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Review: Using magnesium hydroxide as the alkali source for peroxide bleaching of mechanical pulps - process chemistry and industrial implementation

2010· article· en· W2324745185 on OpenAlexaff
Yonghao Ni, Zhibin He

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2010
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Oxide Properties and Applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsChemistryPeroxideMagnesiumSodium hydroxideAlkali metalHydrogen peroxideInorganic chemistryHydroxideOxalatePulp (tooth)Organic chemistry

Abstract

fetched live from OpenAlex

Abstract Magnesium hydroxide, which is a weak alkali, can be used as the alkali source for peroxide bleaching of mechanical pulps. This magnesium hydroxide-based peroxide bleaching process has been commercialized in the paper industry. In this paper, we review the literature results regarding the process chemistry of the Mg(OH)2-based peroxide bleaching process, including the kinetics, peroxide decomposition, anionic trash/COD/oxalate formation, the effect on pulp properties. The benefits associated with the Mg(OH)2- based peroxide bleaching process are discussed. We also discuss the mill implementation of the Mg(OH)2- based peroxide bleaching process in the pulp and paper industry. A case is presented where magnesium hydroxide completely replaces sodium hydroxide as the alkali source. A partial magnesium substitution for sodium hydroxide can also be an option.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.075
GPT teacher head0.403
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations14
Published2010
Admission routes1
Has abstractyes

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