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Record W2969738476 · doi:10.1021/acsphotonics.9b00491

Near-Infrared Colloidal Manganese-Doped Quantum Dots: Photoluminescence Mechanism and Temperature Response

2019· article· en· W2969738476 on OpenAlexafffund
Hui Zhang, Jiabin Liu, Chao Wang, Gurpreet Singh Selopal, David Barba, Zhiming M. Wang, Shuhui Sun, Haiguang Zhao, Federico Rosei

Bibliographic record

VenueACS Photonics · 2019
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Shandong ProvinceQingdao UniversityUniversity of Electronic Science and Technology of ChinaCanada Research ChairsFonds de recherche du Québec – Nature et technologiesUnited Nations Educational, Scientific and Cultural Organization
KeywordsLead sulfidePhotoluminescenceDopingQuantum dotMaterials scienceBand gapSemiconductorManganeseIonOptoelectronicsInfraredNanoparticleBlueshiftNanotechnologyAnalytical Chemistry (journal)ChemistryOpticsPhysics

Abstract

fetched live from OpenAlex

Doping in semiconductor quantum dots (QDs) is a promising approach for introducing unique properties compared to pure QDs. Although manganese (Mn) ion-doped wide band gap QDs have been widely studied, the optical properties of Mn-doped narrow band gap semiconductors are not well understood. Here, we report the synthesis of oil-soluble Mn-doped lead sulfide (PbS) QDs of identical size with different Mn contents. Compared to pure PbS QDs, the photoluminescence (PL) peak positions of Mn-doped PbS QDs exhibit a significant red-shift, resulting in a larger Stokes shift. The large Stokes shift of Mn-doped QDs is due to the electronic state of Mn ions, in which the photogenerated electron is transferred to the energy states of Mn ions and then recombined with holes. Mn-doped PbS QDs exhibit a faster temperature-dependent PL response compared to pure PbS QDs, demonstrating that the Mn-doped PbS QDs are promising alternatives for use as thermal sensors.

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 categoriesMeta-epidemiology (narrow)
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 score0.017
Threshold uncertainty score1.000

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.214
Teacher spread0.206 · 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

Citations28
Published2019
Admission routes2
Has abstractyes

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