A Brief Review of Recent Theoretical Advances in Fe-Based Catalysts for CO2 Hydrogenation.

Catalytic hydrogenation presents a promising approach for converting CO2 into valuable chemicals and fuels, crucial for climate change mitigation. Iron-based catalysts have emerged as key contributors, particularly in driving the reverse water-gas shift and Fischer-Tropsch synthesis reactions. Recent research has focused on enhancing the efficiency and selectivity of these catalysts by incorporating alkali metal promoters or transition metal dopants, enabling precise adjustments to their composition and properties. This review synthesizes recent theoretical advancements in CO2 hydrogenation with iron-based catalysts, employing density functional theory and microkinetic modeling. By elucidating the underlying mechanisms involving metallic iron, iron oxides, and iron carbides, we address current challenges and provide insights for future sustainable CO2 hydrogenation developments.

[Development of Human Vital Signs and Body Posture Monitoring and Positioning Alarm Systems].

In view of the high incidence of malignant diseases such as malignant arrhythmias in the elderly population, accidental injuries such as falls, and the problem of no witnesses when danger occurs, the study developed a human vital signs and body posture monitoring and positioning alarm system. Through the collection and analysis of electrocardiogram (ECG), respiration (RESP) and acceleration (ACC) signals, the system monitors human vital signs and body posture in real time, automatically judges critical states such as malignant arrhythmias and accidental falls on the local device side, and then issues alarm information, opens the positioning function, and uploads physiological information and patient location information through 4G communication. Experiments have shown that the system can accurately determine the occurrence of ventricular fibrillation and falls, and issue position and alarm information.

Mesoscopic simulation of multi-scheme particle separation in deterministic lateral displacement devices using two-piece hybrid pillars.

Pillar shape exploration in deterministic lateral displacement (DLD) technique holds great promise for developing high-performance microfluidic devices with versatile sorting schemes. A recent innovative design using filter-like micropillars was proposed to improve cell separation, but its significance might be greatly underestimated due to an inaccurate understanding of the underlying mechanism. In this study, we employ mesoscopic hydrodynamic simulations to explore the movement and separation of rigid spherical particles in DLD arrays using various two-piece hybrid (TPH) pillars, where each pillar consists of two individual pieces separated by a tunable inter-piece channel. In comparison with the conventional one-piece pillars, the back piece of TPH-pillars is found to hierarchically tailor the flow profile of the front piece on the basis of the row shift fraction and the inter-piece channel width, resulting in unique tunable multi-scheme separation at low, intermediate, and high row shift fractions, respectively. At the intermediate regime, in particular, the first flow lane that determines the critical separation size could be physically fenced out by the inter-piece channel, and a delicate coupling of hydrodynamic filtration and DLD has been revealed to induce a constant critical size in the whole regime. This work theoretically demonstrates the feasibility and significance of TPH-pillars, which may open up a new direction of the geometry design by exploiting rich multi-piece hybrid structures to expand the versatility of the DLD technique.

Dissipative Particle Dynamics Simulation of the Sensitive Anchoring Behavior of Smectic Liquid Crystals at Aqueous Phase.

Rational design of thermotropic liquid crystal (LC)-based sensors utilizing different mesophases holds great promise to open up novel detection modalities for various chemical and biological applications. In this context, we present a dissipative particle dynamics study to explore the unique anchoring behavior of nematic and smectic LCs at amphiphile-laden aqueous-LC interface. By increasing the surface coverage of amphiphiles, two distinct anchoring sequences, a continuous planar-tilted-homeotropic transition and a discontinuous planar-to-homeotropic transition, can be observed for the nematic and smectic LCs, respectively. More importantly, the latter occurs at a much lower surface coverage of amphiphiles, demonstrating an outstanding sensitivity for the smectic-based sensors. The dynamics of reorientation further reveals that the formation of homeotropic smectic anchoring is mainly governed by the synchronous growth of smectic layers through the LCs, which is significantly different from the mechanism of interface-to-bulk ordering propagation in nematic anchoring. Furthermore, the smectic LCs have also been proven to possess a potential selectivity in response to a subtle change in the chain rigidity of amphiphiles. These simulation findings are promising and would be valuable for the development of novel smectic-based sensors.

A Scoping Review of Drug Epidemic Models.

The phenomenon of drug epidemics has been a global issue in the past decades, causing enormous damages to the physical and mental health of drug users and social well-being. Despite great efforts to curb drug epidemics at the governmental or social level, the total number of drug users has still been on the rise in recent years, along with illicit production and trafficking around the world. Inspired by dynamical epidemic models of infectious disease, a flourishment of promising results has been observed in the exploration of drug epidemic models. In this review, we aim to provide a scoping review of all existing drug epidemic modeling studies, and it has been shown that most studies focused on analyses of theoretical behaviors of the model systems, lacking emphasis on practical applications in real settings. We found that the drug epidemic models were characterized by a longer time scale, no incubation period, no significant prevention vaccines interfered, and population specificity. This review could assist policymakers and public health workers in gaining deeper insights into modeling tools, and help modelers improve their works, thus narrowing gaps between mathematical epidemiology and public health studies.

Modeling the Dynamics of Drug Spreading in China.

Drug abuse remains one of the major public health issues at the global level. In this article, we propose a drug epidemic model with a complete addiction-rehabilitation-recovery process, which allows the initiation of new users under the influence of drug addicts undergoing treatment and hidden drug addicts. We first conduct qualitative analyses of the dynamical behaviors of the model, including the existence and positivity of the solutions, the basic reproduction number, global asymptotic stabilities of both the drug-free and the drug-persistent equilibria, as well as sensitivity analysis. Then we use the model to predict the drug epidemic in China during 2020-2030. Finally, we numerically simulate the potential impact of intervention strategies on different drug users. The results show that the drug epidemic will decrease significantly during 2020-2030, and the most effective intervention strategy to eliminate drug epidemics is to strengthen the investigation and rehabilitation admission of hidden drug users.