Bactericidal nanoparticle coatings are very promising for hindering the indirect transmission of pathogens through cross-contaminated surfaces.

Counteracting the spreading of multi-drug-resistant pathogens, taking place through surface-mediated cross-contamination, is amongst the higher priorities in public health policies. For these reason an appropriate design of antimicrobial nanostructured coatings may allow to exploit different antimicrobial mechanisms pathways, to be specifically activated by tailoring the coatings composition and morphology. Furthermore, their mechanical properties are of the utmost importance in view of the antimicrobial surface durability. Indeed, the coating properties might be tuned differently according to the specific synthesis method. The present review focuses on nanoparticle based bactericidal coatings obtained via magneton-spattering and supersonic cluster beam deposition. The bacteria–NP interaction mechanisms are first reviewed, thus making clear the requirements that a nanoparticle-based film should meet in order to serve as a bactericidal coating. Paradigmatic examples of coatings, obtained by magnetron sputtering and supersonic cluster beam deposition, are discussed. The emphasis is on widening the bactericidal spectrum so as to be effective both against gram-positive and gram-negative bacteria, while ensuring a good adhesion to a variety of substrates and mechanical durability. It is discussed how this goal may be achieved combining different elements into the coating.

Accessing fluid infiltration in nanogranular coatings is an outstanding challenge, of relevance for applications ranging from nanomedicine to catalysis. A sensing platform, allowing to quantify the amount of fluid infiltrated in a nanogranular ultrathin coating, with thickness in the 10 to 40 nm range, is here proposed and theoretically investigated by multiscale modelling. The scheme relies on impulsive photoacoustic excitation of hypersonic mechanical breathing modes in engineered gas-phase synthesised nanogranular metallic ultathin films and time-resolved acousto-optical read-out of the breathing modes frequency shift upon liquid infiltration. A superior sensitivity, exceeding 26x10³ cm²/g, is predicted upon equivalent areal mass loading of a few ng/mm². The capability of the present scheme to discriminate among different infiltration patterns is discussed. The platform is an ideal tool to investigate nanofluidics in granular materials and naturally serves as a distributed nanogetter coating, integrating fluid sensing capabilities. The proposed scheme is readily extendable to other nanoscale and mesoscale porous materials.

Nanocomposite systems and nanoparticle (NP) films are crucial for many applications and research fields. The structure-properties correlation raises complex questions due to the collective structure of these systems, often granular and porous, a crucial factor impacting their effectiveness and performance. In this framework, we investigate the optical and morphological properties of Ag nanoparticles (NPs) films and of Ag NPs/TiO₂ porous matrix films, one-step grown by supersonic cluster beam deposition. Morphology and structure of the Ag NPs film and of the Ag/TiO₂ (Ag/Ti 50-50) nanocomposite are related to the optical properties of the film employing spectroscopic ellipsometry (SE). We employ a simple Bruggeman effective medium approximation model, corrected by finite size effects of the nano-objects in the film structure to gather information on the structure and morphology of the nanocomposites, in particular porosity and average NPs size for the Ag/TiO₂ NP film. Our results suggest that SE is a simple, quick and effective method to measure porosity of nanoscale films and systems, where standard methods for measuring pore sizes might not be applicable.

Objectives: To test whether 3T MRI radiomics of breast malignant lesions improves the performance of predictive models of complete response to neoadjuvant chemotherapy when added to other clinical, histological and radiological information. Methods: Women who consecutively had pre-neoadjuvant chemotherapy (NAC) 3T DCE-MRI between January 2016 and October 2019 were retrospectively included in the study. 18F-FDG PET-CT and histological information obtained through lesion biopsy were also available. All patients underwent surgery and specimens were analyzed. Subjects were divided between complete responders (Pinder class 1i or 1ii) and non-complete responders to NAC. Geometric, first order or textural (higher order) radiomic features were extracted from pre-NAC MRI and feature reduction was performed. Five radiomic features were added to other available information to build predictive models of complete response to NAC using three different classifiers (logistic regression, support vector machines regression and random forest) and exploring the whole set of possible feature selections. Results: The study population consisted of 20 complete responders and 40 non-complete responders. Models including MRI radiomic features consistently showed better performance compared to combinations of other clinical, histological and radiological information. The AUC (ROC analysis) of predictors that did not include radiomic features reached up to 0.89, while all three classifiers gave AUC higher than 0.90 with the inclusion of radiomic information (range: 0.91-0.98). Conclusions: Radiomic features extracted from 3T DCE-MRI consistently improved predictive models of complete response to neo-adjuvant chemotherapy. However, further investigation is necessary before this information can be used for clinical decision making.