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Record W2763403875 · doi:10.1039/c7nr04950a

Controlled synthesis of near-infrared quantum dots for optoelectronic devices

2017· article· en· W2763403875 on OpenAlexaff
Hui Zhang, Gurpreet Singh Selopal, Yufeng Zhou, Xin Tong, Daniele Benetti, Lei Jin, Fabiola Navarro‐Pardo, Zhiming Wang, Shuhui Sun, Haiguang Zhao

Bibliographic record

VenueNanoscale · 2017
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsInfraredOptoelectronicsQuantum dotMaterials scienceNanotechnologyQuantumOpticsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

We designed a facile approach for the synthesis of PbS quantum dots (QDs) using thiourea and lead acetate as sources of sulfur and lead, respectively. The sizes of the PbS QDs could be systematically controlled by simply adjusting the reaction parameters. Cd post-treatment via a cation exchange method was performed to increase the stability of QDs. As a proof of concept, colloidal PbS QDs synthesized by using air-stable thiourea were employed as light harvesters for both (i) solar driven photoelectrochemical (PEC) hydrogen generation and (ii) QDs sensitized solar cells (QDSSCs). For PEC hydrogen generation, similar saturated photocurrent densities are observed by using thiourea compared to bis(trimethylsilyl) sulfide, which is air-sensitive and unstable. For QDSSCs, the devices fabricated with QDs synthesized from thiourea reveal a better performance compared to devices fabricated with QDs synthesized from traditional bis(trimethylsilyl) sulfide. Our work demonstrates that this synthetic method is a promising alternative to the existing methodologies of PbS QDs and holds great potential for future solar technologies.

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)0.001
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 score0.010
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.271
Teacher spread0.248 · 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

Citations21
Published2017
Admission routes1
Has abstractyes

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