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Record W3137870273 · doi:10.1002/adma.202008690

Solvent‐Assisted Kinetic Trapping in Quaternary Perovskites

2021· article· en· W3137870273 on OpenAlexaff
Darshan H. Parmar, João M. Pina, Hitarth Choubisa, Golam Bappi, Koen Bertens, Edward H. Sargent

Bibliographic record

VenueAdvanced Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsMaterials sciencePerovskite (structure)HalideCrystalliteChemical engineeringAbsorption (acoustics)Phase (matter)Inorganic chemistryComposite materialOrganic chemistryMetallurgyChemistry

Abstract

fetched live from OpenAlex

Abstract Engineering halide perovskites through alloying allows synthesis of materials having tuned electronic and optical properties; however, synthesizing many of these alloys is hindered by the formation of demixed phases arising due to thermodynamically unstable crystal structures. Methods have been developed to make such alloys, such as solid‐phase reactions, chemical vapor deposition, and mechanical grinding; but these are incompatible with low‐temperature solution‐processing and monolithic integration, precluding a number of important applications of these materials. Here, solvent‐phase kinetic trapping (SPKT), an approach that enables the synthesis of novel thermodynamically unfavored perovskite alloys, is developed. SPKT is used to synthesize Cs 1− x Rb x PbCl 3 and report the first instance of ultraviolet emission in polycrystalline perovskite thin films. SPKT leads to materials exhibiting superior thermal and photostability compared to non‐kinetically trapped materials of the same precursors. Transient absorption spectroscopy of the kinetically trapped material reveals improved optical properties: greater absorption, and longer ground‐state bleach lifetimes. SPKT may be applied to other perovskites to realize improved material properties while benefiting from facile solution‐processing.

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 score0.011
Threshold uncertainty score0.724

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)0.0010.000

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 head0.012
GPT teacher head0.237
Teacher spread0.224 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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