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An air gap membrane distillation (AGMD) module was developed by incorporating a poly(etheretherketone) (PEEK) hollow fiber membrane (HFM) having a nonporous wall. This PEEK HFM was placed inside a polyvinylidene fluoride (PVDF) hydrophobic porous wall HFM with a larger bore diameter. The outside diameter (OD) of PVDF HFM is 925 μm, small enough to be capable of achieving a high surface area packing density of 1297 m2/m3. The air gap thickness was very small, 121 μm. Hot brine flowed on the outside of the PVDF HFM; the colder liquid was passed through the lumen of the PEEK-based condenser hollow fibers. Water vapor condensed in the air gap formed between the inner surface of the porous PVDF HFM and the outer surface of the nonporous condenser PEEK fiber. With 85o C hot brine flowing at 40 mL•min􀀀1 and 5o C coolant flowing at 8 mL•min􀀀1, the water vapor flux was 9.05 kg/m2•h with a salt rejection of 98.7 %. Simulation by COMSOL Multiphysics predicted water flux and interfacial temperature of HFM, which supported the experimental observations. Moreover, the influence of module geometry, membrane characteristics and internal flow configuration on permeate flux, thermal efficiency, gained output ratio (GOR), and temperature and concentration polarization were evaluated. Principal component analysis (PCA) was used to illustrate the interconnections among various parameters and their respective contributions to water flux and other performance indicators. Air gap thickness had the strongest influence on temperature polarization. 

Per- and polyfluoroalkyl substances (PFAS) have garnered attention as a pressing environmental issue due to their enduring presence and suspected adverse health effects. This study assessed the rejection or removal ef- ficacy of PFAS by commercial reverse osmosis (RO) and nanofiltration (NF) membranes and examined the im- pacts of surfactants, ion valency and solution temperature that are inadequately explored. The results reveal that the presence of cationic surfactants such as cetyltrimethylammonium bromide (CTAB) increased the rejection of two selected PFAS compounds, perfluorooctanoic acid (PFOA) and perfluorobutanoic acid (PFBA), by binding with negatively charged PFAS and preventing them from passing through membrane pores via size exclusion, whereas the presence of anionic surfactants such as sodium dodecyl sulfate (SDS) increased the PFAS rejection because the increased electrostatic repulsion prevented PFAS from approaching and adsorbing onto the mem- brane surface. Moreover, aqueous ions (e.g., Al³⁺ and PO³−) with higher ion valency enabled higher rejection of PFOA and PFBA through increased effective molecular size and increased electronegativity. Finally, only high solution temperature at 45 ◦C significantly reduced PFAS rejection efficiency because of the thermally expanded membrane pores and thus the increased leakage of PFAS. Overall, this research provides valuable insights into the various factors impacting PFAS rejection in commercial RO and NF processes. These findings are crucial for developing efficient PFAS removal methods and optimizing existing treatment systems, thereby contributing significantly to the ongoing efforts to combat PFAS contamination. 

The emerging unmanned aerial vehicle (UAV) such as a quadcopter offers a reliable, controllable, and flexible way of ferrying information from energy harvesting powered IoT devices in remote areas to the IoT edge servers. Nonetheless, the employment of UAVs faces a major challenge which is the limited fly range due to the necessity for recharging, especially when the charging stations are situated at considerable distances from the monitoring area, resulting in inefficient energy usage. To mitigate these challenges, we proposed to place multiple charging stations in the field and each is equipped with a powerful energy harvester and acting as a cluster head to collect data from the sensor node under its jurisdiction. In this way, the UAV can remain in the field continuously and get the data while charging. However, the intermittent and unpredictable nature of energy harvesting can render stale or even obsolete information stored at cluster heads. To tackle this issue, in this work, we proposed a Deep Reinforcement Learning (DRL) based path planning for UAVs. The DRL agent will gather the global information from the UAV to update its input environmental states for outputting the location of the next stop to optimize the overall age of information of the whole network. The experiments show that the proposed DDQN can significantly reduce the age of information (AoI) by 3.7% reliably compared with baseline techniques. 

As the next-generation battery substitute for IoT system, energy harvesting (EH) technology revolutionizes the IoT industry with environmental friendliness, ubiquitous accessibility, and sustainability, which enables various self-sustaining IoT applications. However, due to the weak and intermittent nature of EH power, the performance of EH-powered IoT systems as well as its collaborative routing mechanism can severely deteriorate, rendering unpleasant data package loss during each power failure. Such a phenomenon makes conventional routing policies and energy allocation strategies impractical. Given the complexity of the problem, reinforcement learning (RL) appears to be one of the most promising and applicable methods to address this challenge. Nevertheless, although the energy allocation and routing policy are jointly optimized by the RL method, due to the energy restriction of EH devices, the inappropriate configuration of multi-hop network topology severely degrades the data collection performance. Therefore, this article first conducts a thorough mathematical discussion and develops the topology design and validation algorithm under energy harvesting scenarios. Then, this article developsDeepIoTRouting, a distributed and scalable deep reinforcement learning (DRL)-based approach, to address the routing and energy allocation jointly for the energy harvesting powered distributed IoT system. The experimental results show that with topology optimization,DeepIoTRoutingachieves at least 38.71% improvement on the amount of data delivery to sink in a 20-device IoT network, which significantly outperforms state-of-the-art methods. 

A host:guest array can discriminate citrus varietal peel extracts, despite the overwhelming excess of one major component in each sample.