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Record W1545295833 · doi:10.1063/1.2429063

Structural and electronic properties of Al12X+ (X=C, Si, Ge, Sn, and Pb) clusters

2007· article· en· W1545295833 on OpenAlexaff
Guanghao Chen, Yoshiyuki Kawazoe

Bibliographic record

VenueThe Journal of Chemical Physics · 2007
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsHatch (Canada)
FundersInstitute for Materials Research, Tohoku UniversityVirginia Commonwealth University
KeywordsBinding energyIcosahedral symmetryElectronic structureAtom (system on chip)Cluster (spacecraft)CrystallographyValence electronGround stateDegenerate energy levelsValence (chemistry)ChemistryAtomic physicsElectronMolecular physicsMaterials scienceComputational chemistryPhysics

Abstract

fetched live from OpenAlex

Using the first-principles method with the generalized gradient approximation, the authors have studied the structural and electronic properties of Al(12)X(+) (X=C, Si, Ge, Sn, and Pb) clusters in detail. The ground state of Al(12)C(+) is a low symmetry C(s) structure instead of an icosahedron. However, the Si, Ge, Sn, and Pb atom doped cationic clusters favor icosahedral structures. The ground states for Al(12)Si(+) and Al(12)Ge(+) are icosahedra, while the C(5nu) structures optimized from an icosahedron with a vertex capped by a tetravalent atom have the highest binding energy for Al(12)Sn(+) and Al(12)Pb(+) clusters. The I(h) structure and the C(5nu) structure are almost degenerate for Al(12)Ge(+), whose binding energy difference is only 0.03 eV. The electronic properties are altered much by removing an electron from the neutral cluster. The binding strength of a valence electron is enhanced, while the binding energy of the cluster is reduced much. Due to the open electronic shell, the band gaps between the highest occupied molecular orbital and the lowest unoccupied molecular orbital are approximately 0.3 eV for the studied cationic clusters.

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 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 score0.002
Threshold uncertainty score0.190

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 science0.0000.000
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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

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