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Advances in Lead-Free Perovskite Single Crystals: Fundamentals and Applications

2021· article· en· W3173804327 on OpenAlexaff
Naveen Kumar Tailor, Shaoni Kar, Pranjal Mishra, Albert These, Christian Kupfer, Hanlin Hu, Muhammad Awais, Makhsud I. Saidaminov, M. Ibrahim Dar, Christoph J. Brabec, Soumitra Satapathi

Bibliographic record

VenueACS Materials Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Victoria
FundersMinistry of Electronics and Information technologyUniversity Grants Commission
KeywordsPerovskite (structure)Lead (geology)Materials scienceNanotechnologyEngineering physicsGrain boundarySingle crystalTransistorSemiconductorOptoelectronicsElectrical engineeringChemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

With rapid progress in the deployment of metal halide perovskites in various device applications such as solar cells, light-emitting devices, field-effect transistors, photodetectors, etc., the next eminent focus is on the single crystals of these materials. With a lack of grain boundaries and low trap densities, remarkably long charge carrier diffusion lengths, and high ambient and operational stabilities, this class of materials seems greatly promising. Yet, the growing concern for lead toxicity in commercial semiconductor devices has entailed a thrust in the research of alternative lead-free perovskites, including their single crystalline forms. However, there is still no consolidated account of the state-of-the-art in this domain and accordingly, countless feasible systems still remain unexplored. To bridge this gap, we attempt to provide here, an up-to-date overview of lead-free perovskite single crystals with respect to their synthesis methods, structural diversity, stability, photophysical and electrical properties, and device applications. We discuss various approaches to designing, modeling, fabricating, and characterizing new single-crystal systems and conclude with some critical insights for further investigating this field of research.

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 score0.080
Threshold uncertainty score0.754

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations116
Published2021
Admission routes1
Has abstractyes

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