Brookhaven National Laboratory Scientist Yutao Li Advances Industrial Automation Research via DOE Energy I-Corps Program
29 January 2026
Brookhaven National Laboratory (BNL), a key U.S. Department of Energy (DOE) facility in Upton, New York, has marked a significant milestone in industrial automation research with scientist Yutao Li's participation in the DOE Energy I-Corps program. Published on January 29, 2026, this development underscores the growing bridge between federally funded laboratory research and practical B2B applications in the automation sector. Li, alongside colleagues Raymond Blackwell and Huandong Chen, engaged in an intensive two-month training regimen designed to transform cutting-edge automation technologies into commercially viable solutions for American manufacturers and system integrators.
The Energy I-Corps program, launched in 2015, equips researchers with essential business acumen, including market opportunity evaluation, stakeholder interviews, and commercialization strategies. For Li's team, this meant rigorously questioning their automation technology's target markets—focusing on sectors like manufacturing, power generation, and integrated processes where automation can drive efficiency gains. Industry mentor Michael Clarkin played a pivotal role, challenging the team to integrate hardware with software solutions, ensuring robustness for real-world industrial deployments such as control systems and search detection mechanisms.
A defining pivot occurred after dozens of stakeholder interviews, shifting focus from pure software to hybrid systems that meet industry demands for simplicity, reliability, and immediate deployability. Li showcased these advancements at Automate 2025, North America's premier automation conference, managing a booth and interfacing with over 25 industry leaders from categories like motors, drives, actuators, and machine tools. Feedback emphasized 'out-of-the-box' readiness, aligning with needs in oil and gas machinery, metalworking, and power distribution where downtime costs are prohibitive.
This initiative aligns with PlantAutomation-Technology.com's focus on Industrial R&D and Integrated Processes and IT solutions. BNL's efforts exemplify how DOE-supported research accelerates technology transfer to U.S. vendors and plant operators. Li noted the program's impact: 'It forces you to ask hard questions about market size, funding, and immediate impact,' highlighting a shift from lab-centric innovation to customer-driven development. Clarkin praised Li's growth, from skepticism to strategic insight, fostering collaborations essential for scaling automation in competitive B2B landscapes.
Looking ahead, Energy I-Corps positions BNL technologies for partnerships in electronics, semiconductors, electrical components, and beyond. Program director Upadhya emphasized its role in commercializing research addressing market needs through industry collaborations. For facility managers and system integrators, this means access to validated automation tools enhancing productivity in turbines, fans, blowers, and conveyors. Li's journey as BNL's first participant sets a precedent, blending scientific rigor with business savvy to propel U.S. industrial automation forward.
Broader implications extend to regulatory compliance and cybersecurity in automation ecosystems. As manufacturers adopt these technologies, DOE's model ensures alignment with Industry 4.0 standards, mitigating risks in power generation and distribution switchgears. The program's success metrics—hundreds of trained researchers since 2015—signal robust pipelines for innovations in industrial metrology, bearings, and rolling machinery. Stakeholders in construction, mining, and farm machinery stand to benefit from enhanced auto-regulating systems derived from such pivots.
In summary, Yutao Li's Energy I-Corps experience at BNL not only refines automation research but also catalyzes economic impact for American industrial players, reinforcing DOE's commitment to translating lab breakthroughs into deployable B2B solutions across specified categories.