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Record W2027723902 · doi:10.1021/ie901273m

Influence of Soda−Air−AQ Pulping of Straw on Silica Precipitation, Paper Strength, and Performance of CPVA as a Dry Strength Additive

2009· article· en· W2027723902 on OpenAlexaff
Pedram Fatehi, Ahmet Tutuş, Yonghao Ni, Huining Xiao

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2009
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPulp (tooth)ChemistrySoda pulpingStrawPulp and paper industryPrecipitationAdsorptionScanning electron microscopeChemical engineeringMaterials scienceComposite materialKraft processOrganic chemistryKraft paperInorganic chemistry

Abstract

fetched live from OpenAlex

In this study, the impact of soda−air−anthraquinone (AQ) pulping conditions on delignification and silica precipitation onto pulp fibers and paper sheets was studied. The results showed that the yield and silica precipitation slightly increased upon application of AQ in soda−air pulping. Scanning electron microscopy (SEM) and elemental analysis confirmed the deposition of silica-related particles on fibers and on paper sheets for both soda−air and soda−air−AQ pulping. Furthermore, increasing the cooking temperature had a stronger impact on delignification and silica deposition than did increasing the cooking time for soda−air−AQ pulping. In another set of experiments, the influence of cationic poly(vinyl alcohol) (CPVA) with two different molecular weights on improving the strength properties of straw pulps produced under various soda−air−AQ pulping conditions was studied. The adsorption of CPVA onto straw pulp and its effect on silica retention for paper sheets were comprehensively investigated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.273
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2009
Admission routes1
Has abstractyes

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