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Record W3012204009 · doi:10.1021/acs.chemmater.9b05110

Single-Precursor Intermediate Shelling Enables Bright, Narrow Line Width InAs/InZnP-Based QD Emitters

2020· article· en· W3012204009 on OpenAlexafffund
Laxmi Kishore Sagar, Golam Bappi, Andrew Johnston, Bin Chen, Petar Todorović́, Larissa Levina, Makhsud I. Saidaminov, F. Pelayo Garcı́a de Arquer, Sjoerd Hoogland, Edward H. Sargent

Bibliographic record

VenueChemistry of Materials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFull width at half maximumPhotoluminescenceQuantum dotOptoelectronicsMaterials scienceQuantum yieldOpticsPhysicsFluorescence

Abstract

fetched live from OpenAlex

Bright, narrow spectrum infrared emitters, particularly Cd- and Pb-free materials, are of interest for bioimaging, photodetection, and telecommunications. InAs-based quantum dots (QDs) are promising emitters in this spectral range; however, efforts to increase the photoluminescence quantum yield (PLQY) tend to broaden the PL line width as a consequence of interfacial defect formation when thick shells, lattice-mismatched with the core, are employed. Here we report that developing a single-precursor complex for InZnP growth enables uniform shell growth that maintains the excellent size dispersion (6%) of the cores. The introduction of this intermediate layer is key to facilitate the subsequent growth of different shells to improve radiative recombination without sacrificing size uniformity. The growth of InAs/InZnP/ZnSe leads to a PL full-width at half-maximum (fwhm) of 100 meV at 1.12 eV with a PLQY of 14%. We then further introduce an additional GaP layer to increase the radiative/nonradiative relative rate. InAs/InZnP/GaP/ZnSe QDs reach a PLQY of 23% while maintaining a narrow fwhm.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0050.000

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 head0.035
GPT teacher head0.222
Teacher spread0.187 · 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

Citations30
Published2020
Admission routes2
Has abstractyes

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