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Record W2291078680 · doi:10.1021/acsami.5b11614

Fabrication of Semiordered Nanopatterned Diamond-like Carbon and Titania Films for Blood Contacting Applications

2016· article· en· W2291078680 on OpenAlexaff
Deepika Nandakumar, Avi Bendavid, Philip Martin, Kenneth D. Harris, Andrew J. Ruys, Megan S. Lord

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsNational Institute for Nanotechnology
FundersAustralian Research CouncilUniversity of New South Wales
KeywordsMaterials scienceFabricationNanotechnologyDiamondCarbon fibersDiamond-like carbonThin filmMetallurgyComposite materialComposite number

Abstract

fetched live from OpenAlex

Biomaterials with the ability to interface with, but not activate, blood components are essential for a multitude of medical devices. Diamond-like carbon (DLC) and titania (TiO2) have shown promise for these applications; however, both support platelet adhesion and activation. This study explored the fabrication of nanostructured DLC and TiO2 thin film coatings using a block copolymer deposition technique that produced semiordered nanopatterns with low surface roughness (5-8 nm Rrms). These surfaces supported fibrinogen and plasma protein adsorption that predominantly adsorbed between the nanofeatures and reduced the overall surface roughness. The conformation of the adsorbed fibrinogen was altered on the nanopatterned surfaces as compared with the planar surfaces to reveal higher levels of the platelet binding region. Planar DLC and TiO2 coatings supported less platelet adhesion than nanopatterned DLC and TiO2. However, platelets on the nanopatterned DLC coatings were less spread indicating a lower level of platelet activation on the nanostructured DLC coatings compared with the planar DLC coatings. These data indicated that nanostructured DLC coatings may find application in blood contacting medical devices in the future.

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 score0.001
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.255
Teacher spread0.242 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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