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Enhancement of saccharification of corn stover by cellulolytic enzyme produced from biomass-degrading bacteria

2022· article· en· W4212808129 on OpenAlexaff
Yanwen Wu, Sarita Shrestha, Haipeng Guo, Jinchi Zhang, Haisong Wang, Wensheng Qin

Bibliographic record

VenueBioResources · 2022
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsLakehead University
Fundersnot available
KeywordsCorn stoverCellulaseStoverXylanaseHydrolysisFood scienceChemistryBiomass (ecology)Lignocellulosic biomassBeta-glucosidaseBacteriaEnzymeAgronomyFermentationBiochemistryBiology

Abstract

fetched live from OpenAlex

Enzymatic saccharification of corn stover can be enhanced by partially replacing commercial enzymes with bacterial crude enzyme extracts. Thus, in this study, three bacteria (Bacillus sp. A0, Bacillus sp. CH20S1, and Exiguobacterium sp. AS2B) were cultured in a media with corn stover as the substrate to produce crude enzyme extract and saccharify corn stover. The cultural conditions were monitored and optimized to maximize CMCase and xylanase activity in the crude enzyme extracts. After 72 h of hydrolysis of corn stover with diluted crude enzymes (DCE) from the three strains, reducing sugars ranging from 48.2 to 71.7 mg g-1 were released from non-pretreated and pretreated corn stover. Furthermore, the maximum reducing sugars of 316 and 321 mg g-1 were observed when 12 and 4 FPU g-1 of commercial cellulase were added to the DCE of the CH20S1 strain, respectively. It was shown that an effective combination of bacterial DCE with commercial enzymes could achieve higher saccharification of lignocellulosic biomass, which might be cost-efficient compared to their single-use. Overall, this study aims to show the enhanced enzymatic saccharification of corn stover.

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 score0.039
Threshold uncertainty score0.991

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)0.0010.000

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 head0.012
GPT teacher head0.190
Teacher spread0.178 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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