Influence of Fiber Hybridization on the Mechanical Behavior of Kenaf-Sisal Epoxy Composites Kodidasu Pranathi, Abdul Gafoor, Shaik Mohammed Adib Ayub, M. Arthi, Emad M. Elsehly, et al. Engineering Advanced Materials for Manufacturing Energy and Smart Systems, 2026 The natural composites are widely preferred nowadays in versatile application due to easily degradable and moderate mechanical properties when compare to polymer matrix composites. The kenaf fiber are used as reinforcement in composites wherever high flexural strength and modulus is required whereas sisal fiber is preferred for tensile strength, impact and wear resistance. In our work hybrid composite comprising kenaf and sisal as reinforcement and epoxy as matrix is fabricated and its mechanical properties are explored and compared with the conventional composite fabricated using only kenaf or sisal as reinforcement. The hybrid composite shows excellent flexural strength but inferior tensile strength when compare to conventional composite. The failure of the specimens are investigated using scanning electron microscopy (SEM). In this chapter detailed insight is presented for emphasizing the need for tailored fiber selection and processing to achieve optimal mechanical properties.
Surface modification of polyethersulfone membranes with alkaline protease-activated L-histidine zwitterion carbon dots to improve anti-protein fouling Gül Kaya, Kasim Ocakoglu, Mohammed Saleh, Nadir Dizge, Deepanraj Balakrishnan, et al. Environmental Sciences Europe, 2025 In this study, L-histidine zwitterionic carbon dots (HZCDs) were synthesized using the hydrothermal method. The synthesized HZCDs were used to modify polyethersulfone (PES) membranes. Additionally, the HZCDs-modified membranes were activated using the protease enzyme to prepare protease-activated composite membranes. The prepared materials underwent extensive characterization and validation using various techniques, including Transmission Electron Microscopy (TEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD) analyses. The blending or activation of HZCDs by the protease enzyme reduced the contact angle of the prepared membranes. The contact angle decreased from 78.75° to 50.12° and 40.02° for 2.0 wt.% HZCDs-PES and PES/Protease-HZCDs membranes, respectively. As the contact angle decreased, the hydrophilic nature of the prepared membranes increased, reflecting a strong affinity for water and efficient wettability. In this context, the pure water flux (PWF) values of PES membranes increased from 140.5 ± 5.3 to 248.7 ± 8.4 L/m 2 .h with rising HZCDs amount from 0 to 2 wt.% HZCDs-PES. Additionally, PWF values for protease-activated composite membranes increased from 140.5 ± 5.3 to 321.1 ± 9.2 L/m 2 . h. BSA flux values of PES membranes increased from 56.4 ± 2.4 to 82.9 ± 0.9 L/m 2 .h with increasing HZCDs amount from 0 to 2.0 wt.% HZCDs-PES. Besides, BSA values for protease-activated composite membranes increased from 56.4 ± 2.4 to 89.8 ± 2.2 L/m 2 .h. The purpose of this modification was to impart hydrophilic properties to the PES membrane and address the issue of membrane fouling, which is a common problem in filtration processes. 2.0 wt.% HZCDs-PES and enzyme-activated membranes PES membranes demonstrated 100% BSA removal efficiency. Also, 2.0 wt.% HZCDs-blended membranes and 2.0 wt.% protease-HZCDs-blended membranes demonstrated remarkable antifouling properties up to 87.7% and 88.8% flux recovery ratio (FRR), respectively. In contrast, BSA flux recovery reached only 67.8% for the pristine PES. When compared to pristine PES membranes, enzyme-activated membranes demonstrated superior filtration and protein rejection efficiencies.
An intelligent leaf disease prediction for corn and maize using convolutional neural network A. Arulmurugan, Ajanthaa Lakkshmanan, R. Kaviarasan, E. Rajkumar, A. Punitha, et al. Expert Artificial Neural Network Applications for Science and Engineering, 2025 About 40% of Indians work in agriculture directly and 20% indirectly. The most widely grown crop, maize, is utilized in biofuels and the food chain. Many Indian small-scale farmers depend on farming for their livelihood and necessities. Diseases affect maize production and farmer profits. Bad weather and temperature changes spread the disease. Farming and agricultural enterprises use technology increasingly with digital. Farmers can use huge volumes of data on crop and soil conditions, climate change, and other environmental factors to manage plants and animals using machine learning. This study classifies three major maize diseases and identifies healthy pictures using modified deep transfer learning. This study studied the most frequent diseases: blight, grey leaf spot, and rust. Convolutional Resnet-18 predicted classes. Data was split widely for many scenarios to create transfer learning on Resnet-18 using the maize leaf image. It has four classifications and 96% mean accuracy over existing systems.
