Boron carbide (B₄C) coating on diamond particle is synthesized by heating diamond particles in a powder mix of H₃BO₃ and B in Ar atmosphere. The composition, bond state and coverage fraction of boron carbide coating on diamond particles are investigated. The boron carbide coating favors to grow on diamond (100) surface rather than on diamond (111) surface. Cu matrix composites reinforced with B₄C-coated diamond particles were made by powder metallurgy. The addition of B₄C coating gave rise to a dense composite. The influence of B₄C coating on both tensile strength and thermal conductivity of the composite were investigated. When the B₄C fully covered on diamond particles, the composite exhibited a greatly increase in tensile strength (115 MPa) which was much higher than that for uncoated-diamond/Cu (60 MPa) composites. Meanwhile, a high thermal conductivity of 687 W/mK was achieved in the B₄C-coated-diamond/Cu composites.

A metal-based matrix is usually used for the fabrication of diamond bits in order to achieve favorable properties and easy processing. In the effort to reduce the cost and to attain the desired bit properties, researchers have brought more attention to diamond composites. In this paper, Fe-based impregnated diamond composites for drill bits were fabricated by using a pressureless infiltration sintering method at 970 °C for 5 min. In addition, boron was introduced into Fe-based diamond composites. The influence of boron on the density, hardness, bending strength, grinding ratio, and microstructure was investigated. An Fe-based diamond composite with 1 wt % B has an optimal overall performance, the grinding ratio especially improving by 80%. After comparing with tungsten carbide (WC)-based diamond composites with and without 1 wt % B, results showed that the Fe-based diamond composite with 1 wt % B exhibits higher bending strength and wear resistance, being satisfactory to bit needs.

The deployment of the robotic system that executes specific task is being challenged by the prevalence of dynamic objects in real‐world scenes. Two robotic tasks sparked by this challenge, known as dynamic Simultaneous Localization and Mapping (SLAM) and moving object perception, are crucial for enhancing system robustness and reinforcing environment awareness. Existing public datasets are diverse in platforms, sensor combinations, scenarios, and label annotations, but few adequately benchmark the above tasks. To fill this gap, we introduce the multimodal and campus‐scapes (M2CS) dataset, providing robot‐centric synchronized LiDAR‐Inertial‐Visual‐GNSS data with 3D moving object annotation in specific dynamic scenarios. The dataset exhibits variation in dynamic object types and densities, annotating over 160,000 Light Detection and Ranging (LiDAR) scans and releasing ground truth of trajectories acquired by the GNSS‐RTK/INS system. The dataset evaluates existing SLAM and moving object perception methods, driving relevant research to overcome this challenge. We publish the M2CS dataset on the website ( https://github.com/Zhaohuanfeng/M2CS ) and hope it promotes research on robotics in complex environment.