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Record W3045465470 · doi:10.1021/acs.jpclett.0c01934

Watching Paint Dry: Operando Solvent Vapor Annealing of Organic Solar Cells

2020· article· en· W3045465470 on OpenAlexafffund
Chase L. Radford, Richard D. Pettipas, Timothy L. Kelly

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of Saskatchewan
KeywordsOrganic solar cellVapor pressureCrystallinityMaterials scienceSolventChemical engineeringAnnealing (glass)PolymerAnalytical Chemistry (journal)ChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The commercialization of organic solar cell (OSC) technology will require highly reproducible techniques for controlling the morphology of bulk heterojunction blends. Variable-pressure solvent vapor annealing (VP-SVA) is one method for postprocessing organic solar cells with high precision; it can prevent the overannealing of cells that plagues conventional SVA processes. To gain insight into the dynamics of the VP-SVA process, we carried out operando measurements on OSCs with correlated in situ grazing-incidence wide-angle X-ray scattering (GIWAXS) measurements. We show that the partial pressure of solvent vapor controls the length scale of film reordering, with optimal restructuring taking place below the saturation vapor pressure of the solvent. The experiments reveal how the film crystallinity, domain sizes, and percolation pathways evolve over the course of the VP-SVA process and how subtle differences in these morphological parameters differentiate good OSCs from champion 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)0.000
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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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