The interface engineering and structure design of an alloying-type metal foil anode for lithium ion batteries: a review.

An alloying-type metal foil serves as an integrated anode that is distinct from the prevalent powder-casting production of lithium ion batteries (LIBs) and emerging lithium metal batteries (LMBs), and also its energy density and processing technology can be profoundly developed. However, besides their apparent intriguing advantages of a high specific capacity, electrical conductivity, and the ease of formation, metal foil anodes suffer from slow lithiation kinetics, a trade-off between specific capacity and cycle life, and a low initial Coulombic efficiency (ICE) owing to their multi-scaled structural geometry, huge volume change, and induced interfacial issues during the alloying process. In this review, we attempt to present a comprehensive overview on the recent research progress with respect to alloying-type metal foil anodes toward high-energy-density and low-cost LIBs. The failure mechanism of metal foil anodes during lithiation/delithiation and existing challenges are also summarized. Subsequently, the structural design and interface engineering strategies that have witnessed significant achievements are highlighted, which can promote the practical development of LIBs, including artificial SEI, alloying, structural design, and grain refinement. Furthermore, scientific perspectives are proposed to further improve the overall performance and decouple the complex mechanisms in terms of interdisciplinary fields of electrochemistry, metallic materials science, mechanics, and interfacial science, demonstrating that metal foil anode-based LIBs require more research efforts.

Versatile potassium vanadium fluorophosphate (KVPO4F) composites as Dual-Function cathode and anode materials for Potassium-Ion hybrid capacitors.

Potassium-based energy storage has emerged as a promising alternative for advanced energy storage systems, driven by the abundance of potassium, fast ion migration, and low standard electrode potential. Hybrid capacitors, which combine the desirable characteristics of batteries and supercapacitors, offer a compelling solution for efficient energy storage. In this study, we present the development of versatile composite materials, specifically potassium vanadium fluorophosphate (KVPO4F) composites, utilizing a sol-gel method. These composites enable tunable potassium storage and charge transport kinetics within regulated voltage windows, serving as both cathode and anode materials. The anode composite, composed of KVPO4F and hierarchical porous carbon (HPC), exhibited exceptional stability over 400 cycles within a low-voltage window. On the other hand, the cathode composite, consisting of battery-like KVPO4F and physisorption activated carbon (AC), demonstrated great potential as a cathode material, striking a balance between specific energy and cycle life within a regulated high-voltage window. By integrating KVPO4F/C as the anode and KVPO4F/AC as the cathode, we successfully created potassium-ion hybrid capacitors (PIHCs) that showcased an impressive capacity retention of 83% after 10,000 cycles within a high voltage window of 0.5-4.3 V. Furthermore, to explore the application of these materials in miniaturized energy storage, we fabricated potassium-ion micro hybrid capacitors (PIMHCs) with interdigitated electrodes. These devices exhibited a high areal energy density of 18.8 µWh cm-2 at a power density of 111.6 µW cm-2, indicating their potential for compact energy storage systems. The results of this study demonstrate the versatility and efficacy of the developed KVPO4F composite materials, highlighting their potential for future advancements in potassium-based energy storage technologies.

Low-loss microwave photonic switching for satellite communication.

For traditional switching architecture, packet switching performs fine granularity data packet forwarding, but its digital signal processing (DSP) has high power consumption (PC). All-optical switching provides rapid exchange of wavelength resources, which has coarse granularity. In scenarios where the PC is limited, such as broadband satcom, a switching architecture with lower PC and finer granularity than optical switching would be useful. In this paper, we propose a novel, to the best of our knowledge, low-loss microwave photonic switching architecture that can exchange subband signals across beams and frequency bands. The switching process is realized by exchanging optical carriers instead of payload signals, which does not degrade the signal power, guaranteeing the signal-to-noise ratio (SNR). We conducted a proof-of-concept experiment of 2 × 2 switching with two 1.2-GBaud quadrature phase-shift keying (QPSK) signals; an error vector magnitude (EVM) of or less than 13.87% is realized after forwarding. The proposed system has the advantages of low PC, high SNR, and fine granularity, and is very promising for flexible forwarding in future satcom systems.

The complete plastid genome of Thyrsostachys siamensis (Poaceae, Bambusoideae).

Thyrsostachys is oligotypic genus of Bambusinae, while its phylogenetic position had been unclear. Here, the complete plastid genome of the type species, T. siamensis, was sequenced and analyzed in this work. The complete genome is a typical quadripartite structure with 139,522 bp in length, comprising of a large single-copy region (LSC, 83,032 bp), a small single-copy region (SSC, 12,892 bp), and a pair of invert repeats regions (IR, 21,799 bp). The genome contains 138 genes, 89 protein-coding genes, 41 tRNA genes, and 8 rRNA genes. The GC content of genome was 38.9%. Phylogenetic analysis indicated T. siamensis was sister to Dendrocalamus birmanicus within Bambusinae.

Machine learning-driven new material discovery.

New materials can bring about tremendous progress in technology and applications. However, the commonly used trial-and-error method cannot meet the current need for new materials. Now, a newly proposed idea of using machine learning to explore new materials is becoming popular. In this paper, we review this research paradigm of applying machine learning in material discovery, including data preprocessing, feature engineering, machine learning algorithms and cross-validation procedures. Furthermore, we propose to assist traditional DFT calculations with machine learning for material discovery. Many experiments and literature reports have shown the great effects and prospects of this idea. It is currently showing its potential and advantages in property prediction, material discovery, inverse design, corrosion detection and many other aspects of life.