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Record W3127869641 · doi:10.1039/d1ra00555c

Access to thermally robust and abrasion resistant antimicrobial plastics: synthesis of UV-curable phosphonium small molecule coatings and extrudable additives

2021· article· en· W3127869641 on OpenAlexafffund
Joseph Bedard, Alexander Caschera, Daniel A. Foucher

Bibliographic record

VenueRSC Advances · 2021
Typearticle
Languageen
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsToronto Metropolitan UniversityToronto Public Health
FundersMitacs
KeywordsBiocideAntimicrobialBiofilmBiofoulingMaterials scienceCoatingArylAlkylAbrasion (mechanical)BacteriaPhosphoniumChemical engineeringChemistryComposite materialOrganic chemistryPolymer chemistry

Abstract

fetched live from OpenAlex

The threat of antibiotic-resistant, biofilm-forming bacteria necessitates a preventative approach to combat the proliferation of robust, pathogenic strains on "high touch surfaces" in the food packaging, biomedical, and healthcare industries. The development of both biocide-releasing and tethered, immobilized biocide surface coatings has risen to meet this demand. While these surface coatings have demonstrated excellent antimicrobial efficacy, there are few examples of antimicrobial surfaces with long-term durability and performance. To this end, UV-curable phosphoniums bearing benzophenone anchors with either an alkyl, aryl, or fluoroalkyl group were synthesized and their efficacy as thermally stable antimicrobial additives in extruded plastics or as surface attached coatings probed. The surface topology and characteristics of these materials were studied to gain insight into the mechanism of their antimicrobial activity. Efficacy against both Gram negative and Gram positive bacteria as either a coating or additive showed compete reductions of the initial bacterial load. Crucially, the materials maintained the ability to kill biofilm-forming bacteria even after being subject to several cycles of abrasion.

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 score0.007
Threshold uncertainty score0.646

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 head0.241
Teacher spread0.226 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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