MétaCan
Menu
Back to cohort
Record W4280590915 · doi:10.1002/asia.202200416

Identifying of Pure and Defected Ti<sub>2</sub>C Materials Using Gas Probe Molecules: First Principles Calculations

2022· article· en· W4280590915 on OpenAlexaff
Fengxiang Zhang, Ziyue Song, Wei Hu, Yujin Zhang

Bibliographic record

VenueChemistry - An Asian Journal · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsMoleculeAdsorptionDensity of statesMaterials scienceMolecular orbitalFermi levelCrystal (programming language)CrystallographyChemical physicsChemistryPhysical chemistryElectronCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

Abstract Employing first principles calculations, we systematically investigated the geometrical and electronic structures of pure, titanium defected (DTi) and carbon defected (DC) Ti2C materials. We found the defected Ti2C exhibits stronger metallic property than pure Ti2C due to the enhanced density of Ti‐d orbital state near the Fermi level. We then studied the adsorption as well as the infrared spectrum (IR) response of the four kinds of gas molecules (CH4, NH3, CO and NO) on pure, DTi and DC Ti2C surfaces. Simulations show that CO and NO molecules are chemically adsorbed on all Ti2C surface with similar adsorption sites. However, CH4 and NH3 molecules would be dissociated on Ti2C surface. Negative values of crystal orbital Hamilton population as well as the PDOS calculations show that the red shift in IR spectra of CO and NO molecules originates from the decreasing bonding strength of probe molecules. The present work provides rich insight for the adsorption and identification for different Ti2C materials.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.267
Teacher spread0.233 · 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 designSimulation or modeling
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueChemistry - An Asian JournalSame topicMXene and MAX Phase MaterialsFrench-language works237,207