China’s agriculture has long faced three major problems: excessive chemical fertilizer application, residual mulch film pollution, and low utilization rates of livestock and poultry manure. Excessive fertilizer input leads to soil acidification and compaction, and nutrient use efficiency continues to decline. Traditional polyethylene (PE) mulch film is difficult to degrade, and its year-by-year accumulation forms microplastic pollution, damaging soil microbial communities and arable land health. At the same time, livestock waste such as cow manure is large in volume and widely distributed, but its resource utilization pathways are limited, and crop-livestock cycling is not smooth. The green transformation of agriculture urgently needs integrated technologies that simultaneously ensure stable yields, reduce fertilizer use, reduce plastic pollution, and improve soil fertility.

A widely burned agricultural waste rapeseed straw is used as an effective, eco-friendly corrosion inhibitor for cold-rolled steel in methanesulfonic acid.

Researchers have turned lightweight balsa wood into a strong, water-resistant composite by filling its pores with polylactic acid (PLA) built from small molecules inside the wood itself.

With the unique advantages that very low Earth orbit (VLEO) satellites have demonstrated in fields such as high-resolution remote sensing, the contradiction between the demand for large-volume data downlink and the extremely short visibility windows of ground stations has become increasingly prominent. Sun-synchronous orbit VLEO satellites typically have only one to two ground station passes per day, each lasting only a few minutes, far from sufficient to meet the high-throughput data downlink requirements generated by missions such as high-resolution imaging and video surveillance. However, existing relay architectures are unsuitable for small satellites due to constraints in mass, power consumption, and latency; low-rate terminals can only provide basic telemetry; conventional proportional–integral–derivative controllers struggle to maintain accuracy under nonlinear and strong disturbance environments; and nonlinear model predictive control imposes excessive computational burdens. Therefore, how to coordinately optimize the resource allocation and control strategies of VLEO satellites and relay constellations to achieve an effective balance between data transmission efficiency and onboard computational capability has become a key technical challenge in supporting high-capacity VLEO missions.

With the increasing threat posed by space debris to the safe operation of on-orbit spacecraft, the Whipple shield, as a critical means of defending spacecraft against hypervelocity impacts, has evolved from conventional single-layer configurations to advanced architectures employing high-performance materials such as fiber-reinforced composites. Advanced shields such as carbon fiber–reinforced composite laminates involve additional energy dissipation through failure modes including fiber fracture, delamination, and melting, resulting in shielding mechanisms that differ significantly from those of traditional Whipple shields. However, the currently predominant ballistic limit equations lack accuracy and generalizability for different advanced configurations, making them unsuitable for direct comparison of shielding performance. Hypervelocity impact experiments are costly and cannot be performed for every configuration, while existing numerical studies have mostly focused on the macroscopic morphology of debris clouds, with insufficient quantitative analysis of the hazardous fragments within them. Therefore, how to establish a method capable of quantitatively evaluating and comparing the shielding performance of different advanced Whipple shields has become a critical technical issue urgently to be addressed in the field of spacecraft space debris protection.

Circularly polarized light (CPL) carries spin angular momentum information and holds great promise for magnetic recording, spin optical communication, biological sensing and quantum computation. Conventional CPL detectors rely heavily on bulky and complex external optical components, which restrict device integration and miniaturization. Chiral organic semiconductors possessing an intrinsic capability for direct CPL discrimination, have emerged as promising candidates for CPL detectors. However, their development faces an intrinsic trade-off between charge carrier mobility and absorption dissymmetry factor.

Encouragingly, tremendous efforts have been devoted to resolving this contradiction, contributing to the development of a series of high-performance chiral organic semiconductors and thereby CPL detectors. Now, writing in Science Bulletin, researchers at the Institute of Chemistry, Chinese Academy of Sciences, systematically review representative progress of chiral organic semiconductors with a particular emphasis on the planarity-helicity dilemma and spectral tailoring. The review further highlights advances in CPL detectors and emerging applications, and discusses key challenges and future opportunities toward high-performance, miniaturized, and integrated CPL detection technologies.

A new artificial intelligence (AI) framework that combines ultrasound (US) with digital breast tomosynthesis (DBT) may improve breast-level risk classification by raising specificity while preserving sensitivity. 

A multicenter study of 2,746 patients with acute pancreatitis (AP) introduces ORACLE, an artificial intelligence model that predicts organ failure from CT imaging. The model achieved AUCs of 0.81–0.89, outperforming conventional scoring systems, and provided a median early warning of 3.5 hours before clinical onset. Its 97.2% negative predictive value may help clinicians stratify risk.A multicenter study of 2,746 patients with acute pancreatitis (AP) introduces ORACLE, an artificial intelligence model that predicts organ failure from CT imaging. The model achieved AUCs of 0.81–0.89, outperforming conventional scoring systems, and provided a median early warning of 3.5 hours before clinical onset. Its 97.2% negative predictive value may help clinicians stratify risk.