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Although overall battery performance is limited by the electrochemistry of the component materials, the actual performance can be limited by a number of factors. Zhu et al. review different electrode architectures for lithium-ion batteries. In particular, they look at the relations between the kinetics and dimensionality of the different electrode constituents. Making things smaller can improve transport of electrons and ions, but at the cost of making the overall architecture more complex. The authors discuss the overall design rules and criteria to guide battery design. Science , this issue p. eaao2808 Developing high-performance, affordable, and durable batteries is one of the decisive technological tasks of our generation. Here, we review recent progress in understanding how to optimally arrange the various necessary phases to form the nanoscale structure of a battery electrode. The discussion begins with design principles for optimizing electrode kinetics based on the transport parameters and dimensionality of the phases involved. These principles are then used to review and classify various nanostructured architectures that have been synthesized. Connections are drawn to the necessary fabrication methods, and results from in operando experiments are highlighted that give insight into how electrodes evolve during battery cycling.

Potassium‐ion batteries, which possess unique advantages such as lower K+/K redox potential compared to sodium and superior interfacial charge transfer dynamics, demonstrate considerable viability for grid‐level energy storage deployment. However, the development of potassium electrodes remains constrained by sluggish solid‐state diffusion of K+ within electrodes and progressive structural failure by the large volume variation during (de)intercalation, which requires a thorough understanding ionic transport, size effects, and electro–chemo–mechanical properties of electrodes, to achieve rational design and controlled synthesis. This review initiated with a comprehensive evaluation of potassium‐based batteries from five aspects: energy density, power density, cycle life, safety, and cost. Afterward, a systematical examination for key aspects of potassium electrodes from a unique perspective is provided, starting with the fundamental scientific issues of transport properties (key features, anomalous cases, regulation, measurement and prediction) and size effects (kinetics, thermodynamics, potassium storage and transport mechanisms), while further discussing the specific electro–chemo–mechanical properties (composition–structure regulation, nanostructure and interface engineering). Additionally, this review highlights the construction of high‐entropy electrodes and the pivotal role of machine learning in developing potassium electrodes. This review aims to provide critical guidance for future basic research and industrial applications of potassium electrode materials. Potassium‐ion battery is a promising candidate for large‐scale energy storage applications. In this review, transport properties, size effects, and electro–chemo–mechanical properties of electrodes are systematically summarized in order to achieve the rational design and controlled synthesis of potassium electrodes.

The practical application of aqueous zinc-ion batteries (ZIBs) is limited by the growth of dendrite during cycling. How to rationally design and construct an efficient artificial interface layer by selecting suitable building units to control the dendrite growth is still a challenge. Herein, a porous boron nitride nanofibers (BNNFs) artificial interface layer was constructed, and its working mechanisms were revealed by both experiments (electrochemical characterization and in-situ optical microscope) and theoretical calculations (density functional theory (DFT) and finite element simulation). The insulated BNNFs layer leads to position-selected electroplating between BNNFs layer and Zn foil. The unique negatively charged surface and porosity of BNNFs contribute to the self-concentrating and pumping features of Zn ions, thus suppressing the concentration polarization on the Zn surface. Additionally, densely arranged porous BNNFs have a shunt effect on Zn ions diffusion, resulting in uniform distributions of Zn ions and electric field. The introduced BNNFs layer not only makes Zn deposition uniform but also restrains the dendrite growth, therefore the Zn+BNNFs symmetric cells perform ultralong stable cycling for 1,600 h at 1 mA·cm −2 and more than 500 h at 10 mA·cm −2 . Moreover, Zn+BNNFs‖CNT/MnO 2 battery presents a high initial capacity of 293.6mAh·g −1 and an excellent retention rate of 97.6% at 1 A·g −1 after 400 cycles, while Zn ‖ CNT/MnO 2 battery only maintains 37.1% discharge capacity. This artificial interface layer with negatively charged BNNFs exhibits excellent dendrite-inhibit and may have enormous prospects in other metal batteries.

Transition metal sulfides have a great potential for energy storage due to the pronouncedly higher capacity (owing to conversion to metal or even alloy) than traditional insertion electrode materials. However, the poor cycling stability still limits the development and application in lithium and sodium ion batteries. Here, taking SnS as a model material, a novel general strategy is proposed to fabricate a 3D porous interconnected metal sulfide/carbon nanocomposite by the electrostatic spray deposition technique without adding any expensive carbonaceous materials such as graphene or carbon nanotube. In this way, small nanorods of SnS are generated with sizes of ≈10–20 nm embedded in amorphous carbon and self‐assembled into a 3D porous interconnected nanocomposite. The SnS:C is directly deposited on the Ti foil as a current collector and neither conductive additives nor binder are needed for battery assembly. Such electrodes exhibit a high reversible capacity, high rate capability, and long cycling stability for both lithium and sodium storage. A novel general strategy for fabrication of a 3D porous interconnected metal sulfide/carbon nanocomposite is proposed, using the electrostatic spray deposition technique without adding any expensive carbonaceous materials such as graphene or carbon nanotubes. Such SnS/C composite exhibits a high reversible capacity, high rate capability and long cycling stability for both lithium and sodium storage.

A novel generalizable strategy is proposed by Y. Yu and co‐workers in article 1500200 for fabrication of a 3D porous interconnected metal sulfide/carbon nanocomposite by the electrostatic spray deposition technique. Such a technique is simple but versatile and can be easily applied to other compounds. The obtained SnS/C composite exhibits a high reversible capacity, high rate capability and long cycling stability for both lithium and sodium storage.

The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic echo. However, the multiplicative transfer function (MTF) approximation makes it unsuitable for real-time applications especially in highly reverberant environments, and the natural gradient makes it hard to balance well between fast convergence speed and stability. In this paper, we propose two more effective SBSS methods based on auxiliary-function-based independent vector analysis (AuxIVA) and independent low-rank matrix analysis (ILRMA). The convolutive transfer function (CTF) approximation is used instead of MTF so that a long impulse response can be modeled with a short latency. The optimization schemes used in AuxIVA and ILRMA are carefully regularized according to the constrained demixing matrix of NAEC. Experimental results validate significantly better echo cancellation performance of the proposed methods.

Flow matching and diffusion bridge models have emerged as leading paradigms in generative speech enhancement, modeling stochastic processes between paired noisy and clean speech signals based on principles such as flow matching, score matching, and Schr\"odinger bridge. In this paper, we present a framework that unifies existing flow and diffusion bridge models by interpreting them as constructions of Gaussian probability paths with varying means and variances between paired data. Furthermore, we investigate the underlying consistency between the training/inference procedures of these generative models and conventional predictive models. Our analysis reveals that each sampling step of a well-trained flow or diffusion bridge model optimized with a data prediction loss is theoretically analogous to executing predictive speech enhancement. Motivated by this insight, we introduce an enhanced bridge model that integrates an effective probability path design with key elements from predictive paradigms, including improved network architecture, tailored loss functions, and optimized training strategies. Experiments on denoising and dereverberation tasks demonstrate that the proposed method outperforms existing flow and diffusion baselines with fewer parameters and reduced computational complexity. The results also highlight that the inherently predictive nature of this generative framework imposes limitations on its achievable upper-bound performance.

Lightweight models are essential for real-time speech enhancement applications. In recent years, there has been a growing trend toward developing increasingly compact models for speech enhancement. In this paper, we propose an Ultra-Lightweight U-net optimized by Network Architecture Search (UL-UNAS), which is suitable for implementation in low-footprint devices. Firstly, we explore the application of various efficient convolutional blocks within the U-Net framework to identify the most promising candidates. Secondly, we introduce two boosting components to enhance the capacity of these convolutional blocks: a novel activation function named affine PReLU and a causal time-frequency attention module. Furthermore, we leverage neural architecture search to discover an optimal architecture within our carefully designed search space. By integrating the above strategies, UL-UNAS not only significantly outperforms the latest ultra-lightweight models with the same or lower computational complexity, but also delivers competitive performance compared to recent baseline models that require substantially higher computational resources. Source code and audio demos are available at https://github.com/Xiaobin-Rong/ul-unas.

Deep learning-based speech enhancement methods have significantly improved speech quality and intelligibility. Convolutional neural networks (CNNs) have been proven to be essential components of many high-performance models. In this paper, we introduce adaptive convolution, an efficient and versatile convolutional module that enhances the model's capability to adaptively represent speech signals. Adaptive convolution performs frame-wise causal dynamic convolution, generating time-varying kernels for each frame by assembling multiple parallel candidate kernels. A lightweight attention mechanism is proposed for adaptive convolution, leveraging both current and historical information to assign adaptive weights to each candidate kernel. This enables the convolution operation to adapt to frame-level speech spectral features, leading to more efficient extraction and reconstruction. We integrate adaptive convolution into various CNN-based models, highlighting its generalizability. Experimental results demonstrate that adaptive convolution significantly improves the performance with negligible increases in computational complexity, especially for lightweight models. Moreover, we present an intuitive analysis revealing a strong correlation between kernel selection and signal characteristics. Furthermore, we propose the adaptive convolutional recurrent network (AdaptCRN), an ultra-lightweight model that incorporates adaptive convolution and an efficient encoder-decoder design, achieving superior performance compared to models with similar or even higher computational costs.

In this paper, we present TrimTail, a simple but effective emission regularization method to improve the latency of streaming ASR models. The core idea of TrimTail is to apply length penalty (i.e., by trimming trailing frames, see Fig. 1-(b)) directly on the spectrogram of input utterances, which does not require any alignment. We demonstrate that TrimTail is computationally cheap and can be applied online and optimized with any training loss or any model architecture on any dataset without any extra effort by applying it on various end-to-end streaming ASR networks either trained with CTC loss [1] or Transducer loss [2]. We achieve 100 \\(\\sim\\) 200ms latency reduction with equal or even better accuracy on both Aishell-1 and Librispeech. Moreover, by using TrimTail, we can achieve a 400ms algorithmic improvement of User Sensitive Delay (USD) with an accuracy loss of less than 0.2.