Structural Characterization of the Complex between Hen Egg‐White Lysozyme and ZrIV‐Substituted Keggin Polyoxometalate as Artificial Protease

Successful co‐crystallization of a noncovalent complex between hen egg‐white lysozyme (HEWL) and the monomeric Zr^IV‐substituted Keggin polyoxometalate (POM) (Zr1 K1), (Et₂NH₂)₃[Zr(PW₁₁O₃₉)] ( 1 ), has been achieved, and its single‐crystal X‐ray structure has been determined. The dimeric Zr^IV‐substituted Keggin‐type polyoxometalate (Zr1 K2), (Et₂NH₂)₁₀[Zr(PW₁₁O₃₉)₂] ( 2 ), has been previously shown to exhibit remarkable selectivity towards HEWL hydrolysis. The reported X‐ray structure shows that the hydrolytically active Zr^IV‐substituted Keggin POM exists as a monomeric species. Prior to hydrolysis, this monomer interacts with HEWL in the vicinity of the previously identified cleavage sites found at Trp28‐Val29 and Asn44‐Arg45, through water‐mediated H‐bonding and electrostatic interactions. Three binding sites are observed at the interface of the negatively charged Keggin POM and the positively charged regions of HEWL at: 1) Gly16, Tyr20, and Arg21; 2) Asn44, Arg45, and Asn46; and 3) Arg128.     

Using Schedule Risk Analysis with resource constraints for project control

Schedule Risk Analysis (SRA) has shown to provide reliable activity sensitivity information for taking corrective actions during project control. More precisely, by selecting a small subset of activities with high sensitivity values for taking corrective actions, the project outcome can be improved. In resource constrained projects, disrupted activities can affect both their successors as well as other activities when resource conflicts are induced. Since SRA focuses solely on the project network to determine the sensitivity of activities, the traditional SRA metrics do not accurately reflect the activity sensitivity for resource constrained projects. In this paper, the traditional SRA metrics are extended for resource constrained projects, and a novel resource-based sensitivity metric is introduced (RC-SRA metrics).A computational experiment is conducted to investigate the ability of the RC-SRA metrics to identify activities with higher sensitivity values. In addition, two activity selection strategies, defined as the normal strategy and sequential strategy, are designed to select activities for taking corrective actions. Further, two types of corrective actions are proposed to reduce the activity duration or resource demand in case of delays, respectively. Finally, the impact of dynamically updating the RC-SRA metrics during project execution is examined.The computational results show that the normal activity selection strategy is recommended for serial projects, while the sequential strategy is preferred for parallel projects. The results also indicate that reducing the activity durations performs better than reducing the resource demand of activities. Finally, it is shown that updating the RC-SRA metrics dynamically during project execution improves the efficiency of the corrective action taking process.     

Transition-metal-free catalysts for the sustainable epoxidation of alkenes: from discovery to optimisation by means of high throughput experimentation

Transition-metal-free oxides were studied as heterogeneous catalysts for the sustainable epoxidation of alkenes with aqueous H?O? by means of high throughput experimentation (HTE) techniques. A full-factorial HTE approach was applied in the various stages of the development of the catalysts: the synthesis of the materials, their screening as heterogeneous catalysts in liquid-phase epoxidation and the optimisation of the reaction conditions. Initially, the chemical composition of transition-metal-free oxides was screened, leading to the discovery of gallium oxide as a novel, active and selective epoxidation catalyst. On the basis of these results, the research line was continued with the study of structured porous aluminosilicates, gallosilicates and silica-gallia composites. In general, the gallium-based materials showed the best catalytic performances. This family of materials represents a promising class of heterogeneous catalysts for the sustainable epoxidation of alkenes and offers a valid alternative to the transition-metal heterogeneous catalysts commonly used in epoxidation. High throughput experimentation played an important role in promoting the development of these catalytic systems.     

Computational optimization of the configuration of a spatially resolved spectroscopy sensor for milk analysis

A global optimizer has been developed, capable of computing the optimal configuration in a probe for spatially resolved reflectance spectroscopy (SRS). The main objective is to minimize the number of detection fibers, while maintaining an accurate estimation of both absorption and scattering profiles. Multiple fibers are necessary to robustify the estimation of optical properties against noise, which is typically present in the measured signals and influences the accuracy of the inverse estimation. The optimizer is based on a robust metamodel-based inverse estimation of the absorption coefficient and a reduced scattering coefficient from the acquired SRS signals. A genetic algorithm is used to evaluate the effect of the fiber placement on the performance of the inverse estimator to find the bulk optical properties of raw milk. The algorithm to find the optimal fiber placement was repeatedly executed for cases with a different number of detection fibers, ranging from 3 to 30. Afterwards, the optimal designs for each considered number of fibers were compared based on their performance in separating the absorption and scattering properties, and the significance of the differences was tested. A sensor configuration with 13 detection fibers was found to be the combination with the lowest number of fibers which provided an estimation performance which was not significantly worse than the one obtained with the best design (30 detection fibers). This design resulted in the root mean squared error of prediction (RMSEP) of 1.411 cm⁻¹ (R² = 0.965) for the estimation of the bulk absorption coefficient values, and 0.382 cm⁻¹ (R² = 0.996) for the reduced scattering coefficient values.