Uncovering the Impact of Spectroscopic Data Reduction Techniques on the Process Control Mode Pattern Recognition: The Case of Industrial Penicillin Production

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

Effect of nanoparticle organization on molecular mobility and mechanical properties of polymer nanocomposites

\u3cp\u3eInfluence of nanoparticle (NP) spatial organization on relaxation and mechanical properties of polymer nanocomposites (PNCs) was investigated. For the first time, the properties of PNCs with various nanostructures at the constant chemical composition were related to their experimentally determined structural parameters - effective interfacial surface and interparticle distance. Segmental scale reinforcement active below and above glass transition was attributed to the immobilization and frustration of polymer segments caused by attractive polymer-particle interactions. A novel reinforcing mechanism of chain bound clusters related to their internal structure was revealed while negligible reinforcement from NP-NP interactions of contact aggregates was found. The mechanical response of PNCs was correlated with appropriate relaxation properties. It provided the first experimental proof that deformation yielding dynamics of PNCs is controlled by glass transition segmental mobility. Main features of various NP spatial organizations were characterized. Chain bound clusters showed the most significant reinforcement above the glass transition temperature (T\u3csub\u3eg\u3c/sub\u3e). Moreover, the hierarchical nature of chain bound clusters caused broadening of the ductile response compared to other nanostructures and also to the neat matrix. The most pronounced enhancement of elastic modulus, yield stress, and creep durability was found for individually dispersed NPs. The acquired nanostructure-property relationships will provide a foundation for the future design of hierarchic and multidomain nanocomposites.\u3c/p\u3

Effect of nanoparticle organization on molecular mobility and mechanical properties of polymer nanocomposites

Influence of nanoparticle (NP) spatial organization on relaxation and mechanical properties of polymer nanocomposites (PNCs) was investigated. For the first time, the properties of PNCs with various nanostructures at the constant chemical composition were related to their experimentally determined structural parameters - effective interfacial surface and interparticle distance. Segmental scale reinforcement active below and above glass transition was attributed to the immobilization and frustration of polymer segments caused by attractive polymer-particle interactions. A novel reinforcing mechanism of chain bound clusters related to their internal structure was revealed while negligible reinforcement from NP-NP interactions of contact aggregates was found. The mechanical response of PNCs was correlated with appropriate relaxation properties. It provided the first experimental proof that deformation yielding dynamics of PNCs is controlled by glass transition segmental mobility. Main features of various NP spatial organizations were characterized. Chain bound clusters showed the most significant reinforcement above the glass transition temperature (Tg). Moreover, the hierarchical nature of chain bound clusters caused broadening of the ductile response compared to other nanostructures and also to the neat matrix. The most pronounced enhancement of elastic modulus, yield stress, and creep durability was found for individually dispersed NPs. The acquired nanostructure-property relationships will provide a foundation for the future design of hierarchic and multidomain nanocomposites

Mechanical Response of Hybrid Cross-Linked Networks to Uniaxial Deformation: A Molecular Dynamics Model

Networks combining physical and covalent chemical cross-links can exhibit a large amount of dissipated inelastic energy along with high stretchability during deformation. We present our analysis of the influence of the extent of covalent cross-linking on the inelasticity of hydrogels. Four model networks, which are similar in structure but strongly differ in elasticity, have been studied. The aim was the identification of a key structural factor responsible for observing a hysteresis or an elastic deformation. In the employed molecular dynamics study this factor is derived from the underlying structure of each particular hydrogel network. Several structural characteristics have been investigated like the extent of damage to the network, chains sliding, and the specific properties of load-bearing chains. By means of such a key factor, one can predict the deformation behavior (hysteresis or elasticity) of some material, provided a precise description of its structure exists and it resembles any of the four types of a network. The results can be applied in the design of bio-inspired materials with tailored properties

Effect of Foliar Copper-Containing Superabsorbent Polymers on Nutritional Characteristics and Mycotoxin Contamination of Wheat Kernel

Novel use of superabsorbent polymers to deliver copper-based foliar fertilization was tested as a means against fungal mycotoxin production and monitor its effect on nutritional characteristics of wheat. Experiment was located in Žabčice, South Moravia region of the Czech Republic, tested wheat variety was 'Julie'. Differences in fertilization medium (water or superabsorbent polymers) and copper treatments (control, CuO, CuSO4, Cu-EDTA, CuO-nano) were tested. Effect of different variants on percentage of ash, crude protein, crude fat and crude fiber, acid detergent fiber, ash-free neutral detergent fiber, lignin, cellulose and starch were determined. Moreover, concentration of deoxynivalenol and T-2 toxin depending on the variant was measured. The highest amounts of crude fat and crude fiber were observed in variant of Cu-nano with superabsorbent polymers, the lowest in CuO respectively. Higher amount of cellulose was found in SAP control, lowest in water control. There were no significant differences in other nutritional parameters. Of the evaluated mycotoxins, we did not detect the deoxynivalenol in any of our samples. For the second evaluated mycotoxin, there was also no difference observed in T-2 toxin production