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Record W4313424552 · doi:10.1002/smll.202205761

An Omniphobic Spray Coating Created from Hierarchical Structures Prevents the Contamination of High‐Touch Surfaces with Pathogens

2023· article· en· W4313424552 on OpenAlexafffund
Noor Abu Jarad, Kenneth Rachwalski, Fereshteh Bayat, Shadman Khan, Amid Shakeri, Roderick Maclachlan, Martin Villegas, Eric D. Brown, Leyla Soleymani, Tohid F. Didar

Bibliographic record

VenueSmall · 2023
Typearticle
Languageen
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsMcMaster University
FundersCanada Research ChairsMcMaster University
KeywordsCoatingMaterials sciencePolydimethylsiloxaneNanotechnologyWettingBiofoulingChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Engineered surfaces that repel pathogens are of great interest due to their role in mitigating the spread of infectious diseases. A robust, universal, and scalable omniphobic spray coating with excellent repellency against water, oil, and pathogens is presented. The coating is substrate‐independent and relies on hierarchically structured polydimethylsiloxane (PDMS) microparticles, decorated with gold nanoparticles (AuNPs). Wettability studies reveal the relationship between surface texturing of micro‐ and/or nano‐hierarchical structures and the omniphobicity of the coating. Studies of pathogen transfer with bacteria and viruses reveal that an uncoated contaminated glove transfers pathogens to >50 subsequent surfaces, while a coated glove picks up 10 4 (over 99.99%) less pathogens upon first contact and transfers zero pathogens after the second touch. The developed coating also provides excellent stability under harsh conditions. The remarkable anti‐pathogen properties of this surface combined with its ease of implementation, substantiate its use for the prevention of surface‐mediated transmission of pathogens.

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 score0.187
Threshold uncertainty score0.277

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 communication0.0000.000
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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