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Record W4307501598 · doi:10.1002/adma.202208178

Refining Perovskite Heterojunctions for Effective Light‐Emitting Solar Cells

2022· article· en· W4307501598 on OpenAlexafffund
Peng Chen, Juntao Hu, Maotao Yu, Peicheng Li, Rui Su, Zaiwei Wang, Lichen Zhao, Shunde Li, Yingguo Yang, Yuzhuo Zhang, Qiuyang Li, Deying Luo, Qihuang Gong, Edward H. Sargent, Rui Zhu, Zheng‐Hong Lu

Bibliographic record

VenueAdvanced Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Beijing MunicipalityChina Postdoctoral Science FoundationNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsPerovskite (structure)Materials scienceHeterojunctionLight-emitting diodeElectroluminescenceOptoelectronicsSemiconductorDiodeHalideHybrid solar cellQuantum efficiencyPhotovoltaic systemSolar cellPolymer solar cellNanotechnologyInorganic chemistryCrystallographyChemistry

Abstract

fetched live from OpenAlex

Abstract Solar cells capable of light‐harvesting during daytime and light‐emission at night are multifunctional semiconductor devices with many potential applications. Here, it is reported that halide perovskite heterojunction interfaces can be refined to yield stable and efficient solar cells. The cell can also operate effectively as an ultralow‐voltage light‐emitting diode (LED) with a peak external quantum efficiency of electroluminescence (EQE EL ) of 3.3%. Spectroscopic and microscopic studies reveal that double‐heterojunction refinement with wide‐bandgap salts is key to densifying the packing of perovskite grains and enlarging the bandgaps of the perovskite surfaces that are in contact with charge‐transport semiconductors. The refined perovskite enables a simple device with dual actions of solar cells and LEDs. This type of all‐in‐one device has the potential to be used in multifunctional harvesting–storage–utilization (HSU) systems.

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 score0.227
Threshold uncertainty score0.892

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 studies0.0010.000
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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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

Citations19
Published2022
Admission routes2
Has abstractyes

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