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Record W2954967403 · doi:10.3390/ma12132160

The Effect of Substrate Biasing during DC Magnetron Sputtering on the Quality of VO2 Thin Films and Their Insulator–Metal Transition Behavior

2019· article· en· W2954967403 on OpenAlexafffund
Chunzi Zhang, Ozan Güneş, Yuanshi Li, Xiaoyu Cui, Masoud Mohammadtaheri, Shi-Jie Wen, Rick Wong, Q. Yang, Safa Kasap

Bibliographic record

VenueMaterials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCisco SystemsSilicon Valley Community Foundation
KeywordsMaterials scienceBiasingThin filmSputter depositionTransmittanceSputteringBand gapOptoelectronicsAnalytical Chemistry (journal)VoltageNanotechnologyChemistry

Abstract

fetched live from OpenAlex

In this work, VO2 thin films were deposited on Si wafers (onto (100) surface) by DC magnetron sputtering under different cathode bias voltages. The effects of substrate biasing on the structural and optical properties were investigated. The results show that the metal–insulator transition (MIT) temperature of VO2 thin films can be increased up to 14 K by applying a cathode bias voltage, compared to deposition conditions without any bias. The decrease in the transition efficiency and increase in the transition temperature are attributed to the enlarged grain size, increased defects, and the residual stress in the VO2 thin films induced by biasing. The optical transmittance measurements for different thickness films indicate an attenuation coefficient of 3.1 × 107 m−1 at 2000 nm or an extinction coefficient of 4.9 in the metal phase. The optical transmittance vs wavelength characteristics point to an indirect bandgap of 0.6 ± 0.05 eV and significant scattering in the bulk and/or at the interface.

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)0.004
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 score0.109
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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)0.0010.000

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 head0.015
GPT teacher head0.249
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.

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

Citations11
Published2019
Admission routes2
Has abstractyes

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