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Record W4386131031 · doi:10.26599/pbm.2016.9260004

Upgrading Paper-grade Softwood Kraft Pulp to Dissolving Pulp by Cold Caustic Extraction

2016· article· en· W4386131031 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePaper and Biomaterials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsDissolving pulpPulp and paper industryPulp (tooth)HemicelluloseKraft processKraft paperSoftwoodCelluloseKappa numberDissolutionChemistryChromatographyExtraction (chemistry)Raw materialMaterials scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Cold caustic extraction has potential applications in the production of dissolving-grade pulps due to its ability to selectively remove hemicellulose from lignocellulosic materials. In this study, we demonstrate the conversion of paper-grade kraft pulp into dissolving pulp by a single-stage cold caustic extraction. Under the extraction conditions of 12 wt% NaOH lye, 11% pulp consistency, a temperature of 35℃, and 2 h, a paper-grade softwood kraft pulp was purified to high-grade dissolving pulp with 97.1% <i>α</i>-cellulose content, 1.2% pentosane content, and narrowed molecular weight distribution. The resulting extraction filtrate was concentrated by nano-filtration to obtain the hemicellulose content of 59.0 g/L, while the permeate was a clear Na OH solution with 10.9 wt% concentration. A process configuration was also proposed, integrating this cold caustic extraction process with existing pulp and paper production and multi-purpose utilization of the extraction filtrate.

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.

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)0.000
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 score0.017
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.300
Teacher spread0.281 · 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