Enhancing Methane Production from Food Waste via Anaerobic Digestion Using Waste-Derived Hydrogels: Improved VFA Conversion and Microbial Niche Formation
Bibliographic record
Abstract
Anaerobic digestion (AD) offers a sustainable approach to food waste valorization through biogas production. Hydrogels, known for high water retention, porosity, and microbial compatibility, are increasingly explored as AD additives to enhance substrate diffusion, pH buffering, and microbial colonization. This study investigates waste-derived hydrogel materials, i.e., pure hydrogel (PH), biochar-hydrogel (BH), and LECA-hydrogel (LH), as multifunctional additives to enhance methane yield under varying organic loading rates (OLRs). At low OLR (0.3 g VS/L/d), BH40 (40 wt % biochar-hydrogel) achieved the highest methane yield (3.71 ± 0.21 L/g VS), producing 27.9% more methane than PH40 (40 wt % hydrogel) due to its buffering and conductive properties that supported syntrophic microbial activity and volatile fatty acids (VFAs) conversion. Conversely, at high OLR (0.9 g VS/L/d), PH40 yielded the highest methane (4.07 ± 0.28 L/g VS), attributed to improved pH stability and VFA utilization. Microbial analysis revealed PH40 enriched key methanogenic taxa, including Bacilli, Synergistia, and Cloacimonadia . Principal component analysis revealed hydrogel additives shaped distinct microbial communities, with PH40 promoting a methanogen-enriched cluster. Overall, this study highlights the novel use of waste-derived hydrogels as dual-function AD enhancers, demonstrating their cost-effective potential to improve methane yield while contributing to circular bioeconomy and sustainable waste-to-energy solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".