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Abstract The extensive use of petroleum-based, non-bio-degradable plastics has resulted in severe environmental pollution and long-term ecological damage. In response, bio-degradable polymers have emerged as sustainable alternatives capable of reducing plastic waste and minimizing environmental impact. However, the conventional synthesis and processing of these materials often involve hazardous chemicals, energy-intensive methods, and non-renewable resources, which limit their overall sustainability. Green chemistry offers an effective framework for addressing these challenges by promoting environmentally benign materials, safer reaction conditions, renewable feedstocks, and waste-minimizing processes. This review presents a comprehensive overview of recent developments in bio-degradable polymers synthesized using green chemistry principles. It discusses sustainable sources of raw materials, eco-friendly polymerization techniques, green solvents, enzymatic catalysis, and energy-efficient technologies employed in polymer production. Major classes of natural and synthetic bio-degradable polymers, including cellulose, chitosan, polylactic acid, polyhydroxyalkanoates, and polycaprolactone, are critically examined with respect to their properties, biodegradation behavior, and environmental performance. Furthermore, this article highlights emerging applications of bio-degradable polymers in packaging, agriculture, biomedical engineering, and consumer products, emphasizing their role in promoting circular economy practices. Current challenges, such as high production costs, limited mechanical strength, and inadequate waste management infrastructure, are also addressed. Finally, future perspectives and research directions are outlined to support large-scale commercialization and policy-driven adoption of sustainable polymeric materials. Overall, this review aims to provide valuable insights into the integration of green chemistry and bio-degradable polymer science for achieving long-term environmental sustainability.

The orally administered self nano emulsifying drug delivery system (SNEDDS), an uniform blend with nano scaled globules, is composed of oil, surfactant and co-surfactant. The aim of the investigation was to develop and optimize Nicardipine loaded liquid SNEDDS via Box Behnken design and characterised based on physicochemical features, DSC and stability profile. Further to achieve sustain drug release, the optimized SNEDDS was compressed as self nano emulsifying tablet (SNET). Furthermore, the in vivo and pharmacokinetic study of the drug loaded SNET (NT) were performed. The NOF indicated droplet size (75.62 nm ± 2.01), self emulsification time (37 s ± 1.01), and 87.6

The objective of the present investigation was to preparesolid lipid nanoparticles of the BCS class II drug Glimepiride (GP) comprising coconut oil and Poloxamer 407, for improving the in vitro dissolution profile of the drug.Additionally, formulations were statistically optimized (32 full factorial experimental design) for focusing on the impact of independent variables on dependent factors of the nanoformulation (particle size, encapsulation efficiency, and drug content) through response surface methodology. Orally given lipophilic drug moieties, especially those in the BCS class II and IV categories, may have a number of issues that lead to poor absorption, bioavailability, and significant intra-and inter-subject variance.A biocompatible colloidal lipidic nanocarrier, solid lipid nanoparticles, are considered as potent substitute against traditional polymeric nanocarriers for delivering BCS class II and IV categories of drugs. Glimepiride solid lipid nanoparticles was prepared by solvent evaporation method and then physico-chemical parameters, thermal analysis, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analysis, and stability profile of the formulations were assessed. The optimized Glimepiride SLNsdemonstrated particle size of 329.58 ± 0.63 nm, significant encapsulation efficiency (91.41 ± 0.13

This study investigates the impact of alternatively incorporated ZnO, Si and ZnCdTe layers in the active region of the device, a finding that has not been previously reported in detail. The performance of the Quantum-well based hetero-structure ZnO/Si/ZnCdTe PIN photo-detector has been explained and compared to its GaN/Si/InGaAs counterpart in UV-Visible-NIR wavelength region. The Nonlinear Quantum Modified Drift-Diffusion (QM-D-D) model is employed for electro-optical characteristic studies in the devices, which find application in UV-Visible-NIR detection. The results indicate that the designed hetero-structure ZnO/Si/ZnCdTe PIN photo-detector offers higher external quantum efficiency and photo-responsivity compared to its GaN/Si/InGaAs counterpart in UV-Visible-NIR wavelength region. The noise reduction in the designed photo-detector makes it more suitable for use as low-noise photon detectors. Furthermore, the study evaluates the suitability of 3 × 5 array-based photo-detectors in terms of photo responsivity and external/internal quantum efficiency. The validity of the indigenously developed QM-D-D model is confirmed through experimental verification. In addition, failure analysis and fabrication feasibility of the new class of designed PIN photo-detector is presented in this paper. To the best of the authors’ knowledge, this is the first report on quantum-well based hetero-structure ZnO/Si/ZnCdTe PIN photo-detector which can be used to detect the photon in UV through Visible to NIR range of EM spectrum.

The integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies in underground coal mining has received growing attention due to its potential to improve operational efficiency and reduce energy consumption. IoT-based sensing systems enable real-time monitoring of environmental and operational conditions, while AI techniques support predictive analysis and decision-making. Existing studies indicate that such integration can contribute to improved ventilation control, predictive maintenance, and system-level energy management. This review synthesizes recent developments in IoT and AI applications in underground mining and examines their role in supporting energy-efficient operations. The analysis also highlights current limitations related to scalability, long-term validation, and data reliability. Overall, the literature suggests that IoT–AI integration offers promising opportunities for improving energy performance and sustainability in underground coal mining.