Materials

A thorough understanding of the microbial ecology of meat products, dominated by critical pathogens such as Salmonella spp., Campylobacter jejuni, Escherichia coli, and Listeria monocytogenes, and marked by risks of resistant biofilm formation and vulnerabilities specific to informal commercial sectors, underscores the need to transition from conventional inert barriers to active nanostructured packaging systems. This review critically analyses the current state of antimicrobial nanomaterials, dissecting their molecular mechanisms of action and dynamic interactions designed to preserve sensory and nutritional food quality. Beyond technical effectiveness, the paper highlights the inherent tension between technological innovation and toxicological uncertainties, addressing major challenges related to migration kinetics in complex lipid matrices and the uneven global regulatory landscape. Main limitations of frequently investigated materials, along with regulatory discrepancies among international authorities and safety variables, are discussed to contextualise the current barriers to industrial implementation. We conclude that although nanotechnology represents a transformative force for extending shelf life, safety validation through rigorous assessment of migration remains imperative to harmonise scientific progress with public health protection. This integrative perspective highlights the imperative of calibrating nanostructural architecture to the bioactive profile, providing strategic design directions essential for the sustainable translation of experimental innovation to industrial scale.

The aim of this work was to develop a model using Artificial Neural Networks (ANN) to predict stem cutting parameters for giant miscanthus. Experimental studies were conducted to determine biometric traits: maximum stem diameter (Dmax), minimum stem diameter (Dmin), stem wall thickness (THwall), and strength parameters (cutting force, cutting work) for two giant miscanthus genotypes, depending on the internode number (NrNod) and water content (MC). A total of 600 measurement results were obtained, which were randomly divided into training (60%), test (20%), and validation (20%) subsets. Two semantic models were adopted: one for predicting stem cutting force (ann1) and one for predicting cutting work (ann2). The independent variables (ANN inputs) were: Gen, MC, NrNod, Dmax, Dmin, and THwall. The ANN creation process was performed using Statistica Neural Networks. For each of the two semantic models (ANN1 and ANN2), 100 neural networks were developed, with the top 10 ANNs retained for further analysis. The criterion for selecting the best neural network was the root mean square error (RMSE) for the test subset. For ANN1, the RMSE values varied from 6.89 N to 8.70 N. For ANN2, the RMSE values varied from 0.086 J to 0.102 J. For the most accurate ANN1-03 (MLP 7-10-1), used to predict grass cutting force, the RMSE values were 6.46 N–6.89 N–4.70 N for the training, test, and validation subsets. For the most accurate ANN2-02 (MLP 7-10-1), used to predict grass cutting work, the RMSE values were 0.0646 J–0.0857 J–0.0596 J for the training, test, and validation subsets.

Dual-phase layered microstructures containing alternating regions of soft and hard phases can produce alloys with a unique combination of strength and ductility. In this study, the molecular dynamics (MD) method was utilized to simulate nanoindentation of a Ni/NiTi/Ni nanostructured film (NSF). This film features a unique alternating FCC/B2/FCC microstructure, in which the B2-phase NiTi acts as a superelastic shape memory alloy (SMA). The results indicate that Ni/NiTi/Ni NSF significantly reduces its hardness due to the superelasticity of the B2 phase. The presence of the NiTi interlayer effectively blocks the propagation path of dislocations and stacking faults by transforming the local dislocations transferred from the upper layer into a large-scale coordinated phase transition, significantly reducing local deformation misalignment. As the thickness of the surface film λ increases, the dislocation slip plane propagating horizontally appears in the upper pure Ni layer. The thicker the surface film, the more horizontal slip planes are formed. This study provides new insights into the contact mechanical behavior of nanostructured films based on NiTi shape memory alloys from the perspective of atomic scale.