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Record W1528453088 · doi:10.15376/biores.7.3.3051-3063

Aspects on bleaching and tempo-mediated oxidation of wheat straw pulp fractions

2012· article· en· W1528453088 on OpenAlexaff
Anette Heijnesson Hultén, Jiri Basta, Marie Samuelsson, Thomas Greschik, Paul Ander, Geoffrey Daniel

Bibliographic record

VenueBioResources · 2012
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsAkzoNobel (Canada)
Fundersnot available
KeywordsStrawPulp (tooth)Kappa numberPulp and paper industryHypochloriteFractionationChemistryLigninEnvironmental scienceKraft processEngineeringChromatographyKraft paperDentistry

Abstract

fetched live from OpenAlex

This study outlines research concerning wheat straw fines, including their impact on pulp bleachability, their modification by chemical means, and their use as a dry strength enhancer in a paper application. Results show that primary fines constituting about 23% of the whole wheat straw pulp had a large negative impact on pulp bleachability. The differences in response to bleaching were due to the different properties of the fines, i.e. higher kappa number, higher metal ion content, lower brightness, and lower viscosity compared to that of fibers. It was also shown that the fines fraction is more difficult to oxidize and does not reach as high fiber charge levels as that of the other fractions. However, at a dosage of ≥ 3 mmol hypochlorite/g it was possible to prepare nanofibers from wheat straw fines that showed a good strength-enhancing effect in a paper product. The fact that removal of fines from wheat straw pulps by fractionation improves bleachability and also drainage properties at the same time, while simultaneously the fines can be modified and used as a strength enhancer, is very interesting when evaluating the potential of this type of annual plant as a source for production of different paper products.

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 score0.278

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 head0.009
GPT teacher head0.207
Teacher spread0.197 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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