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Record W1968009784 · doi:10.3390/molecules170910119

Dry Etching of Copper Phthalocyanine Thin Films: Effects on Morphology and Surface Stoichiometry

2012· article· en· W1968009784 on OpenAlexafffund
Jaron G. Van Dijken, Michael J. Brett

Bibliographic record

VenueMolecules · 2012
Typearticle
Languageen
FieldMaterials Science
TopicCopper-based nanomaterials and applications
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsNanopillarX-ray photoelectron spectroscopyMaterials scienceCopperStoichiometryPhthalocyanineThin filmEtching (microfabrication)Analytical Chemistry (journal)ArgonCopper phthalocyaninePlasma etchingChemical engineeringNanotechnologyChemistryNanostructureOptoelectronicsMetallurgyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We investigate the evolution of copper phthalocyanine thin films as they are etched with argon plasma. Significant morphological changes occur as a result of the ion bombardment; a planar surface quickly becomes an array of nanopillars which are less than 20 nm in diameter. The changes in morphology are independent of plasma power, which controls the etch rate only. Analysis by X-ray photoelectron spectroscopy shows that surface concentrations of copper and oxygen increase with etch time, while carbon and nitrogen are depleted. Despite these changes in surface stoichiometry, we observe no effect on the work function. The absorbance and X-ray diffraction spectra show no changes other than the peaks diminishing with etch time. These findings have important implications for organic photovoltaic devices which seek nanopillar thin films of metal phthalocyanine materials as an optimal structure.

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 score0.002

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 studies0.0000.000
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 head0.011
GPT teacher head0.256
Teacher spread0.245 · 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

Citations7
Published2012
Admission routes2
Has abstractyes

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