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Record W4293249521 · doi:10.1149/10806.0063ecst

Thin Film Photodetectors Based on Zinc Oxide Nanoinks

2022· article· en· W4293249521 on OpenAlexaff
Sahil Dawka, Pengjun Duan, Raju Sapkota, Chris Papadopoulos

Bibliographic record

VenueECS Transactions · 2022
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPhotodetectorMaterials sciencePhotocurrentThin filmOptoelectronicsFabricationBroadbandNanoparticleOpticsNanotechnology

Abstract

fetched live from OpenAlex

We report on the fabrication and characterization of ZnO thin film photodetectors using planetary ball-milled nanoparticle suspensions (nanoinks). Milling parameters, speed and time, were varied between 200 rpm and 1000 rpm, and 10 and 60 minutes, respectively to produce suspensions with particles below 100 nm in diameter. The resulting PBM nanoinks were used to create ZnO thin films whose photoconductance was measured under broadband UV/visible illumination. Thin film ZnO photodetector devices showed strong response upon light exposure with up to an order of magnitude increase in photocurrent compared to baseline (dark) for the conditions studied. This work demonstrates the use of low-cost PBM nanoinks for the active materials in solution-processed thin film photodetectors based on ZnO and allows film properties to be tailored and optimized for different applications in a straightforward manner by adjusting grinding conditions.

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 communication0.0010.001
Open science0.0010.000
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 head0.018
GPT teacher head0.220
Teacher spread0.203 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueECS TransactionsSame topicZnO doping and propertiesFrench-language works237,207