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Record W4281986763 · doi:10.1021/acsomega.2c01964

Diatomite Modified with an Alkyl Ketene Dimer for Hydrophobicity of Cellulosic Paper

2022· article· en· W4281986763 on OpenAlexaff
Zicheng Chen, Guangyuan Fan, Xiangyang He, Lei Xu, Xuefeng Zhang, Zhibin He, Lanhe Zhang

Bibliographic record

VenueACS Omega · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New Brunswick
FundersEducation Department of Jilin Province
KeywordsPapermakingSizingKeteneCationic polymerizationAlkylEmulsionPolyacrylamideChemical engineeringChemistryMaterials sciencePulp and paper industryProcess engineeringOrganic chemistryComposite materialPolymer chemistryEngineering

Abstract

fetched live from OpenAlex

Multifunctionalization of papermaking chemicals is one of the main developing strategies. Fillers and internal sizing agents are often mutually restricted in practice. Therefore, it is feasible to prepare a new papermaking chemical by combining the functions of both. A process of diatomite modified with an alkyl ketene dimer (AKD) was developed in this study. The modified diatomite (AD) can concurrently play the role of a mineral filler and sizing agent in the papermaking process. With the equal dosage of AKD, the AD showed better sizing and retention performance than the commercial AKD emulsion in the case of cationic polyacrylamide (CPAM) and the CPAM/bentonite retention system. The sizing mechanism of the AD can be interpreted to be due to numerous hydrophobic sites and the microsurface structure of the paper sheet caused by the AD. Since ketones were not detected in Fourier-transform infrared spectra of the paper sheet filled by the AD, the chemical reaction may not be indispensable for its sizing performance. What is more, an interesting "sticky" hydrophobicity phenomenon was observed when filling with AD. The approach in this study to prepare the "sticky" hydrophobic paper sheet can find its applications in some nontraditional application fields of cellulosic paper.

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 score0.012
Threshold uncertainty score0.558

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 head0.023
GPT teacher head0.274
Teacher spread0.251 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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