Impact of defects on the decomposition of chemical warfare agent simulants in Zr-based metal organic frameworks

Metal organic frameworks (MOFs) containing zirconium secondary building units (SBUs) in UiO-67 and related MOFs, are highly active for neutralizing both the chemical warfare agents and simulants, such as dimethyl methylphosphonate (DMMP). However, two recent publications gave conflicting reports of DMMP reaction with UiO-67 under ultra high vacuum (UHV) conditions, with one reporting chemisorption and reaction (Wang et al., J Phys Chem C, 2017, 121, 11261–11272) and the other reporting only physisorption and reversible desorption (Ruffley et al., J Phys Chem C, 2019, 123, 19748–19758) from very similar temperature programmed desorption experiments. We show that the discrepancy between these experiments may be explained by different levels of missing linker defects in the UiO-67 samples. We present density functional theory calculations showing that SBU sites having two-adjacent missing linkers exhibit reaction barriers that are about 30 kJ/mol lower than SBU sites having a single missing linker. We also show that topology of the undercoordinated sites plays an important role in the reaction barrier under UHV conditions.     

Occupational exposure and health risks of volatile organic compounds of hotel housekeepers: Field measurements of exposure and health risks

Hotel housekeepers represent a large, low-income, predominantly minority, and high-risk workforce. Little is known about their exposure to chemicals, including volatile organic compounds (VOCs). This study evaluates VOC exposures of housekeepers, sources and factors affecting VOC levels, and provides preliminary estimates of VOC-related health risks. We utilized indoor and personal sampling at two hotels, assessed ventilation, and characterized the VOC composition of cleaning agents. Personal sampling of hotel staff showed a total target VOC concentration of 57 ± 36 µg/m³ (mean ± SD), about twice that of indoor samples. VOCs of greatest health significance included chloroform and formaldehyde. Several workers had exposure to alkanes that could cause non-cancer effects. VOC levels were negatively correlated with estimated air change rates. The composition and concentrations of the tested products and air samples helped identify possible emission sources, which included building sources (for formaldehyde), disinfection by-products in the laundry room, and cleaning products. VOC levels and the derived health risks in this study were at the lower range found in the US buildings. The excess lifetime cancer risk (average of 4.1 × 10⁻⁵) still indicates a need to lower exposure by reducing or removing toxic constituents, especially formaldehyde, or by increasing ventilation rates.     

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM_2.5, black carbon, CO); household air pollution (PM_2.5, CO); personal exposure (PM_2.5, CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM_2.5 (R² = 0.45) and CO (R² = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R² = 0.76) and a root mean-squared error of 85 µg/m³, and the survey-only-based regression model was able to predict PM2.5 exposures with an R² of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R² of 0.57 and RMSE of 98 µg/m³.     

Rethinking the Citric Acid Cycle: Connecting Pyruvate Carboxylase and Citrate Synthase to the Flow of Energy and Material

In 1937, Sir H. A Krebs first published the Citric Acid Cycle, a unidirectional cycle with carboxylic acids. The original concept of the Citric Acid Cycle from Krebs’ 1953 Nobel Prize lecture illustrates the unidirectional degradation of lactic acid to water, carbon dioxide and hydrogen. Here, we add the heart lactate dehydrogenase•proton-linked monocarboxylate transporter 1 complex, connecting the original Citric Acid Cycle to the flow of energy and material. The heart lactate dehydrogenase•proton-linked monocarboxylate transporter 1 complex catalyses the first reaction of the Citric Acid Cycle, the oxidation of lactate to pyruvate, and thus secures the provision of pyruvic acid. In addition, we modify Krebs’ original concept by feeding the cycle with oxaloacetic acid. Our concept enables the integration of anabolic processes and allows adaption of the organism to recover ATP faster.     

Nonlinear modal analysis via non‐parametric machine learning tools

Modal analysis is an important tool in the structural dynamics community; it is widely utilised to understand and investigate the dynamical characteristics of linear structures. Many methods have been proposed in recent years regarding the extension to nonlinear analysis, such as nonlinear normal modes or the method of normal forms, with the main objective being to formulate a mathematical model of a nonlinear dynamical structure based on observations of input/output data from the dynamical system. In fact, for the majority of structures where the effect of nonlinearity becomes significant, nonlinear modal analysis is a necessity. The objective of the current paper is to demonstrate a machine learning approach to output‐only nonlinear modal decomposition using kernel independent component analysis and locally linear‐embedding analysis. The key element is to demonstrate a pattern recognition approach which exploits the idea of independence of principal components from the linear theory by learning the nonlinear manifold between the variables. In this work, the importance of output‐only modal analysis via “blind source” separation tools is highlighted as the excitation input/force is not needed and the method can be implemented directly via experimental data signals without worrying about the presence or not of specific nonlinearities in the structure.     

Development and validation of a crisis self‐efficacy index

This study develops a crisis self‐efficacy index to provide a tool for crisis communication researchers and practitioners to understand behavioural aspects of crisis response better. Evaluations of public's crisis self‐efficacy using this index inform strategic message development to protect the public and minimize crisis damages by identifying the public/s most in need of self‐efficacy enhancing interventions. The index also provides practitioners a useful longitudinal index to evaluate progress in crisis preparedness programs and track changes in efficacy. A four‐stage survey index development process using structural equation modelling identifies four underlying constructs of crisis self‐efficacy: action, preventive, achievement and uncertainty management.     

Interrelated Mechanism by Which the Methide Quinone Celastrol,Obtained from the Roots of Tripterygium wilfordii,Inhibits Main Protease 3CLpro of COVID-19 and Acts as Superoxide Radical Scavenger

We describe the potential anti coronavirus disease 2019 (COVID-19) action of the methide quinone inhibitor, celastrol. The related methide quinone dexamethasone is, so far, among COVID-19 medications perhaps the most effective drug for patients with severe symptoms. We observe a parallel redox biology behavior between the antioxidant action of celastrol when scavenging the superoxide radical, and the adduct formation of celastrol with the main COVID-19 protease. The related molecular mechanism is envisioned using molecular mechanics and dynamics calculations. It proposes a covalent bond between the S(Cys145) amino acid thiolate and the celastrol A ring, assisted by proton transfers by His164 and His41 amino acids, and a π interaction from Met49 to the celastrol B ring. Specifically, celastrol possesses two moieties that are able to independently scavenge the superoxide radical: the carboxylic framework located at ring E, and the methide-quinone ring A. The latter captures the superoxide electron, releasing molecular oxygen, and is the feature of interest that correlates with the mechanism of COVID-19 inhibition. This unusual scavenging of the superoxide radical is described using density functional theory (DFT) methods, and is supported experimentally by cyclic voltammetry and X-ray diffraction.     

Lupin seed coat as a promising food ingredient: physicochemical,nutritional, antioxidant properties,and effect of genotype and environment

The high proportion of seed coat of legume lupins results in big milling lose during kernel flour production, though the seed coat could be value-added as human food. The physicochemical and nutritional properties and antioxidant capacities of seed coats of six Australian sweet lupin cultivars grown at two locations were evaluated. Results showed that genotype, environment and their interaction were significant for seed coat percentage, proximate composition, dietary fibre content, polyphenols and antioxidant capacities. Strong correlations between seed coat lightness and polyphenol content were found. A comparison using multivariate analysis of the seed coat properties showed clear separation based on growing sites. This study indicates the enormous potential of Australian sweet lupin seed coat as an ‘antioxidant dietary fibre’ food source. The results could also benefit to breed varieties with desirable levels of nutrients and phytochemicals.     

Enhanced dielectric properties of three phase dielectric MWCNTs/BaTiO3/PVDF nanocomposites for energy storage using fused deposition modeling 3D printing

This research studied the effect of fused deposition modeling (FDM) 3D printing on three phase dielectric nanocomposites using poly(vinylidene) fluoride (PVDF), BaTiO₃ (BT), and multiwall carbon nanotubes (CNTs). PVDF polymer and BT ceramics are piezo-, pyro- and di-electric materials extensively used for sensor and energy storage/harvesting applications due to their unique characteristic of dipole polarization. To increase dielectric property, CNTs have been recently utilized for uniform dispersion of BT nanoparticles, ultrahigh polarization density, and local micro-capacitor among matrix. It was proved that 3D printing process provides homogeneous dispersion of nanoparticles, alleviating agglomeration of nanoparticles and reducing micro-crack/voids in matrix which can potentially enhance their dielectric property than traditional methods. In this research, these three-phase nanocomposites are fabricated through FDM 3D printing process and characterized for dielectric property. Increasing both BT and CNT nanoparticles improves dielectric properties, while CNTs have a percolation threshold near 1.7?wt%. The most desirable combination of dielectric constant and loss properties (118 and 0.11 at 1?kHz) is achieved with nanocomposites containing 1.7?wt%-CNT/45?wt%-BT/PVDF. These results provide not only a technique to 3D print dielectric nanocomposites with improved dielectric property but also large-scale electronic device manufacturing possibility with freedom of design, low cost, and faster process.