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Record W4295883439 · doi:10.15376/biores.7.1.161-172

Mg(OH)2-based hydrogen peroxide bleaching of CMP pulps at high consistency

2011· article· en· W4295883439 on OpenAlexaff
Rabi Behrooz, Somayeh Ghasemi, Ghasem Asadpour Atoii, Pedram Fatehi

Bibliographic record

VenueBioResources · 2011
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead UniversityUniversity of New Brunswick
Fundersnot available
KeywordsHydrogen peroxidePulp (tooth)Kappa numberChemistryEffluentPulp and paper industryAqueous solutionNuclear chemistryPeroxidePapermakingKraft processOrganic chemistryWaste managementKraft paper

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the bleaching performance of a Mg(OH)2-based hydrogen peroxide process at a high consistency. In this work, an industrially produced chemimechanical pulp (CMP) was bleached via Mg(OH)2- or NaOH-based hydrogen peroxide processes at 10% and 25% consistencies. The results showed that the pulp bleached under the conditions of 1.5% Mg(OH)2 and 3% H2O2 at 25% consistency had a similar brightness to, a lower yellowness index, and a higher opacity than the pulp produced under the conditions of 2.1% NaOH, 3% Na2SiO3, and 3% H2O2 at the same consistency. The temperature (70 ºC) and time (150 min) of the bleaching were the same for both processes. Under the conditions stated above, the Mg(OH)2-based process had a higher yield than the NaOH-based process did. The bleaching effluent of the Mg(OH)2-based process had a higher residual H2O2, but a lower Chemical Oxygen Demand (COD) load and turbidity, compared with that of the NaOH-based process. However, the strength properties and water retention value (WRV) of the pulp bleached via the Mg(OH)2-based process were lower, while its bulk was higher than those of the pulp bleached via 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 score0.019
Threshold uncertainty score0.612

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 head0.014
GPT teacher head0.166
Teacher spread0.151 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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