MétaCan
Menu
Back to cohort
Record W4206563152 · doi:10.1021/acsnano.1c10636

Guanidinium-Pseudohalide Perovskite Interfaces Enable Surface Reconstruction of Colloidal Quantum Dots for Efficient and Stable Photovoltaics

2022· article· en· W4206563152 on OpenAlexaff

Bibliographic record

VenueACS Nano · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersInstitute for Information and Communications Technology PromotionNational Research Foundation of KoreaHanyang University
KeywordsPassivationPhotovoltaicsQuantum dotPerovskite (structure)ColloidThiocyanateLayer (electronics)Matrix (chemical analysis)Nanoparticle

Abstract

fetched live from OpenAlex

Complete surface passivation of colloidal quantum dots (CQDs) and their strong electronic coupling are key factors toward high-performance CQD-based photovoltaics (CQDPVs). Also, the CQD matrices must be protected from oxidative environments, such as ambient air and moisture, to guarantee air-stable operation of the CQDPVs. Herein, we devise a complementary and effective approach to reconstruct the oxidized CQD surface using guanidinium and pseudohalide. Unlike conventional halides, thiocyanate anions provide better surface passivation with effective replacement of surface oxygen species and additional filling of defective sites, whereas guanidinium cations promote the construction of epitaxial perovskite bridges within the CQD matrix and augment electronic coupling. Additionally, we replace a defective 1,2-ethanedithiol-treated CQD hole transport layer (HTL) with robust polymeric HTLs, based on a judicious consideration of the energy level alignment established at the CQD/HTL interface. These efforts collectively result in high-performance and stable CQDPVs with photocurrents over 30 mA cm –2, ∼80% quantum efficiency at excitonic peaks and stable operation under humid and ambient conditions. Elucidation of carrier dynamics further reveals that interfacial recombination associated with band alignment governs both the CQDPV performance and stability.

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 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 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 score0.017
Threshold uncertainty score0.516

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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 teacher head, 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

Citations34
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueACS NanoSame topicPerovskite Materials and ApplicationsFrench-language works237,207