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Record W4388753953 · doi:10.1021/acsami.3c11074

Superomniphobic and Photoactive Surface Presents Antimicrobial Properties by Repelling and Killing Pathogens

2023· article· en· W4388753953 on OpenAlexafffund
Roderick Maclachlan, Farhaan Kanji, Sadman Sakib, Shadman Khan, Cédric Pattyn, Sara M. Imani, Tohid F. Didar, Leyla Soleymani

Bibliographic record

VenueACS Applied Materials & Interfaces · 2023
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsMaterials scienceAntimicrobialMicrobiologyContaminationNanotechnologyBiology

Abstract

fetched live from OpenAlex

Healthcare-acquired infections place a significant burden on the cost and quality of patient care in hospitals. Reducing contamination on surfaces within healthcare environments is critical for halting the spread of these infections. Herein, we report a bifunctional─repel and kill─surface developed using photoactive TiO 2 nanoparticles integrated into a hierarchical scaffold (OmniKill). To quantify the repellency of OmniKill, we developed a touch-based assay, capable of simulating the transfer of individual pathogens, multiple pathogens, or pathogen-latent fecal matter from hands to surfaces. OmniKill repels bacterial pathogens by at least 2.77-log (99.8%). The photoactive material within OmniKill further reduces the viability of transferred pathogens on the surface by an additional 2.43-log (99.6%) after 1 h of light exposure. The antipathogenic effects─repel and kill─remain robust under complex biological contaminates such as feces. These findings show the potential use of OmniKill in reducing the physical transmission of bacterial pathogens in healthcare settings.

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)0.000
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 score0.760

Codex and Gemma teacher scores by category

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 communication0.0010.000
Open science0.0000.001
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 head0.023
GPT teacher head0.236
Teacher spread0.213 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

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