A Multiscale-Porous Anion Exchange Membrane for Convenient and Scalable Electrokinetic Concentration of Cationic Species

A device able to electrokinetically concentrate cationic samples has many potential medical and industrial applications, but until now has remained undeveloped due to the lack of a commercial anion-permselective material leading to a prohibitively complex fabrication procedure. Herein, a novel multiscale-porous anion exchange membrane (MP-AEM) that enables the convenient and scalable electrokinetic concentration of cationic species is proposed. A mechanically enhanced multiscale-porous structure with a solid framework is realized by adopting polyester resin as an additive to overcome the intrinsic limitations of the AEM material. The scalable MP-AEM-embedded electrokinetic concentrator is devised based on the peculiar properties of the MP-AEM that for allow both ion and fluid transport. With the MP-AEM, the concentrator is fabricated in a highly streamlined manner consisting only of a simple insertion and assembly. The concentration performance of the MP-AEM-embedded electrokinetic concentrator is demonstrated with a positively charged fluorescent dye and a fluorescein-labeled protein, and the results show enrichment factors of 250 and 500, respectively. The MP-AEM makes cationic electrokinetic concentration more accessible and scalable, thereby enabling further progress in a wide range of fields.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Inferring kinetic dissolution of NaCl in aqueous glycol solution using a low-cost apparatus and population balance model

An experimental methodology for inferring brine dissolution rate in monoethylene glycol (MEG) solutions at different temperatures using a webcam combined with a mathematical model is presented. The measurement system is designed to track the RGB (red, green, and blue) colour variations during the dissolution process. A dynamic model augmented with the population balance equation is applied to describe the dissolution process. Moreover, the dissolution rate is consistently related to the temperature and MEG concentration through the driving force based on the Gibbs energy and chemical affinity. The applied low-cost measurement apparatus proved to be a useful resource for tracking the dissolution dynamics in a wide range of undersaturation.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

Random poly(ε‐caprolactone‐L‐alanine) by direct melt copolymerization

A series of random polyesteramides (PEAs) with a range of molar composition from 90/10 to 50/50 were synthesized by direct melt polycondensation of ε‐caprolactone and l ‐alanine. Their structure was fully characterized by Fourier transform IR and NMR spectroscopy. The resulting copolymers are completely amorphous with the exception of PEA‐90/10 which possesses a semicrystalline structure. These PEAs present increasing glass transition temperatures at increasing l ‐alanine contents and exhibit fairly good thermal stability with 10% mass loss temperatures reaching 315 °C. © 2020 Society of Industrial Chemistry