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Prof. SUI Yi

School of Mechanical and Automotive Engineering, Qingdao University of Technology

Research Publications & English Decoded Briefs

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Surface Technology (表面技术)2026DOI: 10.16490/j.cnki.issn.1001-3660.2026.08.001

Research Progress on Chromium-free Passivation Technology for Galvanized Steel Sheets

Galvanized steel sheets are widely used in construction, automotive, appliance, and power industries due to their corrosion resistance, which can be further enhanced by passivation. Traditional chromate passivation, while effective due to self-repairing ability and chemical stability, poses severe health and environmental risks from hexavalent chromium. This review systematically categorizes recent chromium-free passivation technologies into inorganic, organic, and organic/inorganic composite systems. Inorganic systems include molybdates, rare earth salts (e.g., cerium, lanthanum), titanium salts, and silicates; organic systems include silanes, tannic acid, and acrylic resins. Film formation mechanisms and anticorrosion properties are examined. Individual systems exhibit limitations: molybdate films have micro-defects and limited thickness uniformity; rare earth films crack upon drying; organic films offer flexibility and adhesion but insufficient barrier properties. Organic/inorganic composite passivation integrates inorganic barrier function with organic interfacial binding and functional regulation, significantly improving film integrity and durability. The review concludes with challenges and prospects for chromium-free passivation.

Journal of Semiconductors (半导体学报 - 中国科学院半导体研究所)2025DOI: 10.1088/1674-4926/25020034

Robotic computing system and embodied AI evolution: an algorithm-hardware co-design perspective

Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algorithms and supporting hardware. In recent years, the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models. This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance. Therefore, algorithm-hardware co-design has emerged as the primary methodology, which analyzes algorithm behaviors on hardware to identify common computational properties. These properties can motivate algorithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency. We then reviewed recent works on robotic and embodied AI algorithms and computing hardware to demonstrate this algorithm-hardware co-design methodology. In the end, we discuss future research opportunities by answering two questions: (1) how to adapt the computing platforms to the rapid evolution of embodied AI algorithms, and (2) how to transform the potential of emerging hardware innovations into end-to-end inference improvements.