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Record W4308052481 · doi:10.1021/acsami.2c10204

High-Efficiency Perovskite Solar Cells with Sputtered Metal Contacts

2022· article· en· W4308052481 on OpenAlexafffund
Tengxiao Ji, Roxanna S. Delima, David Dvořák, Yang Cao, Shaoxuan Ren, Thomas D. Morrissey, Xin Lu, Curtis P. Berlinguette

Bibliographic record

VenueACS Applied Materials & Interfaces · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersCanada First Research Excellence FundCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institute for Advanced Research
KeywordsSputteringMaterials scienceFabricationSputter depositionMetalDeposition (geology)Perovskite (structure)OptoelectronicsLayer (electronics)Thin filmNanotechnologyMetallurgyChemical engineering

Abstract

fetched live from OpenAlex

Sputter deposition produces dense, uniform, adhesive, and scalable metal contacts for perovskite solar cells (PSCs). However, sputter deposition damages the other layers of the PSC. We here report that the damage caused by sputtering metal contacts can be reversed by aerial oxidation. We support this claim by making PSCs sputtered with Au contacts that exhibit higher efficiencies (18.7%) and stabilities than those made with thermally evaporated Au contacts (18.4%). We performed a series of experiments that show that the post-sputtering oxidation step reconstructs the molecular order of the hole transport layer (HTL) and reverses Au atom diffusion into the HTL. This potential restoration was previously neglected in PSC fabrication recipes because metal contact deposition is generally performed after the HTL oxidation. This result is important for scaling PSCs because sputtering is a superior method for manufacturing optimal-quality coatings or large-area devices.

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 score0.003

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.181
Teacher spread0.174 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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