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Record W2730750085 · doi:10.3390/fermentation3030031

Phenols Removal from Hemicelluloses Pre-Hydrolysate by Laccase to Improve Butanol Production

2017· article· en· W2730750085 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFermentation · 2017
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversité LavalPolytechnique MontréalCentre National en Électrochimie et en Technologies Environnementales
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovationsBioFuelNet Canada
KeywordsLaccaseHydrolysateChemistryButanolAcetoneFermentationPhenolsFlocculationChromatographyFood scienceOrganic chemistryEthanolHydrolysisEnzyme

Abstract

fetched live from OpenAlex

Phenolic compounds are important inhibitors of the microorganisms used in the Acetone-Butanol-Ethanol (ABE) fermentation. The degradation of phenolic compounds in a wood pre-hydrolysate, a potential substrate for the production of ABE, was studied in this article. First, physicochemical methods for detoxification such as nanofiltration and flocculation were applied and the best combination was selected. With a flocculated sample, the concentration of phenolic compounds decreases from 1.20 to 0.28 g/L with the addition of a solid laccase at optimum conditions, which is below the phenolic compounds limit of inhibition. This results in an increase in butanol production, more than double, compared to a pre-hydrolysate non-treated with laccase enzymes.

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.

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 score0.020
Threshold uncertainty score0.552

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 head0.009
GPT teacher head0.236
Teacher spread0.226 · 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