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Record W4210276773 · doi:10.1515/npprj-2019-0077

The effects of wood chip compression on cellulose hydrolysis

2022· article· en· W4210276773 on OpenAlexafffund
Miguel E. Villalba, Heather L. Trajano, James A. Olson

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrolysisEnzymatic hydrolysisCelluloseSoftwoodLigninCompression (physics)Yield (engineering)Pulp (tooth)Materials scienceComposite materialCompressive strengthChemistryChemical engineeringPulp and paper industryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Previously it was shown that wood chip compression or enzyme impregnation prior to refining reduces energy consumption and improves pulp quality. This work characterizes the effect of different magnitudes and rates of compression on the extent of enzymatic hydrolysis. A laboratory compressor and a controlled uniaxial load set-up were used to apply different compression ratios and compression times to mixed-softwood wood chips. The chips were subsequently subjected to enzymatic hydrolysis with a high-yield exoglucanase preparation to demonstrate changes in cellulose hydrolysis. Enzymatic hydrolysis yield increased with compression ratio but was unaffected by compression time. Increasing compression ratio increased removal of soluble molecules such as sugars and acid-soluble lignin. Microscopy imaging showed increased cell wall buckling and fracturing with increased compression. The morphological changes led to improved enzyme diffusion and resulted in higher available surface area. The improved cellulose hydrolysis is due to changes in wood morphology as well as the removal of extractives.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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