Chitosan is a natural polymer that has been widely utilized for many purposes in the food, textile, agriculture, water treatment, cosmetic and pharmaceutical industries. Based on its characteristics, including biodegradability, non-toxicity and antimicrobial properties, it has been employed effectively in wound healing applications. Importantly, however, it is necessary to improve chitosan’s capacities by combination with zinc oxide (ZnO), titanium dioxide (TiO₂) and silver (Ag) nanoparticles (NPs). In this review of many of the latest research papers, we take a closer look at the antibacterial effectiveness of chitosan combined with ZnO, TiO₂ and Ag NPs and also evaluate the specific wound healing application potentials.

Microalgae have been considered as one of the most promising biomass feedstocks for various industrial applications such as biofuels, animal/aquaculture feeds, food supplements, nutraceuticals, and pharmaceuticals. Several biotechnological challenges associated with algae cultivation, including the small size and negative surface charge of algal cells as well as the dilution of its cultures, need to be circumvented, which increases the cost and labor. Therefore, efficient biomass recovery or harvesting of diverse algal species represents a critical bottleneck for large-scale algal biorefinery process. Among different algae harvesting techniques (e.g., centrifugation, gravity sedimentation, screening, filtration, and air flotation), the flocculation-based processes have acquired much attention due to their promising efficiency and scalability. This review covers the basics and recent research trends of various flocculation techniques, such as auto-flocculation, bio-flocculation, chemical flocculation, particle-based flocculation, and electrochemical flocculation, and also discusses their advantages and disadvantages. The challenges and prospects for the development of eco-friendly and economical algae harvesting processes have also been outlined here.

The measurement of deposited aerosol particles in the respiratory tract via in vivo and in vitro approaches is difficult due to those approaches’ many limitations. In order to overcome these obstacles, different computational models have been developed to predict the deposition of aerosol particles inside the lung. Recently, some remarkable models have been developed based on conventional semi-empirical models, one-dimensional whole-lung models, three-dimensional computational fluid dynamics models, and artificial neural networks for the prediction of aerosol-particle deposition with a high accuracy relative to experimental data. However, these models still have some disadvantages that should be overcome shortly. In this paper, we take a closer look at the current research trends as well as the future directions of this research area.

To enhance the performance of lithium-ion batteries, zinc oxide (ZnO) has generated interest as an anode candidate owing to its high theoretical capacity. However, because of its limitations such as its slow chemical reaction kinetics, intense capacity fading on potential cycling, and low rate capability, composite anodes of ZnO and other materials are manufactured. In this study, we introduce binary and ternary composites of ZnO with other metal oxides (MOs) and carbon-based materials. Most ZnO-based composite anodes exhibit a higher specific capacity, rate performance, and cycling stability than a single ZnO anode. The synergistic effects between ZnO and the other MOs or carbon-based materials can explain the superior electrochemical characteristics of these ZnO-based composites. This review also discusses some of their current limitations.

This review clarifies particulate matter (PM) pollution, including its levels, the factors affecting its distribution, and its health effects on passengers waiting at bus stations. The usual factors affecting the characteristics and composition of PM include industrial emissions and meteorological factors (temperature, humidity, wind speed, rain volume) as well as bus-station-related factors such as fuel combustion in vehicles, wear of vehicle components, cigarette smoking, and vehicle flow. Several studies have proven that bus stops can accumulate high PM levels, thereby elevating passengers’ exposure to PM while waiting at bus stations, and leading to dire health outcomes such as cardiovascular disease (CVD), respiratory effects, and diabetes. In order to accurately predict PM pollution, an artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) have been developed. ANN is a data modeling method of proven effectiveness in solving complex problems in the fields of alignment, prediction, and classification, while the ANFIS model has several advantages including non-requirement of a mathematical model, simulation of human thinking, and simple interpretation of results compared with other predictive methods.