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Record W4410131691 · doi:10.1002/aenm.202500921

Phase Transition in Halide Double Perovskites for Solar‐To‐Chemical Energy Conversion

2025· article· en· W4410131691 on OpenAlexaff
Chunhua Wang, Zhirun Xie, Pengshan Xie, Huiqiang Liang, Yang Ding, Yannan Wang, Michael K.H. Leung, Guosong Zeng, Johnny C. Ho, Sateesh Bandaru, Ning Han, Bao‐Lian Su, Yun Hau Ng

Bibliographic record

VenueAdvanced Energy Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersCity University of Hong Kong
KeywordsMaterials scienceHalidePhase transitionPhase (matter)Solar energy conversionChemical physicsSolar energyChemical engineeringEngineering physicsInorganic chemistryCondensed matter physicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Halide double perovskites have recently garnered significant interest in solar energy conversion applications owing to their non‐toxic and high solar absorption properties. However, unanticipated structural distortion within these materials can compromise their performance, suppressing the structural distortion is essential and remains challenging. Here, it is reported that using phenylethylamine triggering the disorder‐order phase transition can largely increase the ordering extent of octahedra in double perovskite Cs 2 AgBiBr 6 , which can suppress self‐trapped exciton and defect and enable rapid charge separation, leading to exceptional photo‐physics/chemistry properties with over sixfold photoactivity enhancement in the photocatalytic C−H bond activation compared to less ordered structures. This work provides an effective strategy to solve the challenging problem of the disorder phenomenon of halide double perovskites for boosting solar‐to‐chemical energy conversion.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.253
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2025
Admission routes1
Has abstractyes

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