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Record W2963974679

Protein Interactions With Nitrogen-Doped Amorphous Carbon Surfaces

2019· dissertation· en· W2963974679 on OpenAlexfundno aff
Jason Maley

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
FundersCanadian Light Source
KeywordsDopingCarbon fibersNitrogenMaterials scienceAmorphous carbonNanotechnologyChemical engineeringAmorphous solidChemistryOptoelectronicsCrystallographyEngineeringComposite materialOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Amorphous carbon is a very promising material for biocompatible devices. It can be made by a variety of plasma-assisted deposition techniques and is readily doped with other elements, such as nitrogen, which allows tuneable mechanical and tribological properties, including high hardness, low coefficient of friction, and high chemical resistance. It has also been applied to polymer surfaces like poly(tetrafluoroethylene) which gives it the potential for coating applications to hemocompatible devices such as vascular grafts. Although advances in biomaterials used in both surgical and biomedical applications have steadily improved over the past 30 years, improvements towards their biocompatibility and longevity are still needed. Proteins immediately adsorb to the biomaterial interface when it is exposed to bodily fluids such as blood, and this protein layer mediates cellular adsorption on the biomaterial, ultimately playing a major role in the overall success of the biomaterial. Despite advances over the past 40 years in understanding protein interactions at biomaterial interface, there is still a lack of understanding on many of the mechanisms and factors affecting protein adsorption. The main objective of the work presented in this thesis is to (1) develop a surface plasmon resonance (SPR) assay to measure the initial binding kinetics of two major serum proteins, human serum albumin (HSA) and fibrinogen (Fib), to amorphous carbon films prepared with different amounts of nitrogen incorporation. The nitrogen incorporation was controlled by adjusting the %N2 plasma discharge gas during plasma enhanced chemical sputtering using a graphite target onto a Au sensor surface. The initial binding kinetics measurements from SPR experiments found the dissociation kinetics (kd) for both Fib and HSA were comparable onto fullerene-like carbon nitride films (FL-CNx). The association kinetics (ka) was determined to be an important factor for protein adsorption, and the ka was an order of magnitude larger for Fib than HSA. In addition, nitrogen incorporation into the FL-CNx initially decreased ka for both Fib and HSA. However, increasing the nitrogen incorporation due to higher %N2 plasma discharge gas ratios during FL-CNx film deposition increased the ka values for both Fib and HSA. Atomic force microscopy, Raman spectroscopy, and sessile contact angle measurements on the FL-CNx films indicated that the surface hydrophobicity, and the film structure played roles in the changes in protein binding kinetics. The FL-CNx films prepared in the original deposition chamber were also found to contain trace amounts of metals, mainly Fe, incorporated into the films during the deposition process. A second objective (2) of the thesis was to characterize the trace Fe in FL-CNx films deposited onto poly(tetrafluoroethylene). X-ray photoelectron spectroscopy, Fe L-edge x-ray absorption near edge spectroscopy, and electron spin resonance spectroscopy were used to elucidate the Fe structure in the FL-CNx films. The Fe was found to exist in different Fe(III)-oxide and Fe(II) oxide forms, and the Fe valency and concentration was dependent on the %N2 plasma discharge gas during film deposition, and differences were observed for the Fe in the surface and bulk regions of the film. A third objective (3) of this thesis was to design a “metal free” plasma deposition chamber. The films generated using this new chamber were amorphous carbon nitride (a-C:N), and the nitrogen incorporation was controlled by changing the %N2 plasma discharge during a-C:N film deposition. The SPR measurements on the a-C:N films found that the kd values were very similar for HSA and Fib, indicating that the protein/surface interaction is very stable and independent of the protein. The ka(Fib) > ka(HSA) by and order of magnitude. The incorporation of nitrogen into a-C:N film initially decreased the ka for both HSA and Fib, but incorporation of nitrogen using higher %N2 plasma discharge gas during a-C:N film formation increased the ka values for both HSA and Fib. Film characterization suggested that changes in the a-C:N film surface wettability, the type of nitrogen functionalization within the film matrix, and the electronic workfunction may play a role in the changes in ka values measured for HSA and Fib. In addition, it was found that Fe-doping (1.3 at.% Fe) into a-C:N film did not change the HSA and Fib binding kinetics compared to the “metal-free” a-C:N film. Overall, a SPR assay was successfully developed and the initial binding kinetics of HSA and Fib onto amorphous carbon surfaces prepared with different amounts of nitrogen incorporation are reported for the first time. The kinetic results show that the major differences in the binding strength between the two different proteins are the differences in the protein’s ka (recognition rate) towards the surface. This fundamental assay can be expanded in future experiments to study specific surface properties and quantitatively measure the effects of protein binding.

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)0.000
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
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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