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Record W4390144208 · doi:10.1002/aic.18319

Synergy of carbanion siting and hydrogen bonding in super‐nucleophilic deep eutectic solvents for efficient <scp>CO<sub>2</sub></scp> capture

2023· article· en· W4390144208 on OpenAlexaff
Meisi Chen, Wenjie Xiong, Weida Chen, Shangyu Li, Feng Zhang, Youting Wu

Bibliographic record

VenueAIChE Journal · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsCarbanionChemistryIonic liquidEutectic systemHydrogen bondNucleophileAbsorption (acoustics)Physical chemistryNucleophilic additionInorganic chemistryMedicinal chemistryOrganic chemistryCatalysisMoleculeMaterials science

Abstract

fetched live from OpenAlex

Abstract Carbanion‐based ionic liquids are proposed and utilized as the key components for the construction of five super‐nucleophilic deep eutectic solvents (SNDESs) in the paper. The super‐nucleophilic nature of carbanion‐based ionic liquids (ILs) is found to enable the capture of CO 2 with large absorption capacity. However, the absorption is very slow in the IL due to high viscosity. The synergy of carbanion siting and hydrogen bonding is found to enable high and fast absorption of CO 2 in [N 2222 ][CH(CN) 2 ]‐ethylimidazole (Eim), and a synergistic absorption mechanism is proposed and validated from spectroscopic analyses and quantum calculations. The enthalpy change of CO 2 absorption in [N 2222 ][CH(CN) 2 ]‐Eim is calculated to be −39.6 kJ mol −1 according to the thermodynamic model, and the moderate value implies that both absorption and desorption of CO 2 in the DES are favored and well balanced. The carbanion and hydrogen bond mediated by SNDESs provides a novel insight into the efficient CO 2 capture.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.236
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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 teacher head, 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

Citations31
Published2023
Admission routes1
Has abstractyes

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