Recent developments suggest that the race to power electric vehicles (EV) with solid-state batteries (SSB) has gained momentum. In January 2024, Toyota Motor Corporation (Toyota City, Japan) confirmed its previously stated plans to start producing SSB EV in the 2027-2028 timeframe [1] . In May 2024, it emerged that the Chinese government plans to invest more than six billion CNY (830 million USD) in projects intended to speed up SSB development [2] . In June 2024, the automaker Nio (Shanghai, China) began supplying customers with EVs containing “semi-solid-state” batteries—a hybrid technology that could serve as a stepping stone to fully solid versions [3] . In September 2024, SAIC Motor (Shanghai, China), China’s largest automobile manufacturer, announced that it would deliver its first SSB-powered vehicles in 2025 [4] .
On 9 October 2024, in a high-profile vote of confidence for the promise of using artificial intelligence (AI) in scientific discovery, the Royal Swedish Academy of Sciences awarded Demis Hassabis (co-founder and chief executive officer) and John M. Jumper (director) of Google DeepMind (London, UK) the 2024 Nobel Prize in Chemistry for their pioneering work in developing the AI-powered protein structure prediction model AlphaFold2 (AF2) [1] . Also sharing the prize was David Baker (half to Hassabis and Jumper; half to Baker), professor of biochemistry at the University of Washington (Seattle, WA, USA), for his work on computational protein design that started with the mid-1990s development of Rosetta, a since-evolving suite of software tools that model protein structures using physical principles [2] —and now also AI [3] .
In February 2024, 192 lasers at the National Ignition Facility (NIF) in Livermore, CA, USA, began pouring 2.2 MJ of energy into a gold container smaller than the tip of a person’s little finger, heating it to more than three million degrees (
The Thwaites Glacier in western Antarctica (
Meta-devices have significantly revitalized the study of nonlinear optical phenomena. At the nanoscale, the detrimental effects of phase mismatching between fundamental and harmonic waves can be substantially reduced. This review analyzes the theoretical frameworks of how plasmonic and dielectric materials induce nonlinear optical properties. Plasmonic and dielectric nonlinear meta-devices that can excite strong resonant modes for efficiency enhancement are explored. We outline different strategies designed to shape the radiation pattern in order to increase the collection capability of nonlinear signals emitted from meta-devices. In addition, we discuss how nonlinear phase manipulation in meta-devices can integrate the benefits of efficiency enhancement and radiation shaping, not only boosting the energy density of the nonlinear signal but also facilitating a wide range of applications. Finally, potential research directions within this field are discussed.
The unique property of chirality is widely used in various fields. In the past few decades, a great deal of research has been conducted on the interactions between light and matter, resulting in significant technical advancements in the precise manipulation of light field wavefronts. In this review, which focuses on current chiral optics research, we introduce the fundamental theory of chirality and highlight the latest achievements in enhancing chiral signals through artificial nano-manufacturing technology, with a particular focus on mechanisms such as light scattering and Mie resonance used to amplify chiral signals. By providing an overview of enhanced chiral signals, this review aims to provide researchers with an in-depth understanding of chiral phenomena and its versatile applications in various domains.
Optical singularities are topological defects of electromagnetic fields; they include phase singularity in scalar fields, polarization singularity in vector fields, and three-dimensional (3D) singularities such as optical skyrmions. The exploitation of photonic microstructures to generate and manipulate optical singularities has attracted wide research interest in recent years, with many photonic microstructures having been devised to this end. Accompanying these designs, scattered phenomenological theories have been proposed to expound the working mechanisms behind individual designs. In this work, instead of focusing on a specific type of microstructure, we concentrate on the most common geometric features of these microstructures—namely, symmetries—and revisit the process of generating optical singularities in microstructures from a symmetry viewpoint. By systematically employing the projection operator technique in group theory, we develop a widely applicable theoretical scheme to explore optical singularities in microstructures with rosette (i.e., rotational and reflection) symmetries. Our scheme agrees well with previously reported works and further reveals that the eigenmodes of a symmetric microstructure can support multiplexed phase singularities in different components, such as out-of-plane, radial, azimuthal, and left- and right-handed circular components. Based on these phase singularities, more complicated optical singularities may be synthesized, including C points, V points, L lines, Néel- and bubble-type optical skyrmions, and optical lattices, to name a few. We demonstrate that the topological invariants associated with optical singularities are protected by the symmetries of the microstructure. Lastly, based on symmetry arguments, we formulate a so-called symmetry matching condition to clarify the excitation of a specific type of optical singularity. Our work establishes a unified theoretical framework to explore optical singularities in photonic microstructures with symmetries, shedding light on the symmetry origin of multidimensional and multiplexed optical singularities and providing a symmetry perspective for exploring many singularity-related effects in optics and photonics.
Highlights
Developing a unified theoretical scheme to explore multidimensional optical singularities generated by photonic microstructures with Rosette symmetries.
Revealing various multiplexed optical singularities in different dimensions intrinsically supported by the microstructures, including phase singularities, C points, V points, L lines, Néel- and bubble-type optical skyrmions, and singularity-related optical lattices.
Shedding light on the symmetry origins of multidimensional optical singularities.
Unveiling the excitation condition of optical singularities, which can serve as a general principle to derive various selection rules in the process of photonic spin-orbit interaction.
Object imaging beyond the direct line of sight is significant for applications in robotic vision, remote sensing, autonomous driving, and many other areas. Reconstruction of a non-line-of-sight (NLOS) screen is a complex inverse problem that comes with ultrafast time-resolved imager requirements and substantial computational demands to extract information from the multi-bounce scattered light. Consequently, the echo signal always suffers from serious deterioration in both intensity and shape, leading to limited resolution and image contrast. Here, we propose a concept of vectorial digitelligent optics for high-resolution NLOS imaging to cancel the wall’s scattering and refocus the light onto hidden targets for enhanced echo. In this approach, the polarization and wavefront of the laser spot are intelligently optimized via a feedback algorithm to form a near-perfect focusing pattern through a random scattering wall. By raster scanning the focusing spot across the object’s surface within the optical-memory-effect range of the wall, we obtain nearly diffraction-limited NLOS imaging with an enhanced signal-to-noise ratio. Our experimental results demonstrate a resolution of 0.40 mm at a distance of 0.35 m, reaching the diffraction limit of the system. Furthermore, we demonstrate that the proposed method is feasible for various complex NLOS scenarios. Our methods may open an avenue for active imaging, communication, and laser wireless power transfer.
Optical data storage (ODS) is a low-cost and high-durability counterpart of traditional electronic or magnetic storage. As a means of enhancing ODS capacity, the multiple recording layer (MRL) method is more promising than other approaches such as reducing the recording volume and multiplexing technology. However, the architecture of current MRLs is identical to that of recording data into physical layers with rigid space, which leads to either severe interlayer crosstalk or finite recording layers constrained by the short working distances of the objectives. Here, we propose the concept of hybrid-layer ODS, which can record optical information into a physical layer and multiple virtual layers by using high-orthogonality random meta-channels. In the virtual layer, 32 images are experimentally reconstructed through holography, where their holographic phases are encoded into 16 printed images and complementary images in the physical layer, yielding a capacity of 2.5 Tbit·cm−3. A higher capacity is achievable with more virtual layers, suggesting hybrid-layer ODS as a possible candidate for next-generation ODS.
Advancements in mode-division multiplexing (MDM) techniques, aimed at surpassing the Shannon limit and augmenting transmission capacity, have garnered significant attention in optical fiber communication, propelling the demand for high-quality multiplexers and demultiplexers. However, the criteria for ideal-mode multiplexers/demultiplexers, such as performance, scalability, compatibility, and ultra-compactness, have only partially been achieved using conventional bulky devices (e.g., waveguides, gratings, and free space optics)—an issue that will substantially restrict the application of MDM techniques. Here, we present a neuro-meta-router (NMR) optimized through deep learning that achieves spatial multi-mode division and supports multi-channel communication, potentially offering scalability, compatibility, and ultra-compactness. An MDM communication system based on an NMR is theoretically designed and experimentally demonstrated to enable simultaneous and independent multi-dataset transmission, showcasing a capacity of up to 100 gigabits per second (Gbps) and a symbol error rate down to the order of 10-4, all achieved without any compensation technologies or correlation devices. Our work presents a paradigm that merges metasurfaces, fiber communications, and deep learning, with potential applications in intelligent metasurface-aided optical interconnection, as well as all-optical pattern recognition and classification.
Efficient utilization of electrostatic charges is paramount for numerous applications, from printing to kinetic energy harvesting. However, existing technologies predominantly focus on the static qualities of these charges, neglecting their dynamic capabilities as carriers for energy conversion. Herein, we report a paradigm-shifting strategy that orchestrates the swift transit of surface charges, generated through contact electrification, via a freely moving droplet. This technique ingeniously creates a bespoke charged surface which, in tandem with a droplet acting as a transfer medium to the ground, facilitates targeted charge displacement and amplifies electrical energy collection. The spontaneously generated electric field between the charged surface and needle tip, along with the enhanced water ionization under the electric field, proves pivotal in facilitating controlled charge transfer. By coupling the effects of charge self-transfer, contact electrification, and electrostatic induction, a dual-electrode droplet-driven-tribo-electric nanogenerator (DD-TENG) is designed to harvest the water-related energy, exhibiting a two-order-of-magnitude improvement in electrical output compared to traditional single-electrode systems. Our strategy establishes a fundamental groundwork for efficient water drop energy acquisition, offering deep insights and substantial utility for future interdisciplinary research and applications in energy science.
Assessing the benefits and costs of digitalization in the energy industry is a complex issue. Traditional cost-benefit analysis (CBA) might encounter problems in addressing uncertainties, dynamic stakeholder interactions, and feedback loops arising out of the evolving nature of digitalization. This paper introduces a methodological framework to help address the intricate inter connections between digital applications and business models in the energy industry. The proposed framework leverages system dynamics to achieve two primary objectives. It investigates how digitalization generally influences the value proposition, value capture, and value creation dimensions of business models. It also quantifies the financial and social impacts of digitalization from a dynamic perspective. The proposed dynamic CBA allows for a more precise quantification of the benefits and costs, associated with evidence-based decision-making. Findings from an illustrative case study challenge the static assumptions of conventional methods. These methods often presume continuous operation, neglecting reinvestment and operational feedback loops, and resulting in negative net present values. Conversely, the outcomes of the proposed method indicate positive net present values when accounting for factors such as reinvestment rates and the willingness to invest in digitalization projects. The principles outlined in this paper can enable a more accurate assessment of digitalization projects, thus catalyzing the development of new CBA applications and guidelines for digitalization.