MétaCan
Menu
Back to cohort
Record W3004167009 · doi:10.3390/nano10020222

Porous Si-SiO2 UV Microcavities to Modulate the Responsivity of a Broadband Photodetector

2020· article· en· W3004167009 on OpenAlexaff
María R. Jiménez-Vivanco, Godofredo García, J. Carrillo, Francisco Morales-Morales, Antonio Coyopol, Miguel Gracia, R. Doti, Jocelyn Faubert, J. E. Lugo

Bibliographic record

VenueNanomaterials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversité de Montréal
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsResponsivityPhotodetectorPhotocurrentOptoelectronicsMaterials sciencePhotodetectionWavelengthOpticsPhotonPhysics

Abstract

fetched live from OpenAlex

Porous Si-SiO2 UV microcavities are used to modulate a broad responsivity photodetector (GVGR-T10GD) with a detection range from 300 to 510 nm. The UV microcavity filters modified the responsivity at short wavelengths, while in the visible range the filters only attenuated the responsivity. All microcavities had a localized mode close to 360 nm in the UV-A range, and this meant that porous Si-SiO2 filters cut off the photodetection range of the photodetector from 300 to 350 nm, where microcavities showed low transmission. In the short-wavelength range, the photons were absorbed and did not contribute to the photocurrent. Therefore, the density of recombination centers was very high, and the photodetector sensitivity with a filter was lower than the photodetector without a filter. The maximum transmission measured at the localized mode (between 356 and 364 nm) was dominant in the UV-A range and enabled the flow of high energy photons. Moreover, the filters favored light transmission with a wavelength from 390 nm to 510 nm, where photons contributed to the photocurrent. Our filters made the photodetector more selective inside the specific UV range of wavelengths. This was a novel result to the best of our knowledge.

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 categoriesInsufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.238
Teacher spread0.221 · 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.

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

Citations20
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueNanomaterialsSame topicSilicon Nanostructures and PhotoluminescenceFrench-language works237,207