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Record W2605685989 · doi:10.1139/cjp-2017-0119

Nickel-decorated B<sub>12</sub>P<sub>12</sub> nanoclusters as a strong adsorbent for SO<sub>2</sub> adsorption: Quantum chemical calculations

2017· article· en· W2605685989 on OpenAlexvenueno aff
Ali Shokuhi Rad, Ali Mirabi, Majid Peyravi, Mahmoud Mirzaei

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsnot available
FundersIran Nanotechnology Initiative Council
KeywordsPhysisorptionChemisorptionNanoclustersAdsorptionPhysicsDensity functional theoryNickelMoleculeAtom (system on chip)Density of statesPhysical chemistryDipoleAtomic physicsChemical physicsNanotechnologyMaterials scienceCondensed matter physicsChemistryQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, we have researched the interaction of SO 2 molecule onto boron phosphide (B 12 P 12 ) and Ni-decorated B 12 P 12 nanoclusters using density functional theory (DFT). While SO 2 has weak physisorption on the surface of pristine B 12 P 12 (–7.4 kJ/mol), high chemisorption is found in the case of Ni-decorated B 12 P 12 depending on the location of the Ni-decorated atom (–140.9, –167.7, and –166.5 kJ/mol). We found three major sites for appropriate decoration of Ni on the surface of a nanocluster, so we tried to find the maximum SO 2 adsorption of this modified surface by taking into account the calculations of adsorption energy, bond distance, dipole moment study, charge analysis, frontier orbital analysis, and density of states of all relaxed systems. Our observations reveal that Ni-decorated B 12 P 12 are highly sensitive for SO 2 molecules, which is beneficial for design of sensitive sensor.

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), Scholarly communication
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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.276
Teacher spread0.245 · 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
Published2017
Admission routes1
Has abstractyes

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