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Record W4402837353 · doi:10.70464/mjbet.v1i1.1179

Effect of Commercial Cationic Starch on Paper Properties Made from Recycled Pulp

2024· article· en· W4402837353 on OpenAlexaboutno aff
Jia Geng Boon, Jing Xian Liew, Jeng Young Liew

Bibliographic record

VenueMalaysian Journal of Bioengineering and Technology (MJBeT) · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCationic polymerizationPulp (tooth)StarchPulp and paper industryChemistryMaterials scienceFood scienceChemical engineeringPolymer chemistryDentistryEngineeringMedicine

Abstract

fetched live from OpenAlex

The production of packaging paper is growing steadily annually globally. Packaging paper such as liner paper and medium paper are mainly made from recycled pulp. The strength of pulp deteriorated over the cycle of recycling. To ensure the packaging paper strength produced from recycled paper is up to par, the additive is mandatory. Starch is one of the common and economical additives used in papermaking. In this research, the performance of commercial cationic starch as an additive in packaging papermaking is evaluated. 4 different percentages of starch, including 2%, 4%, 6%, and 8%, were studied. Five replicates of 60gsm hand sheets were produced for each level, and 0% was introduced as a control. The properties such as thickness, Canadian Standard Freeness Test, Cobb test, tensile index, folding endurance, and tear index were evaluated according to the TAPPI Standard. From the result, an 8% dosage of this commercial cationic starch exhibited the best performance in mechanical tests without compromising the drainage of water significantly.

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)0.001
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.251
Teacher spread0.243 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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