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Record W4390095870 · doi:10.1088/1361-6528/ad1841

Atomistic modeling of electromechanical properties of piezoelectric zinc oxide nanowires

2023· article· en· W4390095870 on OpenAlexafffund
S. A. Meguid, S. I. Kundalwal, A. Alian

Bibliographic record

VenueNanotechnology · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceReaxFFPiezoelectricityInteratomic potentialNanowireMolecular dynamicsZincCharge densityNanotechnologyChemical physicsComposite materialComputational chemistryChemistryPhysics

Abstract

fetched live from OpenAlex

Currently, numerous articles are devoted to examining the influence of geometry and charge distribution on the mechanical properties and structural stability of piezoelectric nanowires (NWs). The varied modeling techniques adopted in earlier molecular dynamics (MD) works dictated the outcome of the different efforts. In this article, comprehensive MD studies are conducted to determine the influence of varied interatomic potentials (partially charged rigid ion model, [PCRIM] ReaxFF, charged optimized many-body [COMB], and Buckingham), geometrical parameters (cross-section geometry, wire diameter, and length), and charge distribution (uniform full charges versus partially charged surface atoms) on the resulting mechanical properties and structural stability of zinc oxide (ZnO) NWs. Our optimized parameters for the Buckingham interatomic potential are in good agreement with the existing experimental results. Furthermore, we found that the incorrect selection of interatomic potentials could lead to excessive overestimate (61%) of the elastic modulus of the NW. While NW length was found to dictate the strain distribution along the wire, impacting its predicted properties, the cross-section shape did not play a major role. Assigning uniform charges for both the core and surface atoms of ZnO NWs leads to a drastic decrease in fracture properties.

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 score0.135
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.214
Teacher spread0.195 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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