MétaCan
Menu
Back to cohort
Record W1989118706 · doi:10.1126/science.1194237

Direct Observation and Quantification of CO <sub>2</sub> Binding Within an Amine-Functionalized Nanoporous Solid

2010· article· en· W1989118706 on OpenAlexaff
Ramanathan Vaidhyanathan, S.S. Iremonger, George K. H. Shimizu, Peter G. Boyd, Saman Alavi, Tom K. Woo

Bibliographic record

VenueScience · 2010
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of OttawaUniversity of Calgary
Fundersnot available
KeywordsAmine gas treatingSorbentSorptionMoleculeNanoporousAdsorptionChemistryMolecular bindingBinding energyPhysical chemistryComputational chemistryMaterials scienceOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Understanding the molecular details of CO(2)-sorbent interactions is critical for the design of better carbon-capture systems. Here we report crystallographic resolution of CO(2) molecules and their binding domains in a metal-organic framework functionalized with amine groups. Accompanying computational studies that modeled the gas sorption isotherms, high heat of adsorption, and CO(2) lattice positions showed high agreement on all three fronts. The modeling apportioned specific binding interactions for each CO(2) molecule, including substantial cooperative binding effects among the guest molecules. The validation of the capacity of such simulations to accurately model molecular-scale binding bodes well for the theory-aided development of amine-based CO(2) sorbents. The analysis shows that the combination of appropriate pore size, strongly interacting amine functional groups, and the cooperative binding of CO(2) guest molecules is responsible for the low-pressure binding and large uptake of CO(2) in this sorbent material.

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 science0.0000.000
Research integrity0.0010.000
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 head0.030
GPT teacher head0.278
Teacher spread0.247 · 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

Citations916
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueScienceSame topicMetal-Organic Frameworks: Synthesis and ApplicationsFrench-language works237,207