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Record W4308444573 · doi:10.1021/acscentsci.2c01077

A Roadmap for Efficient and Stable All-Perovskite Tandem Solar Cells from a Chemistry Perspective

2022· review· en· W4308444573 on OpenAlexafffund
Pu Wu, Deepak Thrithamarassery Gangadharan, Makhsud I. Saidaminov, Hairen Tan

Bibliographic record

VenueACS Central Science · 2022
Typereview
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Victoria
FundersMinistry of Education of the People's Republic of ChinaNatural Science Foundation of Jiangsu ProvinceMitacsNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsTandemPerovskite (structure)FabricationNanotechnologyEnergy conversion efficiencyPerspective (graphical)Materials scienceEngineering physicsChemistryComputer scienceOptoelectronicsPhysicsMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Multijunction tandem solar cells offer a promising route to surpass the efficiency limit of single-junction solar cells. All-perovskite tandem solar cells are particularly attractive due to their high power conversion efficiency, now reaching 28% despite being made with relatively easy fabrication methods. In this review, we summarize the progress in all-perovskite tandem solar cells. We then discuss the scientific and engineering challenges associated with both absorbers and functional layers and offer strategies for improving the efficiency and stability of all-perovskite tandem solar cells from the perspective of chemistry.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.025
GPT teacher head0.273
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations51
Published2022
Admission routes2
Has abstractyes

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