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PMC11278999_p7
|
PMC11278999
|
sec[0]/p[7]
|
1. Introduction
| 4.105469 |
biomedical
|
Study
|
[
0.99658203125,
0.00041365623474121094,
0.002902984619140625
] |
[
0.99951171875,
0.00016951560974121094,
0.00041484832763671875,
0.00004112720489501953
] |
Here, we present a study that addresses a current knowledge gap in hydrochar utilization for ORR and CO 2 RR, thereby enhancing the understanding and application of this promising material in electrocatalysis. This involves the preparation of N-doped hydrochars at moderate temperatures, thorough characterization, and an in-depth exploration of their electrocatalytic prowess for both ORR and CO 2 RR. Significantly, novel electrocatalytic inks have been formulated by combining hydrochars with an anion exchange ionomer (AEI) to harness their synergistic effects. To the best of our knowledge, there are no existing literature reports on catalytic electrodes that simultaneously integrate both a hydrochar and AEI.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p8
|
PMC11278999
|
sec[1]/sec[0]/p[0]
|
2.1. Synthesis and Characterization of Hydrochars
| 2.265625 |
biomedical
|
Study
|
[
0.62939453125,
0.0010538101196289062,
0.36962890625
] |
[
0.9638671875,
0.03509521484375,
0.0007162094116210938,
0.0002765655517578125
] |
Table 1 summarizes the experimental conditions for the carbonization of pine needles using urea as the N-dopant. In addition, the yield, density (δ), and elemental composition of the resulting hydrochars are reported.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p9
|
PMC11278999
|
sec[1]/sec[0]/p[1]
|
2.1. Synthesis and Characterization of Hydrochars
| 2.101563 |
biomedical
|
Other
|
[
0.97265625,
0.0009441375732421875,
0.02630615234375
] |
[
0.15087890625,
0.84716796875,
0.0012302398681640625,
0.0005936622619628906
] |
The correlogram , produced by using the Minitab ® version 21.4 statistical software, provides a visual representation of the relationships within the dataset.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p10
|
PMC11278999
|
sec[1]/sec[0]/p[2]
|
2.1. Synthesis and Characterization of Hydrochars
| 3.664063 |
biomedical
|
Study
|
[
0.98681640625,
0.00049591064453125,
0.0126800537109375
] |
[
0.998046875,
0.0016851425170898438,
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0.00006669759750366211
] |
The positive correlations are visualized in red, indicating the variables that change together in the same direction, while the negative correlations are represented in blue, suggesting the variables that vary inversely. The numerical values in the correlogram reflect the correlation coefficients for all the variable pairs, with the colour intensity indicating the strength of the correlation. There is a robust negative correlation between yield and density. Higher HTC temperatures and longer reaction times lead to decreased yields and higher apparent densities. Time emerges as a more significant parameter influencing yield and density, compared to temperature, under the investigated HTC conditions.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11278999_p11
|
PMC11278999
|
sec[1]/sec[0]/p[3]
|
2.1. Synthesis and Characterization of Hydrochars
| 4.1875 |
biomedical
|
Study
|
[
0.9951171875,
0.00051116943359375,
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] |
[
0.99951171875,
0.00015532970428466797,
0.0002779960632324219,
0.00004035234451293945
] |
Examining the influence of the HTC temperature and time on the elemental composition of hydrochars reveals a highly significant correlation with the N content. The data provided in Table 1 report, indeed, that the nitrogen percentage (N%) increased from 2.50% to 4.45% as the temperature increased from 230 °C for 1 h to 260 °C for 6 h. This is likely due to the intensified thermal decomposition processes occurring at higher temperatures and longer durations. Under these conditions, chemical bonds break more extensively, releasing gases, including nitrogen-containing compounds. This phenomenon contributes to the observed increase in nitrogen content (nitrogen doping) in the resulting carbonaceous products. A highly robust positive correlation between the HTC time and carbon (C) content is observed. The longer reaction times allow for extended decomposition and carbonization processes, facilitating the greater expulsion of volatile components and resulting in a higher carbon content. Although less pronounced, a positive correlation is also evident between the C content and HTC temperature. Conversely, a highly significant negative correlation is observed between both the temperature (T) and time (t) and hydrogen content, with temperature exerting a greater influence. These observations align well with the trends observed in regard to density and yield for the produced carbonaceous structures (see above).
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11278999_p12
|
PMC11278999
|
sec[1]/sec[0]/p[4]
|
2.1. Synthesis and Characterization of Hydrochars
| 2.830078 |
biomedical
|
Study
|
[
0.84130859375,
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] |
[
0.98486328125,
0.0142822265625,
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0.00014603137969970703
] |
The Raman and the FTIR spectra of the samples are reported in Figure 2 . The various carbon materials obtained from hydrochars depend greatly on the ratio between the sp 2 (graphite-like) and sp 3 (diamond-like) regions. The Raman spectra of the samples reflect this dependency and appear very complex showing the typical features of hydrogenated amorphous carbon with graphitic carbon domains .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p13
|
PMC11278999
|
sec[1]/sec[0]/p[5]
|
2.1. Synthesis and Characterization of Hydrochars
| 4.246094 |
biomedical
|
Study
|
[
0.99755859375,
0.00037860870361328125,
0.0019683837890625
] |
[
0.99951171875,
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0.00004792213439941406
] |
The D and G bands can be identified in the spectra. The D band is related to the presence of defects or disorder in the graphite lattice. The G band is associated with the in-plane vibrational motion of the sp 2 hybridized carbon atoms in the hexagonal lattice of graphite . Many factors can contribute to the broadening of the signals: (i) the size of carbon clusters: larger clusters may exhibit different vibrational properties compared to smaller ones; (ii) the cluster distribution: variations in the distribution of carbon clusters within the material; (iii) the nature of chemical bonding: changes in bonding configurations, such as the presence of functional groups or impurities. However, it has been observed that in chars obtained at relatively low temperatures and not aged, the D and G bands do not represent graphitic structures or defects but rather polyaromatic rings of different sizes. In particular, the D band at around 1380–1400 cm −1 could be related to fused aromatic rings and the G band at around 1520–1550 cm −1 could be assigned to aromatic ring breathing . This statement seems to be confirmed by the XPS results. The position and relative shift in the D and G bands in Raman spectra can provide information about the structural characteristics of hydrochars. It is mentioned that the shift in these bands is associated with the degree of aromatization, and this is influenced by the temperature of formation , although there is controversy regarding the correlation of changes in the D and G bands with the char structure . Comparing the three spectra in Figure 2 a, the sample treated at higher temperatures for longer times (260_6) presents a maximum shift in D and G bands and a higher intensity for the G band, indicating an increase in the number or size of crystallites.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11278999_p14
|
PMC11278999
|
sec[1]/sec[0]/p[6]
|
2.1. Synthesis and Characterization of Hydrochars
| 3.695313 |
biomedical
|
Study
|
[
0.99072265625,
0.00034427642822265625,
0.009002685546875
] |
[
0.9326171875,
0.06646728515625,
0.0007710456848144531,
0.0002288818359375
] |
Other peaks are present in the Raman spectra: signals centred at 1325 cm −1 are the result of the vibration of sp 3 carbons, while at around 1470–1490 cm −1 , there is the contribution of C–H vibrations originated from single or fused aromatic rings .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p15
|
PMC11278999
|
sec[1]/sec[0]/p[7]
|
2.1. Synthesis and Characterization of Hydrochars
| 4.195313 |
biomedical
|
Study
|
[
0.99853515625,
0.00035572052001953125,
0.0012445449829101562
] |
[
0.99951171875,
0.00014483928680419922,
0.0002181529998779297,
0.000046193599700927734
] |
The FTIR analysis is shown in Figure 2 b. The spectra of samples 200_3 and 230_1 seem very similar, while the spectrum of 260_6 shows a different pattern. The hydroxyl band appears at 3000 cm −1 and can provide information about the water content of the different hydrochars, indicating the hygroscopic nature of the material. The intensity of the band is reduced with increasing HTC temperature, higher for samples 200_3 and 230_1, lower for 260_6. Aliphatic C-H stretching vibrations , indicating sp 3 carbons, are more pronounced for the samples treated at a low temperature. All the samples show the presence of carboxylic/carbonylic groups at 1735 cm −1 , but the peak is more intense for samples 230_1 and 260_6; other peaks are present at 1608 cm −1 corresponding to aromatic C=C stretching. An aromatic C=C ring structure and CH 3 deformations are at 1420 and 1370 cm −1 , respectively. The sample 260_6 shows a band at 1223 cm −1 , slightly visible for the sample 230_1, corresponding to aromatic C-O stretching. Aliphatic ether C–O and alcohol C–O at 1021 cm −1 appear stronger for the sample 230_1 and 200_3 .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p16
|
PMC11278999
|
sec[1]/sec[0]/p[8]
|
2.1. Synthesis and Characterization of Hydrochars
| 4.003906 |
biomedical
|
Study
|
[
0.98681640625,
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0.01268768310546875
] |
[
0.9990234375,
0.0006003379821777344,
0.00013518333435058594,
0.00003790855407714844
] |
The highly graphitic sample 260_6 has the highest water contact angle , in agreement with the low intensity of the O-H band in the FTIR spectrum, whereas the intermediate value belongs to the sample 230_1 (97°). The lowest value (89°) corresponds to a lower degree of graphitization (200_3) and indicates a higher content of hydrophilic oxygenated surface groups.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11278999_p17
|
PMC11278999
|
sec[1]/sec[0]/p[9]
|
2.1. Synthesis and Characterization of Hydrochars
| 4.089844 |
biomedical
|
Study
|
[
0.99853515625,
0.0002015829086303711,
0.0014925003051757812
] |
[
0.99951171875,
0.0004131793975830078,
0.00017189979553222656,
0.000035762786865234375
] |
In Figure 3 are reported the XPS spectra of the C 1s region for all the samples. The deconvolution of the C 1s signal into four synthetic peaks showed that carbon is mostly present as aliphatic and aromatic carbon (component A, red line) and alcoholic or ether (furans, pyrans, etc.) carbon (component B, blue line) . The other signals are due to ketone and C-N bonds (component C, pink line) and carboxylic carbon (component D, green line).
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11278999_p18
|
PMC11278999
|
sec[1]/sec[0]/p[10]
|
2.1. Synthesis and Characterization of Hydrochars
| 3.091797 |
biomedical
|
Study
|
[
0.98291015625,
0.0004038810729980469,
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] |
[
0.9921875,
0.007419586181640625,
0.000263214111328125,
0.00010192394256591797
] |
The intensity distribution of the four components varies with the type of sample as reported in Figure 3 . Nitrogen is always present in pyrrolic/aminic/amidic bonds (400.2–400.6 eV) . The O 1s spectra, reported in the SI , confirm these assignments.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p19
|
PMC11278999
|
sec[1]/sec[0]/p[11]
|
2.1. Synthesis and Characterization of Hydrochars
| 4.105469 |
biomedical
|
Study
|
[
0.99755859375,
0.0002110004425048828,
0.002407073974609375
] |
[
0.99951171875,
0.0004246234893798828,
0.00027298927307128906,
0.00003451108932495117
] |
Comparing the composition reported via XPS with the results of the elemental analysis, it is possible to note that via XPS the carbon/oxygen ratio increases in the order 200_3, 230_1, 260_6, while via elemental analysis, the minimum value is for the 230_1 sample, indicating a greater content of oxygenated groups. In general, in the XPS analysis, the carbon content is higher with respect to the elemental analysis. Considering that XPS is a surface technique, the results suggest a higher concentration of carbon on the surface, in agreement with previous observations .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p20
|
PMC11278999
|
sec[1]/sec[0]/p[12]
|
2.1. Synthesis and Characterization of Hydrochars
| 1.883789 |
biomedical
|
Other
|
[
0.92578125,
0.004955291748046875,
0.0692138671875
] |
[
0.1021728515625,
0.890625,
0.004962921142578125,
0.0024051666259765625
] |
A highly simplified structure of a hydrochar is proposed in Figure 4 .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11278999_p21
|
PMC11278999
|
sec[1]/sec[0]/p[13]
|
2.1. Synthesis and Characterization of Hydrochars
| 3.757813 |
biomedical
|
Study
|
[
0.85986328125,
0.0006003379821777344,
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] |
[
0.99853515625,
0.0011548995971679688,
0.00018787384033203125,
0.00004750490188598633
] |
The BET isotherms of the sample 200_3 are characteristic of a macroporous material without micropores; the BET surface area (4.7 m 2 /g) is typical of non-activated hydrochars. The correlation between the porosity and density was analyzed by Brewer et al. . As reported previously, the porosity of a hydrochar increases until 230 °C, while a higher temperature has a negative response . The densities reported in Table 1 are consistent, with the lowest density observed for the sample 230_1.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11278999_p22
|
PMC11278999
|
sec[1]/sec[0]/p[14]
|
2.1. Synthesis and Characterization of Hydrochars
| 2.222656 |
biomedical
|
Study
|
[
0.63134765625,
0.0013132095336914062,
0.3671875
] |
[
0.89453125,
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] |
The optical micrographs show that the hydrochar electrodes 200_3 and 260_6 are quite dense, whereas the electrode 230_1 presents a more porous and open microstructure. The hydrochar component is clearly visible on top of the fibrous structure of the carbon paper substrate. The SEM micrographs confirm this observation.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11278999_p23
|
PMC11278999
|
sec[1]/sec[1]/p[0]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 4.144531 |
biomedical
|
Study
|
[
0.99267578125,
0.0003986358642578125,
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[
0.99951171875,
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0.00022733211517333984,
0.000033974647521972656
] |
A typical cyclovoltammetric determination of a hydrochar electrode’s capacitance in the non-Faradaic region is shown in SI . The capacitances, obtained from a linear fit of the current vs. scan rate according to the equation, j = C(dU/dt) , are reported for all the samples in SI (Table S2) . These data can be compared with values obtained via impedance spectroscopy. Figure S8 (SI) presents impedance spectra for all the samples determined under the same conditions. The best-fit values of the various equivalent circuit elements are also reported in the SI (Table S2) . The electrode 230_1, made using a hydrochar synthesized at an intermediate HTC temperature, has clearly the highest capacitance as corroborated via impedance spectroscopy, with the highest value of the circuit element representing the electrode capacitance ( Table S2 ). This sample presents, thus, the highest electrochemically active surface area (ECSA), which is consistent with the optical and SEM micrographs showing that the layer of sample 230_1 is the most dispersed among the hydrochar electrodes.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p24
|
PMC11278999
|
sec[1]/sec[1]/p[1]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 2.855469 |
biomedical
|
Study
|
[
0.96484375,
0.0007915496826171875,
0.034515380859375
] |
[
0.98828125,
0.01062774658203125,
0.0007543563842773438,
0.00018167495727539062
] |
A comparison of linear sweep voltammograms for the oxygen reduction reaction of the hydrochar samples is represented in Figure 5 together with a benchmark Pt/C cloth electrode.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p25
|
PMC11278999
|
sec[1]/sec[1]/p[2]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 2.767578 |
biomedical
|
Study
|
[
0.97216796875,
0.000720977783203125,
0.02703857421875
] |
[
0.9931640625,
0.006038665771484375,
0.0004582405090332031,
0.00016427040100097656
] |
The sample 230_1 has clearly the highest electrocatalytic activity for the ORR, and quite large current densities are attained at 1500 rpm. Linear sweep voltammograms at various RDE speeds are reported in Figure 6 . Similar figures for the other hydrochar electrodes are shown in the SI .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p26
|
PMC11278999
|
sec[1]/sec[1]/p[3]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 3.970703 |
biomedical
|
Study
|
[
0.99267578125,
0.0002503395080566406,
0.0070648193359375
] |
[
0.9970703125,
0.002895355224609375,
0.00017321109771728516,
0.00005048513412475586
] |
Table 2 presents the various electrochemical parameters of the hydrochar electrodes for the ORR in alkaline solution. The potentials vs. the reversible hydrogen electrode (RHE) are calculated from the potential vs. Ag/AgCl according to Equation (1): E(vs. RHE/V) = E(vs. Ag/AgCl) + 0.197 + 13 × 0.059 (1)
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p27
|
PMC11278999
|
sec[1]/sec[1]/p[4]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 3.716797 |
biomedical
|
Study
|
[
0.9609375,
0.0006117820739746094,
0.03826904296875
] |
[
0.9990234375,
0.0009002685546875,
0.00018727779388427734,
0.000057756900787353516
] |
The highest onset and half-wave potentials reported in Table 2 are observed for sample 230_1, indicating the best electrocatalytic properties. This onset potential is to our knowledge the highest value reported in the literature for hydrochars ( Table 3 ). The value of 0.9 V vs. RHE reported in refers to a hydrochar treated at 800 °C with a higher energy consumption.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p28
|
PMC11278999
|
sec[1]/sec[1]/p[5]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 2.695313 |
biomedical
|
Study
|
[
0.79248046875,
0.0011749267578125,
0.206298828125
] |
[
0.9765625,
0.0224151611328125,
0.0006265640258789062,
0.00022339820861816406
] |
The number of exchanged electrons can be obtained from Koutecky–Levich plots. The plots for the hydrochar electrode 230_1 at various overpotentials are reported in Figure 7 . The corresponding figures for the other samples can be found in the supporting information . The number of exchanged electrons increases with the overpotential and reaches four at the highest cathodic potential.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p29
|
PMC11278999
|
sec[1]/sec[1]/p[6]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 2.919922 |
biomedical
|
Study
|
[
0.88525390625,
0.0008559226989746094,
0.11370849609375
] |
[
0.984375,
0.0151214599609375,
0.00036835670471191406,
0.00014591217041015625
] |
The numbers shown in Table 2 at 0.26 V vs. RHE indicate that the hydrochar 200_3 gives mostly two-electron reduction, whereas the samples 230_1 and 260_6 are able to provide a substantial amount of four-electron reduction.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11278999_p30
|
PMC11278999
|
sec[1]/sec[1]/p[7]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 4.003906 |
biomedical
|
Study
|
[
0.9970703125,
0.00029349327087402344,
0.0027332305908203125
] |
[
0.99951171875,
0.00039696693420410156,
0.00019633769989013672,
0.00004363059997558594
] |
Finally, the Tafel plots reported in the SI give slopes indicating two-electron transfer as a rate-determining step; the lower slope for the sample 230_1 ( Table 2 ) is consistent with the better electrocatalytic activity. Altogether, there is a clearly better performance of this sample that can be attributed to a highly electrochemically active surface area, related to the optimal hydrothermal treatment conditions.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11278999_p31
|
PMC11278999
|
sec[1]/sec[1]/p[8]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 2.253906 |
biomedical
|
Study
|
[
0.6591796875,
0.00136566162109375,
0.33935546875
] |
[
0.96923828125,
0.0295257568359375,
0.000858306884765625,
0.00030732154846191406
] |
This advantage is also very clearly observed for the CO 2 reduction reaction (CO 2 RR). The comparison of the hydrochar electrodes is shown in Figure 8 . There is again a large advantage for sample 230_1 and, as for the ORR, the sample 200_3 presents the lowest performance.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11278999_p32
|
PMC11278999
|
sec[1]/sec[1]/p[9]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 2.064453 |
biomedical
|
Study
|
[
0.84423828125,
0.002166748046875,
0.153564453125
] |
[
0.8876953125,
0.1094970703125,
0.001964569091796875,
0.0010309219360351562
] |
The linear sweep voltammograms at various RDE speeds are shown for hydrochar 230_1 in Figure 9 .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11278999_p33
|
PMC11278999
|
sec[1]/sec[1]/p[10]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 4.121094 |
biomedical
|
Study
|
[
0.9970703125,
0.0003478527069091797,
0.002582550048828125
] |
[
0.99951171875,
0.00017023086547851562,
0.00025963783264160156,
0.00004035234451293945
] |
Altogether, the LSVs confirm the superior electrocatalytic performance of 230_1 also for the CO 2 RR. Probably, the optimal porosity of this sample, related to an optimal HTC treatment, explains this result, because the order of current densities is the same as for the ORR, with a much higher current density observed for sample 230_1. CO 2 is very sensitive to the presence of “basic” centres, and it is reported in the literature that the N-doping of carbons leads to an increase in the catalytic activity for the CO 2 RR. However, the hydrochar with the highest nitrogen content according to the elemental analysis ( Table 1 ) does not show the best performance for the CO 2 RR; a similar result was reported recently by Fu et al. .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11278999_p34
|
PMC11278999
|
sec[1]/sec[1]/p[11]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 4.148438 |
biomedical
|
Study
|
[
0.99951171875,
0.00020492076873779297,
0.0004878044128417969
] |
[
0.99951171875,
0.0002856254577636719,
0.00018584728240966797,
0.00004464387893676758
] |
NMR spectroscopy is a powerful investigative method for identifying the liquid products in CO 2 RR and has a low detection limit (<5 μM for methanol) . The 1 H NMR spectrum of sample 230_1 is reported in Figure 10 . The spectrum was collected directly from the CO 2 RR solution using an external lock. In the high-field region, three peaks assigned to acetate, acetaldehyde, and methanol are present. The corresponding signal of acetaldehyde appears at a low field together with formate. Using the three hydrogens of acetaldehyde as an internal standard, the ratios between the products are 1:0.9:0.6:0.4 for acetaldehyde, acetate, methanol, and formate, respectively. In addition to the formation of methanol and formate, the CO 2 RR with these hydrochar electrodes leads thus to the generation of C 2 products providing a means for producing valuable chemicals. The analysis of liquid products using HPLC and gaseous products using gas chromatography is the objective of future research.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p35
|
PMC11278999
|
sec[1]/sec[1]/p[12]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 4.007813 |
biomedical
|
Study
|
[
0.994140625,
0.0003237724304199219,
0.005641937255859375
] |
[
0.99853515625,
0.0008554458618164062,
0.0005812644958496094,
0.00004976987838745117
] |
By improving the microstructure and dispersion of the deposited hydrochar, one can significantly enhance the electrocatalytic activity (e.g., increase the number of exchanged electrons for the ORR), but also reduce the mass transport limitation of oxygen and CO 2 to the active catalytic sites. The data reported here represent a significant improvement of the electrocatalytic performance of hydrochars in comparison with the previous literature, showing the great potential of these materials for electrocatalysis .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p36
|
PMC11278999
|
sec[1]/sec[1]/p[13]
|
2.2. Hydrochar Catalytic Electrodes for ORR and CO 2 RR
| 2.466797 |
biomedical
|
Other
|
[
0.90625,
0.001255035400390625,
0.092529296875
] |
[
0.2291259765625,
0.76904296875,
0.001338958740234375,
0.0005249977111816406
] |
It is important to emphasize the significance of the presence of an excellent anion exchange ionomer (AEI) , specifically PPO-LC-TMA, which is crucial in designing high-performance electrodes.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11278999_p37
|
PMC11278999
|
sec[2]/sec[0]/p[0]
|
3.1. Materials
| 1.946289 |
biomedical
|
Study
|
[
0.89501953125,
0.0009822845458984375,
0.10394287109375
] |
[
0.7529296875,
0.2451171875,
0.0012140274047851562,
0.0007872581481933594
] |
Semi-dried pine needles were collected from an area surrounding the University of Perugia, Italy. The urea supplied by Merck KGaA (Darmstadt, Germany) was used as the nitrogen precursor. Other chemicals were of reagent grade and were used as received from Sigma-Aldrich. Carbon paper (AvCarb EP55) and Pt/C 60% cloth gas diffusion electrodes (0.5 mg/cm 2 ) were purchased from the Fuel Cell Store.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11278999_p38
|
PMC11278999
|
sec[2]/sec[1]/sec[0]/p[0]
|
3.2.1. Pine Needle Waste Pre-Treatment
| 4.0625 |
biomedical
|
Study
|
[
0.9892578125,
0.0003142356872558594,
0.01031494140625
] |
[
0.9951171875,
0.004787445068359375,
0.0002390146255493164,
0.000059604644775390625
] |
Pine needles (PNs) were cut into 1–2 cm pieces and dried at 110 °C for 24 h. The resultant dried biomass was subsequently pulverized, sieved, and subjected to extraction using a toluene–methanol azeotrope in a Soxhlet apparatus for 8 h, achieving a 98% recovery rate. This extraction process aimed to eliminate the oil, waxes, and proteins from the biomass. Following the extraction, any residual solvent traces were efficiently removed under vacuum conditions at 80 °C. The pre-treated feedstock was then directly employed in the subsequent step.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11278999_p39
|
PMC11278999
|
sec[2]/sec[1]/sec[1]/p[0]
|
3.2.2. Hydrothermal Carbonization of Pre-Treated PNs with Urea
| 4.152344 |
biomedical
|
Study
|
[
0.9990234375,
0.0002999305725097656,
0.00064849853515625
] |
[
0.99951171875,
0.00043010711669921875,
0.00021517276763916016,
0.00004696846008300781
] |
The synthesis of N-doped hydrochars was conducted using a 30 mL cylindrical pressure reactor fitted with a 13.0 kW/m 2 heater and internal sensors for precise temperature control. Pre-treated PNs (2 g) were dispersed in 20 mL of water, supplemented with 0.025 g of sodium dodecyl sulfate (SDS) and 1.8 g of urea in a round-bottomed flask. The mixture underwent stirring at 60 °C and 700 rpm for 20 min before being loaded into the reactor. Subsequently, the reactor heater was activated and set to temperatures of 200 °C, 230 °C, and 260 °C, each for durations of 3, 1, and 6 h, respectively. The resulting hydrochar samples were designated as 200_3, 230_1, and 260_6. After the reaction time, the heater was deactivated, and the reactor was allowed to cool to room temperature, with the pressure reduced to the ambient level. Subsequently, product separation was achieved through filtration, and all the liquids were collected in a separate container. The hydrochar underwent drying in an oven at 105 °C for 24 h, followed by mortar and pestle grinding for 15 min and sieving (<0.3 mm), and finally, it was stored in an airtight container for subsequent analysis. The hydrochar yield was calculated using Equation (2): Yield (%) = [weight of dried hydrochar (g)/weight of initial biomass (g)] × 100 (2)
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p40
|
PMC11278999
|
sec[2]/sec[2]/p[0]
|
3.3. Catalytic Ink and Electrode Preparation
| 4.113281 |
biomedical
|
Study
|
[
0.99853515625,
0.00026869773864746094,
0.0011425018310546875
] |
[
0.9990234375,
0.0005941390991210938,
0.00014698505401611328,
0.000044286251068115234
] |
The anion exchange polymer used for the ink preparation was poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) with N-pentyl-N,N,N-trimethylammonium side groups, called in the following PPO-LC (Long Chain)-TMA prepared in the LIME lab . The inks for the catalytic layers were prepared from 40 mg of the hydrochar, 10 mg of PPO-LC-TMA (IEC = 0.953 meq/g) , and 500 μL of DMSO. The mixtures were left to undergo stirring overnight and then sonicated at RT for 30 min. An aliquot of the resulting pastes was uniformly spread over a layer of carbon paper . The electrodes were vacuum dried at 40 °C for 4 h. The deposited mass was 0.25 ± 0.03 mg for all the samples, corresponding to a hydrochar catalyst loading of 0.7 mg/cm 2 .
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p41
|
PMC11278999
|
sec[2]/sec[3]/sec[0]/p[0]
|
3.4.1. Proximate Composition
| 3.386719 |
biomedical
|
Study
|
[
0.73828125,
0.0005559921264648438,
0.261474609375
] |
[
0.96337890625,
0.0361328125,
0.0005826950073242188,
0.00012958049774169922
] |
The moisture (U), the volatile matter (VM), and the ash content were obtained according to EN ISO 18134-3 , EN 15148 , and EN 14775 , EN 51719 , respectively ( Table S1, Supplementary Material ). The fixed carbon (FC) was calculated using Equation (3): FC (wt%) = 100 − VM (wt%) − U (wt%) − Ash (wt%) (3)
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11278999_p42
|
PMC11278999
|
sec[2]/sec[3]/sec[1]/p[0]
|
3.4.2. Elemental Analysis
| 3.96875 |
biomedical
|
Study
|
[
0.9990234375,
0.00013971328735351562,
0.0008440017700195312
] |
[
0.998046875,
0.0015010833740234375,
0.0001823902130126953,
0.00005239248275756836
] |
The elemental composition was determined using a UNICUBE ® elemental analyzer ( Table 1 and Table S1, SI ). The oxygen content was calculated using Equation (4): O (%) = 100% − (C% + H% + N% + Ash%) (4)
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11278999_p43
|
PMC11278999
|
sec[2]/sec[3]/sec[2]/p[0]
|
3.4.3. Bulk Density of Hydrochars
| 4.019531 |
biomedical
|
Study
|
[
0.990234375,
0.00022602081298828125,
0.00962066650390625
] |
[
0.994140625,
0.005435943603515625,
0.0004558563232421875,
0.000060498714447021484
] |
The bulk density was calculated according to the guidelines of the European Biochar Certificate , analogue VDLUFA-Method A 13.2.1 . A dried, water-free sample of at least 300 mL was poured into a graduated cylinder, and its mass was measured. The sample’s volume was then recorded after it underwent ten compressions through falling. Subsequently, the bulk density (on a dry matter basis) in kg/m 3 was calculated using the sample’s mass and volume.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11278999_p44
|
PMC11278999
|
sec[2]/sec[3]/sec[3]/p[0]
|
3.4.4. Brunauer–Emmett–Teller (BET) Analysis
| 2.892578 |
biomedical
|
Study
|
[
0.96533203125,
0.0008635520935058594,
0.0335693359375
] |
[
0.9609375,
0.03814697265625,
0.0008573532104492188,
0.00028228759765625
] |
The BET surface area was measured via nitrogen gas sorption at 77 K. The samples were vacuum degassed before the analysis.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11278999_p45
|
PMC11278999
|
sec[2]/sec[3]/sec[4]/p[0]
|
3.4.5. Water Contact Angle
| 4.027344 |
biomedical
|
Study
|
[
0.99951171875,
0.00017845630645751953,
0.0004520416259765625
] |
[
0.99853515625,
0.0011587142944335938,
0.00030684471130371094,
0.000058650970458984375
] |
The sessile drop method was applied using a Biolin Scientific Attension Theta Flex optical tensiometer. Samples of 3 µL of water were deposited at a 0.1 µL/s rate on the electrode surface. The drop shape and contact angle were analyzed according to the Young–Laplace equation, and an average of several experiments was determined.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p46
|
PMC11278999
|
sec[2]/sec[3]/sec[5]/p[0]
|
3.4.6. Microscopical Observations
| 2.134766 |
biomedical
|
Other
|
[
0.99267578125,
0.0006418228149414062,
0.0068206787109375
] |
[
0.1331787109375,
0.86376953125,
0.0018711090087890625,
0.0011777877807617188
] |
The optical micrographs were produced using a Leitz Aristomet microscope. The Scanning Electron Micrographs (SEM) were obtained using a ZEISS Gemini SEM 500 at an acceleration voltage of 5 kV.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11278999_p47
|
PMC11278999
|
sec[2]/sec[3]/sec[6]/p[0]
|
3.4.7. Spectroscopic Analysis
| 4.1875 |
biomedical
|
Study
|
[
0.99951171875,
0.00017595291137695312,
0.00031566619873046875
] |
[
0.9990234375,
0.0006003379821777344,
0.0003688335418701172,
0.000057578086853027344
] |
1 H-NMR spectra were recorded using a Bruker Avance 700 spectrometer operating at 700.18 MHz. After the CO 2 RR, the solution was collected and measured directly using an external look containing D 2 O. The Fourier Transform Infrared Spectroscopy (FTIR) spectra were obtained using a Perkin Elmer Spectrum 2 IR spectrometer equipped with an ATR ZnSi crystal. The Raman spectra were collected using a Optosky ATR 8300 Micro Raman spectrometer. The excitation wavelength was 785 nm and the laser power was 85 mW. X-Ray Photoelectron Spectra (XPS) were acquired by using an Escalab 250Xi (Thermo Fisher Scientific Ltd., Dartford, UK) with a monochromatic Al Kα source. The powder samples were pressed on pure Au (99.99%) foil. The binding energy (BE) scale was corrected by positioning the C 1s peak of the aliphatic carbon at BE = 285.0 eV and controlling the position of the Fermi level at BE = 0 eV.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11278999_p48
|
PMC11278999
|
sec[2]/sec[3]/sec[7]/p[0]
|
3.4.8. Electrochemical Measurements
| 4.15625 |
biomedical
|
Study
|
[
0.9990234375,
0.0002589225769042969,
0.0004954338073730469
] |
[
0.9990234375,
0.000492095947265625,
0.00027632713317871094,
0.000052034854888916016
] |
The three-electrode cell comprised a rotating disk electrode (RDE, 0.28 cm 2 area, OrigaTrod, OrigaLys), a 4 cm 2 Pt foil as a counter-electrode, and an Ag/AgCl reference electrode (E = 0.197 V vs. NHE). Oxygen-saturated 0.1 M KOH was used as the electrolyte for ORR, and CO 2 saturated 0.1 M KHCO 3 was used for CO 2 RR. Cyclic voltammetry (CV), linear sweep voltammetry (LSV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS) measurements were performed at room temperature using a Biologic VMP3 potentiostat. The scan rates for the CV and the LSV measurements were 20–100 mV/s and 5 mV/s, respectively. The rotating speeds of the RDE were between 500 and 2500 rpm. The impedance spectra were recorded in a frequency range of 1 Hz–1 MHz (a.c. amplitude = 20 mV).
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999994 |
PMC11278999_p49
|
PMC11278999
|
sec[3]/p[0]
|
4. Conclusions
| 2.175781 |
biomedical
|
Study
|
[
0.66357421875,
0.0013427734375,
0.3349609375
] |
[
0.86572265625,
0.1322021484375,
0.0013065338134765625,
0.0008001327514648438
] |
N-doped hydrochar-based catalytic materials were obtained via the hydrothermal carbonization of pine needle waste at moderate temperatures, a process recognized for its energy efficiency compared to the other carbonization methods.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11278999_p50
|
PMC11278999
|
sec[3]/p[1]
|
4. Conclusions
| 4.136719 |
biomedical
|
Study
|
[
0.99853515625,
0.00027561187744140625,
0.0012617111206054688
] |
[
0.99951171875,
0.00021314620971679688,
0.00020182132720947266,
0.00004082918167114258
] |
Three different hydrochars were synthesized and mixed with an anion exchange ionomer to prepare an electrode ink used for the ORR and CO 2 RR. The electrocatalytic performances of the hydrochar synthesized at 230 °C are remarkable for the ORR, exhibiting a four-electron reduction capability and the highest reported value of the onset potential for a hydrochar. This sample exhibited an optimal porosity, and in CO 2 RR, various C 2 products were obtained, including acetaldehyde and acetate, as determined by NMR spectroscopy. The addition of a high-performance hydroxide ion-conducting ionomer to the electrode ink was instrumental in achieving such high electrocatalytic performances.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11278999_p51
|
PMC11278999
|
sec[3]/p[2]
|
4. Conclusions
| 2.378906 |
biomedical
|
Study
|
[
0.6171875,
0.0011692047119140625,
0.38134765625
] |
[
0.888671875,
0.109375,
0.0015134811401367188,
0.00057220458984375
] |
Overall, the development of biomass-waste-derived hydrochar-based catalytic electrodes via low-energy hydrothermal carbonization represents a significant advancement in the field of sustainable catalysis. This represents the first report on catalytic electrodes simultaneously incorporating both a hydrochar and AEI.
|
[
"Assunta Marrocchi",
"Elisa Cerza",
"Suhas Chandrasekaran",
"Emanuela Sgreccia",
"Saulius Kaciulis",
"Luigi Vaccaro",
"Suanto Syahputra",
"Florence Vacandio",
"Philippe Knauth",
"Maria Luisa Di Vona"
] |
https://doi.org/10.3390/molecules29143286
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p0
|
PMC11279011
|
sec[0]/p[0]
|
1. Introduction
| 4.019531 |
biomedical
|
Study
|
[
0.9990234375,
0.00034046173095703125,
0.000583648681640625
] |
[
0.64111328125,
0.002689361572265625,
0.35546875,
0.0004012584686279297
] |
Our understanding of microbes has undergone a fundamental paradigm shift over the past two decades. It is now recognized that eukaryotes are meta-organisms and must be viewed as inseparable functional units along with microorganisms . The plant microbiome is one of the key determinants of plant health and productivity. It can promote multiple plant holobiont functions , such as (i) seed germination and growth, (ii) nutrient supply, (iii) resistance to biotic stress (pathogen defense), (iv) resistance to abiotic stress, and (v) production of biologically active metabolites. The profound influence of the microbiome on its host, as well as the intensive plant–microbiome interactions, suggests that plants and their associated microbiomes are co-evolving . Domestication and breeding greatly affect the diversity, abundance, and composition of plant microbiomes . Some domesticated plants were found to have unique microbial community composition compared to their wild relatives .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p1
|
PMC11279011
|
sec[0]/p[1]
|
1. Introduction
| 3.837891 |
biomedical
|
Study
|
[
0.9951171875,
0.00018012523651123047,
0.00457000732421875
] |
[
0.998046875,
0.00160980224609375,
0.00049591064453125,
0.00004935264587402344
] |
Tomato ( Solanum lycopersicum ) is the most widely cultivated vegetable crop worldwide and an important source of human dietary fiber and nutrients. The breeding history of tomato mainly includes two stages: domestication and genetic improvement . The wild currant tomato ( S. pimpinellifolium , PIM) was domesticated to generate the cherry tomato ( S. lycopersicum var. cerasiforme, CER), which was later improved to develop the cultivated tomato with larger fruits ( S. lycopersicum , BIG) . The effect of domestication and improvement processes on the community structure and function of tomato symbiotic microbiomes, especially in tomato fruits and leaves, has not been systematically studied.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p2
|
PMC11279011
|
sec[0]/p[2]
|
1. Introduction
| 4.058594 |
biomedical
|
Study
|
[
0.99951171875,
0.00036025047302246094,
0.00036454200744628906
] |
[
0.6455078125,
0.004718780517578125,
0.349365234375,
0.0004982948303222656
] |
Most microbiome studies rely solely on rRNA gene sequencing, which can introduce biases into the estimation of microbial diversity and abundance . As an alternative, metagenomic sequencing offers a broader view of the microbiome’s composition and function, along with insights into microbe–host interactions . These techniques enable the simultaneous processing of numerous samples, providing high-throughput capabilities ideal for extensive studies. Metagenomic and metatranscriptomic approaches offer a less biased and more accurate representation of natural microbial communities. By analyzing the microbiomes of genetically distinct plants in similar environmental conditions, researchers can determine the impact of host genetics on microbial community structure and function. Insights gained from these studies may lead to the development of microbial-based biocontrol strategies or the selection of crop varieties that naturally foster disease-suppressive microbial communities.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p3
|
PMC11279011
|
sec[0]/p[3]
|
1. Introduction
| 3.041016 |
biomedical
|
Study
|
[
0.98974609375,
0.0003676414489746094,
0.009857177734375
] |
[
0.919921875,
0.0645751953125,
0.01514434814453125,
0.0004267692565917969
] |
The rhizosphere microbiome of tomato has been extensively studied, mainly focusing on the mining of functional bacteria that can improve tomato biotic and abiotic stress resistance . Little research has been carried out on the microbiome of the above-ground part of tomato plants. In the above-ground part, the microbiota is less influenced by the soil; therefore, plant genotype plays a more important role in symbiotic microbial selection .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p4
|
PMC11279011
|
sec[0]/p[4]
|
1. Introduction
| 4.023438 |
biomedical
|
Study
|
[
0.99853515625,
0.00030493736267089844,
0.0011243820190429688
] |
[
0.99951171875,
0.00019216537475585938,
0.000164031982421875,
0.00004178285598754883
] |
In a previous study , we generated a comprehensive dataset including the genome and fruit pericarp transcriptome (in the orange stage, ~75% ripe) of hundreds of germplasms from PIM, CER, and BIG grown under the same cultivation conditions. The aims of the current study were to (i) understand the changes in the composition and function of the leaf and fruit microbiomes during tomato domestication and improvement, and (ii) obtain potential loci that regulate the leaf and fruit symbiotic microbiomes.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p5
|
PMC11279011
|
sec[1]/sec[0]/p[0]
|
2.1. Genomic and Transcriptomic Data of Tomato
| 4.125 |
biomedical
|
Study
|
[
0.9990234375,
0.0002455711364746094,
0.0007777214050292969
] |
[
0.99951171875,
0.00015032291412353516,
0.00017535686492919922,
0.00003534555435180664
] |
We previously established a dataset containing the genomic and transcriptomic data of hundreds of tomato germplasms. This dataset includes accessions of wild species and red-fruited tomato ( S. pimpinellifolium , S. lycopersicum var. cerasiforme, and S. lycopersicum ), representing various geographical origins, consumption types, and improvement statuses . We selected the leaf genomic and fruit pericarp transcriptomic data from tomato accessions grown in the same environment and sequenced on the same platform to characterize the structure and function of their leaf and fruit microbiomes. As a result, we obtained the genomic data of 60 PIM accessions, 109 BIG accessions, 79 CER accessions, and 13 Wild tomato accessions as well as the transcriptomic data of 25 PIM accessions, 109 BIG accessions, 79 CER accessions, and 4 Wide accessions. The details including SRA numbers of these accessions are listed in Table S1 .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p6
|
PMC11279011
|
sec[1]/sec[1]/p[0]
|
2.2. Kraken Pipeline for Microbial Detection
| 4.117188 |
biomedical
|
Study
|
[
0.99951171875,
0.00023865699768066406,
0.000263214111328125
] |
[
0.99951171875,
0.00034308433532714844,
0.00023543834686279297,
0.000055789947509765625
] |
The reads that failed to be mapped to the tomato reference genome were aligned to all known bacterial, archaeal, and viral microbial genomes using the ultrafast Kraken algorithm. We retrieved a total of 71,782 microbial genomes using the RepoPhlan platform . Among these genomes, there were 5503 viral genomes and 66,279 bacterial or archaeal genomes. We constructed a comprehensive database consisting of 5503 viral genomes and 54,471 bacterial and archaeal genomes which possessed a quality score of 0.8 or higher .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p7
|
PMC11279011
|
sec[1]/sec[1]/p[1]
|
2.2. Kraken Pipeline for Microbial Detection
| 4.128906 |
biomedical
|
Study
|
[
0.99951171875,
0.00019478797912597656,
0.00022292137145996094
] |
[
0.9990234375,
0.0004067420959472656,
0.00029921531677246094,
0.00006008148193359375
] |
The Kraken algorithm decomposes each sequencing read into k-mers (31-mers was used by default) and precisely matches each k-mer to the constructed microbial genome database (59,974 genomes), providing a putative taxonomy assignment of the lowest common ancestor for that read. Matching and classification using the Kraken algorithm are several orders of magnitude faster than performing direct genome alignments. The results were most accurate at the genus level. To remove the rarest species that may cause noise in the subsequent analysis, we retained the genera present in at least 10% of the samples (leaf or fruit pericarp) from PIM, CER, and BIG.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11279011_p8
|
PMC11279011
|
sec[1]/sec[2]/p[0]
|
2.3. Quantification of 16S from Abundant Genera
| 4.109375 |
biomedical
|
Study
|
[
0.99951171875,
0.00028228759765625,
0.0003204345703125
] |
[
0.99951171875,
0.00015997886657714844,
0.0002313852310180664,
0.000050008296966552734
] |
To validate the results obtained from the analysis of the tomato leaf metagenome, we amplified and quantified the 10 most abundant genera found in the tomato accessions. To carry this out, we planted 12 seeds per accession in a glasshouse environment and harvested the leaves. We combined four leaves from four plants to create one biological replicate, and we used a total of three biological replicates. Before extracting the DNA, we cleaned the leaves using a solution of 70% ethanol, 2% bleach, and water. The DNA extraction was performed using the CTAB method. The extracted DNA was then divided into equal concentrations (20 ng/μL) for the quantitative real-time PCR. The StepOnePlus™ Real-Time PCR System and SYBR(R) Green I dye were utilized for the amplification and quantification, following the manufacturer’s protocol (Applied Biosystems, Waltham, MA, USA). In brief, we used a final reaction volume of 10 μL with a primer concentration of 200 nM. We conducted tests on three biological replicates and three technical replicates. The comparative CT method setup was used with default parameters, allowing for a maximum of 40 cycles. We selected previously published primers for the following genera: Clostridium sp., Pasteurella sp., Halothece sp., Candidatus sp., Bacillus sp., Halomonas sp., Hamiltonella sp., Synechococcus sp., Xanthomonas sp., Methylobacterium sp. ( Table S2 ). To facilitate comparisons, all samples were normalized to the V3-V4 region of the 16S rDNA.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11279011_p9
|
PMC11279011
|
sec[1]/sec[3]/p[0]
|
2.4. Analysis and Comparison of Microbiome Structure
| 4.097656 |
biomedical
|
Study
|
[
0.99951171875,
0.000202178955078125,
0.00019669532775878906
] |
[
0.99951171875,
0.0003306865692138672,
0.0003299713134765625,
0.00005644559860229492
] |
To compare the microbial composition of all tomato accessions, the read counts were normalized, and the heteroscedasticity was removed by using voom. The Krona tool was used for visualization. Mothur v1.30.2 was used to calculate the richness and diversity indices (i.e., Chao, Shannon, Simpson, and ACE) of the leaf and fruit microbiomes based on the relative abundance data at the species level. Bray–Curtis distance matrices were calculated with QIIME v1.9.1 based on normalized species data to detect global variations in the composition of microbial communities.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p10
|
PMC11279011
|
sec[1]/sec[3]/p[1]
|
2.4. Analysis and Comparison of Microbiome Structure
| 4.105469 |
biomedical
|
Study
|
[
0.99951171875,
0.00019943714141845703,
0.0001366138458251953
] |
[
0.9990234375,
0.00044655799865722656,
0.0006284713745117188,
0.0000750422477722168
] |
Partial least squares-discriminant analysis (PLS-DA), permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) using the adonis function from the Vegan package (v2.5-3) were also performed to compare microbiome composition between samples. LEfSE was employed to identify distinguishing taxa among microbial communities at multiple levels and to visualize the results using taxonomic bar charts and cladograms .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p11
|
PMC11279011
|
sec[1]/sec[4]/p[0]
|
2.5. Tomato Germplasm Differentiation Using Machine Learning
| 4.179688 |
biomedical
|
Study
|
[
0.99853515625,
0.00031280517578125,
0.0009527206420898438
] |
[
0.9990234375,
0.0002655982971191406,
0.0004699230194091797,
0.00004941225051879883
] |
Gradient Boosting Machine (GBM) models were trained, automatically tuned, and validated using the GBM (v2.1.9) and Caret package (v6.0-94) in R . Random stratified sampling was used to divide all tomato samples into a training set (70%) and a validation set (30%). During model training, the data were first centered and normalized so that each sample had a mean of 0- or 1-unit standard deviation (SD). Two-fold cross-validation was used to create multiple subsets of the training set and perform search optimization of GBM parameters, including the interaction depth (1, 2, or 3) and tree number (50, 100, or 150), to maximize the area under the receiver operating characteristic curve (AUROC) of the final model. The learning rate (shrinkage) was fixed at 0.1, and the minimum observation tree per node was fixed at 5. In the case of imbalance in each class, upsampling was used to facilitate generalization of the model. The performance of the final model, including the ROC curve, PR curve, and confusion matrix (with a 50% threshold of recognition probability for class 1 and class 2), was assessed by applying the final model to the validation set. The ROC and PR curves, as well as the AUROV and AUPR values, were calculated using the PRROC package (v1.3.1), and the confusion matrix was calculated using the Caret package. Variable importance scores for the resulting non-zero model features were estimated using the GBM and Caret packages. The percentage contribution of a particular feature to the model’s prediction was estimated by dividing the variable importance score for that feature by the sum of all variable importance scores for a given model.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p12
|
PMC11279011
|
sec[1]/sec[5]/p[0]
|
2.6. Identification of Regulatory Sites
| 4.117188 |
biomedical
|
Study
|
[
0.99951171875,
0.0003058910369873047,
0.0003230571746826172
] |
[
0.99951171875,
0.000148773193359375,
0.000255584716796875,
0.00005602836608886719
] |
To determine the relationship between microbial community composition and tomato genetic factors, we performed a genome-wide association study (GWAS) using the differential genera identified among PIM, CER, and BIG tomato leaf by the LEfSE analysis as phenotypes. The population structure of tomato accessions was determined by PLINK v1.9, the kinship matrices were calculated using MVP v1.0, and a GWAS was performed based on SNPs. A total of 2.65 million biallelic SNPs were identified across 239 tomato samples . SNPs with a lower genotype rate (>95%) and minor allele frequency (MAF < 0.05) were excluded, and tomato samples with an idiotype deletion rate >= 5% were removed, resulting in a total of 210 samples for the GWAS analysis.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p13
|
PMC11279011
|
sec[2]/sec[0]/p[0]
|
3.1. Microbiome Structure of Tomato Leaf and Fruit Pericarp
| 4.03125 |
biomedical
|
Study
|
[
0.99951171875,
0.000232696533203125,
0.00047469139099121094
] |
[
0.99951171875,
0.0003063678741455078,
0.00017154216766357422,
0.00004500150680541992
] |
Reads that cannot be mapped to the tomato reference genome from the sequencing data of tomato leaf and fruit pericarp samples can be assigned to bacterial and archaeal genomes . An average of 2.02 ± 0.80% reads from the leaf samples belonged to bacterial or archaeal sequences, while the number was 0.92 ± 0.32% in the fruit samples . However, no significant difference was observed in the assigned read numbers among the PIM, CER, BIG, and Wide samples.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 1 |
PMC11279011_p14
|
PMC11279011
|
sec[2]/sec[0]/p[1]
|
3.1. Microbiome Structure of Tomato Leaf and Fruit Pericarp
| 4.035156 |
biomedical
|
Study
|
[
0.99951171875,
0.0002551078796386719,
0.0004296302795410156
] |
[
0.99951171875,
0.0002605915069580078,
0.00019788742065429688,
0.000045359134674072266
] |
The accumulation curve shows that most of the reads that could not be mapped to the tomato reference genome were assigned to corresponding microbial taxa. The number of bacterial genera reached a plateau of 1400 after the first 50 tomato accessions of BIG, CER, and PIM leaf and fruit pericarp samples had been analyzed , while the number bacterial genera obtained from the Wide samples was below the plateau. This result indicated that the obtained reads and characteristic profiles can represent the microbial characteristics of the PIM, CER, and BIG leaf and fruit pericarp samples, making them suitable for subsequent analyses.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p15
|
PMC11279011
|
sec[2]/sec[0]/p[2]
|
3.1. Microbiome Structure of Tomato Leaf and Fruit Pericarp
| 4.097656 |
biomedical
|
Study
|
[
0.99951171875,
0.0002682209014892578,
0.0004355907440185547
] |
[
0.99951171875,
0.00019598007202148438,
0.00029540061950683594,
0.00004482269287109375
] |
The microbiome community composition of tomato leaf and fruit pericarp at the phylum and genus levels was investigated. Proteobacteria and Firmicutes were the most abundant bacterial phyla in both samples, together accounting for about 80% of the total relative abundance . In the tomato leaves, the most abundant phylum was Proteobacteria, followed by Firmicutes . In the fruit pericarps, the most abundant phylum was Firmicutes, followed by Proteobacteria . The bacterial phyla with higher abundance in the leaves and fruit pericarps also included Cyanobacteria, Candidatus, Micrarchaeota, Actinobacteria, Bacteroidetes, Euryarchaeota, and Tenericutes.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p16
|
PMC11279011
|
sec[2]/sec[0]/p[3]
|
3.1. Microbiome Structure of Tomato Leaf and Fruit Pericarp
| 4.070313 |
biomedical
|
Study
|
[
0.99951171875,
0.0002053976058959961,
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] |
[
0.99951171875,
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0.00004500150680541992
] |
Clostridium , Pasteurella , Halomonas , Bacillus , Halothece , Candidatus , Hamiltonella , Synechococcus , and Xanthomonas were the most abundant bacterial genera in the leaf samples , while Clostridium , Alkaliphilus , Candidatus , Arthromitus , Geosporobacter , Mordavella , Oscillibacter , and Blautia were the most abundant genera in the fruit pericarp samples . The composition of leaf symbiotic microbiome was obtained using metagenome sequencing, while that of fruit pericarp was obtained using metatranscriptome sequencing. The differences in the microbial composition between the leaf and fruit pericarp samples might be ascribed to the different tissues (leaf and fruit); however, we could not exclude the possibility that different analysis methods used for these two types of data may also lead to such results.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11279011_p17
|
PMC11279011
|
sec[2]/sec[0]/p[4]
|
3.1. Microbiome Structure of Tomato Leaf and Fruit Pericarp
| 4.121094 |
biomedical
|
Study
|
[
0.9990234375,
0.0002491474151611328,
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] |
[
0.99951171875,
0.0001881122589111328,
0.00023686885833740234,
0.00003647804260253906
] |
Domestication and genetic improvement processes altered the relative abundance of symbiotic microbes in the tomato leaf samples at the phylum level. In the tomato leaves, the relative abundance of Firmicutes showed an increasing trend from PIM (38.50%) to CER (44.05%) to BIG (46.98%) . Meanwhile, the relative abundance of Bacteroidetes increased, that of Cyanobacteria and Actinobacteria decreased, and that of Proteobacteria did not change significantly. Among the classes within Proteobacteria, the abundance of Gammaproteobacteria decreased during the domestication process, while that of Alphaproteobacteria and Betaproteobacteria increased .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p18
|
PMC11279011
|
sec[2]/sec[0]/p[5]
|
3.1. Microbiome Structure of Tomato Leaf and Fruit Pericarp
| 4.089844 |
biomedical
|
Study
|
[
0.99853515625,
0.0002765655517578125,
0.0010404586791992188
] |
[
0.99951171875,
0.00022113323211669922,
0.00025963783264160156,
0.000037789344787597656
] |
The relative abundance of fruit pericarp symbiotic microorganisms also changed at the phylum level during tomato domestication and improvement. The relative abundance of Firmicutes in the BIG and CER fruits was lower than that of PIM fruits, while the relative abundance of Candidatus, Micrarchaeota, Actinobacteria, and Bacteroidetes was higher than that of PIM. Similar to the leaf symbiotic microbial composition, among the classes within Proteobacteria, the relative abundance of Gammaproteobacteria decreased during domestication, while that of Alphaproteobacteria and Betaproteobacteria increased .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11279011_p19
|
PMC11279011
|
sec[2]/sec[0]/p[6]
|
3.1. Microbiome Structure of Tomato Leaf and Fruit Pericarp
| 4.066406 |
biomedical
|
Study
|
[
0.9990234375,
0.00030040740966796875,
0.0004546642303466797
] |
[
0.99951171875,
0.00014448165893554688,
0.0001952648162841797,
0.00004398822784423828
] |
We recognized that sample manipulation during DNA extraction or sequencing could potentially introduce microorganisms not typically found in tomato leaves. To exclude the possibility of artificially introducing abundant genera during sample collection, we employed quantitative PCR to detect 10 highly prevalent genera in tomato accessions ( Table S2 ). We selected new plants under the assumption that the genera we identified are representative members of the tomato leaf microbiome. Remarkably, we successfully quantified the presence of all taxa and observed a consistent distribution across the accessions ( Table S2 ). Notably, Clostridium , Pasteurella , and Halomonas remained the most abundant genera, effectively ruling out the likelihood of artificially introduced highly abundant genera.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p20
|
PMC11279011
|
sec[2]/sec[1]/p[0]
|
3.2. Differences in Leaf and Fruit Pericarp Microbiome Structure among PIM, CER, and BIG
| 2.248047 |
biomedical
|
Study
|
[
0.97216796875,
0.0005059242248535156,
0.027374267578125
] |
[
0.6904296875,
0.302001953125,
0.006435394287109375,
0.000904083251953125
] |
CER was domesticated from PIM and was later genetically improved to the cultivated BIG . After domestication and genetic improvement, the yield, disease resistance, fruit size, and flavor of wild tomato all changed significantly. However, whether domestication and improvement processes also affect tomato leaf and fruit pericarp microbiome structure and function remains elusive.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p21
|
PMC11279011
|
sec[2]/sec[1]/p[1]
|
3.2. Differences in Leaf and Fruit Pericarp Microbiome Structure among PIM, CER, and BIG
| 4.042969 |
biomedical
|
Study
|
[
0.9990234375,
0.00024235248565673828,
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] |
[
0.99951171875,
0.00021696090698242188,
0.00023818016052246094,
0.000037789344787597656
] |
Domestication and genetic improvement altered the alpha diversity of the leaf microbiome. The diversity (Shannon index) and richness (sobs and Chao indices) of the PIM leaf microbiome were significantly higher than those of BIG . The richness (sobs and Chao indices) of the PIM leaf microbiome was significantly higher than that of CER , while the diversity (Shannon index) of the PIM leaf microbiome was also higher than that of CER but not statistically significant . The richness and diversity of the CER leaf microbiome were higher than those of BIG, but the differences were not statistically significant .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p22
|
PMC11279011
|
sec[2]/sec[1]/p[2]
|
3.2. Differences in Leaf and Fruit Pericarp Microbiome Structure among PIM, CER, and BIG
| 4.066406 |
biomedical
|
Study
|
[
0.9990234375,
0.00024235248565673828,
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] |
[
0.99951171875,
0.00019478797912597656,
0.00027632713317871094,
0.00003701448440551758
] |
The effects of domestication and improvement on the alpha diversity of the fruit pericarp microbiome were not as significant as those on the leaf microbiome. The richness (sobs and Chao indices) of the PIM fruit pericarp microbiome was higher than that of BIG, but not statistically significant . The diversity indices of (Shannon and Simpson) the PIM fruit pericarp microbiome were significantly higher than those of BIG . Similarly, the diversity indices of the PIM fruit microbiome were significantly higher than those of CER , while no significant difference was observed in richness . The richness and diversity of the CER fruit microbiome were higher than those of BIG, but with no statistical significance .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p23
|
PMC11279011
|
sec[2]/sec[1]/p[3]
|
3.2. Differences in Leaf and Fruit Pericarp Microbiome Structure among PIM, CER, and BIG
| 4.144531 |
biomedical
|
Study
|
[
0.99951171875,
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] |
[
0.99951171875,
0.000141143798828125,
0.0004324913024902344,
0.000058710575103759766
] |
To assess the similarity between microbial communities, principal coordinates analysis (PCoA) was performed based on the Bray–Curtis algorithm at the species level. The results showed no obvious separation among the three tomato clades for both the leaf and fruit pericarp microbial communities . Then, partial least squares discriminant analysis (PLS-DA), a supervised analysis suitable for high-dimensional data, was performed . The leaf and fruit pericarp microbiomes of PIM, CER, and BIG were clustered separately, indicating significant differences in the overall structure of the microbial communities among the three tomato clades. The leaf microbiome of CER was located between those of PIM and BIG, and was closer to BIG; the same pattern was observed for the fruit pericarp microbiome of the three. This is consistent with the processes of tomato domestication (from PIM to CER) and genetic improvement (from CER to BIG). The p -values ( p -value ≤ 0.001, Table S4 ) calculated from PERMANOVA and ANOSIM, based on the Bray–Curtis distance, further demonstrated the significant differences in the leaf and fruit microbiomes among the three tomato clades.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p24
|
PMC11279011
|
sec[2]/sec[2]/p[0]
|
3.3. Random Forest Model Based on Microbial Community Composition Accurately Predicted Tomato Clade
| 4.015625 |
biomedical
|
Study
|
[
0.99853515625,
0.0002644062042236328,
0.001220703125
] |
[
0.99951171875,
0.0002472400665283203,
0.00035190582275390625,
0.000042319297790527344
] |
Stochastic gradient-boosting machine learning models were constructed to distinguish between tomato clades using the normalized data of leaf and fruit pericarp microbiomes, respectively, of PIM, CER, and BIG. The prediction performance of the model constructed based on both leaf and fruit pericarp microbiome data (model-both) was good, while the model constructed based on leaf microbiome data (model-leaf) outperformed the one using fruit pericarp microbiome data (model-fruit) .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p25
|
PMC11279011
|
sec[2]/sec[2]/p[1]
|
3.3. Random Forest Model Based on Microbial Community Composition Accurately Predicted Tomato Clade
| 4.019531 |
biomedical
|
Study
|
[
0.99658203125,
0.0002944469451904297,
0.00299835205078125
] |
[
0.99951171875,
0.00030112266540527344,
0.0002906322479248047,
0.00004380941390991211
] |
The sensitivity and specificity of the model were highest when predicting PIM, followed by BIG, with the poorest performance observed in predicting CER. The AUROC for model-leaf and model-fruit in predicting PIM were 0.9824 and 0.9373, respectively. For BIG, the corresponding values were 0.8965 and 0.8096, and for CER, they were 0.6457 and 0.6267. These results may be attributed to the transitional role of CER during the domestication and genetic improvement processes of tomatoes .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p26
|
PMC11279011
|
sec[2]/sec[3]/p[0]
|
3.4. Differential Microbes in Leaf and Fruit Microbiomes of PIM, CER, and BIG
| 4.015625 |
biomedical
|
Study
|
[
0.99853515625,
0.00023806095123291016,
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] |
[
0.99951171875,
0.00026679039001464844,
0.00020420551300048828,
0.00003719329833984375
] |
The above observations indicated that the processes of tomato domestication (from PIM to CER) and genetic improvement (from CER to BIG) resulted in changes in the leaf and fruit microbiomes. PIM, CER, and BIG each possessed unique leaf and fruit microbiomes. Through an LEfSe analysis, we identified microbial taxa unique to PIM, CER, or BIG in both leaf metagenomic data and fruit pericarp metatranscriptomic data .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p27
|
PMC11279011
|
sec[2]/sec[3]/p[1]
|
3.4. Differential Microbes in Leaf and Fruit Microbiomes of PIM, CER, and BIG
| 4.152344 |
biomedical
|
Study
|
[
0.9990234375,
0.0002810955047607422,
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] |
[
0.99951171875,
0.0001519918441772461,
0.00025653839111328125,
0.0000432133674621582
] |
The LEfSe analysis identified bacterial taxa specific to PIM, CER, and BIG in the tomato leaf and fruit pericarp microbiomes , the abundance of which was altered by domestication and improvement processes. Consistent with the transitional position of CER during tomato domestication and improvement, the relative abundance of many differential bacterial taxa of CER leaf and fruit pericarp microbiomes was between that of PIM and BIG . For example, the relative abundance of Bacillus in the leaf microbiome increased from 5.77% for PIM to 7.09% for CER, and then to 7.37% for BIG . The abundance of Bacillus in the fruit pericarps also increased during the processes of domestication and improvement, though it was not identified as a differential genus in the fruit pericarp microbiome by the LEfSe analysis (LDA = 4.84, p -value > 0.05) .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p28
|
PMC11279011
|
sec[2]/sec[3]/p[2]
|
3.4. Differential Microbes in Leaf and Fruit Microbiomes of PIM, CER, and BIG
| 4.109375 |
biomedical
|
Study
|
[
0.9990234375,
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[
0.99951171875,
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In the leaf microbiome, the relative abundance of Methylobacterium , Vibrio , and Chamaesiphon increased, while that of Plantactinospora , Paraburkholderia , Ictalurivirus , Capnocytophaga , and Candidatus Portiera , decreased during the tomato domestication and improvement processes . Although the above genera were present in the fruit microbiome and their abundances were also affected by tomato domestication and improvement processes, they were not considered as differential genera by LEfSe analysis . It is worth noting that the abundance of the differential genus Sphingobium identified in both leaf and fruit microbiomes showed an increasing trend from PIM to CER to BIG ( Table S5 ).
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p29
|
PMC11279011
|
sec[2]/sec[4]/p[0]
|
3.5. Functional Profiling of the Tomato Leaf and Fruit Microbiomes
| 3.585938 |
biomedical
|
Study
|
[
0.99755859375,
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[
0.9990234375,
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The processes of tomato domestication and improvement alter the leaf and fruit pericarp microbiome assembly. To elucidate whether changes in the microbiome assembly also alter the function of leaf and fruit microbiomes, we annotated the functional categories of the microbiomes ( Table S6 ) and identified differential functional categories among PIM, CER, and BIG .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p30
|
PMC11279011
|
sec[2]/sec[4]/p[1]
|
3.5. Functional Profiling of the Tomato Leaf and Fruit Microbiomes
| 4.144531 |
biomedical
|
Study
|
[
0.99951171875,
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[
0.99951171875,
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0.00006628036499023438
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Transcription, translation, primary metabolism, flagellar assembly, and secondary metabolism (terpenoids, antibiotics, and xenobiotics) were the most enriched GO terms. The LEfSe analysis identified characteristic GO terms of the PIM leaf microbiome, including carbohydrate metabolic process , and characteristic GO terms of the BIG leaf microbiome, including calcium ion import, magnet ion binding, iron–sulfur cluster binding, and proton-transporting ATP synthase activity.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p31
|
PMC11279011
|
sec[2]/sec[4]/p[2]
|
3.5. Functional Profiling of the Tomato Leaf and Fruit Microbiomes
| 3.847656 |
biomedical
|
Study
|
[
0.99951171875,
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[
0.99755859375,
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] |
The carbohydrate metabolic process was a characteristic GO term of the PIM fruit pericarp microbiome. In addition, the sucrose metabolic process, nitrogen compound metabolic process, cellular amino acid metabolic process, and glutamine biosynthetic process were characteristic GO terms of the PIM fruit microbiome. After domestication and improvement, the characteristic GO terms of BIG included cobalt ion binding, response to salt stress, and catalase activity.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p32
|
PMC11279011
|
sec[2]/sec[5]/p[0]
|
3.6. GWAS Analysis Identifies Loci Regulating Tomato Symbiotic Microbiomes
| 4.140625 |
biomedical
|
Study
|
[
0.99951171875,
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[
0.99951171875,
0.000148773193359375,
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To identify key loci that regulate the structure and composition of the tomato leaf microbiome, we performed a GWAS on the characteristic genera of the leaf microbiome of PIM, CER, and BIG, but no significantly associated SNPs were identified. Next, we performed a GWAS on the 40 specific microbial species identified by the LEfSe analysis (FDR-adjusted p -value < 0.05, Wilcoxon rank-sum test; absolute LDA score > 2; Table S7 ) in the three tomato clades. Twelve SNPs distributed on seven chromosomes were identified to be significantly associated ( p -value < 1 × 10 −6 ) with sixteen characteristic species in the leaf microbiome. Among these SNPs, one was located in the coding region of Solyc09g005770 , but did not cause a frameshift mutation.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p33
|
PMC11279011
|
sec[2]/sec[5]/p[1]
|
3.6. GWAS Analysis Identifies Loci Regulating Tomato Symbiotic Microbiomes
| 4.097656 |
biomedical
|
Study
|
[
0.99951171875,
0.000225067138671875,
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] |
[
0.99951171875,
0.0001461505889892578,
0.00028061866760253906,
0.0000470280647277832
] |
Vibrio vulnificus was the most differential bacterial species identified by LEfSe among PIM, CER, and BIG . V. vulnificus , widely known as a human pathogen, exists in marine ecosystems and serves as an endophyte and is symbiotic with seagrass such as Zostera marina . Kwak et al. revealed the presence of Vibrionaceae in the tomato rhizosphere , while Park et al. reported that V. vulnificus could infect Arabidopsis and cause diseased phenotypes . In this study, the relative abundance of V. vulnificus in the tomato leaf microbiomes increased during domestication, from 0.005% in PIM to 0.014% in CER, and finally to 0.02% in BIG. This change may be related to the reduced disease resistance during tomato improvement, but the role of V. vulnificus on tomato growth and development remains to be further studied.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279011_p34
|
PMC11279011
|
sec[2]/sec[5]/p[2]
|
3.6. GWAS Analysis Identifies Loci Regulating Tomato Symbiotic Microbiomes
| 4.046875 |
biomedical
|
Study
|
[
0.99853515625,
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] |
[
0.99951171875,
0.0003986358642578125,
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0.00004380941390991211
] |
The relative abundance of s__ Capnocytophaga _sp._oral_taxon_878 was also altered during tomato domestication and improvement . Its relative abundance in the tomato leaf microbiome decreased during domestication, from 0.041% in PIM to 0.021% and 0.02% in CER and BIG, respectively. s__ Capnocytophaga _sp._oral_taxon_878 belongs to the Flavobacteriaceae family, which has been widely reported to promote tomato growth and improve disease resistance .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p35
|
PMC11279011
|
sec[2]/sec[5]/p[3]
|
3.6. GWAS Analysis Identifies Loci Regulating Tomato Symbiotic Microbiomes
| 4.101563 |
biomedical
|
Study
|
[
0.99951171875,
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[
0.99951171875,
0.0002789497375488281,
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0.000058710575103759766
] |
Interestingly, 10 of the 12 significant SNPs appeared in more than one microbial as sociation analysis, indicating that specific regions on the tomato genome can affect the abundance of multiple microbial taxa. Among them, the SNP on chromosome 2 was significantly associated with the abundance of four microbial taxa in tomato. This SNP was located at chromosome 2:14,426,758 bp, and there was only one gene in the upstream and downstream 500-kb regions of this SNP. Solyc02g014020 was annotated to the HAT family and encodes a transposon protein. However, its function is unknown.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p36
|
PMC11279011
|
sec[3]/p[0]
|
4. Discussion
| 4.191406 |
biomedical
|
Study
|
[
0.9990234375,
0.0003941059112548828,
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] |
[
0.9990234375,
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] |
In this study, we analyzed the effects of domestication and improvement processes on the symbiotic microbiome structure of tomato leaves and fruit pericarps, as well as their genetic basis. The leaf microbiome was analyzed using metagenome sequencing to capture a broad snapshot of the genetic potential, while the pericarp microbiome was analyzed using metatranscriptome sequencing to focus on actively expressed genes, reflecting the dynamic response of the microbiome under fruiting conditions. Our results showed that those metagenomic and metatranscriptomic data that could not be aligned to the tomato reference genome contained sequences of the tomato leaf and fruit pericarp symbiotic microbiomes. Based on these sequences, we obtained the composition of symbiotic microbiomes of tomato leaf and fruit pericarp, and established models to accurately predict different tomato clades (PIM, CER, and BIG). The processes of tomato domestication and improvement have changed the structure of these symbiotic microbiomes. In addition, we also conducted GWAS analyses to identify SNPs significantly associated with characteristic microbial species of tomato leaf. Future studies are required to verify the underlying mechanisms of modulating the microbiome community for crop improvement purposes.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279011_p37
|
PMC11279011
|
sec[3]/p[1]
|
4. Discussion
| 4.003906 |
biomedical
|
Study
|
[
0.9970703125,
0.0002987384796142578,
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] |
[
0.9931640625,
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0.00005930662155151367
] |
To reveal the changes in the structure and function of crop symbiotic microbial com- munities during crop domestication is of great practical significance. Many studies have been conducted to try to elucidate such changes; however, most of these studies focused on changes in the composition of root symbiotic microbial communities, while relatively little research has been carried out regarding leaf and fruit symbiotic microbial communities . The processes of domestication and improvement have gradually reduced the richness and diversity of symbiotic microbial communities in tomato leaves and fruit pericarps . The effects of domestication on the alpha diversity of the rhizobacterial communities of various crops were inconsistent. The domestication process increased the rhizobacterial community diversity of maize and rice . The alpha diversity of soybean and sunflower rhizobacterial communities decreased during domestication .
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p38
|
PMC11279011
|
sec[3]/p[2]
|
4. Discussion
| 4.140625 |
biomedical
|
Study
|
[
0.99951171875,
0.00023090839385986328,
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] |
[
0.99853515625,
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The structure of symbiotic microbes is affected by various factors such as the culture environment, host genotype, and plant compartment . A comparison of the soil, rhizosphere, and seed microbiomes showed that soil bacterial community composition had a high impact on the bacterial community of the below-ground compartments (e.g., rhizosphere and root endosphere). However, the effect was progressively lowering from the rhizosphere to the root endosphere and finally to the seeds . Therefore, the effects of domestication may not be equal for microbiota colonizing different plant compartments. Investigating the changes in the leaf or fruit pericarp microbiome structure during the domestication process may exclude the influence of environmental factors such as soil type on the microbiome structure, and may effectively reveal the changes in the plant symbiotic microbiome caused by domestication.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p39
|
PMC11279011
|
sec[3]/p[3]
|
4. Discussion
| 4.246094 |
biomedical
|
Study
|
[
0.9990234375,
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[
0.99951171875,
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Members of Bacillus are known to have multiple beneficial traits which help the plants directly or indirectly through acquisition of nutrients, regulation of phytohormones, protection from pathogens and other abiotic stressors . Bacterial seed endophytes of domesticated Cucurbits antagonizing leaf fungal and oomycete pathogens, as well as the majority of pathogen-suppressing endophytes belong to Bacillus . During domestication and genetic improvement processes, the abundance of symbiotic Bacillus in the tomato leaves gradually increased , from 5.77198% in PIM to 7.08867% in CER, and then to 7.36705% in BIG. The LEfSe analysis identified Bacillus as a biomarker genus in the BIG leaves. The abundance of the symbiotic Bacillus in tomato fruits also increased , being 6.05%, 6.80%, and 6.85% in PIM, CER, and BIG fruits, respectively. Bacillus was not identified as a characteristic genus by the LEfSe analysis based on fruit transcriptomic data, probably due to the low amount of PIM transcriptomic data. The increase in the abundance of Bacillus over the domestication and improvement processes was also observed in the maize rhizobacterial communities . Our data, as well as related studies with similar conclusions, indicate that the relative abundance of Bacillus increases during the processes of domestication and genetic improvement, which may be a common phenomenon in crops.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p40
|
PMC11279011
|
sec[3]/p[4]
|
4. Discussion
| 4.191406 |
biomedical
|
Study
|
[
0.99951171875,
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] |
[
0.99853515625,
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] |
Sphingobium was identified as a differential taxon in both the tomato leaves and fruit pericarps, and its abundance showed an increasing trend from PIM to CER to BIG . Studies based on Arabidopsis leaves showed that Sphingobium has the greatest potential to affect phyllosphere microbial community structure as keystone species . Sphingobium is also one of the hubs in the microbial network of rice leaves . The domestication and genetic improvement processes also changed the relative abundance of Sphingobium in the maize rhizosphere microbiome . Domestication and breeding may select microbiomes that confer enhanced resistance to pathogens or abiotic stresses such as drought, salinity, and temperature extremes. This could be due to an increase in antagonistic microbes that inhibit pathogen growth through competitive exclusion or the production of antimicrobials. Microbes can improve plant water use efficiency or help in the synthesis of stress-protective compounds, thus aiding the plant under stress conditions.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p41
|
PMC11279011
|
sec[3]/p[5]
|
4. Discussion
| 4.25 |
biomedical
|
Study
|
[
0.99951171875,
0.00028395652770996094,
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] |
[
0.99853515625,
0.00021374225616455078,
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] |
Exploring the genetic mechanism of this phenomenon is of theoretical and practical significance. Based on leaf metagenomic data, we identified 16 SNPs that were significantly associated with differential microbial taxa during domestication. Most of these SNPs were located in the intergenic or noncoding regions ( Table S8 ). Studies on the genes regulating the plant symbiotic microbiome are mainly focused on the root microbiome . Veronica et al. used leaf metagenomic data from more than 3000 rice germplasms to identify 22 SNPs associated with differential strains, of which 10 SNPs were located in the intergenic or noncoding regions. Studies on symbiotic microorganisms in tomato and rice leaves have shown that SNPs located in the intergenic or noncoding regions may play a key role in the regulation of symbiotic microbial structure. SNP on chromosome 2 was significantly associated with the abundance of four microbial taxa in tomato . There was only one gene in the upstream and downstream 500 kb regions of this SNP. We speculated that this SNP may be involved in regulating the symbiotic microbiome of tomato leaf in a number of ways, including a. regulating Solyc02g014020 gene expression and b. participating in the regulation of symbiotic microorganisms as non-coding RNAs ). In conclusion, the hypotheses and mechanisms discussed provide a framework for understanding how domestication and genetic improvement of tomato plants might influence their symbiotic microbiomes. These changes in the microbiome can have profound effects on plant health, stress tolerance, and productivity. Further research in this area can help optimize breeding strategies to not only improve plant traits but also to harness the benefits of beneficial plant–microbe interactions.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279011_p42
|
PMC11279011
|
sec[4]/p[0]
|
5. Conclusions
| 4.125 |
biomedical
|
Study
|
[
0.9990234375,
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[
0.99951171875,
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Plant breeding or genetic improvement could be used to intentionally modulate the composition of the microbiome and its function, recruiting disease antagonists and plant-growth promoters that enhance plant growth and health. The interactions of endophytic groups with the host plant and other microbial consortia on the physiology of the plant, however, are still poorly understood. Our study showed that the domestication and genetic improvement processes altered the structure and function of tomato leaf and fruit pericarp symbiotic microbiomes. We performed GWAS analyses to identify SNPs significantly associated with tomato leaf characteristic species. This lays the foundation for further exploration of the genetic mechanisms underlying the structural and functional changes of the symbiotic microbiome during crop domestication and genetic improvement.
|
[
"Fei Li",
"Hongjun Lyu",
"Henan Li",
"Kuanling Xi",
"Yin Yi",
"Yubin Zhang"
] |
https://doi.org/10.3390/microorganisms12071351
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11279023_p0
|
PMC11279023
|
sec[0]/p[0]
|
1. Introduction
| 3.511719 |
biomedical
|
Study
|
[
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[
0.99853515625,
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] |
The aim of this study was to analyze the effect of the HFMI treatment of each weld bead on the properties of a butt joint with a ceramic backing welded using a robotic method 135 (MAG) and to determine the effect of HMFI on the stress level and mechanical properties.
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279023_p1
|
PMC11279023
|
sec[0]/p[1]
|
1. Introduction
| 3.375 |
other
|
Study
|
[
0.06256103515625,
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[
0.84912109375,
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Welding techniques constitute the predominant discipline in global steel construction manufacturing. Concomitant with welding are the numerous technological challenges encountered by welding engineers on a daily basis . Among these challenges, the main ones are the deformations and stresses that result from the welding processes; they are the phenomena induced by closely related and characteristic welding processes, encompassing phase transitions related to volume change, uneven and rapid heating and cooling, and alterations in properties such as Young’s modulus (E), yield strength (Re), or thermal expansion coefficient during heating and cooling . The prevailing method employed to alleviate stresses and strains post-welding is heat treatment, notably stress relief annealing. This treatment utilizes electric or gas furnaces, or in localized applications, induction or resistance devices. Another method which can be implemented and is widely used is the optimization of welding parameters and sequences in order to reduce distortion . An alternative to conventional post-weld heat treatments is hammer peening . This method involves inter-pass peening or peening the face of welds, be they butt or fillet welds, to introduce compressive stresses through plastic deformations. The advantage of this method lies in its capability to facilitate both local and globally effective post-weld peening . Peening can be executed through various systems and different conventional methods, such as electric or pneumatic impact weld dressing. Additionally, relatively recent methods of stress reduction by peening include “ultrasonic peening treatment” (UPT), “high-frequency impact treatment” (HiFIT), “ultrasonic peening” (UP), “pneumatic impact treatment” (PIT), and “ultrasonic needle peening” (UNP) . These methods have primarily been developed to enhance impact efficiency, machining precision, and operator comfort by minimizing the impact on the operator . European standards presently lack information regarding whether the use of high-frequency mechanical impact (HFMI) treatment is an essential variable in the welding process. Consequently, research is imperative to address key issues when qualifying metal arc welding technologies to meet the requirements of EN ISO 15614-1 and to examine the influence of HFMI on the results of the required tests . Over the recent 70 years, the yield point of structural steel has surged by more than five times, commencing with low-alloy steel (Re about 200 MPa), progressing through higher-strength normalized low-alloy steel (Re about 350 MPa), steels produced with a thermo-mechanical treatment (Re up to 700 MPa), and culminating with quenched and tempered steels boasting a yield point of approximately 1300 MPa . Fine-grained steels, as a classification, do not constitute a distinct group based on their production process, chemical composition, or mechanical properties. Rather, this designation pertains to steels characterized by a fine-grained microstructure in a condition set for delivery—a feature advantageous due to low grain growth in the heat-affected zone during the welding process . Fine-grained steels are produced using normalizing, thermo-mechanical treatment, and quenching with tempering. The mechanical properties of fine-grained steels depend on both their chemical composition and the production process . The primary challenge during the welding of quenched and tempered fine-grained steels is cold cracking. To optimize the strength and cracking resistance of a welded joint, it is imperative for the strength of the filler metal to be either equal to or slightly lower than the base material. The use of a filler metal with higher strength is not recommended. Welds should be strategically positioned in areas of construction with minimal stresses to mitigate the risks of cracking . Currently, the hammering process has not been considered as one of the technological factors when qualifying welding technology. The tests carried out showed that the hammering process does not reduce the strength properties of the joints, and the results obtained allowed the technology to be qualified based on the applicable standards. Such knowledge is used in practice and allows in some cases to reduce the cost of heat treatment, especially in the case of repaired joints. The novelty of this research is based on a different approach compared to the well-known and already investigated fatigue strength improvement with high-frequency mechanical impact treatment. The direction of this research is based on the thesis that mechanical impact treatment can be considered as a partial replacement or complementary stress reduction method in terms of methods such as post-weld heat treatment, especially for materials where implementation of regular annealing has number of limitations such as S690QL. These limitations stem from the heat treatment state of delivery of quenched and tempered materials. Tempered steels in general can be subjected to stress annealing, adhering to the limits, which are in general 30–40 °C below the steel tempering temperature, which can cause a reduction in hardness and strength because it may temper the steel further, effectively softening it. If this limit is not adhered to, PWHT treatment carried out in a tempering range will result in a reduction in yield and tensile strength.
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279023_p2
|
PMC11279023
|
sec[0]/p[2]
|
1. Introduction
| 1.499023 |
other
|
Study
|
[
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[
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0.299072265625,
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The aim of this article was to connect manufacturing approaches together with advanced and more detailed methodologies which can be implemented during the analysis of welding technology qualifications. Major research studies from a scientific point of view were focused on the determination of stress states while taking into the account that the method used (Barkhausen) could be implemented in heavy industry circumstances.
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279023_p3
|
PMC11279023
|
sec[1]/p[0]
|
2. Materials and Methods
| 2.580078 |
biomedical
|
Study
|
[
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0.31298828125
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[
0.98583984375,
0.013824462890625,
0.0002510547637939453,
0.0001951456069946289
] |
This analysis was based on a comparison of three butt joints made of S690QL plate ( Table 1 ), in the as-welded condition, with the HFMI of each bead and with the heat treatment carried out via stress relief annealing. The consumable used for welding was Multimet brand solid IMT NiMoCr electrode wire with diameter Ø1.2 mm ( Table 2 ).
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279023_p4
|
PMC11279023
|
sec[1]/sec[0]/p[0]
|
2.1. Preparation of Welded Joints
| 1.692383 |
other
|
Other
|
[
0.17431640625,
0.0010242462158203125,
0.82470703125
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[
0.40576171875,
0.59228515625,
0.0008649826049804688,
0.0009746551513671875
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As part of this work, 3 test plates were made of S690QL steel with dimensions of 10 × 150 × 600 mm. For each test plate, there were two plates with a 1/2 V beveled weld groove. These plates were mounted on a CLOOS robotic test bench together with a ceramic backing placed in the axis of the welded joint .
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999995 |
PMC11279023_p5
|
PMC11279023
|
sec[1]/sec[0]/p[1]
|
2.1. Preparation of Welded Joints
| 3.28125 |
biomedical
|
Study
|
[
0.61279296875,
0.0011034011840820312,
0.385986328125
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[
0.98095703125,
0.01861572265625,
0.00029969215393066406,
0.00017821788787841797
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The ceramic backing was utilized to ensure the accurate fusion and formation of the weld root on one side of the robotic bench. The welding procedure was executed on a CLOOS robotic workstation, guaranteeing uniform welding parameters across each of the test plates. These parameters encompassed arc voltage, welding current, number of stitches, welding speed, shielding gas composition (92% Ar + 8% CO 2 ), and the stick-out distance from the welded element. The welding station operator meticulously recorded the welding parameters, and each plate underwent a three-layer welding process (refer to Table 3 ). The resultant outcomes of the average linear energy demonstrate that welding on the robotic workstation facilitated the attainment of consistent welding conditions concerning the linear energy of each bead.
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279023_p6
|
PMC11279023
|
sec[1]/sec[1]/p[0]
|
2.2. High-Frequency Mechanical Impact
| 2.173828 |
other
|
Other
|
[
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0.00322723388671875,
0.595703125
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[
0.2176513671875,
0.78076171875,
0.0007343292236328125,
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] |
The high-frequency (90 Hz) peening of each weld bead was linked with stress reduction in the weld by implementation of compressive stresses into the joint. The Weld Line 10 pneumatic hammer from PITEC GmBH was used for this. The correctness of treatment was achieved when 100% of the surface of each bead including the face was treated . The HFMI was carried out manually .
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999999 |
PMC11279023_p7
|
PMC11279023
|
sec[1]/sec[2]/p[0]
|
2.3. Post-Weld Heat Treatment
| 1.634766 |
other
|
Other
|
[
0.1063232421875,
0.0008206367492675781,
0.89306640625
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[
0.2386474609375,
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0.0011110305786132812,
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] |
The third of the test plates after welding was subjected to heat treatment, which is commonly used to reduce the stress and deformation caused by welding processes. The stress relief annealing process in an electric furnace was divided into three stages: controlled heating, annealing, and controlled cooling .
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279023_p8
|
PMC11279023
|
sec[1]/sec[3]/p[0]
|
2.4. Methodology of Tests and Acceptance Criteria
| 1.296875 |
other
|
Other
|
[
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0.0007185935974121094,
0.9580078125
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[
0.155029296875,
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As part of the post-welding tests basic tests were carried out based on the standards for the qualification of welding technology, and as a supplementary test, a stress state analysis using the Barkhausen effect was carried out.
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |
PMC11279023_p9
|
PMC11279023
|
sec[1]/sec[3]/p[1]
|
2.4. Methodology of Tests and Acceptance Criteria
| 1.412109 |
other
|
Other
|
[
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0.001018524169921875,
0.92041015625
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[
0.01378631591796875,
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0.000331878662109375
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Tests were conducted in accordance with the requirements of EN ISO 15614-1:2017. In accordance with the requirements of the specification and qualification of metal welding technology ( Welding technology testing—Part 1 ), each test plate was subjected to non-destructive testing: ⮚ Visual test (VT) in accordance with EN ISO 17637 ; ⮚ Penetration test (PT) in accordance with EN ISO 3452-1 ; ⮚ Radiographic testing (RT) in accordance with EN ISO 17636-1 .
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999998 |
PMC11279023_p10
|
PMC11279023
|
sec[1]/sec[3]/p[2]
|
2.4. Methodology of Tests and Acceptance Criteria
| 1.737305 |
biomedical
|
Other
|
[
0.96630859375,
0.00864410400390625,
0.02490234375
] |
[
0.307373046875,
0.68115234375,
0.004253387451171875,
0.00736236572265625
] |
All nondestructive tests on the test plates were positive.
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999996 |
PMC11279023_p11
|
PMC11279023
|
sec[1]/sec[3]/p[3]
|
2.4. Methodology of Tests and Acceptance Criteria
| 2.091797 |
biomedical
|
Other
|
[
0.53125,
0.00147247314453125,
0.467041015625
] |
[
0.03033447265625,
0.96923828125,
0.0004248619079589844,
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The next step was to make specimens for destructive testing in accordance with EN ISO 15614-1:2017: ⮚ Tensile test—2 pieces of specimens according to EN ISO 4136 ; ⮚ Side and root bend test—4 pieces of samples in accordance with EN ISO 5173 ; ⮚ Charpy test—2 sets of samples in accordance with EN ISO 9016 ; ⮚ Vickers hardness test—2 lines of measurement in accordance with EN ISO 9015-1 ; ⮚ Macroscopic test—1 piece in accordance with EN ISO 17639 .
|
[
"Jacek Górka",
"Mateusz Przybyła"
] |
https://doi.org/10.3390/ma17143560
|
N/A
|
https://creativecommons.org/licenses/by/4.0/
|
en
| 0.999997 |