In an expedition for green-energy generation and to lower the cost per watt of solar energy, environmentally friendly biotic colorants were separated from Tectona grandis seeds. The prime colorant in the extract is pelargonidin which sensitizes titanium dioxide (TiO₂)-based photo anodes. The pelargonidin-sensitized TiO₂ nanomaterials endured structural, photosensitive, spectral and current-voltage interpretations. Frontier molecular orbital analysis, physicochemical and electronic parameter computation, UV–visible and DOS spectral analysis, van der Waals prediction and molecular electrostatic potential map were performed theoretically with Gaussian tools, and IR symmetry response was computed using the crystal maker software package. The pelargonidin-sensitized TiO₂-created dye-sensitized solar cells which exhibited capable solar light energy to photon conversion proficiency. For comparative purposes, the commercial P25 Degussa TiO₂-based DSSC was also fabricated and its proficiency was analyzed. The commercial TiO₂ exhibited 57 % higher proficiency in comparison to the sol-gel-derived TiO₂-based DSSC.

The effectiveness of traditional cancer chemotherapy is often limited due to poor tumor selectivity, systemic toxicity, and differences in how patients respond to drugs. Nanomedicine-based drug delivery systems offer promising ways to improve targeted delivery and controlled release of anticancer agents. However, designing these systems is complicated by the interactions between physical and chemical properties, biological factors, and the varying tumor microenvironments. In this study, we present a next-generation, AI-optimized nanomedicine framework for precision cancer therapy. This framework combines machine learning-based predictive modeling with computer-aided design of nano carriers. To estimate the adsorption capability of drugs in the nanocarrier surface, various chemistry models such as Langmuir, Freundlich, and Temkin are discussed. The integrated thermodynamic modeling and kinetic simulations, and biological response analysis in nano medicine are detailed. Advanced AI algorithms, such as supervised learning and deep neural networks, improve tumor targeting efficiency, enhance drug stability, and allow for responsive release profiles tailored to specific cancer environments. The influence of ionic strength toward electrostatic double-layer interactions according to DLVO theory has analyzed. It enables quick virtual testing of nanocarrier formulations, cutting down on trial-and-error in experiments and supporting personalized treatment design. Overall, this review article provides a strong computational model for developing AI-driven nanomedicine. It highlights the potential to improve precision oncology and speed up the clinical use of smart drug delivery systems for cancer treatment.