Core Viewpoint - The article discusses a breakthrough in converting CO2 into high-value C3-C4 diols through a synergistic electrochemical and AI-assisted biosynthesis system, highlighting its significance for green chemistry and carbon neutrality [2][3][4]. Group 1: Research Breakthroughs - A novel carbon-negative emission system has been developed, integrating electrochemical and biocatalytic processes to efficiently convert CO2 into 1,3-propanediol (1,3-PDO) and 1,3-butanediol (1,3-BDO) [4][15]. - The electrochemical module utilizes a CuZn alloy catalyst, achieving an ethanol production rate of 1200 μmol h⁻¹ cm⁻² at an amperometric current density of -1100 mA cm⁻², with a Faradaic efficiency of 35% [6][15]. - The biocatalytic module employs engineered DERA enzymes to extend C–C bonds, significantly enhancing the synthesis efficiency of 1,3-PDO to a record yield of 1.8 g L⁻¹ h⁻¹ [10][15]. Group 2: Technological Innovations - A biomimetic J-T membrane has been developed to address ethanol permeation issues, achieving less than 1% ethanol crossover while maintaining high OH⁻ conductivity [7][15]. - AI-assisted enzyme engineering has led to a 2.5-fold increase in catalytic efficiency for the DERA enzyme, facilitating faster synthesis of target diols [10][15]. - Molecular dynamics simulations revealed that mutations introduced new hydrogen bonding networks, enhancing substrate affinity and catalytic efficiency [11][15]. Group 3: Performance Metrics - The integrated system achieved a production rate of 1.8 g L⁻¹ h⁻¹ for 1,3-PDO and 1.0 g L⁻¹ h⁻¹ for 1,3-BDO, with a carbon atom utilization rate of approximately 80% [15]. - All carbon atoms in the products were confirmed to originate from CO2, showcasing the system's efficiency compared to existing electro-biological hybrid systems, which typically yield less than 0.05 g L⁻¹ h⁻¹ [15][18]. - The research demonstrates significant advancements in catalyst design, membrane separation, and enzyme engineering, emphasizing the potential of interdisciplinary collaboration in green synthesis [16].

Core Viewpoint - Major scientific infrastructure is crucial for supporting original innovation and achieving high-level technological self-reliance in the context of intensified global technological competition [1][4]. Group 1: Development and Current Status - China's major scientific infrastructure has developed into a world-class system through national planning and a phased approach, with facilities like the Shanghai Synchrotron Radiation Facility and the Spallation Neutron Source leading internationally [2][3]. - The current trend is towards systematization, digitalization, and internationalization, integrating technologies like 5G and AI to enhance operational efficiency and facilitate global scientific collaboration [2][3]. Group 2: Strategic Importance - Major scientific infrastructure plays a core role in basic research and industrial applications, providing essential support for fields such as quantum materials and AI training, thereby enhancing the innovation chain from research to application [3][4]. - It serves as a key link in optimizing resource allocation for regional coordinated development, fostering innovation ecosystems across different regions of China [3][4]. Group 3: Challenges and Structural Issues - Despite advancements, China's major scientific infrastructure faces structural challenges, including a tendency to prioritize construction over research and issues with resource allocation and collaboration [6][7]. - There is a need for a systematic approach to overcome these challenges and fully activate the strategic potential of major scientific infrastructure [6][7]. Group 4: Future Directions and Recommendations - To achieve the goal of becoming a technological powerhouse by 2035, major scientific infrastructure must transition from scale expansion to quality enhancement, focusing on strategic areas like quantum technology and deep space exploration [7][8]. - Recommendations include strengthening top-level design, enhancing collaborative mechanisms, innovating funding models, and restructuring talent cultivation systems to better support the infrastructure's capabilities [7][8].