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Record W2023384850 · doi:10.1063/1.4824181

Acridine orange as a biosensitive photovoltaic material

2013· article· en· W2023384850 on OpenAlexaff
Faranak Sharifi, Reg Bauld, Giovanni Fanchini

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsAcridine orangeKelvin probe force microscopeThin filmMaterials scienceAnnealing (glass)Work functionPhotovoltaicsAmorphous solidPhotochemistryChemistryOptoelectronicsAnalytical Chemistry (journal)Chemical engineeringNanotechnologyPhotovoltaic systemOrganic chemistryComposite materialAtomic force microscopy

Abstract

fetched live from OpenAlex

Acridine orange (AO), a biosensitive molecule that is customarily used for labeling nucleic acids including DNA and RNA, is here investigated as a cost effective, water soluble, and photoactive material for the fabrication of potentially biosensitive organic photovoltaics. The electronic energy levels of AO are determined using Kelvin Probe Force Microscopy (KPFM) and UV-Visible spectroscopy. The effect of anticrystallization agents, as well as low-temperature annealing, on the work function of AO is investigated: amorphous AO films are shown to possess a significantly higher work function than microcrystalline AO films and the work function also increases by annealing. Photo-induced processes in AO films are investigated by considering the changes of the KPFM signal under illumination. We demonstrate that acridine orange is able to photogenerate electron-hole pairs at rates comparable to the most commonly used solar-grade photovoltaic materials, including polythiophenes. In addition, the effect of the morphology of different types of AO thin films spun from different solvents is studied in bilayer photovoltaic devices fabricated from stacks of AO and phenyl-C61-butyric acid methyl ester thin films.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

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 head0.007
GPT teacher head0.243
Teacher spread0.237 · 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.

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

Citations8
Published2013
Admission routes1
Has abstractyes

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