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Record W2062058582 · doi:10.1021/jp0571675

A Theoretical Study on Growth Patterns of Ni-Doped Germanium Clusters

2006· article· en· W2062058582 on OpenAlexaff
Jin Wang, Ju‐Guang Han

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor materials and interfaces
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGermaniumDopingCluster (spacecraft)Materials scienceFragmentation (computing)CrystallographyAtomic orbitalChemical physicsChemistryElectronSiliconPhysicsMetallurgyOptoelectronics

Abstract

fetched live from OpenAlex

Ni-doped germanium clusters have been systematically investigated by using the density functional approach. The growth-pattern behaviors, stabilities, charge transfer, and polarities of these clusters are discussed in detail. Obviously different growth patterns appear between small-sized Ni-doped germanium clusters and middle- or larger-sized Ni-doped germanium clusters. The Ni-convex or substituted Ge(n) frames for small-sized clusters as well as Ni-concaved or encapsulated Ge(n) frames for middle- or large-sized clusters are dominant growth patterns. The calculated fragmentation energies manifest that the magic numbers of stabilities are 5, 8, 10, and 13 for Ni-doped germanium clusters; the obtained relative stabilities exhibit that the Ni-encapsulated Ge(10) cluster is the most stable species of all different-sized clusters, which is in good agreement with available experimental observations of CoGe(10)(-). Natural population analysis shows that different charge-transfer phenomena depend on the sizes of the Ni-doped Ge(n) clusters. Additionally, the properties of frontier orbitals and the polarities of Ni-doped Ge(n) clusters are also discussed.

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: Theoretical or conceptual · Consensus signal: none
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)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.242
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations78
Published2006
Admission routes1
Has abstractyes

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