Three Nanjing University affiliates have been named to the 2025 MIT Technology Review Innovators Under 35 (TR35) China list, an announcement made on July 25 in Shanghai. The honorees include Xu Ning, an associate professor from the College of Engineering and Applied Sciences, along with alumni Lai Jiancheng and Fan Lingling.

Professor Xu Ning's Profile

Xu Ning, an associate professor, distinguished researcher, and doctoral supervisor at Nanjing University's College of Engineering and Applied Sciences, focuses her research on photothermal conversion materials and devices, as well as water-energy materials and devices. She has published over 60 papers in high-impact journals such as Nat. Nanotechnol., Nat. Sustain., Nat. Water, Nat. Rev. Clean Technol., Sci. Adv., NSR, Joule, and Adv. Mater., with citations exceeding 10,000. She leads multiple National Natural Science Foundation projects, including the National Excellent Young Scientists Fund, and has received awards such as the First Prize of the Jiangsu Province Science and Technology Award, the China New Star of Science and Technology, and the Nanjing University May Fourth Youth Medal.

Summary of Achievements

Solar-driven photothermal evaporation, which uses solar energy for green water production, is a key technology for addressing global water scarcity. However, challenges remain in achieving high efficiency, long-term stability, and functional expansion. Xu Ning has dedicated her career to researching this technology. Her work has made solar evaporation both efficient and stable, while expanding its applications to the synergistic use of water, energy, and resources, providing new pathways for sustainable development. Drawing inspiration from nature, she proposed a biomimetic three-dimensional photothermal evaporator design that significantly boosts solar-to-thermal conversion efficiency by suppressing conductive and radiative heat losses. She then tackled the stability bottleneck that hinders practical application, particularly the failure of photothermal devices due to salt crystallization in high-salinity environments. She innovatively developed anti-salt mechanisms, such as separating the light-absorbing and evaporating surfaces and using rotational self-cleaning, enabling long-term, efficient, and stable operation under high-salinity conditions. Expanding further, she broadened the technology's functional scope, viewing evaporation not just as a water-production process but as a means to exploit ion migration, separation, and enrichment, as well as multi-dimensional steam utilization. This led to applications in water-electricity co-generation, atmospheric water harvesting, resource recovery, and seawater hydrogen production, transforming the technology from single-purpose water production to a synergistic system for water, energy, and resources. These results have been validated in applications like seawater desalination and municipal wastewater treatment.

Alumnus Lai Jiancheng

A 2012 master's and 2014 doctoral graduate from the School of Chemistry and Chemical Engineering, Lai Jiancheng has built a full-stack technology system from flexible electronic skin and tactile chips to data platforms, providing foundational solutions for the large-scale deployment of robots and smart vehicles. Robots require touch sensitivity comparable to humans, but traditional rigid sensors fail upon bending. Lai Jiancheng began by redesigning materials from the ground up, spending over a decade building a comprehensive technology chain covering materials, devices, chips, algorithms, and system integration. To commercialize laboratory-made flexible skin, he founded TouchView Technology as CEO, developing products like dexterous hand tactile systems, full-body robot tactile systems, and tactile gloves. His goal is to create sensors that simultaneously perceive pressure, shear force, temperature, and proximity like human fingertips without interference. Over two years, he and his team solved the critical engineering challenges of high-density integration and signal decoupling. The current multimodal electronic skin achieves an array density of 400 sensors per square centimeter, accurately capturing forces from a 0.1 kPa airflow to a 50% stretch deformation, reaching international leading standards. He also addressed challenges of low yield and poor consistency in large-scale flexible electronics manufacturing. A standardized production line with an annual capacity of 50,000 units is now operational, with core products integrated into the supply chains of leading humanoid robot and automotive manufacturers. His team is now building a platform to collect tactile-visual data, using widespread hardware deployment to gather real-world tactile data for AI, providing it with the physical common sense it currently lacks.

Alumna Fan Lingling

A 2014 undergraduate from the School of Physics, Fan Lingling has advanced the heterogeneous integration and experimental implementation of micro-nano photonics and electronic hardware, aiming to break the energy bottleneck of traditional chips from a physical foundation. Just as a building needs energy-efficient windows that are both attractive and insulating, a large language model running on a phone must be intelligent without overwhelming power consumption and heat dissipation. Fan Lingling's research seeks new computational and energy pathways within these constraints. She began with photonic materials, developing colorful photonic building films with infrared reflection to improve thermal management while preserving color, reducing building energy consumption by about 10% and carbon footprint by 3%, and used in green building projects. She then applied photonics to AI computing, proposing a photonic convolutional processor based on radio-frequency modulated optical frequency combs. This processor uses the parallel processing capability of light in the frequency domain for high-dimensional convolution, achieving a simulation energy efficiency over six times higher than the NVIDIA A100 GPU, and is being explored by Silicon Valley tech companies for next-generation efficient computing hardware. Entering the large model industry, she extended the "low-power, high-efficiency" approach to AI infrastructure. At Meta GenAI, she contributed to large model inference system optimization, advancing technologies like persistent KV cache to reduce inference latency and GPU resource consumption. Now at Google DeepMind, working with the TPU team, she focuses on on-device large models and video generation models, aiming to adapt complex models to the power and heat constraints of mobile devices. From energy-saving window films and photonic convolutional processors to large model inference optimization and on-device GenAI architectures, her work is moving low-power intelligent computing from materials and devices to real-world product systems. Her future focus includes three areas: smart windows, on-device photonic hardware for large language models, and AI-driven scientific research.

About the Innovators Under 35 (TR35) Program

The "Innovators Under 35" (TR35) global selection, initiated by MIT Technology Review in 1999 during its centennial year, aims to identify outstanding young innovators in technology and industry annually, accelerating global technological innovation. Over more than two decades, TR35 has evolved into the "35 Innovators Under 35" with significant international influence and leadership. The TR35 China selection, established in 2017, has brought numerous outstanding young Chinese innovators, both in China and abroad, to the international stage.