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Record W1991291215 · doi:10.1021/ef900424a

Recovery of Hydrogen and Sulfur by Indirect Electrolysis of Hydrogen Sulfide

2009· article· en· W1991291215 on OpenAlexaff
Haiyan Huang, Yingfeng Yu, Keng H. Chung

Bibliographic record

VenueEnergy & Fuels · 2009
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectrolysisChemistryHydrogen sulfideElectrochemistryInorganic chemistryElectrolyteAbsorption (acoustics)HydrogenSulfurSulfur dioxideAqueous solutionMass transferElectrodeMaterials scienceChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrogen sulfide (H 2 S) absorption and conversion to hydrogen and sulfur were carried out in an acidic aqueous vanadium dioxide (VO 2 ) + solution coupled with indirect electrolysis. In this paper, the mechanisms of absorption and electrochemical reactions of the process are discussed. Parametric studies were conducted to determine the effects of operating parameters on absorption and electrochemical reactions. The results showed that the H 2 S absorption increased with temperature; greater than 90% of H 2 S absorption occurred at 50 °C. The absorption reaction was mass-transfer-limiting. In the electrolysis reaction, the current efficiency reached 97% at 45 °C after an extended electrolysis time. The optimal reaction conditions were at a proton concentration of 7 mol/kg of H 2 O in the electrolyte, (VO 2 ) + concentration of lower than 0.65 mol/kg of H 2 O in the electrolyte, and (VO 2 ) + concentration of higher than 0.55 mol/kg of H 2 O in the absorbent. Sulfur particles that are produced can be easily recovered. Some aspects related to design optimization of the absorption process and electrochemical reactor are also discussed.

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 score0.003

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 science0.0000.001
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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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

Citations78
Published2009
Admission routes1
Has abstractyes

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