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Record W3133087812 · doi:10.1039/d0cp05756h

Role of metcar on the adsorption and activation of carbon dioxide: a DFT study

2021· article· en· W3133087812 on OpenAlexaff
Megha, Arup Banerjee, Tapan K. Ghanty

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsEmployment and Social Development Canada
Fundersnot available
KeywordsCarbon dioxideAdsorptionChemistryChemical engineeringMaterials sciencePhysical chemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Metallocarbohedrenes or metcars belong to one of the classes of stable nanoclusters having a specific stoichiometry. In spite of the available theoretical and experimental studies, the structure of pristine Ti8C12 metcar is still uncertain. We study the geometric structure of a titanium metcar, Ti8C12, together with its electronic properties and chemical activity towards adsorption and activation of CO2 molecule by means of density functional theory. Our results suggest that the CO2 molecule is strongly adsorbed and undergoes a significantly high degree of activation onto the Ti8C12 metcar. The migration of charge from titanium metcar to CO2 molecule attributes the high degree of activation of this molecule. In the infrared vibrational spectra for CO2 molecule adsorbed onto Ti8C12, we find a new signal which is absent in the corresponding spectra for gaseous CO2. In addition to adsorption energy, we also estimate the energy barrier for the dissociation of CO2 molecule to CO and O fragments on a Ti8C12 cluster. As a whole, this work reveals the ground state geometry of Ti8C12 metcar and highlights the role of this metcar in CO2 adsorption and activation, which are the key steps in designing potential catalysts for CO2 capture and its conversion to industrially valuable chemicals.

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 score0.021
Threshold uncertainty score0.770

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 head0.234
Teacher spread0.223 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

Explore more

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