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Record W4413431921 · doi:10.1021/acsomega.5c05517

Molecular Characterization of Three Classes of Bow-Tie-Shaped Graphene Nanoflakes by Polynomials as Alternative for Their Computed Energies and Spectral Patterns

2025· article· en· W4413431921 on OpenAlexaff
S. Prabhu, M. Arulperumjothi, Lorentz Jäntschi, V Manimozhi, R.L. Madhusudhan

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsToronto Metropolitan University
FundersUniversitatea Tehnică din Cluj-Napoca
KeywordsGrapheneCharacterization (materials science)Bow tieMaterials scienceNanotechnologyComputer science

Abstract

fetched live from OpenAlex

Graphene has been recognized as one of the most promising materials for nanoelectronics in recent decades. Despite its excellent mechanical and electrical characteristics, its use in the fabrication of genuine nanoelectronic devices is frequently fraught with challenges. A graphene nanoflake is graphene with a finite size. It can be prepared in a bottom-up or top-down manner. While it has qualities similar to those of graphene, it may be manufactured in a variety of sizes and shapes. A topological index is a numerical value that shows some valuable information about molecular structure, shape, and investigation. It refers to a molecular graph's numerical invariants, which can correlate bioactivity and physio-chemical properties. Researchers have discovered topological indices to be an effective and valuable tool in describing molecular structure throughout time. In this study, we compute such topological indices of bow-tie-shaped graphene nanoflakes.

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 score0.032
Threshold uncertainty score0.416

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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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