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Record W4372231849 · doi:10.1063/5.0141682

Plasmon-enhanced photodetectors fabricated using digital inkjet-printing on chemically nanopatterned silicon wafers

2023· article· en· W4372231849 on OpenAlexafffund
Xiaohang Guo, Debika Banerjee, Ivy M. Asuo, François-Xavier Fortier, Moulay Ahmed Slimani, Sylvain G. Cloutier

Bibliographic record

VenueAIP Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsInstitut National de la Recherche ScientifiqueÉcole de Technologie Supérieure
FundersCanada Research Chairs
KeywordsPhotodetectorMaterials scienceWaferResponsivitySiliconPhotocurrentOptoelectronicsFabricationPlasmonNanotechnology

Abstract

fetched live from OpenAlex

In this study, we have fabricated and characterized three different configurations of photodetectors with digital inkjet printing techniques on different types of silicon substrates, such as pristine n-type silicon and chemically nanostructured n-type silicon, with and without Ag nanoparticle-induced surface-plasmon enhancement. Among these three comparison batches, digitally printed devices on chemically nanostructured n-type silicon with Ag nanoparticle-induced enhancement yield the highest photocurrent enhancement factor of 920×, the lowest rise and decay times of τr = 176 ms and τd = 98 ms, respectively, and the highest responsivity of 24.8 mA W−1 at wavelengths ranging from 380 to 700 nm. Most importantly, we demonstrate that these devices are highly stable after fabrication, losing less than 3% of their efficiency over 60 days under ambient conditions. We firmly believe that this simple device architecture and effective digital fabrication process are most promising for the realization of efficient, stable, and low-cost photodetectors fabricated at large scales.

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 score0.032
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.235
Teacher spread0.222 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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