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Record W2068592706 · doi:10.1021/nl1014926

Large-Scale Density Functional Theory Investigation of Failure Modes in ZnO Nanowires

2010· article· en· W2068592706 on OpenAlexaff
Ravi Agrawal, Jeffrey T. Paci, Horacio D. Espinosa

Bibliographic record

VenueNano Letters · 2010
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Victoria
FundersArmy Research OfficeDivision of Civil, Mechanical and Manufacturing InnovationDivision of Materials Research
KeywordsNanowireMaterials scienceDensity functional theoryPhase transitionCondensed matter physicsNanoscopic scaleMolecular dynamicsPhase (matter)NanotechnologyChemical physicsComputational chemistryChemistryPhysics

Abstract

fetched live from OpenAlex

Electromechanical and photonic properties of semiconducting nanowires depend on their strain states and are limited by their extent of deformation. A fundamental understanding of the mechanical response of individual nanowires is therefore essential to assess system reliability and to define the design space of future nanowire-based devices. Here we perform a large-scale density functional theory (DFT) investigation of failure modes in zinc oxide (ZnO) nanowires. Nanowires as large as 3.6 nm in diameter with 864 atoms were investigated. The study reveals that pristine nanowires can be elastically deformed to strains as high as 20%, prior to a phase transition leading to fracture. The current study suggests that the phase transition predicted at approximately 10% strain in pristine nanowires by the Buckingham pairwise potential (BP) is an artifact of approximations inherent in the BP. Instead, DFT-based energy barrier calculations suggest that defects may trigger heterogeneous phase transition leading to failure. Thus, the difference previously reported between in situ electron microscopy tensile experiments (brittle fracture) and atomistic simulations (phase transition and secondary loading) (Agrawal, R.; Peng, B.; Espinosa, H. D. Nano Lett. 2009, 9 (12), 4177-2183) is elucidated.

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 score0.008
Threshold uncertainty score0.270

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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

Citations37
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueNano LettersSame topicZnO doping and propertiesFrench-language works237,207