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Record W2091152932 · doi:10.1021/ie050194x

Bifunctional Redox Iron/Cerium (Hydr)oxide Process for H<sub>2</sub>S Removal from Pulp and Paper Emissions

2005· article· en· W2091152932 on OpenAlexaff
Cătălin Florin Petre, Faı̈çal Larachi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsChemistryOxidizing agentHydrogen sulfideInorganic chemistryRedoxSulfurSulfideThiosulfateAnoxic watersOxygenEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrogen sulfide is the most abundant contaminant among the total reduced sulfur (TRS) quartet in the kraft mill atmospheric emissions. An advantage of the association of oxygen with TRS in the pulp and paper effluents is exploited in a new bifunctional redox scrubbing process based on iron chemistry for the removal of hydrogen sulfide. As synthesized iron/cerium (hydr)oxide composite materials were tested at ambient and alkaline conditions in an agitated batch reactor for oxidizing dissolved bisulfide (HS - ) in both aerobic and anoxic environments. Polysulfides and thiosulfate were the main detected reaction products. The presence of dissolved oxygen contributed to the sustained active surface iron sites regeneration thereby improving significantly HS - removal. Testing the material in three-cycle bisulfide oxidation without reactivation and in the presence of oxygen demonstrated its long-term efficiency.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.050
GPT teacher head0.297
Teacher spread0.247 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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