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Record W1787156164 · doi:10.15376/biores.7.3.3948-3959

Using cationic polymers to improve alkenyl succinic anhydride (ASA) sizing efficiency in high-yield pulp containing furnish

2012· article· en· W1787156164 on OpenAlexafffund
Qijie Chen, Yonghao Ni, Zhibin He

Bibliographic record

VenueBioResources · 2012
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of New Brunswick
FundersCanada Research Chairs
KeywordsSuccinic anhydrideCationic polymerizationPulp (tooth)SizingPolyacrylamidePolymerEmulsionPulp and paper industryChemical engineeringMaterials scienceChlorideYield (engineering)ChemistryOrganic chemistryPolymer chemistryComposite material

Abstract

fetched live from OpenAlex

High-yield pulp (HYP) such as bleached chemi-thermo-mechanical pulp (BCTMP) from aspen wood, is now being used in the production of high-quality fine papers, and this is particularly true in China. In this study, the effect of using cationic polymers such as poly-aluminum chloride (PAC), polyethylenimine (PEI), cationic polyacrylamide (CPAM), and poly-(diallyldimethylammonium chloride) (PDADMAC) on Alkenyl Succinic Anhydride (ASA) sizing efficiency in HYP-containing pulp furnish was investigated. The results showed that the addition of only ASA emulsion to the HYP-containing furnish, without any cationic additives, did not yield an appreciable degree of sizing in the paper sheet. PAC (or alum) could improve the ASA sizing efficiency. PEI, PDADMAC, CPAM, and NaHCO3 all improved ASA sizing efficiency in the presence of PAC. The charge density of PEI was important for improving the ASA sizing performance. Also, the sequence of adding the cationic polymer affected the ASA sizing performance.

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 score0.069
Threshold uncertainty score0.880

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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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

Citations9
Published2012
Admission routes2
Has abstractyes

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