MS23D: A 3D object detection method using multi-scale semantic feature points to construct 3D feature layer.

LiDAR point clouds can effectively depict the motion and posture of objects in three-dimensional space. Many studies accomplish the 3D object detection by voxelizing point clouds. However, in autonomous driving scenarios, the sparsity and hollowness of point clouds create some difficulties for voxel-based methods. The sparsity of point clouds makes it challenging to describe the geometric features of objects. The hollowness of point clouds poses difficulties for the aggregation of 3D features. We propose a two-stage 3D object detection framework, called MS23D. (1) We propose a method using voxel feature points from multi-branch to construct the 3D feature layer. Using voxel feature points from different branches, we construct a relatively compact 3D feature layer with rich semantic features. Additionally, we propose a distance-weighted sampling method, reducing the loss of foreground points caused by downsampling and allowing the 3D feature layer to retain more foreground points. (2) In response to the hollowness of point clouds, we predict the offsets between deep-level feature points and the object's centroid, making them as close as possible to the object's centroid. This enables the aggregation of these feature points with abundant semantic features. For feature points from shallow-level, we retain them on the object's surface to describe the geometric features of the object. To validate our approach, we evaluated its effectiveness on both the KITTI and ONCE datasets.

Efficient three-dimensional point cloud object detection based on improved Complex-YOLO.

Lidar-based 3D object detection and classification is a critical task for autonomous driving. However, inferencing from exceedingly sparse 3D data in real-time is a formidable challenge. Complex-YOLO solves the problem of point cloud disorder and sparsity by projecting it onto the bird's-eye view and realizes real-time 3D object detection based on LiDAR. However, Complex-YOLO has no object height detection, a shallow network depth, and poor small-size object detection accuracy. To address these issues, this paper has made the following improvements: (1) adds a multi-scale feature fusion network to improve the algorithm's capability to detect small-size objects; (2) uses a more advanced RepVGG as the backbone network to improve network depth and overall detection performance; and (3) adds an effective height detector to the network to improve the height detection. Through experiments, we found that our algorithm's accuracy achieved good performance on the KITTI dataset, while the detection speed and memory usage were very superior, 48FPS on RTX3070Ti and 20FPS on GTX1060, with a memory usage of 841Mib.

Mapping and Modeling Physicochemical Fields in Solid-State Batteries.

The safety and energy density of solid-state batteries can be, in principle, substantially increased compared with that of conventional lithium-ion batteries. However, the use of solid-state electrolytes instead of liquid electrolytes introduces pronounced complexities to the solid-state system because of the strong coupling between different physicochemical fields. Understanding the evolution of these fields is critical to unlocking the potential of solid-state batteries. This necessitates the development of experimental and theoretical methods to track electrochemical, stress, crack, and thermal fields upon battery cycling. In this Perspective, we survey existing characterization techniques and the current understanding of multiphysics coupling in solid-state batteries. We propose that the development of experimental tools that can map multiple fields concurrently and systematic consideration of material plasticity in theoretical modeling are important for the advancement of this emerging battery technology. This Perspective provides introductory material on solid-state batteries to scientists from a broad physical chemistry community, motivating innovative and interdisciplinary studies in the future.

Overpotential-Regulated Stable Cycling of a Thin Magnesium Metal Anode.

To obtain high energy density for magnesium (Mg)-metal batteries, a promising low-cost energy storage technology, a thin Mg-metal anode of tens of micrometers must be used. However, the Coulombic efficiency (CE) and the anode utilization rate (AUR) of thin Mg metal are far from sufficient to sustain a long cycle life. This drawback is closely related to the morphological instability during galvanostatic cycling. In this work, we observed that the morphological evolution of Mg metal can be controlled with a pre-applied overpotential. With a properly pre-applied overpotential (e.g., -0.5 V), we show that the average AUR and the average CE of thin Mg metal (16 µm, equivalent to 6 mA h cm-2) in a Mg/Mo asymmetric cell can be substantially improved from 29.8 to 74.8% and from 97.7 to 99.5%, respectively, under a practical current density of 2 mA cm-2. These advances can theoretically improve the energy density and cycle life of Mg-S batteries to more than 1000 W h kg-1 and 100 cycles, respectively. This work deepens our understanding of the morphological and compositional evolution of Mg metal during stripping and plating processes and suggests a facile and effective method to substantially improve the cycling stability of thin Mg metal.

Improved Electrochemical Performance of Li-Rich Layered Oxide Cathodes Enabled by a Two-Step Heat Treatment.

Lithium-rich layered oxide cathodes with high specific energy have become one of the most popular cathode materials for high-performance lithium-ion batteries. However, spinel phase formation due to the migration of transition metals and the release of lattice oxygen leads to the degradation of electrochemical performance. Here, we develop a synthesis approach for Li-rich layered oxide cathodes by a two-step heat-treatment process, which includes precursor calcination and pellet sintering. Compared with the sample prepared by the traditional one-step calcination, the oxide particles prepared by the two-step heat treatment show increased grain size from 217 to 425 nm. The Li-rich layered oxide cathodes with larger crystal grains indicate a mitigated formation of spinel phase and reduced voltage decay, which result in improved specific capacity, cycle stability, and rate capability. In addition, the thermal stability of the oxides is also improved. The improved electrochemical performance is because of the large single grains having a reduced contact area with a liquid electrolyte and the stable crystal lattice during cycling. Our strategy not only provides a simple and effective way to enhance the stability of the Li-rich layered oxide cathodes but also extends to the preparation of oxide powders with large grains.

Stereolithography Three-Dimensional Printing Solid Polymer Electrolytes for All-Solid-State Lithium Metal Batteries.

Liquid-free all-solid-state lithium metal batteries (ASSLMBs) are promising candidates to meet the requirements of safety and high energy density for energy storages. However, poor interfacial contact is a major obstacle limiting their applications. Herein, we report a solid polymer electrolyte (SPE), originally prepared by stereolithography (SLA) three-dimensional (3D) printing for ASSLMBs. A 3D-Archimedean spiral structured SPE is rationally designed, which can shorten the Li-ion transport pathway from the electrolyte into the electrode, reinforce the interfacial adhesion, and improve the mass loading of active materials. The SLA printed SPE exhibits a high ionic conductivity of 3.7 × 10-4 S cm-1 at 25 °C. Furthermore, Li|3D-SPE|LFP cells achieve reduced interfacial impedance and higher specific capacity of 128 mAh g-1 after 250 cycles than those using structure-free SPE of 32 mAh g-1. This work opens the great promise of SLA 3D printing technology to fabricate high-performance SPEs in ASSLMBs for next-generation energy storages.

Biofilm Nanofiber-Coated Separators for Dendrite-Free Lithium Metal Anode and Ultrahigh-Rate Lithium Batteries.

Rechargeable batteries that combine high energy density with high power density are highly demanded. However, the wide utilization of lithium metal anode is limited by the uncontrollable dendrite growth, and the conventional lithium-ion batteries (LIBs) commonly suffer from low rate capability. Here, we for the first time develop a biofilm-coated separator for high-energy and high-power batteries. It reveals that the coating of Escherichia coli protein nanofibers can improve electrolyte wettability and lithium transference number and enhance adhesion between separators and electrodes. Thus, lithium dendrite growth is impeded because of the uniform distribution of the Li-ion flux. The modified separator also enables the stable cycling of high-voltage Li|Li1.2Mn0.6Ni0.2O2 (LNMO) cells at an extremely high rate of 20 C, delivering a high specific capacity of 83.1 mA h g-1, which exceeds the conventional counterpart. In addition, the modified separator in the Li4Ti5O12|LNMO full cell also exhibits a larger capacity of 68.2 mA h g-1 at 10 C than the uncoated separator of 37.4 mA h g-1. Such remarkable performances of the modified separators arise from the conformal, adhesive, and endurable coating of biofilm nanofibers. Our work opens up a new opportunity for protein-based biomaterials in practical application of high-energy and high-power batteries.