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Record W2132697358 · doi:10.1080/02773813.2011.624666

The Use of Sodium Chlorite in Post-Oxidation of TEMPO-Oxidized Pulp: Effect on Pulp Characteristics and Nanocellulose Yield

2012· article· en· W2132697358 on OpenAlexaff
Shree Mishra, Anne-Sophie Manent, Bruno Chabot, Claude Daneault

Bibliographic record

VenueJournal of Wood Chemistry and Technology · 2012
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsNanocelluloseSodium chloriteChemistryPulp (tooth)CellulosePolymerizationDegree of polymerizationNanofiberOrganic chemistryPolymer chemistryNuclear chemistryChemical engineeringChlorine dioxidePolymer

Abstract

fetched live from OpenAlex

Abstract Presence of aldehydes on cellulose nanofibers (alternatively called nanocellulose) produced from TEMPO (2,2,6,6-tetramethylpiperidine-1-oxyle)-oxidized pulp could interfere with the grafting of selected compounds on the carboxyls group of the oxidized pulp or of the nanofibers produced from such pulps. A simple protocol, called post-oxidation, utilizing sodium chlorite under acidic conditions has been developed for TEMPO-oxidized native cellulose to oxidize the aldehydes to carboxyls. The chemical nature, degree of polymerization, nanocellulose yield, and brightness stability of the post-oxidized pulp was characterized and compared with those of pulps prepared by the post-oxidation methods published in the literature. A sodium chlorite charge of 10% was sufficient for the oxidation of the TEMPO-oxidized pulp, which had a residual aldehydes content of ∼100 mmol/kg. We had observed an increase in degree of polymerization, nanocellulose yield, and brightness stability due to post-oxidation. It was found that aldehyde groups contributed significantly to the brightness reversion and yellowing of the TEMPO-oxidized pulp.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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

Citations41
Published2012
Admission routes1
Has abstractyes

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