MétaCan
Menu
Back to cohort
Record W4403466580 · doi:10.1021/acs.jpclett.4c02372

Modulating the Amine–CO<sub>2</sub> Interaction Strength: Toward Efficient Carbon Capture

2024· article· en· W4403466580 on OpenAlexafffund
Junlin Lan, Chenxu Wang, Meiyue Li, Chunguo Duan, Hao Wang, Junhua Chen, Jens‐Uwe Grabow, Wolfgang Jäger, Yunjie Xu, Qian Gou

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Alberta
FundersFundamental Research Funds for the Central UniversitiesState Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaDeutsche Forschungsgemeinschaft
KeywordsAmine gas treatingCarbon fibersEnvironmental scienceChemical engineeringMaterials scienceEngineeringEnvironmental engineeringComposite material

Abstract

fetched live from OpenAlex

This study contributes to a comprehensive understanding of the interactions between CO 2 and amines at the molecular level by exploring the rotational spectra of binary complexes between CO 2 and eight different amines through pulsed-jet Fourier transform microwave spectroscopy and quantum chemical calculations. The findings reveal a consistent pattern in which CO 2 is bonded to the amino group, primarily through a C···N tetrel bond, while being supported by C–H···O/C hydrogen bonds. Notably, the binding energies increase from primary through tertiary amines and with increasing chain length of the alkyl groups. These groups are found to enhance the electron density at the amino group significantly, thereby facilitating the formation of stronger C···N tetrel bonds. The insights provided into how the interaction strength is modulated by the geometries of amines are deemed essential for the design of more effective CO 2 adsorption materials, thus advancing carbon capture technologies.

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 score0.018
Threshold uncertainty score0.533

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 integrity0.0000.001
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry LettersSame topicCarbon Dioxide Capture TechnologiesFrench-language works237,207