Shree Guru Gobind Singh Tricentenary University, commonly called as SGT University, is located in Budhera, Gurugram district, Haryana, India, in the vicinity of Sultanpur National Park.
Semiconductor quantum dots (SQDs) are extensively used nanomaterial for sensing, electronics, drug delivery, and bioimaging. However, their poor aqueous solubility, lack of uniformity in synthesis, toxicity, and challenges in scalable fabrication frequently limit their applications. Carbon quantum dots (CDs) emerged as new safer alternatives to SQDs with comparable optical properties and diverse applications. Herein, fluorescent, biocompatible, and water-soluble, carbon dots were synthesized via cost-effective hydrothermal method using ascorbic acid (AA) as the sole precursor. The as-synthesized spherical 3-4 nm ascorbic acid-derived carbon dots (AA-CDs) exhibited maximum excitation at 340 nm, emission at 400 nm and displayed fluorescence quantum yield of similar to 29.89% with average fluorescence lifetime decay of similar to 1.12 ns. For technological domain, this study successfully formulated AA-CDs into anti-counterfeiting fluorescent ink that resembled conventional ink under visible light but exhibited bright blue fluorescence under UV excitation. Furthermore, AA-CDs displayed negligible toxicity toward both mycobacterial (e.g., Mycobacterium marinum) and mammalian (phorbol myristate acetate-treated human THP-1 macrophages) cells, supporting their safe applications in biomedicine. Rapid and efficient internalization of AA-CDs into M. marinum and THP-1 macrophage cells was evidenced by presence of bright blue fluorescence inside the cells, which highlighted their potential as nano-probe for real-time bioimaging and tracking cellular processes. Collectively, this study demonstrated AA-CDs as a proof-of-concept dual-function nanomaterial for applications across fluorescent ink-based security technology and nanomedicine.
This work gives an in-depth discussion on prediction and optimisation of the mechanical properties of FDM-printed polyethylene terephthalate glycol (PETG) parts by applying statistical and machine learning methods. Experimentally, the influence of the three important process variables on the surface roughness (SR) and ultimate tensile strength (UTS) was studied: the melting temperature, the height of the layer, and the infill density. The predictive capacity of the Artificial Neural Network (ANN), Random Forest Regression (RFR) and Response Surface Methodology (RSM) models were developed and compared. RFR model was found to be more accurate than other models with the following values of R2 = 0.94 (training) and R2 = 0.83 (testing) and mean absolute error (MAE) = 2.51 when predicting UTS. The mean error between the experimental and predicted values was lower than 5% accounting for the robustness of the developed model. The process parameters were optimised with multi objective Genetic Algorithm (GA) and produced an optimum UTS of 38.39 MPa and a minimum surface roughness of 5.65 um at 82.42% infill density, 0.26 mm layer height, and 230.26 C melting temperature. The findings are also validated by carrying out microstructural study using SEM image. These findings show how the mechanical performance and surface quality of PETG components that are FDM-printed can be enhanced by combining machine learning and evolutionary optimisation.
This research describes a sustainable approach to the synthesis of copper oxide (CuO) nanostructures by using Aloe vera as a green reducing agent, with the aim of their use in asymmetric supercapacitor configurations. CuO nanoparticles were synthesized in the alkaline environment (pH 12) and the as-synthesized nanostructures were characterized using different techniques such as field emission scanning electron microscopy (FE-SEM), X-ray diffraction, UV-Vis, fourier transform infrared (FTIR) spectroscopy, brunauer-emmett-teller (BET) surface area measurements, and electrochemical techniques such as galvanostatic charging-discharging, electrochemical impedance spectroscopy, and cyclic-voltammetry (CV) for the analysis of their structure, morphology, and optical and electrochemical properties. The results revealed the successful fabrication of CuO nanoparticles with uniform morphology and a porous architecture with a high specific surface area of 19.6 m(2) g(-1), which contributed to acapacitance of 280 F g(-1) at 1 mV s(-1) sweep rate. The as-fabricated asymmetric device (CuO//AC) exhibited excellent performance, with a capacitance of 112 F g(-1) at a scan rate of 1 mV s(-1). After 10 000 CV cycles at 200 mV s(-1), it retained 78.2% of its original capacitance, indicating prolonged functional reliability. These findings demonstrate that synthesizing CuO nanoparticles using Aloe vera is an environmentally friendly and sustainable strategies, exhibit excellent supercapacitor performance, indicating good candidate for use in ecofriendly energy storage applications.
Fused Deposition Modeling (FDM) enables the fabrication of multi-material thermoplastic composites with tailored properties through controlled internal geometries. However, optimizing the trade-off between mechanical properties in PETG-TPU (Polyethylene Terephthalate Glycol and Thermoplastic Polyurethane) composites remains a challenge due to complex parameter interactions. This study investigates the tensile and compressive strength of bioinspired PETG-TPU structures using a surrogate-assisted multi-objective optimization framework. Three architected infill geometries (Octet, Gyroid, and Cross 3D), infill density, and layer thickness were examined. Specimens were fabricated using dual-extrusion 3D printing and characterized via ASTM D638 and ASTM D695 standards. An Artificial Neural Network (ANN) was developed to model nonlinear relationships between printing parameters and mechanical responses, achieving a correlation coefficient (R2) above 0.99 with low prediction errors. The ANN was coupled with a Multi-Objective Genetic Algorithm (MOGA) to identify Pareto-optimal solutions for simultaneous strength maximization. Pareto analysis revealed distinct performance trade-offs relative to architectural configurations. The proposed MOGA-ANN framework provides a computationally efficient approach to optimize architected thermoplastic composites, demonstrating the potential of combining bioinspired design with advanced metaheuristics for high-performance additive manufacturing.
ABSTRACT On a global scale, the escalating burden of infectious diseases, predominantly attributed to bacterial pathogens, especially drug‐resistant strains, has progressed into a critical concern for clinical management and public health systems. Antibiotics are the primary therapeutic agents used to treat bacterial infections; however, the ineffectiveness of antibiotics and the emergence of antibiotic‐resistant microorganisms have led researchers to search for novel drugs and therapeutic approaches. Previous studies demonstrated that probiotics benefit both diarrhea and inflammatory bowel disease. The beneficial impact of probiotics and advancements in genetic engineering strategies shift conventional probiotics towards engineered probiotics to cope with drug‐resistant bacterial infections. Expansion of research in this direction has proved that engineered probiotics are beneficial for clearing drug‐resistant bacterial infections. Thus, to explore the possibility of mitigating drug‐resistant infection with engineered probiotics, this review outlines engineered probiotics, a next‐generation live therapeutic approach, its mechanisms, and the therapeutic efficacy against drug‐resistant bacteria compared to conventional probiotics. This review presents the disparate hurdles associated with traditional probiotics and the emergence of engineered probiotics as an effective therapy against antibiotic‐resistant microbes and their mechanisms.
