MétaCan
Menu
Back to cohort
Record W4410310637 · doi:10.1002/adfm.202503074

Tailoring the Microstructure of B‐Cation Octahedra in Halide Double Perovskites for Efficient Selective CO <sub>2</sub> ‐to‐CO Photoreduction

2025· article· en· W4410310637 on OpenAlexaff
Chunhua Wang, Wenjing Shen, Zhirun Xie, Yannan Wang, Yang Ding, Ning Han, Michael K.H. Leung, Biao Liu, Zhi Zhu, Yun Hau Ng

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of ChinaCity University of Hong Kong
KeywordsMaterials scienceHalideOctahedronMicrostructurePerovskite (structure)Inorganic chemistryCrystallographyChemical engineeringCrystal structureMetallurgy

Abstract

fetched live from OpenAlex

Abstract Solar‐driven CO 2 photoreduction using halide perovskites (HPs) has recently garnered significant attention; however, the conversion efficiency remains suboptimal for practical applications. Here, using Cs 2 AgBiBr 6 double perovskites as a model photocatalyst, the interplay between the coordination of octahedral B‐cation sites and photocatalytic performance are reported. The results reveal that modulating the microstructure of octahedra in Cs 2 AgBiBr 6 enables orbital rehybridization, resulting in an upshifted d‐band center and reduced effective masses, thereby improving the adsorption strength of intermediates, lowering reaction energy barriers, and promoting charge separation. Experimentally, it is demonstrated that the tailored Cs 2 AgBiBr 6 achieves selective CO 2 photoreduction toward CO without sacrificial agents, yielding a CO generation rate of 23.73 µmol g −1 h −1 with ≈100% selectivity, 13‐fold higher than pristine Cs 2 AgBiBr 6 and outperforms most reported HP‐based photocatalysts. This work highlights microstructure modulation in HPs as an effective strategy for efficient solar‐to‐fuel 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 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 score0.107
Threshold uncertainty score0.597

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 head0.243
Teacher spread0.235 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicPerovskite Materials and ApplicationsFrench-language works237,207