Syntrophic interactions in anaerobic digestion: how biochar properties affect them?
Bibliographic record
Abstract
Biochar as a biomass derived, low cost, carbon conductive material is considered as an important supplement in the anaerobic digestion (AD) of organic matter. It functions as an electrical grid to allow direct electron transfer from fatty acid oxidizers to methanogenic archaea, thereby promoting syntophy between various microbial groups and leading to efficient methanogenesis. Specific properties of biochar play an important role in promoting syntrophic interactions in AD. As a physical indicator, surface area, porosity, particle size and surface texture of biochar play an important role in governing microbial attachment and enrichment on biochar. This influences the microbial degradation of fatty acids and their subsequent conversion to methane by methanogens. Chemical properties such as the presence of hydrophobic functional groups, molecular nature and redox active groups on biochar surface promote interaction between biochar and microorganisms and provide an increased degree of electron transfer between the attached microorganisms. The above characteristics depend on feedstock and pyrolysis conditions used for biochar production. Unlike previous reviews, herein the desired physical and chemical properties of biochar that promote syntrophy in AD and the factors that influence them have been discussed in detail. Furthermore, engineering of biochar properties by various activation methods to harness favourable characteristics of biochar as an effective AD additive is described. Such a comprehensive account would be useful for engineering efficient biochar-mediated digestions with enhanced syntrophy and overall AD performance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.033 |
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".