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Record W3000171146 · doi:10.1002/adma.201906497

Stabilizing Surface Passivation Enables Stable Operation of Colloidal Quantum Dot Photovoltaic Devices at Maximum Power Point in an Air Ambient

2020· article· en· W3000171146 on OpenAlexafffund
Jongmin Choi, Min‐Jae Choi, Junghwan Kim, Filip Dinic, Petar Todorović́, Bin Sun, Mingyang Wei, Se‐Woong Baek, Sjoerd Hoogland, F. Pelayo Garcı́a de Arquer, Oleksandr Voznyy, Edward H. Sargent

Bibliographic record

VenueAdvanced Materials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Science and ICT, South KoreaDaegu Gyeongbuk Institute of Science and TechnologyNational Research Foundation of KoreaUniversity of TorontoCanada Foundation for InnovationGovernment of OntarioMinistry of Science, ICT and Future PlanningNational Research Foundation
KeywordsPassivationMaterials sciencePhotovoltaic systemQuantum dotOptoelectronicsNanotechnologyEnergy conversion efficiencyInert gasLayer (electronics)Composite materialElectrical engineering

Abstract

fetched live from OpenAlex

Colloidal quantum dots (CQDs) are promising materials for photovoltaic (PV) applications owing to their size-tunable bandgap and solution processing. However, reports on CQD PV stability have been limited so far to storage in the dark; or operation illuminated, but under an inert atmosphere. CQD PV devices that are stable under continuous operation in air have yet to be demonstrated-a limitation that is shown here to arise due to rapid oxidation of both CQDs and surface passivation. Here, a stable CQD PV device under continuous operation in air is demonstrated by introducing additional potassium iodide (KI) on the CQD surface that acts as a shielding layer and thus stands in the way of oxidation of the CQD surface. The devices (unencapsulated) retain >80% of their initial efficiency following 300 h of continuous operation in air, whereas CQD PV devices without KI lose the amount of performance within just 21 h. KI shielding also provides improved surface passivation and, as a result, a higher power conversion efficiency (PCE) of 12.6% compared with 11.4% for control 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.250
Teacher spread0.224 · 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.

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

Citations72
Published2020
Admission routes2
Has abstractyes

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