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Record W4405109953 · doi:10.1021/acsnano.4c09811

Electrical Tunability of Quantum-Dot-in-Perovskite Solids

2024· article· en· W4405109953 on OpenAlexaff
Md Azimul Haque, Tong Zhu, Roba Tounesi, Seungjin Lee, Maral Vafaie, Luis Huerta Hernandez, Bambar Davaasuren, Alessandro Genovese, Edward H. Sargent, Derya Baran

Bibliographic record

VenueACS Nano · 2024
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersKing Abdullah University of Science and Technology
KeywordsQuantum dotPerovskite (structure)Materials scienceNanotechnologyOptoelectronicsEngineering physicsCondensed matter physicsPhysicsCrystallographyChemistry

Abstract

fetched live from OpenAlex

The quantum-dot-in-perovskite matrix (DIM) is an emerging class of semiconductors for optoelectronics enabled by their complementary charge transport properties and stability improvements. However, a detailed understanding of the pure electrical properties in DIM is still in its early stage. Here, we developed PbS quantum dot-in-CsSnI 3 matrix solids exhibiting improved electrical properties and enhanced stability. PbS incorporation reduces the tensile strain of DIM films compared to that of pristine CsSnI 3, consequently increasing the electrical conductivity. Electrical conductivity is tunable between 20 and 130 S/cm as a function of PbS concentration. Notably, a decoupling of electrical conductivity and Seebeck coefficient is observed upon PbS addition into the perovskite matrix, which is attractive for thermoelectric applications. Density functional theory analysis reveals that at low concentrations of PbS, light holes/electrons govern the overall transport properties in DIM, while heavy holes/electrons begin to dominate as the PbS concentration increases. Understanding the electrical properties would help for designing DIMs with specific properties for various technological applications.

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 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 score0.195
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.238
Teacher spread0.230 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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