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Record W2096502170 · doi:10.1002/bit.21827

Enhancement of biogenic sulfide production in a packed‐bed bioreactor via critical inoculum design and carrier material selection

2008· article· en· W2096502170 on OpenAlexaff
Matthew James Lee McMahon, Andrew J. Daugulis

Bibliographic record

VenueBiotechnology and Bioengineering · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsBioreactorPelletsPacked bedBiomass (ecology)Sulfate-reducing bacteriaSulfateChemistrySulfidePulp and paper industryChemical engineeringChromatographyMaterials scienceGeologyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.208
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2008
Admission routes1
Has abstractyes

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