Ion acceleration enhancement in laser-generated plasmas by metallic doped hydrogenated polymers

Laser-generated plasmas in vacuum were obtained by ablating hydrogenated polymers at the Physics Department of the University of Messina and at the PALS Laboratory in Prague. In the first case a 3 ns, 532 nm Nd:Yag laser, at 10^10 W/cm^2 intensity was employed. In the second case a 300 ps, 438 nm iodine laser, at 5x10^14 W/cm^2 intensity was employed. Different ion collectors were used in a time-of-flight configuration to monitor the ejected ions from the plasma at different angles with respect to the direction normal to the target surface. Measurements demonstrated that the mean ion velocity, directed orthogonally to the target surface, increases for ablation of polymers doped with metallic elements with respect to the nondoped one. The possible mechanism explaining the results can be found in the different electron density of the plasma, due to the higher number of electrons coming from the doping elements. This charge enhancement increases the equivalent ion voltage acceleration, i.e. the electric field generated in the non-equilibrium plasma placed in front of the ablated target surface

Self-assembly of silver nanoparticles and bacteriophage

Biohybrid nanostructured materials, composed of both inorganic nanoparticles and biomolecules, offer prospects for many new applications in extremely diverse fields such as chemistry, physics, engineering, medicine and nanobiotechnology. In the recent years, Phage display technique has been extensively used to generate phage clones displaying surface peptides with functionality towards organic materials. Screening and selection of phage displayed material binding peptides has attracted great interest because of their use for development of hybrid materials with multiple functionalities. Here, we present a self-assembly approach for the construction of hybrid nanostructured networks consisting of M13 P9b phage clone, specific for Pseudomonas aeruginosa, selected by Phage display technology, directly assembled with silver nanoparticles (AgNPs), previously prepared by pulsed laser ablation. These networks are characterized by UV–vis optical spectroscopy, scanning/transmission electron microscopies and Raman spectroscopy. We investigated the influence of different ions and medium pH on self-assembly by evaluating different phage suspension buffers. The assembly of these networks is controlled by electrostatic interactions between the phage pVIII major capsid proteins and the AgNPs. The formation of the AgNPs-phage networks was obtained only in two types of tested buffers at a pH value near the isoelectric point of each pVIII proteins displayed on the surface of the clone. This systematic study allowed to optimize the synthesis procedure to assembly AgNPs and bacteriophage. Such networks find application in the biomedical field of advanced biosensing and targeted gene and drug delivery. Keywords: Phage display, Silver nanoparticles, Self-assembly, Hybrid architecture, Raman spectroscop

N-TiO2-x Nanocatalysts: PLAL Synthesis and Photocatalytic Activity

N-TiO2-x nanocatalysts are developed by the pulsed laser ablation in liquid (PLAL) technique, a simple and surfactant-free preparation method. The PLAL approach allows synthesizing chemical-morphological fine-tuning water TiO2-based nanomaterials, starting from targets of different nature (powders and commercial high purity targets). The catalytic activity was investigated using methylene blue (cationic dye) and methyl orange (azo dye). A different photocatalytic response was found for the various kinds of N-TiO2-x. In the first 20 min, under UV and visible light, about 50% and 10% of the methyl orange were removed using the N-TiO2-x and TiO2 colloids, respectively. In addition, we observe that the response towards the methylene blue is comparable in all the synthesized samples under UV irradiation while differing by about 30% under a visible lamp. The enhanced photocatalytic response of the N-TiO2-x nanocatalysts with respect to the TiO2 one is dependent on the content of the nitrogen dopant, surface area, and nitrogen-oxygen bonding configurations

Weibull Modeling of Controlled Drug Release from Ag-PMA Nanosystems

Traditional pharmacotherapy suffers from multiple drawbacks that hamper patient treatment such as antibiotic resistances or low drug selectivity and toxicity during systemic applications. Some functional hybrid nanomaterials are designed to handle the drug release process under remote-control. More attention has recently been paid to synthetic polyelectrolytes for their intrinsic properties which allow them to rearrange into compact structures, ideal to be used as drug carriers or probes influencing biochemical processes. The presence of Ag nanoparticles (NPs) in the Poly methyl acrylate (PMA) matrix leads to an enhancement of drug release efficiency, even using a low-power laser whose wavelength is far from the Ag Surface Plasmon Resonance (SPR) peak. Further, compared to the colloids, the nanofiber-based drug delivery system has shown shorter response time and more precise control over the release rate. The efficiency and timing of involved drug release mechanisms has been estimated by the Weibull distribution function, whose parameters indicate that the release mechanism of nanofibers obeys Fick’s first law while a non-Fickian character controlled by diffusion and relaxation of polymer chains occurs in the colloidal phase

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy

Nano-Hybrid Au@LCCs Systems Displaying Anti-Inflammatory Activity

Gold nanoparticles (Au NPs) have received great attention owing to their biocompatible nature, environmental, and widespread biomedical applications. Au NPs are known as capable to regulate inflammatory responses in several tissues and organs; interestingly, lower toxicity in conjunction with anti-inflammatory effects was reported to occur with Au NPs treatment. Several variables drive this benefit-risk balance, including Au NPs physicochemical properties such as their morphology, surface chemistry, and charge. In our research we prepared hybrid Au@LCC nanocolloids by the Pulsed Laser Ablation, which emerged as a suitable chemically clean technique to produce ligand-free or functionalized nanomaterials, with tight control on their properties (product purity, crystal structure selectivity, particle size distribution). Here, for the first time to our knowledge, we have investigated the bioproperties of Au@LCCs. When tested in vitro on intestinal epithelial cells exposed to TNF-α, Au@LCCs sample at the ratio of 2.6:1 showed a significantly reduced TNF gene expression and induced antioxidant heme oxygenase-1 gene expression better than the 1:1 dispersion. Although deeper investigations are needed, these findings indicate that the functionalization with LCCs allows a better interaction of Au NPs with targets involved in the cell redox status and inflammatory signaling

Correction: Condorelli et al. Nano-Hybrid Au@LCCs Systems Displaying Anti-Inflammatory Activity. Materials 2022, 15, 3701

The authors wish to make the following correction to the published paper [...