A Typha Angustifolia-like MoS2/carbon nanofiber composite for high performance Li-S batteries

A Typha Angustifolia-like MoS2/carbon nanofiber composite as both a chemically trapping agent and redox conversion catalyst for lithium polysulfides has been successfully synthesized via a simple hydrothermal method. Cycling performance and coulombic efficiency have been improved significantly by applying the Typha Angustifolia-like MoS2/carbon nanofiber as the interlayer of a pure sulfur cathode, resulting in a capacity degradation of only 0.09% per cycle and a coulombic efficiency which can reach as high as 99%

FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders

This paper proposes a general spectral analysis framework that thwarts a security risk in federated Learning caused by groups of malicious Byzantine attackers or colluders, who conspire to upload vicious model updates to severely debase global model performances. The proposed framework delineates the strong consistency and temporal coherence between Byzantine colluders' model updates from a spectral analysis lens, and, formulates the detection of Byzantine misbehaviours as a community detection problem in weighted graphs. The modified normalized graph cut is then utilized to discern attackers from benign participants. Moreover, the Spectral heuristics is adopted to make the detection robust against various attacks. The proposed Byzantine colluder resilient method, i.e., FedCut, is guaranteed to converge with bounded errors. Extensive experimental results under a variety of settings justify the superiority of FedCut, which demonstrates extremely robust model performance (MP) under various attacks. It was shown that FedCut's averaged MP is 2.1% to 16.5% better than that of the state of the art Byzantine-resilient methods. In terms of the worst-case model performance (MP), FedCut is 17.6% to 69.5% better than these methods

RIDERS: Radar-Infrared Depth Estimation for Robust Sensing

Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning. Adverse weather conditions, including haze, dust, rain, snow, and darkness, introduce significant challenges to accurate dense depth estimation, thereby posing substantial safety risks in autonomous driving. These challenges are particularly pronounced for traditional depth estimation methods that rely on short electromagnetic wave sensors, such as visible spectrum cameras and near-infrared LiDAR, due to their susceptibility to diffraction noise and occlusion in such environments. To fundamentally overcome this issue, we present a novel approach for robust metric depth estimation by fusing a millimeter-wave Radar and a monocular infrared thermal camera, which are capable of penetrating atmospheric particles and unaffected by lighting conditions. Our proposed Radar-Infrared fusion method achieves highly accurate and finely detailed dense depth estimation through three stages, including monocular depth prediction with global scale alignment, quasi-dense Radar augmentation by learning Radar-pixels correspondences, and local scale refinement of dense depth using a scale map learner. Our method achieves exceptional visual quality and accurate metric estimation by addressing the challenges of ambiguity and misalignment that arise from directly fusing multi-modal long-wave features. We evaluate the performance of our approach on the NTU4DRadLM dataset and our self-collected challenging ZJU-Multispectrum dataset. Especially noteworthy is the unprecedented robustness demonstrated by our proposed method in smoky scenarios. Our code will be released at \url{https://github.com/MMOCKING/RIDERS}.Comment: 13 pages, 13 figure

Interface Engineering via Ti3C2Tx MXene Electrolyte Additive toward Dendrite-Free Zinc Deposition

Zinc metal batteries have been considered as a promising candidate for next-generation batteries due to their high safety and low cost. However, their practical applications are severely hampered by the poor cyclability that caused by the undesired dendrite growth of metallic Zn. Herein, Ti3C2Tx MXene was first used as electrolyte additive to facilitate the uniform Zn deposition by controlling the nucleation and growth process of Zn. Such MXene additives can not only be absorbed on Zn foil to induce uniform initial Zn deposition via providing abundant zincophilic-O groups and subsequently participate in the formation of robust solid-electrolyte interface film, but also accelerate ion transportation by reducing the Zn2+ concentration gradient at the electrode/electrolyte interface. Consequently, MXene-containing electrolyte realizes dendrite-free Zn plating/striping with high Coulombic efficiency (99.7%) and superior reversibility (stably up to 1180 cycles). When applied in full cell, the Zn-V2O5 cell also delivers significantly improved cycling performances. This work provides a facile yet effective method for developing reversible zinc metal batteries

Multi-Core-shell structured LiFePO4@Na3V2(PO4)3@C composite for enhanced low-temperature performance of lithium ion batteries

In this work, a multi-core–shell-structured LiFePO4@Na3V2(PO4)3@C (LFP@NVP@C) composite was successfully designed and prepared to address inferior low-temperature performance of LiFePO4 cathode for lithium-ion batteries. Transmission electron microscopy (TEM) confirms the inner NVP and outer carbon layers co-existed on the surface of LFP particle. When evaluated at low-temperature operation, LFP@NVP@C composite exhibits an evidently enhanced electrochemical performance in term of higher capacity and lower polarization, compared with LFP@C. Even at − 10 °C with 0.5C, LFP@NVP@C delivers a discharge capacity of ca. 96.9 mAh·g−1 and discharge voltage of ca. 3.3 V, which is attributed to the beneficial contribution of NVP coating. NASICON-structured NVP with an open framework for readily insertion/desertion of Li+ will effectively reduce the polarization for the electrochemical reactions of the designed LFP@NVP@C composite

Half-Sphere Shell Supported Pt Catalyst for Electrochemical Methanol Oxidation

Bi-functional effect, elevated mass transport and increased durability have been combined within one catalyst for electrochemical methanol oxidation reaction. It has niobium (Nb) doped titanium dioxides (TiO2) nanosized half-sphere shell (HSS) as the substrate material deposited with small amount of Pt nanoparticles. These specially designed HSS nanostructure has significantly increased surface areas which are suitable for Pt nanoparticles to be deposited onto them to form the catalyst denoted as Pt/Nb-TiO2 HSS. It exhibits a remarkably high methanol oxidation activity of 0.21 V vs. RHE which is 0.05 V lower than HiSPEC10000 PtRu/C catalyst, due to the substrate's strong metal support interactions effect, bi-functional effect and the special structure. These HSS nanostructures have also increased the methanol diffusion and mass transport within the anode to give a maximum power output of 0.0931 W of cathode polarization in miniature direct methanol fuel cell (DMFC). It also acts as protection shells, which minimises the dissolution of Pt metal nanoparticles to prevent its diffusion through the membrane

Electrolyte Salts and Additives Regulation Enables High Performance Aqueous Zinc Ion Batteries: A Mini Review

Aqueous zinc ion batteries (ZIBs) are regarded as one of the most ideally suited candidates for large-scale energy storage applications owning to their obvious advantages, that is, low cost, high safety, high ionic conductivity, abundant raw material resources, and eco-friendliness. Much effort has been devoted to the exploration of cathode materials design, cathode storage mechanisms, anode protection as well as failure mechanisms, while inadequate attentions are paid on the performance enhancement through modifying the electrolyte salts and additives. Herein, to fulfill a comprehensive aqueous ZIBs research database, a range of recently published electrolyte salts and additives research is reviewed and discussed. Furthermore, the remaining challenges and future directions of electrolytes in aqueous ZIBs are also suggested, which can provide insights to push ZIBs’ commercialization

Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-P\'erot laser with saturable absorber

Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.Comment: 10 pages, 8 figure

Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on seed examples or existing datasets to construct instruction tuning data, GLAN exclusively utilizes a pre-curated taxonomy of human knowledge and capabilities as input and generates large-scale synthetic instruction data across all disciplines. Specifically, inspired by the systematic structure in human education system, we build the taxonomy by decomposing human knowledge and capabilities to various fields, sub-fields and ultimately, distinct disciplines semi-automatically, facilitated by LLMs. Subsequently, we generate a comprehensive list of subjects for every discipline and proceed to design a syllabus tailored to each subject, again utilizing LLMs. With the fine-grained key concepts detailed in every class session of the syllabus, we are able to generate diverse instructions with a broad coverage across the entire spectrum of human knowledge and skills. Extensive experiments on large language models (e.g., Mistral) demonstrate that GLAN excels in multiple dimensions from mathematical reasoning, coding, academic exams, logical reasoning to general instruction following without using task-specific training data of these tasks. In addition, GLAN allows for easy customization and new fields or skills can be added by simply incorporating a new node into our taxonomy.Comment: Work in progres