Research advances in microfluidic collection and detection of virus, bacterial, and fungal bioaerosols.

Bioaerosols are airborne suspensions of fine solid or liquid particles containing biological substances such as viruses, bacteria, cellular debris, fungal spores, mycelium, and byproducts of microbial metabolism. The global Coronavirus disease 2019 (COVID-19) pandemic and the previous emergence of severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and influenza have increased the need for reliable and effective monitoring tools for bioaerosols. Bioaerosol collection and detection have aroused considerable attention. Current bioaerosol sampling and detection techniques suffer from long response time, low sensitivity, and high costs, and these drawbacks have forced the development of novel monitoring strategies. Microfluidic technique is considered a breakthrough for high performance analysis of bioaerosols. In recent years, several emerging methods based on microfluidics have been developed and reported for collection and detection of bioaerosols. The unique advantages of microfluidic technique have enabled the integration of bioaerosol collection and detection, which has a higher efficiency over conventional methods. This review focused on the research progress of bioaerosol collection and detection methods based on microfluidic techniques, with special attention on virus aerosols and bacterial aerosols. Different from the existing reviews, this work took a unique perspective of the targets to be collected and detected in bioaerosols, which would provide a direct index of bioaerosol categories readers may be interested in. We also discussed integrated microfluidic monitoring system for bioaerosols. Additionally, the application of bioaerosol detection in biomedicine was presented. Finally, the current challenges in the field of bioaerosol monitoring are presented and an outlook given of future developments.

Cost-effective synthesis of hierarchical SAPO-34 zeolites with abundant intracrystalline mesopores and excellent MTO performance.

Hierarchical SAPO-34 catalysts with abundant intracrystalline mesopores have been prepared using morpholine as the template combined with a cheap nonsurfactant cationic surfactant. Significantly, the hierarchical SAPO-34 catalysts exhibit more than 5-fold improved lifetime and 12% enhanced selectivity for ethylene and propylene compared to conventional microporous counterparts in methanol-to-olefin (MTO) reactions.

In Situ Confinement of Ultrasmall Pd Clusters within Nanosized Silicalite-1 Zeolite for Highly Efficient Catalysis of Hydrogen Generation.

Well-dispersed and ultrasmall Pd clusters in nanosized silicalite-1 (MFI) zeolite have been prepared under direct hydrothermal conditions using [Pd(NH2CH2CH2NH2)2]Cl2 as precursor. High-resolution scanning transmission electron microscopy studies indicate that the Pd clusters are encapsulated within the intersectional channels of MFI, and the Pd clusters in adjacent channels visually aggregate, forming nanoparticles (NPs) of ∼1.8 nm. The resultant catalysts show an excellent activity and highly efficient H2 generation toward the complete decomposition of formic acid (FA) under mild conditions. Notably, thanks to the further reduced Pd NP size (∼1.5 nm) and the additionally introduced basic sites, the Pd/S-1-in-K catalyst affords turnover frequency values up to 856 h(-1) at 25 °C and 3027 h(-1) at 50 °C. The easy in situ confinement synthesis of metal clusters in zeolites endows the catalysts with superior catalytic activities, excellent recyclability, and high thermal stability, thus opening new perspectives for the practical application of FA as a viable and effective H2 storage material for use in fuel cells.

Ultrafast synthesis of nano-sized zeolite SAPO-34 with excellent MTO catalytic performance.

Nano-sized SAPO-34 zeolites with high crystallinity are obtained in 10 minutes by fast heating the reaction gel in a stainless steel tubular reactor combined with the seed-assisted method, which show outstanding performance in methanol-to-olefin (MTO) reaction.

Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems.

This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic algorithm based on tournament selection is proposed. Also, HCOEA has adopted a parallel local search operator that implements a clustering partition of the population and multiparent crossover to generate the offspring population. Then, nondominated individuals in the offspring population are used to replace the dominated individuals in the parent population. Meanwhile, the best infeasible individual replacement scheme is devised for the purpose of rapidly guiding the population toward the feasible region of the search space. During the evolutionary process, the global search model effectively promotes high population diversity, and the local search model remarkably accelerates the convergence speed. HCOEA is tested on 13 well-known benchmark functions, and the experimental results suggest that it is more robust and efficient than other state-of-the-art algorithms from the literature in terms of the selected performance metrics, such as the best, median, mean, and worst objective function values and the standard deviations.

Evolutionary parallel local search for function optimization.

This paper proposes a kind of evolutionary parallel local search technique (EPLS) that integrates the reproduction mechanisms from evolutionary algorithms and simplex method. The major aim is to explore the tradeoff between exploration and exploitation for optimizing multimodal functions. It has been cost-efficiently reached by means of parallel local search using simplex method. In each generation, EPLS partitions the population into a group of subpopulations, each of which consists of several individuals with adjacent space locations. EPLS independently locates multiple local optima in these disjoint neighborhoods, thus to reduce the probability of losing the global optimum. The local search in a neighborhood speeds up the convergence rate of simplex method. Recombination, adaptive Gaussian mutation and selection are incorporated into EPLS to further enhance the ability of global exploration and exploitation. The experimental observations and the extensive comparisons show that EPLS remarkably outperforms the standard evolutionary algorithms (EA) and some hybrid ones for almost all the problems tested, thus justifying the rationality and the competitive potential of EPLS for optimizing multimodal functions, especially for those with very rugged and deceptive topological structures.