MétaCan
Menu
Back to cohort
Record W3047518622 · doi:10.1021/acsenergylett.0c01566

Bifunctional Surface Engineering on SnO<sub>2</sub> Reduces Energy Loss in Perovskite Solar Cells

2020· article· en· W3047518622 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Energy Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of KoreaOntario Research Foundation
KeywordsMaterials scienceTin oxidePerovskite (structure)BifunctionalAmmonium fluoridePhotovoltaic systemTinDopingSubstrate (aquarium)Energy conversion efficiencyLayer (electronics)PhotovoltaicsOxideOptoelectronicsNanotechnologyChemical engineeringInorganic chemistryChemistryCatalysisMetallurgy

Abstract

fetched live from OpenAlex

Tin oxide (SnO2) has recently emerged as a promising electron transport layer for perovskite solar cells (PSCs) in light of the material’s optical and electronic properties and its low-temperature processing. However, SnO2 films are prone to surface defect formation, which results in energy loss in PSCs. We report that surface treatment using ammonium fluoride (NH4F) leads to reduced surface defects and that it also induces chemical doping of the SnO2 substrate simultaneously. The effects of NH4F treatment on SnO2 properties are revealed by surface chemical analysis, computational studies, and energy level investigations, and PSCs with the treatment achieve photovoltaic performance of 23.2% in light of higher voltage than in relevant controls.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.396
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.006
GPT teacher head0.157
Teacher spread0.151 · 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