Enhancement of biogenic sulfide production in a packed‐bed bioreactor via critical inoculum design and carrier material selection
Bibliographic record
Abstract
Sulfate reducing bacteria (SRB) are commonly used in environmental bioprocesses for the treatment of acid mine drainage and sulfate wastewaters. Biogenic H(2)S is also a potential source of H(2) fuel with the recent development of H(2)S splitting technologies. In this study, a sulfate reducing packed bed bioreactor (PBR) capable of rapidly achieving high volumetric productivities was developed using a novel method of rational inoculum design and the selection of improved biomass carrier materials. An inoculum with initial composition of approximately 95% Desulfovibrio desulfuricans (ATCC 7757) and 5% SRB consortium was designed based on the pure strain's superior immobilization potential and the SRB consortium's superior kinetics. Diatomaceous earth (DE) pellets, porous glass beads, polyurethane foam and bone char were evaluated as potential biomass carrier materials. The DE pellets immobilized the most biomass and were employed in two packed bed bioreactor fermentations. Using the designed inoculum and DE pellets, a packed bed bioreactor achieved a volumetric productivity of 493 mol H(2)S m(-3) day(-1) (based on a 308 mL working volume) with a dissolved sulfide concentration of 9.9 mM. This occurred after 8.3 days of operation and represents a tenfold reduction in the start-up period compared to other sulfate reducing PBRs described in the literature.
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) | 0.001 | 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 | 0.001 | 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".