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Record W4319829037 · doi:10.1002/apxr.202200088

Copolymer Mediated Engineering of Halide Perovskites and Associated Devices: Current State and Future

2023· article· en· W4319829037 on OpenAlexafffund
Avi Mathur, Saikiran Khamgaonkar, Vivek Maheshwari

Bibliographic record

VenueAdvanced Physics Research · 2023
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceUniversity of WaterlooCanada Foundation for Innovation
KeywordsHalideCopolymerPerovskite (structure)Materials sciencePolymerNanotechnologyChemical engineeringChemistryInorganic chemistryComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract The field of halide perovskites has advanced significantly within a decade, as engineering strategies are addressing many of the challenges and as a result improved environmental stability and electro‐optical properties have been achieved. The use of the copolymer additive strategy has received significant attention in recent years as a variety of polymers with significant differences in properties such as water affinity, polarity, elastic modulus, ion conductivity and basis for interaction with perovskite can be selected and combined with halide perovskite. As a result, there has been a rapid increase in publications reporting the effectiveness of the inexpensive and readily available copolymer additives in altering the physicochemical, mechanical, and electro‐optical properties of the halide perovskites‐based high‐performance devices. This article is an effort to provide insight into the current state of copolymer‐mediated engineering of halide perovskites with a perspective on the reported improvements in the properties and performance of the perovskite‐based devices. Critical analysis is done on the potential of the copolymer–perovskite materials toward realizing commercial 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 score0.401
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.329
Teacher spread0.298 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

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