On July 14, 2026, the Forum on Biochar and Carbon Research brought together researchers and members of the public for an online presentation examining three decades of progress in biochar science.

Industrial equipment such as boilers, pipelines, and heat exchangers must be chemically cleaned to remove scale, corrosion products, and organic deposits. However, this maintenance process generates organic cleaning wastewater containing high concentrations of organic compounds, ammonia, dissolved salts, and metal ions. Because this wastewater is difficult and costly to treat, researchers are exploring whether it can be safely destroyed in existing coal-fired boilers.

Soils worldwide are increasingly contaminated by both microplastics and toxic metals. When these pollutants meet, their combined effects are far more complicated than simple addition. A new review reveals that microplastics can change the mobility, bioavailability, ecological toxicity, and food-chain transfer of heavy metals, but the direction and magnitude of these changes differ substantially among metals.

An international team led by Prof. LIU Yi from the Institute of Atmospheric Physics (IAP) of the Chinese Academy of Sciences investigated how CH₄ emissions in Siberia are evolving under climate change, showing how atmospheric circulation promotes wildfire activity and methanogenesis and thereby drives the recent sharp rise in CH₄ emissions.

Neuroblastoma remains one of the most unpredictable childhood cancers, ranging from tumors that regress without treatment to aggressive disease that resists intensive therapy. 

Neural activity unfolds through three-dimensional circuits in milliseconds or faster, but many optical microscopes still record volumes sequentially. A new PhotoniX Perspective from UCLA researchers reviews how light-field microscopy is being re-engineered for fast neuroimaging, from brain-wide calcium recordings to kilohertz-class volumetric voltage imaging. The authors argue that light-field microscopy should be judged not as a direct replacement for confocal, multiphoton, or light-sheet microscopy, but by information throughput, latency, photon efficiency, temporal accuracy, and robustness to motion at the speed frontier.

A research team has found that microbial communities in a large, thermally stratified reservoir changed more strongly from year to year than between surface and deep waters, while their overall metabolic functions remained comparatively stable. 

A 360-million-year-old fossil plant named Ganiopteris from Jiangxi Province, China, is the earliest seed plant with ovules borne directly on leaves. The fossil preserves complete megasporophylls with ovules on their undersides; these megasporophylls form a strobilus terminating the axis. The discovery reveals that the evolutionary shift from axial- to leaf-borne ovules occurred at least 40 million years earlier than previously known and offers insights into the origin of Permian glossopterids.

The exponential demand for energy-efficient and adaptive computing architectures drives the evolution of artificial intelligence (AI) and machine learning (ML). Neuromorphic computing, inspired by biological neural networks, overcomes the limitations of traditional von Neumann architectures, including high energy consumption and limited scalability. The introduction of two-dimensional (2D) materials, such as transition metal dichalcogenides, hexagonal boron nitride, black phosphorus, and tellurene, enables neuromorphic devices with unprecedented control over electronic and optoelectronic properties. These materials exhibit atomic-scale thickness, high carrier mobility, and tunable bandgaps, facilitating synaptic behaviours such as spike-timing-dependent plasticity and paired-pulse facilitation. This review describes the integration of 2D materials into neuromorphic systems, highlighting applications in wearable electronics, brain–machine interfaces, and quantum neuromorphic platforms. In wearable and edge computing, 2D-based devices enable localized, ultra-low-power data processing. In brain–machine interfaces, they enhance signal transduction and neural interfacing. Quantum effects in 2D materials further enable hybrid quantum–classical neuromorphic architectures for high-dimensional computational tasks. Despite significant advances, challenges in reproducibility, scalability, and stability remain. Addressing these limitations through innovations in synthesis and defect passivation is essential for practical application. This review underscores the transformative potential of 2D-material-based neuromorphic computing for energy-efficient AI.