Evaluation of Aging Methods on the Surface Characteristics of Hydrochar and Germination Indices for Kale Seeds
Bibliographic record
Abstract
Hydrochar derived from hydrothermal carbonization (HTC) has been recognized as a potential absorbent and horticultural substrate. However, its practical application has been limited due to its low adsorption capacity and negative effects on plant growth. To address these issues, three pre-treatment methods (water washing, microbial aging, and freezing-thawing aging) were employed to further improve the physical structure and chemical properties of hydrochar. A seed germination test with kale (Brassica oleracea var. acephala D.C) was conducted to evaluate the phytotoxicity of modified hydrochars. The results showed that microbial aging considerably enhanced the physicochemical properties of the hydrochar. Specifically, under microbial aging, the bulk density of microbial-aged hydrochar (MHC) decreased by 8.1%, the porosity increased by 24.8%, and the water-holding capacity increased by 36.54% compared to fresh hydrochar (FHC). Moreover, the surfaces of MHC and freezing-thawing aged hydrochar (FTHC) were observed with rough and cracked surfaces and macro pore structures. Fourier transform infrared (FTIR) spectroscopy revealed that the functional group’s intensities of the four hydrochar materials varied, and that MHC and FTHC had more oxygen-containing groups than the others. Additionally, the surface areas of MHC and FTHC increased by 318.64% and 238.98% compared to FHC, respectively. The seed germination test indicated the strong inhibitory effect of FHC, while MHC significantly (p < 0.05) improved the seed germination rate and root development. These findings suggest that among the different pre-treatment methods, microbial aging demonstrated the greatest potential for practical application in improving the physicochemical properties of hydrochar and promoting seed germination. This study opens up new avenues for further research on improving hydrochar and suggests that future studies should focus on optimizing the aging process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".