The palladium metal catalysed Heck reaction of 4-iodoanisole with styrene or methyl acrylate has been studied in a continuous plug flow reactor (PFR) using supercritical carbon dioxide (scCO2) as the solvent, with THF and methanol as modifiers. The catalyst was 2% palladium on silica and the base was diisopropylethylamine due to its solubility in the reaction solvent. No phosphine co-catalysts were used so the work-up procedure was simplified and the green credentials of the reaction were enhanced. The reactions were studied as a function of temperature, pressure and flow rate and in the case of the reaction with styrene compared against a standard, stirred autoclave reaction. Conversion was determined and, in the case of the reaction with styrene, the isomeric product distribution was monitored by GC. In the case of the reaction with methyl acrylate the reactor was scaled from a 1.0 mm to 3.9 mm internal diameter and the conversion and turnover frequency determined. The results show that the Heck reaction can be effectively performed in scCO2 under continuous flow conditions with a palladium metal, phosphine-free catalyst, but care must be taken when selecting the reaction temperature in order to ensure the appropriate isomer distribution is achieved. Higher reaction temperatures were found to enhance formation of the branched terminal alkene isomer as opposed to the linear trans-isomer.
The Drive for Muscularity Scale (DMS) is a widely used measure in studies of men's body image, but few studies have examined its psychometric properties outside English-speaking samples. Here, we assessed the factor structure of a Malay translation of the DMS. A community sample of 159 Malay men from Kuala Lumpur, Malaysia, completed the DMS, along with measures of self-esteem, body appreciation, and muscle discrepancy. Exploratory factor analysis led to the extraction of two factors, differentiating attitudes from behaviours, which mirrors the parent scale. Both factors also loaded on to a higher-order drive for muscularity factor. The subscales of the Malay DMS had adequate internal consistencies and good convergent validity, insofar as significant relationships were reported with self-esteem, body appreciation, muscle discrepancy, and body mass index. These results indicate that the Malay DMS has acceptable psychometric properties and can be used to assess body image concerns in Malay men.
The use of dielectric property measurements to define specific trends in the molecular structures of poly(caprolactone) containing star polymers and/or the interbatch repeatability of the synthetic procedures used to generate them is demonstrated. The magnitude of the dielectric property value is shown to accurately reflect: (a) the number of functional groups within a series of materials with similar molecular size when no additional intermolecular order is present in the medium, (b) the polymer molecular size for a series of materials containing a fixed core material and so functional group number, and/or (c) the batch to batch repeatability of the synthesis method. The dielectric measurements are validated by comparison to spectroscopic/chromatographic data.
The stability of enzymes is critical for their application in industrial processes, which generally require different conditions from the natural enzyme environment. Both rational and random protein engineering approaches have been used to increase stability, with the latter requiring extensive experimental effort for the screening of variants. Moreover, some general rules addressing the molecular origin of protein thermostability have been established. Herein, we demonstrate the use of molecular dynamics simulations to gain molecular level understanding of protein thermostability and to engineer stabilizing mutations. Carbonic anhydrase (CA) is an enzyme with a high potential for biotechnological carbon capture applications, provided it can be engineered to withstand the high temperature process environments, inevitable in most gas treatment units. In this study, we used molecular dynamics simulations at 343, 353, and 363 K to study the relationship between structure flexibility and thermostability in bacterial α-CAs and applied this knowledge to the design of mutants with increased stability. The most thermostable α-CA known, TaCA from Thermovibrio ammonificans, had the most rigid structure during molecular dynamics simulations, but also showed regions with high flexibility. The most flexible amino acids in these regions were identified from root mean square fluctuation (RMSF) studies, and stabilizing point mutations were predicted based on their capacity to improve the calculated free energy of unfolding. Disulfide bonds were also designed at sites with suitable geometries and selected based on their location at flexible sites, assessed by B-factor calculation. Molecular dynamics simulations allowed the identification of five mutants with lower RMSF of the overall structure at 400 K, compared to wild-type TaCA. Comparison of free-energy landscapes between wild-type TaCA and the most promising mutants, Pro165Cys-Gln170Cys and Asn140Gly, showed an increased conformational stability of the mutants at 400 K.
Macromolecules that possess three-dimensional, branched molecular structures are of great interest because they exhibit significantly differentiated application performance compared to conventional linear (straight chain) polymers. This paper reports the synthesis of 3- and 4-arm star branched polymers via ring opening polymerisation (ROP) utilising multi-functional hydroxyl initiators and Sn(Oct)2 as precatalyst. The structures produced include mono-functional hydrophobic and multi-functional amphiphilic core corona stars. The characteristics of the synthetic process were shown to be principally dependent upon the physical/dielectric properties of the initiators used. ROP's using initiators that were more available to become directly involved with the Sn(Oct)₂ in the "in-situ" formation of the true catalytic species were observed to require shorter reaction times. Use of microwave heating (MWH) in homopolymer star synthesis reduced reaction times compared to conventional heating (CH) equivalents, this was attributed to an increased rate of "in-situ" catalyst formation. However, in amphiphilic core corona star formation, the MWH polymerisations exhibited slower propagation rates than CH equivalents. This was attributed to macro-structuring within the reaction medium, which reduced the potential for reaction. It was concluded that CH experiments were less affected by this macro-structuring because it was disrupted by the thermal currents/gradients caused by the conductive/convective heating mechanisms. These gradients are much reduced/absent with MWH because it selectively heats specific species simultaneously throughout the entire volume of the reaction medium. These partitioning problems were overcome by introducing additional quantities of the species that had been determined to selectively heat.
Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700 mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.