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Record W2768374339 · doi:10.1109/jstqe.2017.2776522

Filter-Free Narrowband Photodetectors Employing Colloidal Quantum Dots

2017· article· en· W2768374339 on OpenAlexaff
Qiuyang Xiong, Farsad Imtiaz Chowdhury, Xihua Wang

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhotodetectorNarrowbandOptoelectronicsQuantum dotMaterials scienceQuantum efficiencyBand-pass filterPhotodiodeInfraredDiodeLight-emitting diodeOpticsPhysics

Abstract

fetched live from OpenAlex

Tuning the electronic bandgap of quantum dots via the quantum size-effect enables the tailored spectral response of quantum dot photodetectors, light emitting diodes, and solar cells. Chemically synthesized colloidal quantum dot (CQD) provides an option to produce such quantum material in an inexpensive way. Here, we propose an approach to fabricate filter-free narrowband photodetectors in near-infrared and mid-infrared regions using PbS and HgSe CQD materials, respectively. The spectrally selective bandpass response is implemented by taking advantage of the carrier collection narrowing effect using a photodiode device architecture. Narrower bandpass response in near-infrared region is further achieved employing blended PbS CQDs with different sizes. The external quantum efficiency spectra of these CQD detectors are comprehensively studied using numerical calculation.

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 score0.004

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 communication0.0000.001
Open science0.0000.000
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 head0.026
GPT teacher head0.266
Teacher spread0.240 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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