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Record W3217204418 · doi:10.1002/adma.202107420

Mechanochemistry Advances High‐Performance Perovskite Solar Cells

2021· article· en· W3217204418 on OpenAlexaff
Yuzhuo Zhang, Yanju Wang, Xiaoyu Yang, Lichen Zhao, Rui Su, Jiang Wu, Deying Luo, Shunde Li, Peng Chen, Maotao Yu, Qihuang Gong, Rui Zhu

Bibliographic record

VenueAdvanced Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsFormamidiniumMaterials sciencePerovskite (structure)CrystallizationNucleationChemical engineeringEnergy conversion efficiencyHalideMechanochemistryPhotovoltaicsCrystal (programming language)NanotechnologyPhase (matter)Inorganic chemistryPhotovoltaic systemOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Abstract A prerequisite for commercializing perovskite photovoltaics is to develop a swift and eco‐friendly synthesis route, which guarantees the mass production of halide perovskites in the industry. Herein, a green‐solvent‐assisted mechanochemical strategy is developed for fast synthesizing a stoichiometric δ‐phase formamidinium lead iodide (δ‐FAPbI 3 ) powder, which serves as a high‐purity precursor for perovskite film deposition with low defects. The presynthesized δ‐FAPbI 3 precursor possesses high concentration of micrometer‐sized colloids, which are in favor of preferable crystallization by spontaneous nucleation. The resultant perovskite films own preferred crystal orientations of cubic (100) plane, which is beneficial for superior carrier transport compared to that of the films with isotropic crystal orientations using “mixture of PbI 2 and FAI” as precursors. As a result, high‐performance perovskite solar cells with a maximum power conversion efficiency of 24.2% are obtained. Moreover, the δ‐FAPbI 3 powder shows superior storage stability for more than 10 months in ambient environment (40 ± 10% relative humidity), being conducive to a facile and practical storage for further commercialization.

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 score0.054
Threshold uncertainty score0.993

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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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

Citations110
Published2021
Admission routes1
Has abstractyes

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