Calcium alginate beads doped with hydrazine sulphate-treated red mud are investigated as adsorbent for extracting lead ions from water using batch methods of extraction. Different extraction conditions are optimised for maximum lead extraction. Substantial amount of lead is removed, and the adsorption ability is found to be 138.6 mg/g. Surface characterization using FTIR, EDX, and FESEM confirms that lead is “onto” the surface of the adsorbent. Thermodynamic parameters, adsorption isotherms, and kinetics of adsorption are analysed. Adsorption is “physisorption” in nature and spontaneous. The adsorbent developed can be regenerated using 0.1 M HCl. Thus regenerated adsorbent can be used as the adsorbent for further removal of lead at least 10 times, and this enables the complete removal of lead from water by repetitive use of the regenerated adsorbent. The beads facilitate the easy filtration. The methodology developed is successfully applied for removing lead from industrial waste waters.

A novel reversed-phase high performance liquid chromatographic (HPLC) technique for the determination of everolimus (Isomer-B) and its impurities in the tablet dosage form has been optimized using analytical quality by design (QbD) approach. All the compounds are monitored with the photodiode array (PDA) detector at 280 nm and the parameters namely; precision, accuracy, specificity, stability, linearity, limit of quantitation (LOQ) and limit of detection (LOD) are evaluated. The quantitation limits of IMP-A, IMP-B, IMP-C, IMP-D, IMP-E, Sirolimus and TGR are found to be 0.08, 0.08, 0.10, 0.10, 0.10, 0.08 and 0.08, respectively. Recovery studies from 0.9 mg/L to 9.0 mg/L are performed for all impurities and the values were obtained between 85-110 %. Injection volume and test concentrations have been optimized to achieve LOQ values under the reporting threshold. The whole technique is developed and validated as per International Council for Harmonization (ICH) guidelines. The proposed method is robust, sensitive, rapid and successful and helpful in the regions where regulatory agencies recommend HPLC analytical method.

While artificial intelligence (AI) has transformed materials discovery, the primary bottleneck to technological impact remains the transition from lab-scale synthesis to robust, industrial-scale manufacturing. Most promising materials perish in this depth, which is referred as the "valley of death." The current review consolidates and critically evaluates the emerging ecosystem of AI-driven strategies and frameworks designed specifically to bridge this "lab-to-fab" gap. The review shifts our attention from property prediction to the engineering-driven problems of manufacturability. Further, the review discusses the main obstacles to scaling the production of materials, such as reproducibility, process optimization in the context of uncertainty, and techno-economic viability, as well as the AI approaches being developed to overcome them. This includes Natural Language Processing (NLP) for method extraction, graph neural networks for reaction modeling, reinforcement learning for process control, Bayesian optimization for defining process windows, and integrated AI-Techno-Economic Analysis (TEA) frameworks. The article concludes in a forward-looking roadmap for the future of AI in chemical and materials engineering. The proposed conclusion is defined by a paradigm shift from simply finding new materials to creating viable, economical, and scalable pathways to produce them, thereby enabling a new era of synthesis-aware materials innovation.