MétaCan
Menu
Back to cohort
Record W4220820504 · doi:10.1088/1361-6528/ac5b52

Laser modification of Au–CuO–Au structures for improved electrical and electro-optical properties

2022· article· en· W4220820504 on OpenAlexaff
Shuo Zheng, W. W. Duley, Peng Peng, Y. Zhou

Bibliographic record

VenueNanotechnology · 2022
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceIrradiationLaserOptoelectronicsNanomaterialsElectrical resistivity and conductivityResistive touchscreenNanowireConductivityNanotechnologyNanosecondCopper oxideElectrodeOxideOpticsMetallurgyElectrical engineering

Abstract

fetched live from OpenAlex

CuO nanomaterials are one of the metal-oxides that received extensive investigations in recent years due to their versatility for applications in high-performance nano-devices. Tailoring the device performance through the engineering of properties in the CuO nanomaterials thus attracted lots of effort. In this paper, we show that nanosecond (ns) laser irradiation is effective in improving the electrical and optoelectrical properties in the copper oxide nanowires (CuO NWs). We find that ns laser irradiation can achieve joining between CuO NWs and interdigital gold electrodes. Meanwhile, the concentration and type of point defects in CuO can be controlled by ns laser irradiation as well. An increase in the concentration of defect centers, together with a reduction in the potential energy barrier at the Au/CuO interfaces due to laser irradiation increases electrical conductivity and enhances photo-conductivity. We demonstrate that the enhanced electrical and photo-conductivity achieved through ns laser irradiation can be beneficial for applications such as resistive switching and photo-detection.

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 score0.014
Threshold uncertainty score0.325

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 science0.0000.000
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 head0.022
GPT teacher head0.241
Teacher spread0.219 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueNanotechnologySame topicZnO doping and propertiesFrench-language works237,207