The growing concern over bacterial infections and the increasing demand for hygiene have driven the development of antibacterial textiles that offer enhanced protection and public health safety. In this investigation, zinc oxide nanoparticles (ZnO NPs) were synthesized in situ and applied to cotton fabric using the surfactant-assisted ultrasonication method. Various surfactants like cetyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), and Triton-X 100 (TX) were evaluated for their efficiency in stabilizing ZnO NPs and achieving a uniform coating on cotton fabric. These surfactants improved the adhesion of ZnO NPs and enhanced the coating's stability during practical use. The structural, morphological, and physicochemical properties of ZnO-finished fabrics were examined by using ultraviolet (UV), X-ray diffraction (XRD), dynamic light scattering (DLS), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). Moreover, the mechanical, washing durability, and antibacterial properties were also studied. SEM and TGA demonstrate the amount of ZnO NPs loaded onto the fabric before and after repeated washing cycles. Surfactants, especially SDS, minimized the leaching of ZnO NPs from the cotton surface, enhancing the durability and antibacterial performance even after repeated washing. The ZnO-treated cotton using SDS exhibited higher antibacterial effectiveness than CTAB and TX. This study provides a simple, eco-friendly approach to developing robust antibacterial cotton fabrics for the medical and healthcare sectors.
Conductive polymers are essential materials for flexible electronics and energy storage, yet their electrical performance is highly sensitive to multivariate synthesis parameters that are difficult to optimize experimentally. Here, we develop a machine learning framework that accurately predicts polymer conductivity based on nine key synthetic variables. Among five tested algorithms, EXtreme Gradient Boosting (XGBoost) achieves the highest predictive accuracy (test R 2 = 0.933), while Light Gradient Boosting Machine (LightGBM) shows strong performance with minimal overfitting. SHapley Additive exPlanations (SHAP) analysis reveals that dopant species is the dominant determinant of conductivity (59.85%), followed by oxidant species (17.5%) and polymer type (8.5%). The model further uncovers critical nonlinear relationships, including conductivity maximization at 30-60 degrees C, a sharp decline beyond a 2 mol/L dopant concentration, and negligible dependence on reaction duration after equilibrium. Notably, optimal conductivity arises under synergistic conditions of elevated reaction temperature (>80 degrees C) and low oxidant concentration (<0.2 mol/L), which promote molecular alignment and enhance charge carrier mobility. Our findings highlight that dopant-driven acid-base doping strength, rather than classical acid-base doping strength, serves as the primary factor controlling carrier transport in conductive polymers. This insight provides the design principles for organic electronic materials and demonstrates how machine learning can accelerate materials discovery and optimization.
The textile industry is addressing pollution by utilizing mint extract as an environmentally friendly alternative to natural dyes. Mint extract has antimicrobial properties, increased durability, and thermal protection, making it ideal for the perfume and pharmaceutical industries. It can be extracted using various methods, including ultrasound, microwave, and enzyme-assisted extractions. Mint extract has antibacterial properties, such as inhibiting Staphylococcus aureus and Escherichia coli. It can also be used as a bioactive agent on textile substrates and has antiredox properties. However, the use of low biodegradable ionic silver may induce toxic effects. Solvent applications are beneficial for these applications, as they change the high content of essential oils in people, making them available at low prices without toxic effects.
This study aimed to investigate the wound healing effects of peppermint (P) single and its co-emulsion with black seed oil (B) or frankincense oil (F) through an ultrasonication process. Successfully Prepared nano-emulsions were examined through TEM, particle size analyser (PSA), and zeta potential (ZP) techniques. The produced emulsion was applied to non-woven fabric (NWF) by spraying technique. Non-woven fabric surface morphology was produced and examined through a scanning electron microscope (SEM). Antibacterial activities of emulsions treated NWF were examined against Staphylococcus aureus as gram-positive bacteria and Escherichia coli as gram-negative bacteria and their healing effect to second burn degree were examined through reduction of the burn size. Additionally, Histopathological evaluations also evaluated for burn skin samples. Nano-emulsion droplets size lies between 20 and 70 nm and are increased by storage. The Zeta potential of P/F emulsion showed the highest negative charge compared with all prepared emulsions. FTIR-ATR charts showed new peaks for oils treated NWF compared with untreated NWF sample. The P/F emulsion showed higher zeta potential measurement compared with other single or Co-emulsions. For burn healing in-vivo, P/F emulsion treated NWF showed complete healing with original skin colour compared with other groups. While histopathological evaluation showed a homogenous epidermis with more granulation tissue formation than P/B emulsion treated NWF and all single oils emulsions. Antimicrobial evaluation of co-emulsions showed higher results than single oils emulsions. The results of co-emulsion oils showed superior properties and burn healing, especially P/F co-emulsion.
