Nanosensors have gained significant attention in recent years for improving energy conversion and storage performance in solar cells. These nanosensors, typically made from nanoparticles or nanowires, can be embedded within the solar cell to monitor parameters like temperature and light intensity. By monitoring these parameters, nanosensors provide real-time feedback and control to optimize the efficiency and performance of the solar cell. They also play a role in detecting potential issues, such as defects, for proactive maintenance and troubleshooting. The integration of nanosensors in solar cells enables the development of smart energy systems, leading to increased power output, improved stability, and a longer lifespan of solar cells. The deployment of nanosensors in solar cells offer promising trajectory for advancing energy conversion, utilization, and storage capabilities. This review summarizes recent advances in nanosensors in solar cells, with a focus on the role they play in enhancing energy conversion, utilization, and storage performance.

AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration. Sensitivity increases from 60 to 75 % in ordinary aptasensors to 85–95 %, while specificity improves from 70-80 % to 90–98 %. This enhanced performance allows for ultra-low detection limits, such as 10 fM for carcinoembryonic antigen (CEA) and 20 fM for mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), and 1 pM for prostate-specific antigen (PSA) with Differential Pulse Voltammetry (DPV). Similarly, Square Wave Voltammetry (SWV) and potentiometric sensors have detected alpha-fetoprotein (AFP) at 5 fM and epithelial cell adhesion molecule (EpCAM) at 100 fM, respectively. AI integration also enhances reproducibility, reduces false positives and negatives (from 15-20 % to 5–10 %), and significantly decreases response times (from 10-15 s to 2–3 s). These advancements improve data processing speeds (from 10 to 20 min per sample to 2–5 min) and calibration accuracy (<2 % margin of error compared to 5–10 %), while expanding application scope to multi-target biomarker detection. This review highlights how these advancements position AI-optimized electrochemical aptasensors as powerful tools for personalized treatment, point-of-care testing, and continuous health monitoring. Despite a higher cost ($500-$1,500/unit), their enhanced portability and diagnostic performance promise to revolutionize healthcare, environmental monitoring, and food safety, ultimately improving public health outcomes.

Polyester/kenaf composites reinforced with zinc oxide nanoparticles (ZnO NPs) were fabricated. The nanoparticle treatment had a noticeable effect on the mechanical properties of the composites. Kenaf treatments with five different concentrations 1%, 2%, 3%, 4%, and 5% were performed. The mechanical analysis showed an increased flexural strength stability and break elongation in the functionalized ZnO NPs-treated kenaf polyester resin composites. The polymer nanocomposites with 2% ZnO NPs had stable mechanical properties with moderate elastic properties compared with the remaining ZnO NPs systems. The mechanical properties of the composites that contained different layers of kenaf mat demonstrated positive influence on the polymer nanocomposites. The material increased in stiffness with an increase in layers from 1 to 5. The results of weathering confirmed the stability of the polymer nanocomposites with increased stability. The 2% nanoparticle coating with a 5-layer kenaf layer revealed no remarkable changes in the mechanical degradation throughout the 6-month weathering period. The results of the mechanical properties tests suggested that a 2% ZnO NPs concentration with five kenaf layers had the highest moisture resistance, thus; the ZnO NPs acted as a water repellent agent.

Graphene has received tremendous attention among diverse 2D materials because of its remarkable properties. Its emergence over the last two decades gave a new and distinct dynamic to the study of materials, with several research projects focusing on exploiting its intrinsic properties for optoelectronic devices. This review provides a comprehensive overview of several published articles based on density functional theory and recently introduced machine learning approaches applied to study the electronic and optical properties of graphene. A comprehensive catalogue of the bond lengths, band gaps, and formation energies of various doped graphene systems that determine thermodynamic stability was reported in the literature. In these studies, the peculiarity of the obtained results reported is consequent on the nature and type of the dopants, the choice of the XC functionals, the basis set, and the wrong input parameters. The different density functional theory models, as well as the strengths and uncertainties of the ML potentials employed in the machine learning approach to enhance the prediction models for graphene, were elucidated. Lastly, the thermal properties, modelling of graphene heterostructures, the superconducting behaviour of graphene, and optimization of the DFT models are grey areas that future studies should explore in enhancing its unique potential. Therefore, the identified future trends and knowledge gaps have a prospect in both academia and industry to design future and reliable optoelectronic devices.