Fast-Charging Lithium-Ion Batteries Enabled by Magnetically Aligned Electrodes.

With the increasing popularity of electric transportation over the past several years, fast-charging lithium-ion batteries are highly demanded for shortening electric vehicles' charging time. Extensive efforts have been made on material development and electrode engineering; however, few of them are scalable and cost-effective enough to be potentially incorporated into the current battery production. Here, we propose a facile magnetic templating method for preparing LiFePO (LFP) cathodes with vertically aligned graphene sheets to realize fast-charging properties at a practical loading of 20 mg cm.

Structured Liquid--Liquid Emulsion Stabilized by Surface-Engineered Liquid Metal Droplets as "Mutant" Pickering Emulsifiers.

Liquid metals (LMs), i.e., metals and alloys that exist in a liquid state at room temperature, have recently attracted considerable attention owing to their electronic and rheological properties useful in various cutting-edge technologies.

Programmable scanning diffuse speckle contrast imaging of cerebral blood flow.

Significance: Cerebral blood flow (CBF) imaging is crucial for diagnosing cerebrovascular diseases. However, existing large neuroimaging techniques with high cost, low sampling rate, and poor mobility make them unsuitable for continuous and longitudinal CBF monitoring at the bedside.

Aim: We aimed to develop a low-cost, portable, programmable scanning diffuse speckle contrast imaging (PS-DSCI) technology for fast, high-density, and depth-sensitive imaging of CBF in rodents.

Deep-Learning-Enabled Fast Raman Identification of the Twist Angle of Bi-Layer Graphene.

Twisted bilayer graphene (TBG) has drawn considerable attention due to its angle-dependent electrical, optical, and mechanical properties, yet preparing and identifying samples at specific angles on a large scale remains challenging and labor-intensive. Here, a data-driven strategy that leverages Raman spectroscopy is proposed in combination with deep learning to rapidly and non-destructively decode and predict the twist angle of TBG across the full angular range. By processing high-dimensional Raman data, the deep learning model extracts hidden information to achieve precise twist angle identification.

Cryogenic in-memory computing using magnetic topological insulators.

Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.