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FABRICATION OF HIGH STRENGTH PAPER FROM DIFFERENT TYPES OF PHOSPHORYLATED FIBERS USING HOTPRESSING AND FORMING AGENTS

2024· article· en· W4399351859 on OpenAlexaff
Hamid Lamoudan, François Brouillette

Bibliographic record

VenueCellulose Chemistry and Technology · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPapermakingUltimate tensile strengthKraft processPulp (tooth)Kraft paperChemistryPressingHot pressingCationic polymerizationLigninFlocculationComposite materialCellulose fiberPulp and paper industryCelluloseMaterials scienceOrganic chemistryDentistry

Abstract

fetched live from OpenAlex

Phosphorylated fibers offer a broad range of applications, particularly in thermal insulation, notably with wood fibers, provided they exhibit improved mechanical characteristics. Despite encountering challenges in applying traditional papermaking methods, the creation of paper or board sheets with phosphorylated pulp fibers remains a challenge. Findings suggest that phosphorylation-modified fibers show increased roughness. Moreover, in comparison with unbeaten kraft sheets (KF) and thermomechanical pulp sheets (TMP), those made from phosphorylated kraft fibers (PKF), using a cationic coagulant and a flocculant, demonstrate significant enhancements in burst index, break index, and tensile energy absorption by 2.12 times, 1.7 times, and 2.77 times, respectively. Similarly, phosphorylated TMP sheets, prepared with a dual polymeric system (coagulant/flocculant), exhibit improvements of 1.42 times, 1.33 times, and 1.82 times, respectively, in these properties. The study emphasizes the ameliorating effect of cationic polymeric agents on the charge impact of phosphorylated fibers on overall sheet quality, while also highlighting the substantial influence of hot-pressing lignin-containing paper on all determined physical properties.

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)0.000
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 score0.010
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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