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Record W3212089862 · doi:10.1021/acsnano.1c08002

Silicon Surface Passivation for Silicon-Colloidal Quantum Dot Heterojunction Photodetectors

2021· article· en· W3212089862 on OpenAlexafffund
Qiwei Xu, I Teng Cheong, Lingju Meng, Jonathan G. C. Veinot, Xihua Wang

Bibliographic record

VenueACS Nano · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundAlberta InnovatesKillam TrustsAlberta Innovates - Technology Futures
KeywordsPassivationMaterials sciencePhotodetectorPhotocurrentPhotodiodeHeterojunctionOptoelectronicsQuantum efficiencySiliconQuantum dotLead sulfideNanotechnology

Abstract

fetched live from OpenAlex

Sensitizing crystalline silicon (c-Si) with an infrared-sensitive material, such as lead sulfide (PbS) colloidal quantum dots (CQDs), provides a straightforward strategy for enhancing the infrared-light sensitivity of a Si-based photodetector. However, it remains challenging to construct a high-efficiency photodetector based upon a Si:CQD heterojunction. Herein, we demonstrate that Si surface passivation is crucial for building a high-performance Si:CQD heterojunction photodetector. We have studied one-step methyl iodine (CH3I) and two-step chlorination/methylation processes for Si surface passivation. Transient photocurrent (TPC) and transient photovoltage (TPV) decay measurements reveal that the two-step passivated Si:CQD interface exhibits fewer trap states and decreased recombination rates. These passivated substrates were incorporated into prototype Si:CQD infrared photodiodes, and the best performance photodiode based upon the two-step passivation shows an external quantum efficiency (EQE) of 31% at 1280 nm, which represents a near 2-fold increase over the standard device based upon the one-step CH3I passivated Si.

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 score0.007
Threshold uncertainty score0.962

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 science0.0000.000
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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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

Citations44
Published2021
Admission routes2
Has abstractyes

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