Artificial intelligence is fundamentally transforming synthetic biology biosensors (SBBs) from traditional rational design to AI-driven predictive engineering. This review establishes the first systematic integration framework of AI algorithms with the design-build-test-learn (DBTL) full cycle of synthetic biology biosensors

The ongoing escalation of the Iran-U.S. conflict is not only reshaping the competitive landscape of the global sensor industry but also profoundly influencing the strategic segmentation and development trajectories of China's domestic sensor sector.

In precision industries such as semiconductor manufacturing and microelectronics processing, etching and cleaning are core process steps. The accurate control of chemical solution levels directly determines product yield and process stability.

Heavy metal pollution, especially lead (Pb) and mercury (Hg), has always been a major concern in the fields of environment and food safety. Even trace levels of lead and mercury can cause serious damage to the human nervous system. Traditional detection methods, such as inductively coupled plasma mass spectrometry, although precise, are expensive in equipment, complex in operation, and have long detection cycles, making it difficult to meet the needs of rapid on-site detection.

The pneumatic hammer, as an indispensable impact vibration device in industrial production, is widely used in fields such as material conveying, product testing, and oil and gas drilling. The stability of its vibration parameters directly determines production efficiency, product quality, and operational safety.

In high-end fields such as precision manufacturing, scientific research experiments, and aerospace, small vibrations are like invisible killers. Even nanoscale displacements can lead to experimental failures, equipment damage, or product accuracy not meeting standards.