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Record W2009332062 · doi:10.1002/btpr.481

A strategic approach for the design and operation of two‐phase partitioning bioscrubbers for the treatment of volatile organic compounds

2010· article· en· W2009332062 on OpenAlexaff
Sung Ho Yeom, Andrew J. Daugulis, David R. Nielsen

Bibliographic record

VenueBiotechnology Progress · 2010
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsChemistryBenzeneSolubilityPhase (matter)SolventAqueous solutionReaction rate constantHexadecaneChromatographyOrganic chemistryKinetics

Abstract

fetched live from OpenAlex

A strategic approach for the design of two-phase partitioning bioscrubbers (TPPBs) has been formulated using, as a basis, a re-evaluation of extensive literature data available for the degradation of benzene by Achromobacter xylosoxidans Y234 in TPPBs with n-hexadecane as the partitioning phase. Using a previously determined maintenance coefficient for benzene, we determined that an inlet benzene loading rate of 100 mg/h requires 5,928 mg cell mass at biological steady state and 243.0 mg O(2) /h. The total oxygen-transfer rates (TOTRs) into the TPPB increased by 83.5% with 33.3% of organic phase compared with a single aqueous phase and were significantly influenced by gas flow rate, whereas agitation has a minor affect. The fraction of organic phase used was suggested to be the primary parameter with which the TOTR into the TPPB may be altered. Although the presence of an organic solvent in the TPPB remarkably increased the TOTR, the total benzene transfer rate into the TPPB remained largely insensitive due to the intrinsic low Henry's law constant (or relatively high solubility) of benzene in water. Finally, we have integrated the elements of this analysis into a set of heuristic criteria that can serve as a guideline for the design of TPPB systems for future volatile organic compound treatment applications.

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: none
Teacher disagreement score0.505
Threshold uncertainty score0.251

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 head0.045
GPT teacher head0.301
Teacher spread0.257 · 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

Citations15
Published2010
Admission routes1
Has abstractyes

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