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Record W2295370633 · doi:10.1002/cjce.22410

Improving the material efficiency of recycled furnish for papermaking through enzyme modifications

2015· article· en· W2295370633 on OpenAlexvenueno aff
Piyush Verma, Nishi Kant Bhardwaj, Surendra P. Singh

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCellulaseCellulosic ethanolCellulosePulp (tooth)PapermakingPulp and paper industryHydrolysisChemistryDrainageSlurryAmorphous silicaUltimate tensile strengthEnzymatic hydrolysisLigninMaterials scienceChemical engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Fibre fines and fibrils within recycled cellulosic pulp have high amorphous cellulose content. These fines, having a high surface area, restrict the free drainage of water and retain bound water within the pressed sheet but contribute little to the hydrogen bonding potential of the fibre slurry. The advantage of higher freeness, achieved by selective hydrolysis of excess fines through enzymes, can be used for enhancement of the drainage rate, leading to increased paper production. In this study, monocomponent cellulase treatment of recycled pulp for drainage improvement as a result of selective and controlled hydrolysis is investigated. The effectiveness of specific types of enzyme activity, endoglucanase or cellobiohydrolase, is studied. The increased solubilization of amorphous cellulose mediated by endoglucanase treatments improved pulp drainability by 11–25 %, along with providing better paper properties such as tensile index and smoothness.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.048
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.035
GPT teacher head0.265
Teacher spread0.230 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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