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A Type-I n-ZnO Nanowire/n<sup>+</sup>-Si Heterojunction With High Photoresponse For Photovoltaic Applications

2021· article· en· W3197951574 on OpenAlexaff
Hrilina Ghosh, Siva Sivoththaman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHeterojunctionResponsivityOptoelectronicsMaterials scienceUltravioletNanowirePhotovoltaicsPhotodetectorVisible spectrumPhotodetectionPhotovoltaic systemElectrical engineering

Abstract

fetched live from OpenAlex

Due to their excellent electrical and optical properties, zinc oxide (ZnO) nanowires (NWs) have been gaining significant traction in the world of photovoltaics and optoelectronics. However, ability to detect light only in the ultraviolet (UV) spectrum limits the full potential of these NWs. In this paper, we have a developed a cost-effective type-I n-ZnO NW/n+-Si heterojunction device capable of dual band photodetection. The device exhibited high photoresponse to radiation in both visible and UV range. The device had a good On/Off ratio of 23.8 and 19.6 for visible and UV illumination respectively. Responsivity of 0.153 was obtained for EQE of 33.2% at 570nm. In the UV range, responsivity of 0.097 was obtained for EQE of 30.9% at 390nm. Additionally, the device had a strong response to pulsed illumination. The excellent dual band photoresponse of the developed n-ZnO NW/n+-Si heterojunction proves that it has promising potential for photovoltaic applications.

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 science0.0010.000
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 head0.019
GPT teacher head0.238
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 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
Published2021
Admission routes1
Has abstractyes

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