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Record W4389611373 · doi:10.1021/acsami.3c14611

Transient Measurements and Simulations Correlate Exchange Ligand Concentration and Trap States in Colloidal Quantum Dot Photodetectors

2023· article· en· W4389611373 on OpenAlexafffund
Darshan H. Parmar, Benjamin Rehl, Ozan Atan, Sjoerd Hoogland, Edward H. Sargent

Bibliographic record

VenueACS Applied Materials & Interfaces · 2023
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotocurrentPhotodetectorMaterials scienceQuantum dotPhotoluminescenceOptoelectronicsTransient (computer programming)ColloidUltrafast laser spectroscopyLigand (biochemistry)Quantum efficiencyTrap (plumbing)Absorption (acoustics)SpectroscopyChemistryPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Colloidal quantum dot (CQD) photodetectors (PDs) can detect wavelengths longer than the 1100 nm limit of silicon because of their highly tunable bandgaps. CQD PDs are acutely affected by the ligands that separate adjacent dots in a CQD-solid. Optimizing the exchange solution ligand concentration in the processing steps is crucial to achieving high photodetector performance. However, the complex mix of chemistry and optoelectronics involved in CQD PDs means that the effects of the exchange solution ligand concentration on device physics are poorly understood. Here we report direct correspondence between simulated and experimental transient photocurrent responses in CQD PDs. For both deficient and excess conditions, our model demonstrated the experimental changes to the transient photocurrent aligned with changes in trap state density. Combining transient photoluminescence, absorption, and photocurrent with this simulation model, we revealed that different mechanisms are responsible for the increased trap density induced by excess and deficient active layer ligand concentrations.

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 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 score0.004
Threshold uncertainty score0.806

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 studies0.0000.000
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 head0.040
GPT teacher head0.257
Teacher spread0.217 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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