Effects of trace elements on digester performance and microbial community response in anaerobic digestion systems
Bibliographic record
Abstract
Trace elements (TE), as micronutrients for microorganisms, have a significant impact on the stability of anaerobic digestion (AD). Studies have been conducted on process stability and performance of the AD of food waste (FW) by supplementing TEs. In this study, mesophilic batch biomethane potential (BMP) tests using FW were conducted to investigate the effect of TEs (Fe, Ni, Co, Se, and Mo) as single and mixed ions. In view of their scarcity, correlations between the microbial community and digester performance such as first-order hydrolysis coefficient (Kh), volatile fatty acids (VFA), methane yield, and methane production rate (MPR) have been developed. Ni2+ at 1 and 1.5 mg/L increased the methane yield by 27% and 23% respectively. Similarly, Co2+ at 0.1 and 0.5 mg/L increased the yield by 21% and 23% respectively, compared to control. Although Se4+ at all concentrations enhanced the methane yield, Fe2+ at only 50 mg/L increased methane yield by 22%. For mixed TEs, the combination of Ni2+ [1 mg/L] +Co2+ was the best and increased methane for all Co2+ concentrations (0.1, 0.4 and 0.5 mg/L) by 16%, 14% and 12% respectively. Firmicutes and Methanosaeta were the most abundant phyla among hydrolytic and methanogenic microbial groups, respectively, constituting 42%-61% and 60-80% of their respective microbial groups. The most significant positive correlations were observed between aceto/acidogenic microorganisms and final VFA concentrations with Pearson correlation factors of 0.91.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".