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Record W2182082524 · doi:10.1139/cjc-2014-0056

Al<sub>12</sub>N<sub>12</sub> nanocage as a potential sensor for phosgene detection

2014· article· en· W2182082524 on OpenAlexvenueno aff
Mohammad T. Baei, Alireza Soltani, Saeedeh Hashemian, Hossein Mohammadian

Bibliographic record

VenueCanadian Journal of Chemistry · 2014
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhosgeneNanocagesChemistryMoleculeGibbs free energyDensity functional theoryAdsorptionEnthalpyPhysical chemistryChemical physicsComputational chemistryThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

To find a novel sensor for phosgene molecules, phosgene adsorption on Al 12 N 12 nanocage was studied by using density functional calculations in terms of geometry, Gibbs free energy, energy gap, enthalpy changes, vibrational frequencies, work function, molecular electrostatic potential surface, and density of state analysis. It was found that the electronic properties of the Al 12 N 12 nanocage are very sensitive to the presence of phosgene molecules so that the energy gap of the nanocage is changed about 31% after the adsorption process. Based on calculated results, the Al 12 N 12 nanocage is expected to be a promising novel sensor for phosgene molecules without manipulating its structure through doping, chemical functionalization, etc.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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.

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

Citations50
Published2014
Admission routes1
Has abstractyes

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