A document by Guillaume Hopsort. Click on the document to view its contents.

Economical uranium adsorption from seawater remains a crucial task for energy and environmental safety. Aiming for improving the mass transfer rate of uranium adsorption. Herein, a novel 2D porous aromatic framework(PAF) based on nucleophilic substitution of 2,5-dichloro benzonitrile was synthesized, with an ordered prous structure, excellent stability and selectivity of uranium extraction from seawater. PAF shows excellent uranium adsorption capacity of 637 mg/g and 3.22 mg/g in simulated and real seawater because of highly accessible pores on the walls of open channels. In addition, benefiting from the super-hydrophilicity due to the presence of amidoxime groups attributes high selectivity and ultrafast kinetics with an uptake rate of 0.43±0.03 mg/g.day and allowing half-saturation within 1.35±0.09 day. This strategy demonstrates a potential of PAF not only in uranium trap but also possess a power to monitor water quality. This technique can be extended in other applications by sensible planning target ligands

The application of heterogeneous catalysts in dimethyl carbonate (DMC) synthesis from methanol is hindered by low activation efficiency of methanol to methoxy intermediates (CH3O*), which is the key intermediate for DMC generation. Herein, a catalyst of alkali metal K anchored on the CuO/ZnO oxide is rationally designed for offering Lewis acid-base pairs as dual active centers to improve the activation efficiency of methanol. Characterizations of CO2-TPD, NH3-TPD, XPS, and DRIFTS revealed that the addition of Lewis base K observably boosted the dissociation of methanol and combined with Lewis acid CuO/ZnO oxide to adsorb the formed CH3O* stably, thus synergistically promoted the transesterification. Finally, the CuO/ZnO-9%K2O catalyst exhibited the optimal catalytic activity, achieving a high yield of 74.4% with an excellent selectivity of 98.9% for DMC at a low temperature of 90 °C. The strategy of constructing Lewis acid-base pairs provides a reference for the design of heterogeneous catalysts.

Molecular imprinting technology has gained increasing attention and application in protein adsorption and separation. Bacterial growth on the imprinted material would reduce the adsorption selectivity of the imprinted cavity, contaminate the isolation products and shorten the service life of the material. To solve the above problems, carrier materials with dual antibacterial ability are constructed for the first time and novel surface protein imprinted microspheres (GO-PEI/MXene@MIPs) are manufactured. Thanks to the large exterior surface area, the saturation adsorption amount of GO-PEI/MXene@MIPs reaches 312.63 mg/g with an imprinting factor (IF) value of 3.16 within 90 min. Meanwhile, this imprinted material also exhibits a high ability to separate real samples as well as reusability. In addition, this material has excellent broad-spectrum antibacterial effects, which will significantly extend its service life in real-world environments. This study provides a feasible solution for the application of surface protein imprinted materials in real-world environments.

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A methodology is proposed to aid parameter estimation in fundamental models of pharmaceutical processes. This methodology addresses situations with insufficient data to reliably estimate all parameters, when the estimation is complicated by uncertain independent variables. The proposed method uses an augmented sensitivity matrix to rank the combined set of parameters and uncertain inputs from most estimable to least estimable. An updated mean-squared-error criterion is then used to determine the appropriate parameters and inputs that should be estimated, based on the ranked list. A model for one step in a batch pharmaceutical production process with an uncertain initial reactant concentration is used to illustrate the method, revealing that the initial reactant concentration in each batch should be estimated along with three out of six model parameters. Non-estimable parameters are fixed at their initial values to prevent overfitting. The method will aid error-in-variables parameter estimation in many situations involving limited data.

We carried out 3-D simulations of monodisperse particle suspensions subjected to a constant shear rate with the view to investigate the effect of electrical double layers around the particles on apparent suspension viscosities. To this end, expressions for Debye length, zeta potential and ionic strength (pH) of the liquid were incorporated into our in-house lattice Boltzmann code that uses the Immersed Boundary method and includes sub-grid lubrication models. We varied the solids concentration and particle radius, keeping the particle Reynolds number equal to 0.1. We report on results with respect to the effect of pH (in the range 9 through 12) and Debye length on apparent viscosity and spatial suspension structures, particularly at higher solids volume fractions, and on the effect of flow reversals.

The distribution of gas-liquid two-phase flow is one of significant effects on heterogeneous catalytic reactions. Ceramic membrane gas distributors (CMGD) were applied in improving gas-liquid distribution, and flow behavior of gas as dispersed phase in liquid phase was visualized via high-speed photograph. The average diameters of multi-scale bubbles were measured and modeled ranging from 10-5 to 10-2 m. The coalescence and trajectory of bubbles during rising process were observed, and two typical trajectories straight and spiral types were tracked. In order to inhibit coalescence of bubbles during rising process, internals manufactured by 3D printing were installed in the channel of ceramic membrane. The average bubble size of CMGD decreases 12 % from 392 to 345 μm compared to that of the original CMGD. The CMGD with internals enhances the heterogeneous catalytic reaction performance via providing large quantity of stabile multi-scale bubbles which could match the porous structure of catalyst.

Two-dimensional (2D) membranes have demonstrated potential for molecular separation; however, their applicability for Li/Mg ion separation has been restricted by their negatively-charged and easily-swellable properties in water. Moreover, their practical application has been hindered by the challenge of producing significant quantities of single-layer nanosheets. To overcome these challenges, we have developed a scalable method for synthesizing micro-sized nitrate ZnAl layered double hydroxide (LDH) and subsequent exfoliating to yield monolayer nanosheets for the construction of 2D membranes. The sub-nanometer channels of the LDH membrane is positively charged, which prevents the passage of magnesium ions. These channels also impede the flow of magnesium ions that are more difficult to dehydrate. As a result, the LDH membranes exhibit robust lithium-magnesium separation ability, with a separation ratio of 6 (Li/Mg). This work provides a method for producing high-quality LDH nanosheets and validates the enormous potential of LDH membranes in the field of lithium-magnesium separation.

To design D-amino acid dehydrogenase (DAADH) for enhanced stability, the interactions of the subunit interfaces of DAADH were analyzed. Interaction network analysis of DAADH indicated that there are only weak interactions between the A and B subunits. Several co-evolved residue pairs were selected for mutation to enhance interfacial interactions of subunits, and 11 designed MDHs were obtained. DA06 and DA11 were selected for experimental verification for their salt bridges are 1.4 and 1.2-fold of that of DAwild, respectively. DA11 can maintain 93% activity in 80℃, while it was only 40% for DAwild. Thermostabiliy study indicated the half-life of DA11 was 2-fold of DAwild. Molecular dynamics simulations revealed that the extraordinary stability of the DA11 was due to the formation of extra interfacial salt bridges. The paper provided a strategy of mutations outside the active site of enzyme by co-evolutionary analysis which can reduce the effect of the activity-thermostability trade-off.

Bladed mixers are widely used for processing granular materials where significant mechanical energy is required to produce the desired blend. Some mechanical energy is dissipated within the granular medium, generating heat during this process. However, our knowledge of the heat generation mechanisms without external thermal loads is still lacking. This study uses an overhead stirrer to mix granular materials and investigate heat generation by monitoring the temperature changes in the granular bed. Additionally, first-order kinetic equations are used to extrapolate the experimental data to a thermal equilibrium where the heat generation and heat loss rates are equal. Lead, steel, and glass particles are used under various operating conditions. It is observed that metallic particles heat up faster owing to their lower heat capacity. Also, increasing the rotation speed, fill ratio and particle size result in a greater temperature increase. Moreover, flat blades induce more heat generation compared to tilted blades.

Biomass-derived deep eutectic solvents (DESs) have been introduced as promising pretreatment and fractionation solvents because of their mild processing conditions, easy synthesis, and green solvent components from biomass. In recent DES studies, solvent-based third constituents like water, ethanol, and others improve the processibility of typical binary DESs. However, the impacts of these components are not well understood. Here, two solvent-based constitutions, including water and ethylene glycol, were applied to 3,4-dihydroxybenzoic acid (DHBA)-based DES system for improving the conversion efficiency of cellulose-rich fraction and the properties of lignin fraction. Compositional changes, enzymatic digestibility of carbohydrate components, and transformation of lignin were used to evaluate the impact of each constituent on biomass processing. Ternary DHBA-ChCl DESs exhibited better performances in delignification, fermentable sugar production, and preservation of β–O–4 ether linkage in lignin compared to neat ChCl-DHBA DES.

This paper considers the problem of state observation for nonlinear dynamics. While model-based observer synthesis is difficult due to the need of solving partial differential equations, this work proposes an efficient model-free, data-driven approach based on online learning. Specifically, by considering the observer dynamics as a Chen-Fliess series, the estimation of its coefficients has a least squares formulation. Since the series converges only locally, the coefficients are recursively updated, resulting in an online optimization scheme driven by instantaneous gradients. When the state trajectories are available, the online least squares guarantees an ultimate upper bound of average observation error proportional to the average variation of states. In the realistic situations where the states cannot be measured, the immersed linear dynamics based on the Kazantzis-Kravaris/Luenberger structure is assigned, followed by online kernel principal component analysis for dimensionality reduction. The proposed approach is demonstrated by a limit cycle dynamics and a chaotic system.

This study explores the aerobic Baeyer-Villiger oxidation of cyclohexanone into ε-caprolactone using metalloporphyrin and benzaldehyde, a greener process to replace hazardous concentrated peroxyacid. The reaction mechanism involves a series of free radical reactions, identified through in-situ EPR. In this complex three-component reaction, we developed an intrinsic kinetic model based on the proposed mechanism. Utilizing a hyperbolic equation, the model well fits experimental data, describing biomimetic catalytic behavior of the aerobic Baeyer-Villiger oxidation. The reaction orders for the three reactants corroborate the kinetic model, with the activation energy of oxygen (130.27 kJ/mol) surpassing cyclohexanone (94.85 kJ/mol) and benzaldehyde (40.73 kJ/mol), implying slow initial oxygen activation while rapid subsequent benzaldehyde oxidation, making oxygen transfer and activation key steps. This unified approach to elementary reaction, mechanism, and intrinsic kinetics provides robust forecasts and lays the groundwork for additional studies, such as side reactions control and mass transfer enhancement and reactor design.

In this work, we demonstrate plasma-catalytic synthesis of hydrogen and acrylonitrile (AN) from CH4 and N2. The process involves two steps: 1) plasma synthesis of C2H2 and HCN in a nominally 1:1 stoichiometric ratio with high yield up to 90% and high methane conversion > 90%; and 2) downstream thermocatalytic reaction of these intermediates to make AN. The effect of process parameters on product distributions and specific energy requirements are reported. If the catalytic conversion of C2H2 and HCN in the downstream thermocatalytic step to AN were perfect, which will require further improvements in the thermocatalytic reactor, then at the maximum output of our 1 kW radiofrequency 13.56 MHz transformer, a specific energy requirement of 73 kWh kgAN-1was determined. The expectation is that scaling up the process to higher throughputs would result in decreases in specific energy requirement into the predicted economically viable range less than 10 kWh kgAN-1.

To alleviate the greenhouse gas emissions by the chemical industry, electrification has been proposed as a solution where electricity from renewable sources is used to power processes. The adoption of renewable energy is complicated by its spatial and temporal variations. To address this challenge, we investigate the potential of distributed manufacturing for electrified chemical processes installed in a microgrid. We propose a multi-scale mixed-integer linear programming model for locating modular electrified plants, renewable-based generating units, and power lines in a microgrid that includes monthly transportation and hourly scheduling decisions. We propose a K-means clustering-based aggregation disaggregation matheuristic to solve the model efficiently. The model and algorithm are tested using a case study with 20 candidate locations in Western Texas. Additionally, we define a new concept, “the Value of the Multi-scale Model”, to demonstrate the additional economic benefits of our model compared with a single-scale model.

Hydrogel-based microfluidics offer an in vivo-relevant micro-environments for construction of organs-on-chips. However, the fabrication of heterogeneous microchannels using hydrogels is challenging and fails to mimic the complex structures of organs in vivo. Here we present a new methodology called “layer-by-layer adhesion” for the construction of complex microfluidic chips. A hydrosoluble and photo-crosslinkable adhesive, chitosan methacryloyl (CS-MA), was used to stitch various hydrogels together layer-by-layer to form perfusable microchannels. Our results show that CS-MA can bond different types of hydrogels with adhesion energy ranging from 1.2-140 N/m. Using the layer-by-layer adhesion approach, we constructed heterogeneous hydrogel-based microchannels with various morphologies of snail, spiral, vascular-like, and bilayer. Based on this methodology, liver-on-a-chip was established by entrapping hepatic cells inside a biocompatible Gel-MA layer and covering it with the perfusable microchannels in tough F127-DA layer. The “layer-by-layer adhesion” provides a facile and cytocompatible approach for engineering user-defined hydrogel-based chips potentially for organs-on-chips.

The fragmentation/adhesion behavior of nanoparticle agglomerate collision, which is challenging to model, is a crucial factor affecting fluidization. In this study, a discrete-finite element method (FDEM) based cohesive crack model is developed to simulate normal collisions between a complex agglomerate (around 1 mm) and a wall. In the FDEM model, the complex agglomerate is built from primary agglomerates (around a few micrometers), whose adhesive force and Young’s modulus are measured by an atomic force microscope (AFM). Simulation results agree well with the collision experiments. Furthermore, the effects of adhesive force, solid holdup and Young’s modulus on fragmentation behavior are explored, and a two-parameter Weibull function is found to fit well with the fragment distribution. The current FDEM model provides a link between the collision behavior and agglomerate properties. The detailed fragmentation/adhesion information can be useful for developing a macro model for agglomerate collision in the future.