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Record W2097610911 · doi:10.1002/cjce.5450790418

The effect of mixing on the generation of alkaline peroxymonosulfate

2001· article· en· W2097610911 on OpenAlexaffvenue
Mohammad Shaharuzzaman, Chad P. J. Bennington

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSodium sulfiteChemistryKraft processOxygenMixing (physics)Kraft paperCatalysisSulfiteChemical engineeringPulp and paper industryInorganic chemistrySodiumOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Alkaline peroxymonosulfate (PMS) has been successfully used in the laboratory for bleaching kraft pulp. Used in conjunction with oxygen, the addition of 1.0% PMS to an oxygen delignification system can increase delignification from 49% to 73% without reducing pulp strength. One promising method of achieving this is the catalytic oxidation of sodium sulfite with oxygen. Laboratory generation of PMS is readily achieved, but typically at low yields and low concentrations. Here we investigate the mixing‐sensitivity of its generation under semi‐batch reaction conditions using a number of laboratory mixers. Our primary focus was on the energy dissipation in the reaction zone and its effect on PMS yield and concentration. By managing the chemical contacting strategy and increasing energy dissipation in the reaction zone we were able to generate PMS at higher yields and concentrations than previously reported.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.174
Teacher spread0.167 · 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 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

Citations3
Published2001
Admission routes2
Has abstractyes

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