Tuning Local Atomic Structures in MoS2 Based Catalysts for Electrochemical Nitrate Reduction.

In recent years, there has been a substantial surge in the investigation of transition-metal dichalcogenides such as MoS2 as a promising electrochemical catalyst. Inspired by denitrification enzymes such as nitrate reductase and nitrite reductase, the electrochemical nitrate reduction catalyzed by MoS2 with varying local atomic structures is reported. It is demonstrated that the hydrothermally synthesized MoS2 containing sulfur vacancies behaves as promising catalysts for electrochemical denitrification. With copper doping at less than 9% atomic ratio, the selectivity of denitrification to dinitrogen in the products can be effectively improved. X-ray absorption characterizations suggest that two sulfur vacancies are associated with one copper dopant in the MoS2 skeleton. DFT calculation confirms that copper dopants replace three adjacent Mo atoms to form a trigonal defect-enriched region, introducing an exposed Mo reaction center that coordinates with Cu atom to increase N2 selectivity. Apart from the higher activity and selectivity, the Cu-doped MoS2 also demonstrates remarkably improved tolerance toward oxygen poisoning at high oxygen concentration. Finally, Cu-doped MoS2 based catalysts exhibit very low specific energy consumption during the electrochemical denitrification process, paving the way for potential scale-up operations.

Efficient conversion of low-concentration nitrate sources into ammonia on a Ru-dispersed Cu nanowire electrocatalyst.

Electrochemically converting nitrate ions, a widely distributed nitrogen source in industrial wastewater and polluted groundwater, into ammonia represents a sustainable route for both wastewater treatment and ammonia generation. However, it is currently hindered by low catalytic activities, especially under low nitrate concentrations. Here we report a high-performance Ru-dispersed Cu nanowire catalyst that delivers an industrial-relevant nitrate reduction current of 1 A cm-2 while maintaining a high NH3 Faradaic efficiency of 93%. More importantly, this high nitrate-reduction catalytic activity enables over a 99% nitrate conversion into ammonia, from an industrial wastewater level of 2,000 ppm to a drinkable water level <50 ppm, while still maintaining an over 90% Faradaic efficiency. Coupling the nitrate reduction effluent stream with an air stripping process, we successfully obtained high purity solid NH4Cl and liquid NH3 solution products, which suggests a practical approach to convert wastewater nitrate into valuable ammonia products. Density functional theory calculations reveal that the highly dispersed Ru atoms provide active nitrate reduction sites and the surrounding Cu sites can suppress the main side reaction, the hydrogen evolution reaction.

Case Study: A Bio-Inspired Control Algorithm for a Robotic Foot-Ankle Prosthesis Provides Adaptive Control of Level Walking and Stair Ascent.

Powered ankle-foot prostheses assist users through plantarflexion during stance and dorsiflexion during swing. Provision of motor power permits faster preferred walking speeds than passive devices, but use of active motor power raises the issue of control. While several commercially available algorithms provide torque control for many intended activities and variations of terrain, control approaches typically exhibit no inherent adaptation. In contrast, muscles adapt instantaneously to changes in load without sensory feedback due to the intrinsic property that their stiffness changes with length and velocity. We previously developed a "winding filament" hypothesis (WFH) for muscle contraction that accounts for intrinsic muscle properties by incorporating the giant titin protein. The goals of this study were to develop a WFH-based control algorithm for a powered prosthesis and to test its robustness during level walking and stair ascent in a case study of two subjects with 4-5 years of experience using a powered prosthesis. In the WFH algorithm, ankle moments produced by virtual muscles are calculated based on muscle length and activation. Net ankle moment determines the current applied to the motor. Using this algorithm implemented in a BiOM T2 prosthesis, we tested subjects during level walking and stair ascent. During level walking at variable speeds, the WFH algorithm produced plantarflexion angles (range = -8 to -19°) and ankle moments (range = 1 to 1.5 Nm/kg) similar to those produced by the BiOM T2 stock controller and to people with no amputation. During stair ascent, the WFH algorithm produced plantarflexion angles (range -15 to -19°) that were similar to persons with no amputation and were ~5 times larger on average at 80 steps/min than those produced by the stock controller. This case study provides proof-of-concept that, by emulating muscle properties, the WFH algorithm provides robust, adaptive control of level walking at variable speed and stair ascent with minimal sensing and no change in parameters.