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Record W1604329059 · doi:10.1038/srep10633

Absence of Structural Impact of Noble Nanoparticles on P3HT:PCBM Blends for Plasmon-Enhanced Bulk-Heterojunction Organic Solar Cells Probed by Synchrotron GI-XRD

2015· article· en· W1604329059 on OpenAlexfundno aff
Samuele Lilliu, Mejd Alsari, Oier Bikondoa, J. Emyr Macdonald, Marcus S. Dahlem

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilYork UniversityEuropean Synchrotron Radiation FacilityMasdar Institute of Science and TechnologyUniversity of WarwickCardiff University
KeywordsMaterials scienceSurface plasmon resonanceNanoparticleHeterojunctionPlasmonAbsorption (acoustics)Noble metalSynchrotronCrystallizationActive layerOptoelectronicsNanotechnologyChemical engineeringLayer (electronics)OpticsMetal

Abstract

fetched live from OpenAlex

The incorporation of noble metal nanoparticles, displaying localized surface plasmon resonance, in the active area of donor-acceptor bulk-heterojunction organic photovoltaic devices is an industrially compatible light trapping strategy, able to guarantee better absorption of the incident photons and give an efficiency improvement between 12% and 38%. In the present work, we investigate the effect of Au and Ag nanoparticles blended with P3HT: PCBM on the P3HT crystallization dynamics by synchrotron grazing incidence X-ray diffraction. We conclude that the presence of (1) 80 nm Au, (2) mix of 5 nm, 50 nm, 80 nm Au, (3) 40 nm Ag, and (4) 10 nm, 40 nm, 60 nm Ag colloidal nanoparticles, at different concentrations below 0.3 wt% for Au and below 0.1% for Ag in P3HT: PCBM blends, does not affect the behaviour of the blends themselves.

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 score0.003

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)0.0010.000

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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