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Record W2731606776 · doi:10.24867/jged-2017-1-039

Use of nanoparticle binders for paper coatings: a review

2017· review· en· W2731606776 on OpenAlexaff
Bilge Nazli Altay, Steven Bloembergen, Cem Aydemir, Arif Karademir, Paul D. Fleming

Bibliographic record

VenueJournal of Graphic Engineering and Design · 2017
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsEcoSynthetix (Canada)
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsBiopolymerCoatingStarchMaterials scienceBiodegradationModified starchPolymerNanoparticlePulp and paper industryProcess engineeringNanotechnologyComposite materialChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Starch is a biopolymer that is used as a co-binder alongside synthetic petroleum based latex binders for paper coating applications, though it causes production downtimes due to the problems during cooking process, such as gelling; lack of full expansion of starch granules; increase in viscosity during cooling; bacteria growth in cooked starch and difficulties in viscosity control during storage. On the other hand, synthetic binders negatively affect paper recyclability and biodegradability. To overcome these problems, a new biopolymer binder has been introduced to the paper manufacturing, being used initially as a partial replacement for petroleum based synthetic latex polymers. This study reviews the recent developments of nanoparticle biopolymer binders, referred to as biolatex binders. These binders are shipped dry and can be dispersed in water without cooking requirement while improving paper quality and reducing costs of paper manufacturing. They also provide quality benefits, coater runnability improvements and new higher solids coating formulations for future product advancements

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.845
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.206
GPT teacher head0.374
Teacher spread0.168 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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