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Record W4391295761 · doi:10.1002/aenm.202304234

Indium Iodide Additive Realizing Efficient Mixed Sn─Pb Perovskite Solar Cells

2024· article· en· W4391295761 on OpenAlexaff
Hui Liu, Chongwen Li, Jing Dong, Yao Dai, Pengyang Wang, Biao Shi, Ying Zhao, Xiaodan Zhang

Bibliographic record

VenueAdvanced Energy Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaNankai UniversityNational Natural Science Foundation of China
KeywordsMaterials scienceIndiumPerovskite (structure)IodideInorganic chemistryOptoelectronicsChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Abstract Low‐bandgap mixed tin (Sn)‐lead (Pb) perovskite solar cells promise efficiency beyond the pure‐Pb ones. However, the difference in the interaction rate of SnI 2 and PbI 2 with organic salts causes spatial distribution heterogeneity of Sn 2+ and Pb 2+ in mixed Sn─Pb perovskite layers. This causes a Sn‐rich surface, which can trigger more severe Sn 2+ oxidation and nonradiative recombination. A strategy, of introducing indium ion (In 3+ ) into the perovskite precursor solution to compete with Sn 2+ when reacting with organic salts is developed. Therefore, the nucleation and crystallization of perovskite films are well‐controlled, leading to improved film quality with a more balanced Sn/Pb ratio on the film surface. Additionally, In 3+ has a lower reduction potential compared to Sn 2+ which can generate an extra energy barrier for Sn 2+ oxidation. The improved film quality and reduced surface oxidation result in accelerated electron transfer and reduced carrier recombination rate. The modified devices achieve a power conversion efficiency (PCE) of 23.34%, representing one of the highest PCEs in mixed Sn─Pb solar cells made with PCBM.

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 categoriesMeta-epidemiology (narrow)
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 score0.176
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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.

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

Citations34
Published2024
Admission routes1
Has abstractyes

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