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Record W3087404470 · doi:10.1021/acssuschemeng.0c05183

Potential To Produce Sugars and Lignin-Containing Cellulose Nanofibrils from Enzymatically Hydrolyzed Chemi-Thermomechanical Pulps

2020· article· en· W3087404470 on OpenAlexafffund
Xushen Han, Ran Bi, Hale Oğuzlu, Masatsugu Takada, Jungang Jiang, Feng Jiang, Jie Bao, Jack Saddler

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsCelluloseSoftwoodHydrolysisLigninCellulasePulp (tooth)ChemistryEnzymatic hydrolysisMonosaccharideChemical engineeringNanocelluloseSubstrate (aquarium)HardwoodLignosulfonatesOrganic chemistryPulp and paper industryMaterials scienceComposite materialBotany

Abstract

fetched live from OpenAlex

Softwood mechanical pulps have proven to be quite recalcitrant to enzymatic hydrolysis. However, the unhydrolyzed, residual fibers might have potential as nanofibrillated cellulose feedstocks. In the work reported here, a bleached softwood chemi-thermomechanical pulp (CTMP) was neutrally sulfonated (S-BCTMP) in an attempt to enhance fiber accessibility and enzymatic hydrolysis. A 12 h hydrolysis at 10% solid loading with CTec3 cellulases provided optimum conditions with 22% of the pulp hydrolyzed to monosaccharides and about one-third of the original substrate remaining as lignin-containing cellulose nanofibrils (LCNFs). Prolonged hydrolysis (72 h) resulted in 42% hydrolysis of the original substrate with only 16% of the original S-BCTMP recovered as LCNFs. Although the LCNFs contained high levels of lignin (26.8%–38.5%), they were successfully used to prepare transparent films showing a high contact angle (82.8°) and strong UV-blocking properties. It was apparent that enzyme-mediated modification of CTMP has the potential to produce both fermentable sugars and higher-value LCNFs.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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.

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

Citations45
Published2020
Admission routes2
Has abstractyes

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