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Record W1984511297 · doi:10.3390/nano2030286

Influence of High Shear Dispersion on the Production of Cellulose Nanofibers by Ultrasound-Assisted TEMPO-Oxidation of Kraft Pulp

2012· article· en· W1984511297 on OpenAlexafffund
Éric Loranger, André-Olivier Piché, Claude Daneault

Bibliographic record

VenueNanomaterials · 2012
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsKraft paperKraft processMaterials scienceCellulosePulp (tooth)Dispersion (optics)RheologyPulp and paper industryComposite materialNanocelluloseSodium hypochloriteChemical engineeringShear (geology)NanofiberChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Cellulose nanofibers can be produced using a combination of TEMPO, sodium bromide (NaBr) and sodium hypochlorite, and mechanical dispersion. Recently, this process has been the subject of intensive investigation. However, studies on the aspects of mechanical treatment of this process remain marginal. The main objective of this study is to evaluate the high shear dispersion parameters (e.g., consistency, stator-rotor gap, recirculation rate and pH) and determine their influences on nanocellulose production using ultrasound-assisted TEMPO-oxidation of Kraft pulp. All nanofiber gels produced in this study exhibited rheological behaviors known as shear thinning. From all the dispersion parameters, the following conditions were identified as optimal: 0.042 mm stator-rotor gap, 200 mL/min recycle rate, dispersion pH of 7 and a feed consistency of 2%. High quality cellulose gel could be produced under these conditions. This finding is surely of great interest for the pulp and paper industry.

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)0.001
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 score0.007
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.267
Teacher spread0.250 · 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

Citations34
Published2012
Admission routes2
Has abstractyes

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