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Record W4310363906 · doi:10.1002/adma.202206345

Efficient Inverted Perovskite Solar Cells via Improved Sequential Deposition

2022· article· en· W4310363906 on OpenAlexaff
Peng Chen, Yun Xiao, Lei Li, Lichen Zhao, Maotao Yu, Shunde Li, Juntao Hu, Bin Liu, Yingguo Yang, Deying Luo, Cheng‐Hung Hou, Xugang Guo, Jing‐Jong Shyue, Zheng‐Hong Lu, Qihuang Gong, Henry J. Snaith, Rui Zhu

Bibliographic record

VenueAdvanced Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersSongshan Lake Materials LaboratoryNational Key Research and Development Program of ChinaEngineering and Physical Sciences Research CouncilChina Oxford Scholarship FundBeijing Nova ProgramNatural Science Foundation of Beijing MunicipalityChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials sciencePerovskite (structure)Energy conversion efficiencyOptoelectronicsDeposition (geology)FabricationNanotechnologyPerovskite solar cellChemical engineering

Abstract

fetched live from OpenAlex

Inverted-structure metal halide perovskite solar cells (PSCs) have attractive advantages like low-temperature processability and outstanding device stability. The two-step sequential deposition method shows the benefits of easy fabrication and decent performance repeatability. Nevertheless, it is still challenging to achieve high-performance inverted PSCs with similar or equal power conversion efficiencies (PCEs) compared to the regular-structure counterparts via this deposition method. Here, an improved two-step sequential deposition technique is demonstrated via treating the bottom organic hole-selective layer with the binary modulation system composed of a polyelectrolyte and an ammonium salt. Such improved sequential deposition method leads to the spontaneous refinement of up and buried interfaces for the perovskite films, contributing to high film quality with significantly reduced defect density and better charge transportation. As a result, the optimized PSCs show a large enhancement in the open-circuit voltage by 100 mV and a dramatic lift in the PCE from 18.1% to 23.4%, delivering the current state-of-the-art performances for inverted PSCs. Moreover, good operational and thermal stability is achieved upon the improved inverted PSCs. This innovative strategy helps gain a deeper insight into the perovskite crystal growth and defect modulation in the inverted PSCs based on the two-step sequential deposition method.

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 categoriesInsufficient payload (model declined to judge)
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 score0.064
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)0.0010.000

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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

Citations76
Published2022
Admission routes1
Has abstractyes

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