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Record W4403946392 · doi:10.1063/5.0225112

A comparative AIMD study of electronic excitation-induced amorphization in 3C-SiC, TiC, and ZrC

2024· article· en· W4403946392 on OpenAlexaff
Shuo Song, Ming Jiang, Ji Wen, Xi Tang, Chandra Veer Singh

Bibliographic record

VenueJournal of Applied Physics · 2024
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExcitationMaterials scienceElectronic structureWide-bandgap semiconductorCondensed matter physicsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

In the present study, an ab initio molecular dynamics (AIMD) method was employed to investigate the effect of electronic excitation on the micro-structural evolution of 3C-SiC, TiC, and ZrC. The AIMD results demonstrated that electron excitation induces a crystalline-to-amorphous phase transition in all carbide compounds. The determined threshold electronic excitation concentration for 3C-SiC, TiC, and ZrC at 300 K is 4.06%, 5.28%, and 4.26%, respectively. The mean square displacement of C atoms is larger than those of Si, Zr, and Ti atoms, which results from the smaller atomic mass of the C atom. These results indicate that the structural amorphization of 3C-SiC, TiC, and ZrC is primarily attributed to the displacement of C atoms. It is noted that amorphization induced by electronic excitation represents a solid–solid transition rather than a solid–liquid transition. It is further verified that the ⟨Si−C⟩ bond is a covalent characteristic, whereas the ⟨Ti−C⟩ or ⟨Zr−C⟩ bond is a mixture of ionic, metallic, and covalent characteristics, which may lead to different radiation tolerances of carbide compounds. The present results suggest that electronic excitation may contribute to the structural amorphization of carbides under low- or medium-energy electron and ion irradiation, and advance the fundamental comprehension of the radiation resistances of carbide compounds.

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 score0.299
Threshold uncertainty score0.290

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 head0.020
GPT teacher head0.260
Teacher spread0.240 · 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
Published2024
Admission routes1
Has abstractyes

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