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Record W2347974277

Effect of Deinking Process on Wastepaper Fiber Properties

2012· article· en· W2347974277 on OpenAlexaff
Rumin Wang

Bibliographic record

VenueTransactions of China Pulp and Paper · 2012
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsScience North
Fundersnot available
KeywordsDeinkingFiberPulp and paper industryMaterials scienceScanning electron microscopeWaste paperChemical engineeringComposite materialChemistryWaste managementEngineering
DOInot available

Abstract

fetched live from OpenAlex

Two kinds of deinking processes,neutral deinking process and alkaline deinking process,were used for old newspaper(ONP) deinking.Olympus inverted biological microscope was used to analyze fiber and ink particles dispersing behaviors in the deinking system.Waste-paper fiber structures before and after deinking were characterized by FTIR.Scanning electron microscopy(SEM) was used to analyze fiber surface change after alkaline and neutral deinking.The results showed that the effect of different deinking process on the fiber morphology and structure is obvious.After deinking,the average fiber length and kink index decrease and fines increase.Influence of alkaline deinking on the fiber morphology is more significant than that of neutral deinking.Compared to alkaline deinking,the fiber morphology and structure after neutral deinking have few change,there is less hydroxyl group on the fiber surface and the neutral deinked fiber surface has less fibril and less damage.

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 score0.003

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 communication0.0000.001
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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