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Record W2172267870 · doi:10.1002/aic.14634

Protic ionic liquids for the selective absorption of H<sub>2</sub>S from CO<sub>2</sub>: Thermodynamic analysis

2014· article· en· W2172267870 on OpenAlexaff
Kuan Huang, Xiaomin Zhang, Yun Xu, Youting Wu, Xingbang Hu, Yun Hua Xu

Bibliographic record

VenueAIChE Journal · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsIonic liquidChemistryFormateSolubilitySelectivityAbsorption (acoustics)Methyl formateIonic bondingInorganic chemistryPhysical chemistryOrganic chemistryIonMethanolMaterials scienceCatalysis

Abstract

fetched live from OpenAlex

The solubilities of H2S and CO2 in four protic ionic liquids (PILs)—methyldiethanolammonium acetate, methyldiethanolammonium formate, dimethylethanolammonium acetate, and dimethylethanolammonium formate were determined at 303.2–333.2 K and 0–1.2 bar. It is shown PILs have higher absorption capacity for H2S than normal ionic liquids (ILs) and the Henry's law constants of H2S in PILs (3.5–11.5 bar at 303.2 K) are much lower than those in normal ILs. In contrast, the solubility of CO2 in PILs is found to be a magnitude lower than that of H2S, implying these PILs have both higher absorption capacity for H2S and higher ideal selectivity of H2S/CO2 (8.9–19.5 at 303.2 K) in comparison with normal ILs. The behavior of H2S and CO2 absorption in PILs is further demonstrated based on thermodynamic analysis. The results illustrate that PILs are a kind of promising absorbents for the selective separation of H2S/CO2 and believed to have potential use in gas sweetening. © 2014 American Institute of Chemical Engineers AIChE J 60: 4232–4240, 2014

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: Simulation or modeling · Consensus signal: none
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 head0.009
GPT teacher head0.230
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations159
Published2014
Admission routes1
Has abstractyes

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