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.

This paper utilizes response surface methodology (RSM) design-based analyses to optimize starch-based wound dressings that were characterized based on weight loss (WL%), swelling index (SI%), and mechanical strength (TS). The wound-dressing materials were prepared by employing a modified casting method, using various concentrations of starch (0.5–2 w/w%), polyvinyl alcohol (PVA) (0.5–2 weight%), citric acid (1.5–4 weight%), and glycerol (1.5–4 weight%) to yield wound-dressing films with appropriate combinations of in vitro degradation, swelling index, and tensile strength. As a result of the response surface method-based analysis, the swelling index, in vitro degradation, and tensile strength were linearly related to variations in the formulation of organic components. Based on our experimental investigations, the optimized film (formulation: 1 weight% PVA, 2 weight% starch, 1.5 weight% citric acid, and 1.5 weight% glycerol) exhibited an outstanding swelling index (343.52%), suitable in vitro degradation (53.22%), and excellent tensile strength (8.82 MPa). The response surface plots for the dependent variables, swelling index (SI%), weight loss (WL%), and mechanical strength (TS), showed that in all dual relations, the PVA-starch combination significantly affected all dependent variables; however, the PVA-citric acid interaction showed the most excellent effect on the swelling index. As a result, every component of the resulting film had a lesser amount of all ingredients to achieve better properties at a lower material cost. Starch-based/PVA films have been identified in this paper as optimal and more affordable wound-dressing films.

This study explores the use of citric acid and L-ascorbic acid as reducing agents in CNC/CNF/rGO nanocomposite fabrication, focusing on their effects on electrical conductivity and mechanical properties. Through comprehensive analysis, L-ascorbic acid showed superior reduction efficiency, producing rGO with enhanced electrical conductivity up to 2.5 S/m, while citric acid offered better CNC and CNF dispersion, leading to higher mechanical stability. The research employs an advanced optimization framework, integrating regression models and a neural network with 30 hidden layers, to provide insights into composition–property relationships and enable precise material tailoring. The neural network model, trained on various input variables, demonstrated excellent predictive performance, with R² values exceeding 0.998. A LASSO model was also implemented to analyze variable impacts on material properties. The findings, supported by machine learning optimization, have significant implications for flexible electronics, smart packaging, and biomedical applications, paving the way for future research on scalability, long-term stability, and advanced modeling techniques for these sustainable, multifunctional materials.

Artificial Neural Networks (ANN) and Data analysis are powerful tools used for supporting decision-making. They have been employed in diverse fields and one of them is nanotechnology used, for example, in predicting particles size. Liposomes are nanoparticles used in different biomedical applications that can be produced in Dean Forces-based Periodic Disturbance Micromixers (PDM). In this work, ANN and data analysis techniques are used to build a liposome size prediction model by using the most relevant variables in a PDM, i.e., Flow Rate Radio (FRR) and Total Flow Rate (TFR). The ANN was designed in MATLAB and fed data from 60 experiments, which were 70% training, 15% validation and 15% testing. For data analysis, regression analysis was used. The model was evaluated; it showed 98.147% of regression number for training and 97.247% in total data compared with 78.89% regression number obtained by data analysis. These results demonstrate that liposomes’ size can be better predicted by ANN with just FRR and TFR as inputs, compared with data analysis techniques when the temperature, solvents, and concentrations are kept constant.