With the rapid iteration of portable electronics and electric vehicles, developing high-capacity batteries with ultra-fast charging capability has become a holy grail. Here we report rechargeable aluminum-ion batteries capable of reaching a high specific capacity of 200 mAh g−1. When liquid metal is further used to lower the energy barrier from the anode, fastest charging rate of 104 C (duration of 0.35 s to reach a full capacity) and 500% more specific capacity under high-rate conditions are achieved. Phase boundaries from the active anode are believed to encourage a high-flux charge transfer through the electric double layers. As a result, cationic layers inside the electric double layers responded with a swift change in molecular conformation, but anionic layers adopted a polymer-like configuration to facilitate the change in composition.

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Engineering the electric double layer (EDL) next to the electrode surface as an impedance-dominating location inside an ionogel provides us an opportunity to detect sound underwater, especially sound from different directions. In response to these vector stimuli, subtle changes in the interface/EDL were easily captured by high-frequency alternating current (AC) modulations. In contrast to capacitive mechanisms under direct current (DC) operations, this AC mode generates an electric field at the interface which is orders of magnitude weaker than its DC counterpart. This removes any electrochemical reaction in the electrolytic environment, resulting in an exceptional signal-to-noise ratio (SNR) over 3000 min of continuous operations. Moreover, this ionogel-based microphone is found to be responsive to the whole range of low-frequency sounds, producing 60 dB (1000 times) stronger signals than the commercial hydrophone. Another unique feature of this microphone is its directivity even when the wavelength of the incoming sound far exceeds the size of the device, filling a property gap that affects the latest piezoelectric ceramic-based sound navigation ranging (SONARs).

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Dendrites are important microstructures in single-crystal superalloys. The distribution of dendrites is closely related to the heat treatment process and mechanical properties of single-crystal superalloys. The primary dendrite arm spacing (PDAS) is an important length scale to describe the distribution of dendrites. In this work, the second-generation single crystal superalloy HT901 with a diameter of 15 mm was imaged under a metallurgical microscope. An automatic dendrite core identification and full-field quantitative statistical analysis method is proposed to automatically detect the dendrite core and calculate the local PDAS. The Faster R-CNN algorithm combined with test time augmentation (TTA) technology is used to automatically identify the dendrite cores. The local multi-directional algorithm combined with Voronoi tessellation is used to determine the local nearest neighbor dendrite and calculate the local PDAS and coordination number. The accuracy of using Faster R-CNN combined with TTA to detect the dendrite core of HT901 reaches 98.4%, which is 15.9% higher than using Faster R-CNN alone. The algorithm calculates the local PDAS of all dendrites in H901 and captures the Gaussian distribution of the local PDAS. The average PDAS determined by the Gaussian distribution is 415 μm, which is only a small difference from the average spacing λ¯ (420 μm) calculated by the traditional method. The technology analyzes the relationship between the local PDAS and the distance from the center of the sample. The local PDAS near the center of HT901 are larger than those near the edge. The results suggests that the method enables the rapid, accurate and quantitative dendritic distribution characterization.

Designing and fabricating solid-state batteries with a high-rate capability and a long cycle life remains a feat. Here, for the first time, a free-standing gel polymer electrolyte (GPE) that holds...

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