AbstractIn the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process.
The rapid growth of electronic information technology has led to an increase in electromagnetic wave radiation pollution. Therefore, it is imperative to look into shielding materials with superior electromagnetic interference (EMI) shielding capabilities. Because of its exceptional benefits, nanofibers made by electrospinning are capable of the shielding from electromagnetic radiation. This work tries to bring together the details of electrospinning, electrospun carbon and electrospun composite fibers. Furthermore, the preparation, properties and EMI shielding performance of electrospun carbon and composite fibers are presented. This article attempts to provide an overview of the advancements made in the field of carbon fibrous material prepared via electrospinning for shielding applications.
In the realm of hemodialysis, Polyethersulfone (PES) membranes dominate due to their exceptional stability and mechanical properties, capturing 93
Present work reports solution combustion synthesis (SCS) of Co0.5Zn0.5Fe2O4 spinel ferrite, its chemical modification with polymethylmethacrylate (PMMA), synthesis of polyaniline (PANI), and electromagnetic interference (EMI) shielding properties of their composites. Both as-prepared and PMMA-modified Co0.5Zn0.5Fe2O4 adopt cubic spinel structure as shown by X-ray diffraction studies. Field emission scanning electron microscopy images show the agglomerated and microporous nature of the ferrites. High resolution transmission electron microscopy image of as-prepared Co0.5Zn0.5Fe2O4 shows lattice fringes related to (311) plane of the spinel structure. X-ray photoelectron spectroscopy studies reveal the presence of Co2+, Zn2+, and Fe3+ in tetrahedral and octahedral coordinations in the ferrite. EMI shielding properties are evaluated for PMMA-modified Co0.5Zn0.5Fe2O4 and PANI composites in different weight ratios. Composites containing PMMA-modified ferrite and PANI with 50:50 and 10:90 weight ratios show the best performance among all the composites in 2−20 GHz frequency range. Optimized PMMA-modified Co0.5Zn0.5Fe2O4 and PANI composite with the ratio of 10:90 shows shielding effectiveness (SE) values of −16.6 to −24.2 dB in 2−20 GHz frequency region.
The longevity of waste asphalt can be considerably improved through cold recycling techniques combined with various additives. This research investigates the cold regeneration of aged asphalt concrete using Reclaimed Asphalt Pavement (RAP), Portland cement, cationic bitumen emulsion, and additional aggregates. The primary goal is to evaluate the performance enhancements in terms of average density, compressive strength, water resistance, and swelling across different mix compositions. Three distinct mixtures were formulated and assessed. Mix No. 1, composed solely of RAP, showed the lowest average density and highest swelling, indicating poor performance due to the lack of binding agents. Mix No. 2, which incorporated RAP, Portland cement, and water, exhibited the highest density and compressive strength, highlighting the crucial role of Portland cement in improving structural integrity. Mix No. 3, a more complex mixture including RAP, aggregates, Portland cement, water, and bitumen emulsion, displayed balanced properties with enhanced moisture resistance and reduced swelling. The experimental findings emphasize the effectiveness of adding Portland cement and bitumen emulsion to improve the mechanical and durability characteristics of recycled asphalt mixtures. Specifically, Mix No. 2 and Mix No. 3 demonstrated significant performance improvements, making them suitable for road maintenance applications. This study advocates for the widespread use of cold recycling methods with additive integration to achieve sustainable and cost-effective pavement restoration solutions.
