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Record W2482410638 · doi:10.1039/c6cp03068h

Strain-induced electrostatic enhancements of BiFeO<sub>3</sub>nanowire loops

2016· article· en· W2482410638 on OpenAlexafffund
Jun Liu, K. Prashanthi, Zhi Li, Ryan McGee, Kaveh Ahadi, Thomas Thundat

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsKelvin probe force microscopeNanowireElectrostatic force microscopeStrain (injury)Materials scienceMicroscopyAtomic force microscopyNanotechnologyElectrostaticsCondensed matter physicsChemistryOpticsPhysics

Abstract

fetched live from OpenAlex

Semiconductor nanowires (NWs), due to their intriguing structural and physical properties, offer tremendous potential for future technological applications. The existence of strain in NWs can greatly affect, for instance, their mechanical, electrical and optical properties. Here, we report an extraordinary electrostatic response of semiconductor BiFeO3 NW loops, based on Kelvin probe force microscopy (KPFM) and electrostatic force microscopy (EFM). A substantial ∼300 mV surface potential difference, accompanied by an ∼29% higher surface charge density, was found on the NW loop. We also found that the electrostatic enhancement is strongly related to the strain present at the curvature of the NW loops. We propose that the electric polarization coupled with mechanical strain (piezoelectric effect) or strain gradient (flexoelectricity) as possible reasons to account for our observation. These findings provide new insights into multiferroic based semiconductor NWs under external stimuli as well as significant inspiration towards strain sensors and electromechanical devices with multifunctional sensing abilities.

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 categoriesMeta-epidemiology (narrow)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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.

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

Citations11
Published2016
Admission routes2
Has abstractyes

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