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Protein-Resistant Materials via Surface-Initiated Atom Transfer Radical Polymerization of 2-Methacryloyloxyethyl Phosphorylcholine

2010· article· en· W2039399596 on OpenAlexaff
Zhilin Jin, Wei Feng, Shiping Zhu, Heather Sheardown, John L. Brash

Bibliographic record

VenueJournal of Biomaterials Science Polymer Edition · 2010
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLysozymeProtein adsorptionAtom-transfer radical-polymerizationAdsorptionGraftingPolymer chemistryX-ray photoelectron spectroscopyPolyurethaneMaterials scienceContact angleMonomerPhosphorylcholinePolymerizationChemical engineeringPolymerChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Poly(2-methacryloyloxyethyl phosphorylcholine) (poly(MPC)) was grafted from various polymeric substrates to prepare protein-resistant materials. The poly(MPC) chain length was adjusted via the ratio of monomer to sacrificial initiator in solution. The surfaces were characterized by water contact angle and X-ray photoelectron spectroscopy (XPS). The protein-resistant properties of the poly(MPC)-grafted surfaces were evaluated by single adsorption experiments with fibrinogen and lysozyme. It was shown that the simple three-step grafting method could be applied to modify various biomaterial surfaces including polyurethane and silicones. The adsorption of fibrinogen and lysozyme to the modified surfaces was greatly reduced compared to the unmodified surfaces, and adsorption decreased with increasing poly(MPC) chain length. On polyurethane film grafted with poly(MPC) of chain length 100, the reduction in adsorption was approx. 96% for lysozyme and approx. 99% for fibrinogen.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.016
GPT teacher head0.285
Teacher spread0.269 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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