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Record W1840744766 · doi:10.15376/biores.5.4.2258-2267

Impact of acid washing and chelation on Mg(OH)2-based hydrogen peroxide bleaching of mixed hardwoods CMP at high consistency

2010· article· en· W1840744766 on OpenAlexafffund
Somayeh Ghasemi, Rabi Behrooz, Pedram Fatehi, Yonghao Ni

Bibliographic record

VenueBioResources · 2010
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrogen peroxidePulp (tooth)ChelationChemistryEffluentPeroxideNuclear chemistrySulfuric acidPulp and paper industryInorganic chemistryWaste managementOrganic chemistry

Abstract

fetched live from OpenAlex

The removal of transition metal ions is crucial for improving the efficiency of subsequent peroxide bleaching. Acid-washing and chelation have been proposed for such a purpose. However, their influences on the Mg(OH)2-based peroxide bleaching of hardwood pulps at a high consistency have not been well documented in the literature. In this work, we studied the influence of acid-washing using sulfuric acid or chelation using diethylenetriaminepentaacetic (DTPA) on the Mg(OH)2- or NaOH-based hydrogen peroxide bleaching efficiency, effluent properties of bleaching filtrates, and paper properties. The results showed that for Mg(OH)2-based peroxide bleaching, the pulp yield and water retention value of acid-washed pulp were higher than those of the chelated pulp; the chemical oxygen demand (COD) and turbidity of the bleaching filtrates for the acid-washed pulp were lower than those of the chelated pulp. The bleached acid-washed pulp had lower strength properties than bleached chelated pulp did. Additionally, at a high pulp consistency (25%), the Mg(OH)2-based process had a higher bleaching efficiency and superior bleaching effluent properties, but a lower strength properties, in comparison with the NaOH-based process.

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 score0.003
Threshold uncertainty score0.549

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 head0.006
GPT teacher head0.205
Teacher spread0.200 · 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

Citations5
Published2010
Admission routes2
Has abstractyes

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