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Record W4414649250 · doi:10.1021/acsenergylett.5c01923

Improved Interconnecting Layer for Perovskite–Organic Tandem Solar Cells

2025· article· en· W4414649250 on OpenAlexaff
Yun Xiao, Tianyu Huang, Nan Chen, Peng Chen, Deying Luo, Xin Jiang, Xiaohan Jia, Juntao Hu, Dengke Wang, Pascal Kaienburg, Suhas Mahesh, Anna Jungbluth, Rui Su, Congmeng Li, Qiang Lou, Chen Yang, Bingjun Wang, Irfan Habib, Hao Ye, Hang Zhou, Hui Li, Lei Meng, Xiaojun Li, Hongyu Yu, Moritz Riede, Zheng‐Hong Lu, Rui Zhu, Henry J. Snaith

Bibliographic record

VenueACS Energy Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersMajor Science and Technology Projects in Yunnan ProvinceResearch EnglandNational Key Research and Development Program of ChinaEngineering and Physical Sciences Research CouncilChina Oxford Scholarship FundChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsTandemOhmic contactFabricationLayer (electronics)Perovskite (structure)Band gapPhotovoltaic systemVoltage

Abstract

fetched live from OpenAlex

Monolithic perovskite–organic tandem solar cells (POTSCs) have attracted considerable attention in recent years due to their compatible fabrication routes and advances in single-cell efficiencies. To further boost the performance of POTSCs, reducing the voltage losses that mainly arise from wide bandgap (WBG, >1.7 eV) perovskite subcells and interconnecting layers (ICLs) is critical. Here, a new ICL with a configuration of C 60 /YbO x /Au/MoO x is demonstrated for constructing the monolithic POTSC. The YbO x -based ICL benefits from an ohmic contact and high transparency, resulting in improved POSTC performance. The champion device presents a PCE of 23.2% owing to a high V OC of 2.11 V (approximately equal to the sum of individual V OC ’s of the subcells) without compromising the short-circuit current density and fill factors. This work opens an avenue for developing efficient ICLs in POTSCs.

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 score0.239
Threshold uncertainty score0.653

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 head0.006
GPT teacher head0.202
Teacher spread0.195 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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