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Record W2780620403 · doi:10.1021/acs.accounts.7b00467

Structure/Property Relations in “Giant” Semiconductor Nanocrystals: Opportunities in Photonics and Electronics

2017· article· en· W2780620403 on OpenAlexafffund
Fabiola Navarro‐Pardo, Haiguang Zhao, Zhiming Wang, Federico Rosei

Bibliographic record

VenueAccounts of Chemical Research · 2017
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of ChinaUniversity of Electronic Science and Technology of ChinaGovernment of CanadaUnited Nations Educational, Scientific and Cultural Organization
KeywordsPhotoluminescenceQuantum dotMaterials scienceAbsorption (acoustics)OptoelectronicsSemiconductorPassivationBand gapNanocrystalPhotonicsQuantum yieldNanometreNanotechnologyAbsorption edgeOpticsFluorescenceLayer (electronics)

Abstract

fetched live from OpenAlex

ConspectusSemiconductor nanocrystals exhibit size-tunable absorption and emission ranging from the ultraviolet (UV) to the near-infrared (NIR) spectral range, high absorption coefficient, and high photoluminescence quantum yield. Effective surface passivation of these so-called quantum dots (QDs) may be achieved by growing a shell of another semiconductor material. The resulting core/shell QDs can be considered as a model system to study and optimize structure/property relations. A special case consists in growing thick shells (1.5 up to few tens of nanometers) to produce “giant” QDs (g-QDs). Tailoring the chemical composition and structure of CdSe/CdS and PbS/CdS g-QDs is a promising approach to widen the spectral separation of absorption and emission spectra (i.e., the Stokes shift), improve the isolation of photogenerated carriers from surface defects and enhance charge carrier lifetime and mobility. However, most stable systems are limited by a thick CdS shell, which strongly absorbs radiation below 500 nm, covering the UV and part of the visible range. Modification of the interfacial region between the core and shell of g-QDs or tuning their doping with narrow band gap semiconductors are effective approaches to circumvent this challenge. In addition, the synthesis of g-QDs composed of environmentally friendly elements (e.g., CuInSe2/CuInS2) represents an alternative to extend their absorption into the NIR range. Additionally, the band gap and band alignment of g-QDs can be engineered by proper selection of the constituents according to their band edge positions and by tuning their stoichiometry during wet chemical synthesis. In most cases, the quasi-type II localization regime of electrons and holes is achieved. In this type of g-QDs, electrons can leak into the shell region, while the holes remain confined within the core region. This electron–hole spatial distribution is advantageous for optoelectronic devices, resulting in efficient electron–hole separation while maintaining good stability.This Account provides an overview of emerging engineering strategies that can be adopted to optimize structure/property relations in colloidal g-QDs for efficient photon management or charge separation/transfer. In particular, we focus on our recent contributions to this rapidly expanding field of research. We summarize the design and synthesis of a variety of colloidal g-QDs with the aim of tuning the optical properties, such as absorption/emission in a wide region of the solar spectrum, which allows enlargement of their Stokes shift. We also describe the band alignment within these systems, charge carrier dynamics, and charge transfer from g-QDs into semiconducting oxides. We show how these tailored g-QDs may be used as active components in luminescent solar concentrators, photoelectrochemical cells for hydrogen generation, QD-sensitized solar cells and optical nanothermometers. In each case, we aim at providing insights on structure/property relationships and on how to optimize them toward improving device performance. Finally, we describe perspectives for future work, sketching new directions and opportunities in this field of research at the intersection between chemistry, physics, materials science and engineering.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.148
GPT teacher head0.364
Teacher spread0.216 · 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

Citations60
Published2017
Admission routes2
Has abstractyes

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