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Record W4235126042 · doi:10.32920/ryerson.14648721.v1

Enzymatic Hydrolysis of Wheat Straw in a Stirred-Tank Reactor

2021· preprint· en· W4235126042 on OpenAlexafffund
Ejaz Ahmad Khan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStrawHydrolysisContinuous stirred-tank reactorYield (engineering)BiofuelChemistryEnzymatic hydrolysisMixing (physics)Pulp and paper industryImpellerEthanolBioreactorMaterials scienceChemical engineeringWaste managementBiochemistryOrganic chemistryComposite materialMechanical engineeringInorganic chemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

In an attempt to elucidate the effect of some operating conditions on the rate and extent of enzymatic hydrolysis of lignocellulosic materials in a stirred tank reactor, wheat straw was hydrolyzed by mixing with two pitched-blade impellers mounted on a shaft under various atmospheric conditions: static air in headspace, N₂ gas flowing over the surface of the medium, and minimal static air by a lid touching the surface of the medium. The presence of N₂ gas over the reaction medium produced by the highest 6.9 % (w/w) conversion in 36 hours. The initial conversion when N₂ gas flowed in the headspace (2.9 % w/w), and when a lid was used (2.9 % w/w) as compared to conversion in air presence (2.3 % w/w), seems to indicate that the enzyme activity was affected due to oxidation in the presence of air. The observed low conversion yield was probably the result of the non sterile conditions imposed by industrial requirements for the production of biofuel ethanol from agricultural lignocellulosics.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes2
Has abstractyes

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