GLA University

Graphene quantum dots (GQDs) are next-generation class of zero-dimensional carbon-based nanomaterials exhibit variable bandgap energies and quantum dimensional effect suitable for advanced electrochemical energy conversion and storage applications. Their intrinsic properties such as high specific surface area, chemical versatility, and electrochemical stability increase their performance characteristics in electrocatalytic systems, batteries, and supercapacitors. GQDs is an exceptional support for anchoring various active materials, such as metal nanoparticles or metal oxides, providing an accelerated interfacial charge transport through highly conductive pathways between active sites and the electrode resulting improved in overall catalytic efficacy. Moreover, atomic scale disorder, edge sites/defects and functional groups (–OH, –COOH, −C=O etc.) on GQD surface furnish an additional active site for electrocatalytic processes. Heteroatom doping (N, S, B, and P) further create electron density states, promote charge delocalization, es and facilitates favourable adsorption/desorption reaction intermediates. Within this context of energy storage, GQDs can intercalate Li/Na-ions and modulate ion diffusion kinetics due to redox active (surface) functional groups and interlayer spacing. Additionally, their pseudo-capacitance behaviour also significantly contributes to supercapacitors and portraying them as promising electrode materials for hybrid capacitors. Despite these advances, a comprehensive evaluation of the electrochemical performance and structural attributes of GQDs in relation to their applied energy technologies remain unexplored. This review inspects current progress in the design and development of GQDs for electrochemical energy conversion and storage systems. Firstly, we presented a fundamental understanding of a sustainable and cost-effective synthesis strategies, specifically focus on atomically precise structural engineering design. Furthermore, the intrinsic electronic structure and morphological features of GQDs are examined in correlation with their redox behaviour and charge transfer kinetics. Attention is also paid to establishing structure–property-performance correlations that underpin their functional role in electrochemical systems. Moreover, this review provides a critical outlook on the prospects, prevailing challenges, and future directions associated with the deployment of GQDs in next generation energy technology.

One of the major challenges in the designing of Pt-support based electrocatalysts for oxygen reduction reaction (ORR) is the careful tuning of metal-support interaction at the atomic level. Herein, first-principles calculation was conducted to investigate the influence of electron-rich (N) and electron-deficient (B) atom doping in graphene support on the ORR performance of Pt-graphene composite. Four models, Pt@graphene, Pt@N-doped graphene, Pt@B-doped graphene, and Pt@B, N-doped graphene were considered and their electronic studies, including charge density difference, electron localization function, density of states, projected density of states, d-band center, via density functional theory suggested that heteroatom doping in graphene support produces remarkable perturbation in the electronic properties of Pt active sites. This electronic perturbation in Pt active sites influences their ability towards O2 adsorption followed by end-on geometries, thus regulating the ORR kinetics. Free energy calculations suggest that Pt@B, N-doped graphene have the minimum overpotential (0.39 V) mainly due to an appropriate d-band center and proper adsorption energy for the formation of all key reaction intermediates (*O2, *OOH, *O, *OH). Furthermore, enhanced charge transfer and tight interaction help stabilize the reaction intermediates and avoid overbinding. However, pristine graphene and N-doped graphene suffer from lack of adsorption energy, whereas B-doped graphene displayed overbinding, towards ORR intermediates, resulting in a poor ORR performance. Therefore, it is evident that dual-dopant engineering in pristine graphene is a powerful approach to tailor the electronic structure of Pt nanoparticles and enhance their ORR activity.

Nutrition, encompassing vital macro- and micronutrients (including carbs, proteins, fats, vitamins such as D and C, and minerals like iron and calcium), is fundamental to supporting life, fostering healthy ageing, and averting disease. The information on the combined impact of dietary patterns, nutritional content, and lifestyle factors on metabolic health, disease risk, and healthy ageing is summarised in this publication. According to the discussion, both macronutrient and micronutrient surpluses and deficiencies upset metabolic homeostasis, which leads to obesity, insulin resistance, cardiovascular disease, and hepatic steatosis. Dietary habits are one of the three cornerstones of health and longevity, according to biogerontological theories of ageing. The free radical theory emphasises oxidative stress in cellular damage, and the disposable soma hypothesis emphasises the trade-off between reproduction and somatic upkeep. Clinical and epidemiological data indicate that cardiometabolic outcomes, including elevated insulin sensitivity, lipid profiles, and inflammatory markers, are strongly influenced by diet quality, dietary timing (such as time-restricted eating), and overall energy balance. The review also demonstrates how nutrients can control gene expression, signalling pathways, and epigenetic alterations in addition to serving as energy sources. Nutrient availability and disease susceptibility are largely determined by interactions between the gut microbiota, food, and human metabolism. Furthermore, individual reactions to diet are very variable due to genetic, metabolic, and environmental factors, which explains why customised nutrition plans are becoming more and more popular. The findings also show that dietary habits during childhood and adolescence have an impact on long-term disease prevention, and that combining lifestyle treatments (diet and exercise) can consistently reduce the risk of chronic illness. In order to optimise health and wellness over the course of a lifetime, the data generally supports switching from reductionist, single-nutrient dietary methods to integrated, systems-oriented strategies that take into account molecular, clinical, and sociocultural factors.

The incorporation of artificial intelligence (AI) and machine learning (ML) into microalgal research is transforming biomass generation, biofuel synthesis, and wastewater remediation strategies. Sophisticated ML techniques, such as artificial neural networks (ANN), support vector machines (SVM), and genetic algorithms (GA), facilitate precise simulation and forecasting of highly intricate microalgal systems. Although constraints related to data accessibility and model scalability persist, ML-based methodologies are increasingly demonstrating their value in enhancing the sustainability and operational efficiency of microalgal processes. Simultaneously, technoeconomic analysis (TEA) has become an indispensable framework for assessing biorefinery viability through systematic evaluation of life-cycle environmental burdens. Recent progress in TEA methodologies has strengthened iterative design optimization, uncertainty quantification, and user accessibility via open-source computational platforms. Broader systems boundaries now account for policy mechanisms, performance during end-use phase, and international market dynamics, thereby reinforcing TEA’s contribution to sustainable bioeconomic advancement. Collectively, these computational and analytical innovations are expediting the deployment of scalable and economically feasible microalgal technologies. Highlights

The most often occurring primary liver cancer, hepatocellular carcinoma (HCC), continues to be a major worldwide health burden due to few treatment choices and high rates of recurrence and death. Though frequently hampered by acquired resistance and dose-limiting toxicity, sorafenib-a first-in-class oral multikinase inhibitor-is still a standard systemic treatment for late-stage HCC. The pharmacological effects of sorafenib in HCC are critically analysed in this review, along with their molecular mechanisms, resistance pathways, and genetic and epigenetic elements affecting response. Using databases including PubMed, Scopus, Embase, and Web of Science, an extensive literature review was carried out to include FDA-approved medications, clinical trials, and peer-reviewed research concentrating on mechanisms of resistance, therapeutic combinations, and artificial intelligence (AI)-based precision medicine approaches. While VEGFR-mediated angiogenesis is the main target of sorafenib, resistance comes via compensatory activation of alternate pathways, including PI3K/AKT/mTOR and JAK/STAT, as well as efflux transporter upregulation. Preclinical research shows that combining sorafenib with natural compounds like berberine, wogonin, and quercetin improves pro-apoptotic effects and lowers toxicity. Second-generation kinase inhibitors, including Regorafenib and lenvatinib, provide better survival; immune checkpoint inhibitors, including Nivolumab and atezolizumab, show promise in combination treatments. Moreover, artificial intelligence (AI) applications in radiomics, genomics, and predictive modelling are showing high accuracy in helping to optimize HCC diagnosis and therapy selection. Overcoming the constraints of sorafenib demands the development of combination therapies, biomarker-based personalization, and validated artificial intelligence integration into clinical practice, even if it is still a mainstay of advanced HCC treatment.