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Record W2026416387 · doi:10.1080/15363831003721740

Ammonia Adsorption on SiC Nanotubes: A Density Functional Theory Investigation

2011· article· en· W2026416387 on OpenAlexfundno aff
Masoud Darvish Ganji, N. Seyed-aghaei, Mehdi Taghavi, Mahyar Rezvani, F. Kazempour

Bibliographic record

VenueFullerenes Nanotubes and Carbon Nanostructures · 2011
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
FundersIslamic Azad UniversityUniversity of Lethbridge
KeywordsAdsorptionDensity functional theoryCarbon nanotubeMaterials scienceNanotubeAb initioBinding energyAmmoniaSilicon carbideChemical physicsSelective chemistry of single-walled nanotubesAb initio quantum chemistry methodsChemical engineeringNanotechnologyComputational chemistryPhysical chemistryOptical properties of carbon nanotubesMoleculeChemistryAtomic physicsComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The adsorption of NH3 on a (5, 0) single-walled SiC nanotube is studied by using the density functional theory approach. The silicon atoms in sites on SiCNT surface is the most stable adsorption site for N pointing of NH3 toward the nanotube surface, with a binding energy of −1.36 eV (−31.35 kcal mol−1) and a Si–N binding distance of 1.972 Å. We also have tested the stability of the NH3-adsorbed SiCNT with ab initio molecular dynamics calculations, which have been carried out at room temperature. Furthermore, the adsorption of NH3 on the single-walled carbon nanotubes has been investigated. Our first-principles calculations predict that the NH3 adsorptive capability of silicon carbide nanotubes is much better than that of carbon nanotubes. This demonstrates that SiCNTs could be a promising material for gas detection and energy storage.

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 score0.196
Threshold uncertainty score0.870

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 head0.025
GPT teacher head0.222
Teacher spread0.196 · 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

Citations25
Published2011
Admission routes1
Has abstractyes

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