The great promise of hydrogen energy and hydrogen production from water through proton exchange membrane (PEM) or membrane-free electrolysis drives the pursuit of highly active and acid-stable electrocatalysts with dual functionality and reduced cost for overall water splitting in acidic media. Here, we report a new Ru-modified cobalt-based electrocatalyst embedded in a nitrogen-doped carbon (NC) matrix with rationally designed Mott−Schottky heterostructure to realize high activity and stability toward overall water splitting in a strongly acidic environment. Such a composite was facilely prepared by carbonization of cobalt-based MOF, followed by galvanic exchange between cobalt and Ru, and then controlled partial oxidation. The partial oxidation of RuCo implanted inside the NC matrix led to the formation of a class of RuO 2 / Co 3 O 4 −RuCo@NC composites with rich metal−semiconductor interfaces to facilitate the charge-transfer process. As a result, the composite displayed remarkable electrocatalytic activity toward both oxygen/hydrogen evolutions in acidic media. Significantly, they also afforded low overpotentials of 247 and 141 mV for OER and HER, respectively, and a cell voltage of 1.66 V for overall water splitting at 10 mA cm −2 . In addition, excellent operation stability in 0.5 M H 2 SO 4 solutions, comparable to those of them working in alkaline conditions, is demonstrated due to the protection of a coated carbon thin film. The presented work opens a new opportunity toward designing bifunctional electrocatalysts for acidic water electrolysis.

Abstract The synapse is one of the fundamental elements in human brain performing functions such as learning, memorizing, and visual processing. The implementation of synaptic devices to realize neuromorphic computing and sensing tasks is a key step to artificial intelligence, which, however, has been bottlenecked by the complex circuitry and device integration. We report a high-performance charge-trapping memory synaptic device based on two-dimensional (2D) MoS2 and high-k Ta2O5–TiO2 (TTO) composite to build efficient and reliable neuromorphic system, which can be modulated by both electrical and optical stimuli. Significant and essential synaptic behaviors including short-term plasticity, long-term potentiation, and long-term depression have been emulated. Such excellent synaptic behaviors originated from the good nonvolatile memory performance due to the high density of defect states in the engineered TTO composite. The 2D synaptic device also exhibits effective switching by incident light tuning, which further enables pattern recognition with accuracy rate reaching 100%. Such experimental demonstration paves a robust way toward a multitask neuromorphic system and opens up potential applications in future artificial intelligence and sensing technology.

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A constant energy supply is crucial for the exploration of deep‐sea extreme environments, and a self‐powered energy conversion device is ideal for this situation. Dissolved‐oxygen seawater batteries (SWBs) that generate electricity by reducing the dissolved oxygen are promising candidates but the ultralow oxygen concentration in deep sea limits the reaction kinetics. As a result, oxygenophilic electrocatalysts for lean‐oxygen conditions are urgently needed. A microwave heating method is reported that achieves the ultrafast synthesis of atomic dispersed FeNC catalyst (FeNgraphene (G)/carbon nanotube (CNT)), which possesses high activity and strong oxygenophilic interface between graphene and CNTs. DFT calculations and experimental results both show that the high oxygenophilicity is due to the double‐adsorption sites on the G/CNT interface, and the high activity FeN4 active sites is caused by the charge separation. FeNG/CNT catalysts have an outstanding oxygen reduction reaction (ORR) performance in both O2‐saturated alkaline medium and neutral seawater with half‐wave potentials (E1/2) of 0.929 and 0.704 V, respectively, far better than commercial Pt/C. A SWB shows excellent performance in lean‐oxygen seawater (≈0.4 mg L−1), with a discharge voltage of 1.18 V at 10 mA cm−2. These results suggest a critical role for oxygenophilic catalyst specifically for SWBs under lean‐oxygen conditions.

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Synaptic devices are necessary to meet the growing demand for the smarter and more efficient system. In this work, the anisotropic rhenium disulfide (ReS 2) is used as a channel material to construct a synaptic device and successfully emulate the long-term potentiation/depression behavior. To demonstrate that our device can be used in a large-scale neural network system, 165 pictures from Yale Face database are selected for evaluation, of which 120 pictures are used for artificial neural network (ANN) training, and the remaining 45 pictures are used for ANN testing. A three-layer ANN containing more than 10 5 weights is proposed for the face recognition task. Also 120 continuous modulated conductance states are selected to replace weights in our well-trained ANN. The results show that an excellent recognition rate of 100% is achieved with only 120 conductance states, which proves a high potential of our device in the artificial neural network field.

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