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Record W3040169454 · doi:10.1002/smtd.202000303

Low‐Dimensional‐Networked Cesium Lead Halide Perovskites: Properties, Fabrication, and Applications

2020· article· en· W3040169454 on OpenAlexaff
Muhan Cao, Yashvi Damji, Congyang Zhang, Linzhong Wu, Qixuan Zhong, Pengli Li, Di Yang, Yong Xu, Qiao Zhang

Bibliographic record

VenueSmall Methods · 2020
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsWestern UniversityUniversity of Waterloo
FundersHigher Education Discipline Innovation ProjectCollaborative Innovation Center of Suzhou Nano Science and TechnologyPriority Academic Program Development of Jiangsu Higher Education InstitutionsNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsHalideCaesiumFabricationMaterials scienceNanotechnologyPerovskite (structure)Lead (geology)OptoelectronicsChemistryInorganic chemistryCrystallography

Abstract

fetched live from OpenAlex

Abstract All‐inorganic cesium lead halide perovskites have emerged as promising optoelectronic materials with excellent photophysical properties and great potential in a variety of applications. In parallel with the most investigated CsPbX 3 , its derivates, Cs 4 PbX 6 and CsPb 2 X 5 , with low‐dimensional‐networked structures have also attracted great attention. In this review, recent advancements on the low‐dimensional‐networked cesium lead halide perovskites (Cs 4 PbX 6 and CsPb 2 X 5 ) are reviewed systematically. The optical properties and direct controllable synthesis methods are first introduced. The transformation between Cs x Pb y X z and their hybridization are then presented, followed by their wide applications. Finally, challenges and prospects of this class of perovskites are introduced.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 head0.049
GPT teacher head0.273
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 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

Citations58
Published2020
Admission routes1
Has abstractyes

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