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Record W2752396711 · doi:10.1063/1.4995245

Impact of tungsten doping on the dynamics of the photo-induced insulator-metal phase transition in VO2 thin film investigated by optical pump-terahertz probe spectroscopy

2017· article· en· W2752396711 on OpenAlexafffund
Nicolas Émond, Akram Ibrahim, Badr Torriss, Ali Hendaoui, Ibraheem Al‐Naib, T. Ozaki, Mohamed Chaker

Bibliographic record

VenueApplied Physics Letters · 2017
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesCanada Research Chairs
KeywordsMaterials scienceTerahertz radiationDopingThin filmFluenceOptoelectronicsTungstenMetal–insulator transitionUltrashort pulseSpectroscopyAnalytical Chemistry (journal)OpticsMetalNanotechnologyChemistryLaser

Abstract

fetched live from OpenAlex

The influence of tungsten (W) doping on the ultrafast dynamics of the photo-induced insulator-metal phase transition (IMT) is investigated at room temperature in epitaxially grown vanadium dioxide (VO2) thin films by means of optical pump-terahertz (THz) probe spectroscopy. It is observed that the THz transmission variation of the films across the IMT follows a bi-exponential decrease characterized by two time constants, one corresponding to a fast process and the other to a slower process. W-doping (i) reduces the photo-excitation fluence threshold required for triggering the IMT, (ii) accelerates the slow process, and (iii) increases the THz transient transmission variation for corresponding fluences. From the Drude-Smith model, it is deduced that a strong carrier confinement and an enhancement of the transient conductivity occur across the IMT. The IMT is also accompanied by an increase in the carrier concentration in the films, which is enhanced by W-doping. Our results suggest that W-doped VO2 could be advantageously exploited in applications such as ultrafast THz optical switching and modulation 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 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 score0.005
Threshold uncertainty score0.765

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 science0.0010.000
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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

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