Join us for a cutting-edge Carbon Research Webinar featuring Prof. Weihong Yang from KTH-Royal Institute of Technology, Sweden, where he will explore innovative strategies to transition from fossil-based materials to sustainable, bio-based graphite alternatives. This talk will provide insights into the conversion of bioprecursors into fossil-free graphite and its applications in lithium-ion batteries and electrochemical systems. A techno-economic assessment and life cycle analysis (LCA) will also be discussed.

Researchers have systematically mapped the complex cellular pathways leading to glucocorticoid-induced osteonecrosis of the femoral head (GION). The study identifies key apoptotic mechanisms including 11β-HSD enzymes, CD95/CD95L, STAT1-caspase 3, and PI3K/Akt pathways, while presenting emerging therapeutic approaches including targeted drugs, biomaterials, and stem cell therapies.

The use of immune checkpoint inhibitors (ICIs) has significantly improved the efficacy of cancer therapy, but their associated immune-related adverse events (irAEs) can severely compromise treatment safety. This review systematically summarizes the core mechanisms underlying irAEs, which include multi-organ damage resulting from T-cell dysfunction, B-cell-mediated autoantibody abnormalities, cytokine network dysregulation, and monocyte-driven inflammatory cascades. Identified risk factors encompass a range of elements, including host clinical characteristics and underlying diseases, gut microbiota dysbiosis, characteristics of the treatment regimen, tumor type and its histological features, genetic factors and immunogenetic polymorphisms, pre-existing autoimmune conditions or a history of autoimmunity, and a history of previous exposures alongside various environmental factors. The grading criteria, their clinical context and incidence rates, and clinical management strategies for irAEs affecting various organ systems are detailed herein. Future research should aim to deeply analyze the shared mechanisms and temporal dynamics between irAEs and anti-tumor effects, develop targeted irAE prediction and monitoring systems, and optimize strategies for irAE prevention and treatment.

The study found through multi-omics integration analysis that rheumatoid arthritis is a causal factor of idiopathic pulmonary fibrosis, identifying CCL2 and CXCL2 as shared biomarkers between the two conditions, providing new evidence for the early diagnosis and intervention of rheumatoid arthritis-related pulmonary fibrosis.

Chinese researchers have identified key immune checkpoints on natural killer (NK) cells that enable tumors to evade immune destruction. This systematic review reveals how inhibitory receptors like TIGIT, NKG2A, and PD-1 suppress NK cell function in the tumor microenvironment, and demonstrates that blocking these checkpoints – through monoclonal antibodies, gene editing (CRISPR-Cas9), or engineered CAR-NK cells – significantly enhances anti-tumor immunity. The findings provide a roadmap for next-generation immunotherapies targeting solid tumors and blood cancers.

With the rapid development of multispectral detection technology, the demand for multi-band, multifunctional, and compatible infrared camouflage in complex military environments is becoming increasingly urgent. Traditional infrared camouflage technology mainly focuses on the control of single-band emissivity, and the static design is hard to match with the surrounding environment and actual needs. When the target has a significant difference in emissivity compared to the environment, low-emissivity camouflage may expose itself due to abnormal thermal signals. At the same time, existing technologies often overlook the needs of multispectral compatibility, such as the lack of comprehensive management of heat accumulation, laser stealth, and visible light camouflage when achieving infrared camouflage. The adaptability mechanisms in nature provide new insights, such as the unique spectral characteristics of Rosaceae plants. Their band response patterns offer significant inspiration for the design of tunable and compatible visible light camouflage, as well as infrared and laser stealth. The emergence and application of phase change materials provide a possibility to solve this key problem. The reversible switching between crystalline and amorphous states supports the regulation of optical parameters, enabling multifunctional compatible stealth and camouflage in infrared, laser and visible light bands.

Metasurfaces are artificially designed two-dimensional structures composed of a large number of subwavelength-sized units arranged periodically or aperiodically. They can overcome the limitations of natural materials and endow electromagnetic waves with unique manipulation capabilities. The motivation for metasurface research stems from the core challenges of wireless communication: optimizing signal coverage and quality in complex electromagnetic environments.

With the increasing demand for high-frequency bands (millimeter-wave and terahertz) and large-scale connectivity in 5G/6G communications, traditional technologies face challenges such as high hardware complexity, excessive power consumption, and severe channel interference. Due to their low power consumption, low cost, and easy deployment, metasurfaces have emerged as an ideal solution to these problems. 

In particular, the concept of digital-coded metasurfaces, proposed in 2014, introduced programmable elements (such as PIN diodes) to discretize electromagnetic properties into "0/1" digital states. Combined with controllers like FPGAs, these metasurfaces enable real-time dynamic regulation of electromagnetic waves. This breakthrough has transformed metasurfaces from “fixed-function materials” into “intelligent electromagnetic surfaces”, capable of dynamically optimizing wireless channels and even directly participating in signal modulation and transmission.

Panoramic visual-inertial odometry (VIO) systems are crucial for navigating Global Positioning System (GPS)-denied environments, but their potential is hindered by image distortions caused by geometric inconsistent projection models.  

Self-driving cars know their own way in unpredictable traffic, thanks to path planning technology. Among current AI-driven efforts to make path planning more efficient and reliable, a research team has developed an optimization framework proven especially effective in uncertain environments. The results were published June 3 under the title “Action-Curiosity-Based Deep Reinforcement Learning Algorithm for Path Planning in a Nondeterministic Environment” in Intelligent Computing, a Science Partner Journal.