Electrocatalytic splitting of water is becoming increasingly crucial for renewable energy and device technologies. As one of the most important half-reactions for water splitting reactions, the oxygen evolution reaction (OER) is a kinetically sluggish process that will greatly affect the energy conversion efficiency. Therefore, exploring a highly efficient and durable catalyst to boost the OER is of great urgency. In this work, we develop a facile strategy for the synthesis of well-defined phosphorus and fluorine co-doped Ni Co N hybrid nanorods (HNs) by using ionic liquids (ILs; 1-butyl-3-methylimidazolium hexafluorophosphate). In comparison to the IrO catalyst, the as-obtained PF/Ni Co N HNs manifests a low overpotential of 280 mV at 10 mA cm , Tafel slope of 66.1 mV dec , and excellent durability in 1.0 m KOH solution. Furthermore, the iR-corrected electrochemical results indicate it could achieve a current density of 100 mA cm at an overpotential of 350 mV. The combination of cobalt and nickel elements, 1D mesoporous nanostructure, heteroatom incorporation, and ionic liquid-assisted nitridation, which result in faster charge transfer capability and more active surface sites, can facilitate the release of oxygen bubbles from the catalyst surface. Our findings confirm that surface heteroatom doping in bimetallic nitrides could serve as a new class of OER catalyst with excellent catalytic activity.

The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.

Research on falls in elderly people has a great social significance because of the rapidly growing of the aging population. The pre-impact lead time of fall (PLT) is an important part of the human fall theory. PLT is the longest time for a person who is going to fall to take action in order to prevent the fall or to reduce bodily injuries from the fall impact. However, there is no clear definition of PLT so far. There is also no comparative study for active and passive falls. In this study, we proposed a theoretical definition of the PLT, based on a new method of fall event division. We also compared the differences of PLT and the related angles between active and passive falls. Eight healthy adult subjects were arranged to perform three kinds of activities of daily living (sitting, walking and lying), and two kinds fall activities (active and passive) in three directions (forward, backward and lateral fall). Nine inertial sensor modules were used to measure the body segmental kinematic characteristics of each subject in our experimental activities. In this paper, a fall event was suggested to divide into three or four phases and then the critical phase could be divided into three periods (pre-impact, impact, and post-impact). Two fall models were developed for active and passive falls using acceleration data. The average value of PLT for active falls is about 514 ±112 ms and it is smaller than the value for passive falls, which is 731 ±104 ms. The longest PLTs were measured on the chest or waist instead of other locations, such as the thigh and shank. The PLTs of the three kinds of fall activities were slightly different, but there was a significant difference between two fall modes. The PLT showed the correlation to the body angle at the start of PLT, but it was uncorrelated at the end of PLT. The angles at the start of PLT had slight variations (<10 degrees) from the steady standing state except in passive forward falls (max 16 degrees) due to the self-control. The landing angles were significantly different in the both fall modes in all the three directions of fall, indicating the state of the trunk was uncertain when the hip contacted the ground. It can be concluded that it is feasible to prevent falls by using an early preimpact fall alarm device; the present study provides important reference for development of pre-impact fall alarm devices.

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