Given recent worldwide environmental concerns, biodegradability, antibacterial activity, and healing properties around the wound area are vital features that should be taken into consideration while preparing biomedical materials such as wound dressings. Some of the available wound dressings present some major disadvantages. For example, low water vapor transmission rate (WVTR), inadequate exudates absorption, and the complex and high environmental cost of the disposal/recycling processes represent such drawbacks. In this paper, starch/polyvinyl alcohol (PVA) material with inserted nano-sized zinc-oxide particles (nZnO) (average size ≤ 100 nm) was made and altered using citric acid (CA). Both ensure an efficient antibacterial environment for wound-dressing materials. The film properties were assessed by UV–Vis spectrometry and were validated against the UV light transmission percentage of the starch/ polyvinyl alcohol (PVA)/ zinc-oxide nanoparticles (nZnO) composites. Analyses were conducted using X-ray Spectroscopy (EDX) and scanning electron microscopy (SEM) to investigate the structure and surface morphology of the materials. Moreover, to validate an ideal moisture content around the wound area, which is necessary for an optimum wound-healing process, the water vapor transmission rate of the film was measured. The new starch-based materials exhibited suitable physical and chemical properties, including solubility, gel fraction, fluid absorption, biodegradability, surface morphology (scanning electron microscopy imaging), and mechanical properties. Additionally, the pH level of the starch-based/nZnO film was measured to study the prospect of bacterial growth on this wound-dressing material. Furthermore, the in vitro antibacterial activity demonstrated that the dressings material effectively inhibited the growth and penetration of bacteria (Escherichia coli, Staphylococcus aureus).

Pressure measurement in high temperature environments is important in many applications to provide valuable information for performance studies. Information on pressure patterns is highly desirable for improving performance, condition monitoring and accurate prediction of the remaining life of systems that operate in extremely high temperature environments, such as gas turbine engines. A number of technologies have been recently investigated, however these technologies target specific applications and they are limited by the maximum operating temperature. Thick and thin films of SiCN can withstand high temperatures. SiCN is a polymer-derived ceramic with liquid phase polymer as its starting material. This provides the advantage that it can be molded to any shape. CERASET™ also yields itself for photolithography, with the addition of photo initiator 2, 2-Dimethoxy-2-phenyl-acetophenone (DMPA), thereby enabling photolithographical patterning of the pre-ceramic polymer using UV lithography. SiCN fabrication includes thermosetting, crosslinking and pyrolysis. The technology is still under investigation for stability and improved performance. This work presents the preparation of SiCN films to be used as the body of a sensor for pressure measurements in high temperature environments. The sensor employs the phenomenon of drag effect. The pressure sensor consists of a slender sensitive element and a thick blocking element. The dimensions and thickness of the films depend on the intended application of the sensors. Fabrication methods of SiCN ceramics both as thin (about 40–60 µm) and thick (about 2–3 mm) films for high temperature applications are discussed. In addition, the influence of thermosetting and annealing processes on mechanical properties is investigated.

This review paper delves into the realm of hybrid cellulose-based materials and their applications in 2D nano-delivery systems. Cellulose, recognized for its biocompatibility, versatility, and renewability, serves as the core matrix for these nanomaterials. The paper offers a comprehensive overview of the latest advancements in the creation, analysis, and application of these materials, emphasizing their significance in nanotechnology and biomedical domains. It further illuminates the integration of nanomaterials and advanced synthesis techniques that have significantly improved the mechanical, chemical, and biological properties of hybrid cellulose-based materials.

Liposomes nanoparticles (LNPs) are vesicles that encapsulate drugs, genes, and imaging labels for advanced delivery applications. Control and tuning liposome physicochemical characteristics such as size, size distribution, and zeta potential are crucial for their functionality. Liposome production using micromixers has shown better control over liposome characteristics compared with classical approaches. In this work, we used our own designed and fabricated Periodic Disturbance Micromixer (PDM). We used Design of Experiments (DoE) and Response Surface Methodology (RSM) to statistically model the relationship between the Total Flow Rate (TFR) and Flow Rate Ratio (FRR) and the resulting liposomes physicochemical characteristics. TFR and FRR effectively control liposome size in the range from 52 nm to 200 nm. In contrast, no significant effect was observed for the TFR on the liposomes Polydispersity Index (PDI); conversely, FRR around 2.6 was found to be a threshold between highly monodisperse and low polydispersed populations. Moreover, it was shown that the zeta potential is independent of TFR and FRR. The developed model presented on the paper enables to pre-establish the experimental conditions under which LNPs would likely be produced within a specified size range. Hence, the model utility was demonstrated by showing that LNPs were produced under such conditions.

Artificial neural networks (ANN) and data analysis (DA) are powerful tools for supporting decision-making. They are employed in diverse fields, and one of them is nanotechnology; for example, in predicting silver nanoparticles size. To our knowledge, we are the first to use ANN to predict liposome size (LZ). Liposomes are lipid nanoparticles used in different biomedical applications that can be produced in Dean-Forces-based microdevices such as the Periodic Disturbance Micromixer (PDM). In this work, ANN and DA techniques are used to build a LZ prediction model by using the most relevant variables in a PDM, the Flow Rate Radio (FRR), and the Total Flow Rate (TFR), and the temperature, solvents, and concentrations were kept constant. The ANN was designed in MATLAB and fed data from 60 experiments with 70% training, 15% validation, and 15% testing. For DA, a regression analysis was used. The model was evaluated; it showed a 0.98147 correlation coefficient for training and 0.97247 in total data compared with 0.882 obtained by DA.