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Record W1967419288 · doi:10.1002/adfm.200390009

Controlling the Morphology of Nanocrystal–Polymer Composites for Solar Cells

2003· article· en· W1967419288 on OpenAlexfundno aff
Wendy U. Huynh, Janke J. Dittmer, W.C. Libby, Gregory L. Whiting, A. Paul Alivisatos

Bibliographic record

VenueAdvanced Functional Materials · 2003
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsMaterials scienceNanocrystalNanorodPolymerNanometreNanotechnologyDispersion (optics)Chemical engineeringPulmonary surfactantSolventMicrometerComposite materialOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract We have shown recently that the use of high‐aspect‐ratio inorganic nanorods in conjunction with conjugated polymers is a route to obtaining efficient solar cells processed from solution. Here, we demonstrate that the use of binary solvent mixtures in which one of the components is a ligand for the nanocrystals is effective in controlling the dispersion of nanocrystals in a polymer. By varying the concentration of the solvent mixture, phase separation between the nanocrystal and polymer could be tuned from micrometer scale to nanometer scale. In addition, we can achieve nanocrystal surfaces that are free of surfactant through the use of weak binding ligands that can be removed through heating. When combined, the control of film morphology together with surfactant removal result in nanorod–polymer blend photovoltaic cells with a high external quantum efficiency of 59 % under 0.1 mW cm –2 illumination at 450 nm.

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 score0.004

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 communication0.0010.000
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 head0.018
GPT teacher head0.220
Teacher spread0.201 · 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

Citations450
Published2003
Admission routes1
Has abstractyes

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