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Record W4301594600 · doi:10.26434/chemrxiv-2022-20mll

Plasma-Enhanced Molecular Layer Deposition of Phosphane-ene Polymer Films

2022· preprint· en· W4301594600 on OpenAlexafffund
Justin Lomax, Eden Goodwin, Peter G. Gordon, Christine L. McGuiness, Floryan Decampo, Seán T. Barry, Paul J. Ragogna

Bibliographic record

VenueChemRxiv · 2022
Typepreprint
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsCarleton UniversityWestern University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsQuartz crystal microbalanceX-ray photoelectron spectroscopySiloxanePolymerDeposition (geology)Atomic layer depositionLayer (electronics)PhosphineMaterials scienceChemical engineeringPhase (matter)RadicalAnalytical Chemistry (journal)ChemistryNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

A vapour phase molecular layer deposition (MLD) process generating phosphorus-rich phosphane-ene polymer networks was adapted from known solution phase methods and successfully used in a commercial atomic layer deposition tool. By using plasma-enhanced MLD on Si/SiO2 and Al2O3 substrates, film deposition was carried out with a commercially available primary phosphine, iBuPH2, paired with a known volatile cyclic siloxane precursor, tetramethyltetravinylcyclotetrasiloxane (D4Vinyl). The deposition process used radicals generated by an Ar plasma source to facilitate P-H addition to vinyl functionalities on D4Vinyl which yielded a growth per cycle of 0.6 – 2.0 Å, generating 10-120 nm films as determined by AFM and SEM measurements. Characterization of the films were carried out using X-ray photoelectron spectroscopy and oxygen scavenging capabilities were studied using a quartz crystal microbalance, showing an uptake of oxygen by a 12 nm depth of a freshly deposited polymer film.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score1.000

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)0.0020.000

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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.

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

Citations0
Published2022
Admission routes2
Has abstractyes

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