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Record W1959069779 · doi:10.3390/photonics2030916

Characteristics of the Current-Controlled Phase Transition of VO2 Microwires for Hybrid Optoelectronic Devices

2015· article· en· W1959069779 on OpenAlexafffund
Arash Joushaghani, Junho Jeong, Suzanne Paradis, David Alain, J. Stewart Aitchison, Joyce K. S. Poon

Bibliographic record

VenuePhotonics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsDefence Research and Development CanadaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceOptoelectronicsPhase transitionPhotonicsInsulator (electricity)Current (fluid)Metal–insulator transitionVoltageMetalCondensed matter physics

Abstract

fetched live from OpenAlex

The optical and electrical characteristics of the insulator-metal phase transition of vanadium dioxide (VO2) enable the realization of power-efficient, miniaturized hybrid optoelectronic devices. This work studies the current-controlled, two-step insulator-metal phase transition of VO2 in varying microwire geometries. Geometry-dependent scaling trends extracted from current-voltage measurements show that the first step induced by carrier injection is delocalized over the microwire, while the second, thermally-induced step is localized to a filament about 1 to 2 μm wide for 100 nm-thick sputtered VO2 films on SiO2. These effects are confirmed by direct infrared imaging, which also measures the change in optical absorption in the two steps. The difference between the threshold currents of the two steps increases as the microwires are narrowed. Micron- and sub-micron-wide VO2 structures can be used to separate the two phase transition steps in photonic and electronic devices.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.023
GPT teacher head0.291
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2015
Admission routes2
Has abstractyes

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