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Record W4413087838 · doi:10.1002/adom.202501353

Modified Chemical Bath Deposition of Tin Oxide for All‐Scalable Perovskite Solar Modules

2025· article· en· W4413087838 on OpenAlexafffund
Wanlong Wang, Augusto Amaro, Yameen Ahmed, Mohammad Reza Kokaba, Vishal Yeddu, I Teng Cheong, Victor Marrugat‐Arnal, Nicholas Sandor, Makhsud I. Saidaminov

Bibliographic record

VenueAdvanced Optical Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Victoria
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials sciencePerovskite (structure)TinTin oxideOxideChemical bath depositionDeposition (geology)Engineering physicsChemical engineeringScalabilityChemical depositionNanotechnologyOptoelectronicsThin filmMetallurgyComputer scienceDatabase

Abstract

fetched live from OpenAlex

Abstract Chemical bath deposition (CBD) of tin oxide is a commercially viable method for manufacturing the electron transport layer in perovskite solar cells, yet it remains highly sensitive to reaction conditions, particularly pH. This study introduces tin‐like cation, namely Pb2+, as a chemical modulator to stabilize the CBD process. Due to nearly two orders of magnitude higher solubility of lead hydroxide (Pb(OH)2) as compared to tin hydroxides (Sn(OH)2 and Sn(OH)4), Pb(OH)2 acts as an OH− scavenger, reducing free excess OH− concentration and suppressing premature tin oxide (SnOx) precipitation. This enables the formation of smooth, uniform SnOx films. Perovskite photovoltaics fabricated on these SnOx films via ambient and scalable processes achieve a power conversion efficiency of 24.5% for cells (and 17.8% for solar modules), retaining >84% of initial performance after 1000 h of operation under maximum power point tracking (MPPT) at 65 ± 5 °C.

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 score0.005

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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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