Protein nanofiber additives improve biogas yield, kinetics, and digestate dewaterability in anaerobic digestion
Bibliographic record
Abstract
Anaerobic digestion (AD) plays a key role in wastewater treatment, converting organic waste into biogas and biofertilizers. Yet, the efficiency of AD processes are often constrained by slow microbial kinetics, long digestion times, and poor digestate dewaterability. In this study, we introduce bovine serum albumin (BSA)-derived protein nanofibers (PNFs) as a novel multifunctional bio-additive designed to mimic extracellular polymeric substances (EPS). Biochemical methane potential (BMP) assays were conducted under mesophilic conditions to investigate impacts of BSA PNFs on biogas production, whereby incorporation of 900 mg/L (relative to inoculum) led to a synergistic 16 ± 4 % increase in cumulative biogas production ( p < 0.05). Under high organic loading, BSA PNFs reduced the lag phase and improved microbial activity, shortening ultimate digestion time by 40 % from 35 to 20 days. These improvements were linked to enhanced microbial granulation and community stability: BSA PNFs altered the EPS profile, increasing tightly bound protein content and promoting microbial aggregation. Microscopy revealed improved floc morphology and granule formation in PNF-treated samples. PNF integration also enhanced digestate dewaterability, with a 20 ± 2 % reduction in sludge volume index (SVI) and a 2 ± 0.5 % increase in total solids. These findings demonstrate the potential of BSA PNFs to greatly enhance AD performance and sustainability in wastewater treatment. • Bovine serum albumin (BSA) protein nanofibers (PNFs) enhanced biogas yield by 16 ± 4 %. • BSA PNFs cut ultimate digestion times from 35 to 20 days under high load. • PNFs promoted granulation and the stabilization of microbial aggregates by increasing tightly bound protein concentration. • BSA PNFs improved digestate dewaterability by 20 ± 2 %, reducing sludge volume index.
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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) | 0.000 | 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".