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Pilot-Scale Investigation into the Effects of Alkaline Peroxide Pre-Treatments on Low-Consistency Refining of Primary Refined Softwood TMP

2016· article· en· W2233901566 on OpenAlexaff
Xue Feng Chang, Antti Luukkonen, James A. Olson, Rodger P. Beatson

Bibliographic record

VenueBioResources · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsBritish Columbia Institute of TechnologyUniversity of British Columbia
Fundersnot available
KeywordsSoftwoodPeroxidePulp (tooth)Ultimate tensile strengthMaterials sciencePulp and paper industryHydrogen peroxideRefining (metallurgy)Composite materialChemistryMetallurgyOrganic chemistryDentistry

Abstract

fetched live from OpenAlex

Primary refined coarse softwood thermomechanical pulp was treated with alkaline peroxide prior to low-consistency (LC) refining. The effects of the pre-treatments on pulp quality, refinability, and electrical energy consumption were assessed. Four pre-treatments were conducted with alkali charges of 2.5 and 6% and peroxide charges of 3 and 4%. The pulps were refined to specific energies up to 600 kWh/t by multiple passes through an LC refiner at intensities of 90 and 150 kWh/t. It was found that alkaline peroxide treatments increased tear strength and protected the fibre from cutting, especially during high intensity refining below a specific energy of 300 kWh/t. Treatment with 6% NaOH and 4% or 3% H2O2 led to lower brightness gains and scattering coefficients but increased the tensile strength index by 31%, potentially lowering the total electrical energy required to achieve strong pulp. The enhancement of tensile strength caused by the highly alkaline peroxide mostly resulted from increased bonding, which was attributable to acid group generation rather than the promotion of further fibrillation during LC refining.

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)0.001
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 score0.032
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.253
Teacher spread0.238 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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