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Record W2619426754 · doi:10.1021/acsenergylett.7b00194

Stabilities Related to Near-Infrared Quantum Dot-Based Solar Cells: The Role of Surface Engineering

2017· article· en· W2619426754 on OpenAlexafffund
Long Tan, Pandeng Li, Baoquan Sun, Mohamed Chaker, Dongling Ma

Bibliographic record

VenueACS Energy Letters · 2017
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum dotRealization (probability)NanotechnologyMaterials sciencePhotovoltaic systemStability (learning theory)Energy conversion efficiencyDegradation (telecommunications)OptoelectronicsProcess engineeringComputer scienceEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

As compared to the great effort made on improving the power conversion efficiency of quantum dot (QD) solar cells, investigations on relevant stability, regarded as another crucial factor for their widespread implementation, are still limited. In this perspective, we discuss the stability of QD solar cells from different aspects, all highly relying on the surface chemistry of QDs. Specifically, three types of stabilities, closely relevant to the realization of the actual application of QD solar cells, are presented: (i) air-processability, which allows QDs to be processed in air with high batch-to-batch reproducibility; (ii) long-term stability of QD solar cells, directly related to their performance degradation in the long run; and (iii) QD ink stability that is concerned with the storage lifetime of QDs in solution and is compatible with low-cost solution processing techniques. Both air processability and excellent QD ink stability are critical for achieving the desired low-cost, large-scale roll-to-toll manufacturing of solar cells.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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

Citations44
Published2017
Admission routes2
Has abstractyes

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Same venueACS Energy LettersSame topicQuantum Dots Synthesis And PropertiesFrench-language works237,207