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Record W4398159277 · doi:10.1088/1748-605x/ad4e87

Antifouling nanoplatform for controlled attachment of <i>E. coli</i>

2024· article· en· W4398159277 on OpenAlexafffund
Amirhossein Tavangar, Priyatha Premnath, Bo Tan, Krishnan Venkatakrishnan

Bibliographic record

VenueBiomedical Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsToronto Metropolitan UniversitySt. Michael's HospitalHumber Polytechnic
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiofoulingMaterials scienceNanotechnologyBiomedical engineeringMedicineChemistryBiochemistry

Abstract

fetched live from OpenAlex

Abstract Biofouling is the most common cause of bacterial contamination in implanted materials/devices resulting in severe inflammation, implant mobilization, and eventual failure. Since bacterial attachment represents the initial step toward biofouling, developing synthetic surfaces that prevent bacterial adhesion is of keen interest in biomaterials research. In this study, we develop antifouling nanoplatforms that effectively impede bacterial adhesion and the consequent biofilm formation. We synthesize the antifouling nanoplatform by introducing silicon (Si)/silica nanoassemblies to the surface through ultrafast ionization of Si substrates. We assess the effectiveness of these nanoplatforms in inhibiting Escherichia coli ( E. coli ) adhesion. The findings reveal a significant reduction in bacterial attachment on the nanoplatform compared to untreated silicon, with bacteria forming smaller colonies. By manipulating physicochemical characteristics such as nanoassembly size/concentration and nanovoid size, we further control bacterial attachment. These findings suggest the potential of our synthesized nanoplatform in developing biomedical implants/devices with improved antifouling properties.

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 categoriesInsufficient 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 score0.052
Threshold uncertainty score1.000

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)0.0010.000

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 head0.024
GPT teacher head0.327
Teacher spread0.303 · 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
Published2024
Admission routes2
Has abstractyes

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