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Record W3170652562 · doi:10.1039/d1ee02454j

Nanoscale interfacial engineering enables highly stable and efficient perovskite photovoltaics

2021· article· en· W3170652562 on OpenAlexfundno aff
Anurag Krishna, Hong Zhang, Zhiwen Zhou, Thibaut Gallet, Mathias Dankl, Olivier Ouellette, Felix T. Eickemeyer, Fan Fu, Sandy Sánchez, Mounir Mensi, Shaik M. Zakeeruddin, Ursula Röthlisberger, G. N. Manjunatha Reddy, Alex Redinger, Michaël Grätzel, Anders Hagfeldt

Bibliographic record

VenueEnergy & Environmental Science · 2021
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungBundesamt für EnergieHorizon 2020 Framework ProgrammeFonds National de la Recherche LuxembourgNatural Sciences and Engineering Research Council of CanadaUniversité de LilleEuropean CommissionRoyal SocietyNational Science FoundationRoyal Society of ChemistryEuropean Soft Matter Infrastructure
KeywordsPerovskite (structure)PassivationMaterials sciencePhotovoltaicsNanoscopic scaleNanotechnologyGrain boundaryThermal stabilityEnergy conversion efficiencyDiffusionLayer (electronics)OptoelectronicsChemical engineeringPhotovoltaic systemMicrostructureComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

The molecular level interface engineering with a multifunctional ligand 2,5-thiophenedicarboxylic acid suppresses interfacial ion diffusion and inhibits I 2 formation, which leads to high operational stability with T 80 of 3570 h along with PCE of 23.4%.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.003
GPT teacher head0.155
Teacher spread0.152 · 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 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

Citations113
Published2021
Admission routes1
Has abstractyes

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