The hydrothermal method is a cost-effective and eco-friendly route for preparing various nanomaterials. It can use a capping agent, such as a polysaccharide, to govern and define the nanoparticle morphology. Elemental selenium nanostructures (spheres and rods) were synthesized and stabilized using a tailor-made carboxymethyl starch (CMS, degree of substitution = 0.3) under hydrothermal conditions. CMS is particularly convenient because it acts simultaneously as the capping and reducing agent, as verified by several analytical techniques, while the reaction relies entirely on green solvents. Furthermore, the effect of sodium selenite concentration, reaction time and temperature on the nanoparticle size, morphology, microstructure and chemical composition was investigated to identify the ideal synthesis conditions. A pilot experiment demonstrated the feasibility of implementing the synthesized nanoparticles into vat photopolymerization three-dimensional-printed hydrogel carriers based on 2-hydroxyethyl methacrylate (HEMA). When submersed into the water, the subsequent particle release was confirmed by dynamic light scattering (DLS), promising great potential for use in bio-three-dimensional printing and other biomedical applications.

Process Analytical Technologies (PAT) often rely on real-time spectroscopy, allowing for fast-paced process control and monitoring. However, the data generated from real-time spectroscopy for long-running process systems can result in excessively large databases, which can be challenging to manage and may not necessarily lead to better process control. Therefore, it is crucial to reduce the amount of data generated by real-time spectroscopy while still retaining the essential information needed for process control. This work explores various data reduction techniques to address this issue. IndPenSim, a simulated spectroscopic probing dataset, was used as an oracle model to study the impact of data reduction techniques on the resulting process control identification. For analysis, the data pipeline consists of using principal component analysis (PCA) for visualization, followed by truncation and pre-processing (e.g. baseline corrections). Moreover, we have discussed the impact of data size reduction techniques (e.g. spectral data column selection, data binning, and region of interest (ROI), etc.) on the different chemometric models (e.g. PCA, PLS-DA, SIMCA, and KNN, etc.). Finally, the study examined the impact of data reduction on the control strategy for a realistic industrial fed-batch penicillin simulator. The multi-class classification performance was analyzed, and the results were interpreted to determine the best approach for controlling the process. Overall, the study provides valuable insights into data reduction techniques for real-time spectroscopy in PAT, which can improve the efficiency and accuracy of process control and monitoring.