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Record W4291693678 · doi:10.1021/acs.nanolett.2c02756

Quantum-Size-Effect Tuning Enables Narrowband IR Photodetection with Low Sunlight Interference

2022· article· en· W4291693678 on OpenAlexafffund
João M. Pina, Maral Vafaie, Darshan H. Parmar, Ozan Atan, Pan Xia, Yangning Zhang, Amin Morteza Najarian, F. Pelayo Garcı́a de Arquer, Sjoerd Hoogland, Edward H. Sargent

Bibliographic record

VenueNano Letters · 2022
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotodetectorPhotodetectionOptoelectronicsMaterials scienceQuantum efficiencyDark currentAbsorption (acoustics)PhotodiodeOpticsInterference (communication)PhysicsChannel (broadcasting)Telecommunications

Abstract

fetched live from OpenAlex

Infrared photodetection enables depth imaging techniques such as structured light and time-of-flight. Traditional photodetectors rely on silicon (Si); however, the bandgap of Si limits photodetection to wavelengths shorter than 1100 nm. Photodetector operation centered at 1370 nm benefits from lower sunlight interference due to atmospheric absorption. Here, we report 1370 nm-operating colloidal quantum dot (CQD) photodetectors and evaluate their outdoor performance. We develop a surface-ligand engineering strategy to tune the electronic properties of each CQD layer and fabricate photodetectors in an inverted (PIN) architecture. The strategy enables photodetectors with an external quantum efficiency of 75% and a low dark current (1 μA/cm2). Outdoor testing demonstrates that CQD-based photodetectors combined with a 10 nm-line width bandpass filter centered at 1370 nm achieve over 2 orders of magnitude (140× at incident intensity 1 μW/cm2) higher signal-to-background ratio than do Si-based photodetectors that use an analogous bandpass filter centered at 905 nm.

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)0.001
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 score0.007
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations22
Published2022
Admission routes2
Has abstractyes

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