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Record W2321839677 · doi:10.1021/acssuschemeng.6b00328

Pretreatment of Eastern White Pine (<i>Pinus strobes</i> L.) for Enzymatic Hydrolysis and Ethanol Production by Organic Electrolyte Solutions

2016· article· en· W2321839677 on OpenAlexafffund
Xiaofei Tian, Lars Rehmann, Chunbao Xu, Zhen Fang

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsWestern University
FundersChinese Academy of SciencesChina Scholarship CouncilCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsChemistryHemicelluloseLigninCelluloseEnzymatic hydrolysisHydrolysisXyloseEthanol fuelEthanolNuclear chemistryOrganic chemistryFermentation

Abstract

fetched live from OpenAlex

Organic electrolyte solution (OES), composed of room temperature ionic liquids (RTILs) and polar organic solvent, was applied during the pretreatment of eastern white pine (EWP), one of the most recalcitrant woody biomass. The influence of various crucial parameters that govern the dissolution and further pretreatment process were examined. A gradual reduction of the crystallinity of cellulose I, fragmentation and fibrillation, as well as lignin redistribution occurred with an increase of χ [AMIM]Cl (molar portion of [AMIM]Cl) from 0.1 to 0.9; whereas the content of the cellulose, acid insoluble lignin as well as hemicellulose composition did not change. The efficiency of glucose released from EWP through rapid enzymatic hydrolysis (24 h hydrolysis yield) and the final hydrolysis yield (120 h hydrolysis yield) were improved remarkably by up to 460% and 500% after OES pretreatment. No negative effect of OES pretreatment on downstream ethanol fermentation was observed, and the highest ethanol productivity was 11.04 g ethanol/100 g EWP (when χ [AMIM]Cl = 0.9).

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 score0.032
Threshold uncertainty score0.827

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 head0.004
GPT teacher head0.165
Teacher spread0.162 · 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

Citations18
Published2016
Admission routes2
Has abstractyes

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