Neural Network Modelling for Prediction of Zeta Potential

Study on the evaluation method of cigarette astringency in the simulated oral environment

Cigarettes with pronounced astringency can diminish consumers' enjoyment. However, due to the complex composition of cigarettes, quantifying astringency intensity accurately has been challenging. To address this, research was conducted to develop a method for assessing astringency intensity in a simulated oral environment. The astringency intensity of four cigarette brands was determined using the standard sensory evaluation method. The mainstream smoke absorbing solution (MS) was prepared by simulating the cigarette smoking process, and its physicochemical properties (such as total phenol content and pH levels) were analyzed. The lubrication properties of the five solutions were tested using the MFT-5000 wear tester, and factors influencing cigarette astringency were examined. The findings showed that total phenol content and pH of MS were positively and negatively correlated with astringency intensity, respectively. Particularly, the lubrication properties of MS were significantly correlated with astringency intensity, and the correlation coefficient was affected by load and speed during testing. The study concluded that coefficient of friction was a more reliable measure for assessing the extent of astringency in cigarettes than the total phenol content and pH of MS, offering new insights into astringency evaluation and development of high-grade cigarettes.

Chitosan-Based Composite Membranes with Different Biocompatible Metal Oxide Nanoparticles: Physicochemical Properties and Drug-Release Study

Chitosan (CS) composite membranes were prepared using different biocompatible metal oxide nanoparticles (NPs): titanium dioxide (TiO₂); iron oxide (Fe₃O₄); and aluminum oxide (Al₂O₃). For each nanoparticle, the CS-based composite membranes were prepared with two NPs contents in the CS solution, high (H) and low (L) NPs concentrations. To establish both concentrations, the NPs saturation point in the CS polymeric matrix was determined. The influence of NP concentrations on the physicochemical properties of the CS films was assessed. The prepared CS membranes were characterized with different techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and zeta potential. It was found that the addition of NPs in the CS matrix improved both swelling and mechanical properties. Nanocomposite CS membranes could be prepared using Al₂O₃ NPs. Swelling experiments revealed different pH-sensitive mechanisms, which might be beneficial in biomedical applications since solute permeation through CS-based composite membranes could be controlled by adjusting environmental conditions. When aspirin transport (ASA) through the prepared membranes was carried out in different release media, SGF (simulating gastric fluid) and SIF (simulating intestinal fluid without enzymes), it was observed that the Fickian diffusion coefficient (D) was conditioned by the pH of the release solution. In SGIT (simulating gastrointestinal transit) medium, a transition time (t_trans) was detected due to the shrinkage of the CS polymeric chains, and the drug release depended not only on the Fickian's diffusion but also on the shrinkage of the biopolymer, obeying Peppas and Sahlin equation.

Human Serum Albumin Nanoparticles: Synthesis, Optimization and Immobilization with Antituberculosis Drugs

The aim of this study was to create nanoparticles of human serum albumin immobilized with anti-TB drugs (rifampicin, isoniazid) using the desolvation method. Central Composite Design (CCD) was applied to study the effect of albumin, urea, L-cysteine, rifampicin and isoniazid concentration on particle size, polydispersity and loading degree of the drugs. The optimized nanoparticles were spherical in shape with an average particle size of 216.7 ± 3.7 nm and polydispersity of 0.286 ± 4.9. The loading degree of rifampicin and isoniazid in the optimized nanoparticles were 44% and 27%, respectively. The obtained nanoparticles were examined by Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC); the results showed the absence of drug-polymer interactions. The drug release from the polymer matrix was studied using dialysis membranes.

Site Identification by Ligand Competitive Saturation-Biologics Approach for Structure-Based Protein Charge Prediction

Protein-based therapeutics typically require high concentrations of the active protein, which can lead to protein aggregation and high solution viscosity. Such solution behaviors can limit the stability, bioavailability, and manufacturability of protein-based therapeutics and are directly influenced by the charge of a protein. Protein charge is a system property affected by its environment, including the buffer composition, pH, and temperature. Thus, the charge calculated by summing the charges of each residue in a protein, as is commonly done in computational methods, may significantly differ from the effective charge of the protein as these calculations do not account for contributions from bound ions. Here, we present an extension of the structure-based approach termed site identification by ligand competitive saturation-biologics (SILCS-Biologics) to predict the effective charge of proteins. The SILCS-Biologics approach was applied on a range of protein targets in different salt environments for which membrane-confined electrophoresis-determined charges were previously reported. SILCS-Biologics maps the 3D distribution and predicted occupancy of ions, buffer molecules, and excipient molecules bound to the protein surface in a given salt environment. Using this information, the effective charge of the protein is predicted such that the concentrations of the ions and the presence of excipients or buffers are accounted for. Additionally, SILCS-Biologics also produces 3D structures of the binding sites of ions on the proteins, which enable further analyses such as the characterization of protein surface charge distribution and dipole moments in different environments. Notable is the capability of the method to account for competition between salts, excipients, and buffers on the calculated electrostatic properties in different protein formulations. Our study demonstrates the ability of the SILCS-Biologics approach to predict the effective charge of proteins and its applicability in uncovering protein-ion interactions and their contributions to protein solubility and function.

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models' predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand-brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields.

Classification, Synthetic, and Characterization Approaches to Nanoparticles, and Their Applications in Various Fields of Nanotechnology: A Review

Nanoparticles typically have dimensions of less than 100 nm. Scientists around the world have recently become interested in nanotechnology because of its potential applications in a wide range of fields, including catalysis, gas sensing, renewable energy, electronics, medicine, diagnostics, medication delivery, cosmetics, the construction industry, and the food industry. The sizes and forms of nanoparticles (NPs) are the primary determinants of their properties. Nanoparticles’ unique characteristics may be explored for use in electronics (transistors, LEDs, reusable catalysts), energy (oil recovery), medicine (imaging, tumor detection, drug administration), and more. For the aforementioned applications, the synthesis of nanoparticles with an appropriate size, structure, monodispersity, and morphology is essential. New procedures have been developed in nanotechnology that are safe for the environment and can be used to reliably create nanoparticles and nanomaterials. This research aims to illustrate top-down and bottom-up strategies for nanomaterial production, and numerous characterization methodologies, nanoparticle features, and sector-specific applications of nanotechnology.

Virus-like Particles: Fundamentals and Biomedical Applications

Nanotechnology is a fast-evolving field focused on fabricating nanoscale objects for industrial, cosmetic, and therapeutic applications. Virus-like particles (VLPs) are self-assembled nanoparticles whose intrinsic properties, such as heterogeneity, and highly ordered structural organization are exploited to prepare vaccines; imaging agents; construct nanobioreactors; cancer treatment approaches; or deliver drugs, genes, and enzymes. However, depending upon the intrinsic features of the native virus from which they are produced, the therapeutic performance of VLPs can vary. This review compiles the recent scientific literature about the fundamentals of VLPs with biomedical applications. We consulted different databases to present a general scenario about viruses and how VLPs are produced in eukaryotic and prokaryotic cell lines to entrap therapeutic cargo. Moreover, the structural classification, morphology, and methods to functionalize the surface of VLPs are discussed. Finally, different characterization techniques required to examine the size, charge, aggregation, and composition of VLPs are described.

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

The Effect of Submicron Polystyrene on the Electrokinetic Potential of Cell Membranes of Red Blood Cells and Platelets

In recent years, many scientists have studied the effects of polymer micro- and nanostructures on living organisms. As it turns out, plastic can be a component of the blood of livestock, eaten by humans around the globe. Thus, it seems important to investigate possible changes in the physicochemical parameters and morphology of the cell membranes of blood morphotic elements (red blood cells and platelets) under the influence of polymer particles. The article presents research in which cell membranes were exposed to plain polystyrene (PS) and amino-functionalized polystyrene (PS-NH₂) of two different sizes. The polymers were characterized by infrared spectroscopy and dynamic light-scattering methods. To analyze possible changes caused by polymer exposure in the structure of the membranes, their zeta potentials were measured using the electrophoretic light-scattering technique. The concentration of the polymers, as well as the exposure time, were also taken into the consideration during the research. Based on the obtained results, we concluded that 100 and 200 nm PS, as well as 100 nm PS-NH₂, internalize into the cells. On the contrary, 200 nm PS-NH₂ particles attach to cell membranes. Our study clearly shows that particle size and surface chemistry determine the interaction with biological membranes.