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Product Optimization of Biphasic Absorbents for CO<sub>2</sub> Capture

2025· article· en· W4413289274 on OpenAlexaff
Yinchun Liang, Hongyu Duan, Weida Chen, Lihui Sun, Feng Zhang

Bibliographic record

VenueEnergy & Fuels · 2025
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsProduct (mathematics)Process engineeringChemistryComputer scienceMaterials scienceChemical engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

New absorbents combining the advantages of phase separation and easily regenerated products were prepared with 2-methyl-2-amino-1-propanol (AMP) as the main absorbent and tertiary amines as the auxiliary absorbents and phase splitters. The absorption performances of absorbents containing different tertiary amines were systematically compared. Among these tertiary amines, N, N, N ′, N ′-tetramethyl-1,3-propanediamine (TMPDA) exhibited the most effective properties. For the absorbent TMPDA–AMP–H 2 O system (TAH), an absorption load of approximately 2.6 mol/kg was achieved. The kinetic and thermodynamic properties of TAH absorption were evaluated. It was found that the absorption products were readily decomposed into HCO 3 –, and the regeneration efficiency of TAH was above 91% at 358 K, while the regeneration energy consumption was only 2.0 GJ/t CO 2 . It should be noted that the addition of TMPDA effectively inhibited the precipitation of bicarbonate, thus leading to a higher absorption capacity and lower energy consumption per ton of CO 2 loaded.

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)0.0010.000
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 head0.006
GPT teacher head0.211
Teacher spread0.204 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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