MétaCan
Menu
Back to cohort
Record W4387452841 · doi:10.1016/j.xcrp.2023.101634

Selective deactivation of perovskite grain boundaries

2023· article· en· W4387452841 on OpenAlexafffund
Muhammad Awais, Soumya Kundu, Dongyang Zhang, Vishal Yeddu, Mohammad Reza Kokaba, Yameen Ahmed, Wen Zhou, Sergey Dayneko, Furui Tan, Makhsud I. Saidaminov

Bibliographic record

VenueCell Reports Physical Science · 2023
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of CalgaryUniversity of Victoria
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsPassivationGrain boundaryPerovskite (structure)BiphenylMaterials scienceGrain sizeDegradation (telecommunications)Chemical engineeringNanotechnologyChemistryComposite materialLayer (electronics)Electronic engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Grain boundaries in perovskites are a major source of degradation in perovskite solar cells. Here, we report selective passivation of perovskite grain boundaries with the aid of biphenyl-containing moieties. We find that biphenyl ligands selectively react with PbI2-rich interfaces but not with perovskite itself. Such targeted defect deactivation of grain boundaries leads to extended radiative recombination lifetime from 1 to 2.7 μs while allowing for efficient charge transfer from grains. The hydrophobic nature of benzene ring present in biphenyl improves the stability of perovskite in direct reaction with water by a factor of 3. The devices, all fabricated in ambient air, show significantly improved reproducibility (17%–21% efficiency) and increased open-circuit voltage of 1.11 V. This work offers a design principle for selective passivation of grain boundaries and chemical stabilization of hybrid structures.

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 science0.0000.000
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 head0.007
GPT teacher head0.232
Teacher spread0.225 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCell Reports Physical ScienceSame topicPerovskite Materials and ApplicationsFrench-language works237,207