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Record W4213424730 · doi:10.3390/micro2010010

A Biomimetic Strategy for the Fabrication of Micro- and Nanodiamond Composite Films

2022· article· en· W4213424730 on OpenAlexafffund
Kayla Baker, Igor Zhitomirsky

Bibliographic record

VenueMicro · 2022
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanodiamondMaterials scienceDiamondDissolutionComposite numberChemical engineeringNanotechnologyBiocompatibilityCoatingComposite materialMetallurgy

Abstract

fetched live from OpenAlex

This investigation is motivated by increasing interest in diamond and composite films for applications in biomedical and electronic devices. A biomimetic strategy is based on the use of commercial bile acids, such as ursodeoxycholic acid (UDCA) and hyodeoxycholic acid (HDCA). Composite films are developed using UDCA and HDCA as solubilizing agents for poly(ethyl methacrylate) (PEMA) in isopropanol and as dispersing agents for micro- and nanodiamonds. In this approach, the use of traditional toxic solvents for PEMA dissolution is avoided. The ability to obtain high concentrations of high molecular mass PEMA and disperse diamond particles in such solutions is a key factor for the development of a dip-coating method. The PEMA dissolution and diamond dispersion mechanisms are discussed. The composition and microstructure of the films can be varied by variation of the diamond particle size and concentration in the suspensions. The films can be obtained as singular layers of different compositions, multilayers of similar composition, or alternating layers of different compositions. The films combine corrosion protection property and biocompatibility of PEMA with advanced functional properties of diamonds.

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)0.001
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 score0.002
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.273
Teacher spread0.250 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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