Research Quick Takes

Professor Junbo Zhao earned the Top Influencers in AI Energy Award at the AI x Energy Summit in San Diego for his "outstanding leadership and influence" in advancing research in AI energy-related fields.

PhD students Samuel Ong and Simon Agnew '22, Md Saifur Rahman Th'25, and Professor Will Scheideler—with NIST physicist Lee Richter—co-authored "Tailoring Solid Phase Crystallization for Tunable Electronic Transport in Liquid Metal Printed 2D Oxides" published in Advanced Materials Technologies. The study showed highly-aligned, single-orientation grains which yield high-mobility devices, outperforming almost all other vacuum-free metal-oxide semiconductors reported to date. "We've always seen unique grain morphologies in our liquid metal printed metal oxides, so we probed the solid phase crystallization through highly-sensitive x-ray scattering techniques thanks to our collaborator, Dr. Richter. These results mark a critical step towards scalable manufacturing of transparent, high-performance electronics for next-generation flexible displays and sensors," said Ong.

PhD candidates Amritha Anup (first-author, pictured) and Afton Limberg, Mika Bok '27, and Professor Katie Hixon co-authored "Silk cryogel and electrospun scaffold characterization for bone-tendon interface applications" published in Frontiers in Bioengineering and Biotechnology. In this work, tissue engineered silk cryogels and electrospun fibers were combined to model aspects of the mechanical, structural, and biochemical gradients found at the bone-tendon interface. "Injuries to the hard-soft tissue interfaces, such as the bone-tendon interface, affect approximately 32 million people in the US annually. Limitations in surgical repair and the natural healing process emphasizes the need for tissue engineering approaches that restore tissue continuity while supporting the spatial heterogeneity of the native bone-tendon insertion," said Anup.

Professor Ian Baker was awarded the Oleg D. Sherby Award at last week's annual meeting of The Minerals, Metals & Materials Society (TMS) in San Diego. The award was for "contributions to understanding the elevated temperature behavior and processing of metallic alloys as well as snow and ice using advanced characterization methods." "I very much appreciate receiving the Oleg Sherby Award from TMS in recognition of my work on elevated temperature mechanical properties. I joined TMS in 1983 and consider it a key institution for materials research both in the US and worldwide," said Baker.

Professors Ryan Halter, Jonathan Elliott, Vikrant Vaze, and Ethan Murphy—with Geisel Professor Norman Paradis—were issued a US patent for "System and method to detect the presence and progression of diseases characterized by systemic changes in the state of the vasculature." The invention uses a novel technique to obtain multiple tissue measurements which are then "transformed by a multivariate algorithm to outputs that convey the diagnostic and prognostic risk of the disease of interest," according to the patent. "We show that by effectively combining signals from multiple sensors using advanced machine learning algorithms, we can save lives through early detection of hidden internal bleeding," said Vaze.

PhD student Zequn "Vincent" Chen and Professor Wesley Marrero co-authored "A survey on optimization and machine learning-based fair decision making in healthcare" published in Health Care Management Science. The review examines how fair AI and mathematical optimization can improve areas like patient scheduling, disease diagnosis, and treatment recommendations. It identifies potential sources of bias in existing literature, classifies different bias mitigation strategies, and evaluates fairness metrics that help verify whether outcomes are equivalent for privileged and unprivileged groups. "By analyzing the trade-offs of each method, this research facilitates more informed and transparent decision-making in health care," said Marrero.

PhD students Baiheng Li and Huilin Qing, Research Associate Peiyu Wang, and professors Ian Baker and Weiyang "Fiona" Li co-authored "Highly Stable Quasi-Solid-State Sodium Batteries via Facile Grain Boundary Engineering" published in ACS Applied Materials and Interfaces. "This work improves the performance of solid-state batteries by employing a novel and scalable fabrication method for the electrolyte, and paves the way for safer and cheaper next-generation energy storage solutions," said Baiheng.

PhD student Jiahui "Gary" Luo and Professor Wesley Marrero,  with researchers at U Michigan, developed a new simulation framework to analyze liver transplant decisions, which was published in IEEE's 2025 Winter Simulation Conference. The team created a continuous-time simulation that models patient health and organ arrivals while mimicking real-world, varied organ acceptance practices. "The study concludes that high selectivity is a major obstacle to saving lives. Because small quality differences have modest survival effects, accepting a broader range of medically suitable organs can significantly reduce waiting times and maximize the lifesaving potential of the donor pool," said Marrero.

Research Associate Peiyu Wang Th'25, PhD students Huilin Qing, Baiheng Li, and Ruiwen Zhang, and Professor Weiyang (Fiona) Li co-authored "Semi-liquid lithium−sulfur batteries for large-scale energy storage" published in Nature Reviews Clean Technology. This review examines catholyte chemistry and design, static and redox flow configurations, and strategies to improve performance and scalability for large-scale energy storage. "Lithium–sulfur batteries offer high energy density and cost-effectiveness but are limited by the precipitation of solid sulfur species, which has driven interest in semi-liquid systems," said Li.

PhD student Simon Agnew '22, Research Associate Xavier Cadet, and professors Peter Chin and Will Scheideler co-authored "Decoding disorder: Machine learning unlocks multi-wavelength and intensity sensing in a single indium oxysulfide phototransistor" published in Device. The paper presents machine-learning-enabled phototransistors that decode both light wavelength and intensity from a single printed device—no filters or sensor arrays required. This work points toward simpler, lower-cost, and more scalable multi-parameter sensing for flexible optoelectronics. "By combining scalable liquid-metal printing of ultrathin indium oxysulfide with data-driven analysis, we show how disorder—often viewed as a limitation in printed semiconductors—can be turned into a powerful sensing feature," said Scheideler.

Professor Arthur Pétusseau (far right) has teamed up with Research Fellow Dylan Parker, MD, and professors of dermatology Shane Chapman, MD, and Brian Simmons, MD to launch a clinical study focused on the diagnosis of non-melanoma skin cancer. The study, titled "Assessment of Skin Lesions Using a Tissue Oxygen Imager Based on Protoporphyrin IX (PpIX) Fluorescence," will enroll 120 patients to investigate whether oxygen dynamics following gentle palpation can serve as an early marker to distinguish malignant from non-malignant lesions. "Information from this study could help dermatologists better determine when surgical resection is truly necessary, potentially reducing unnecessary procedures, particularly in cosmetically-sensitive areas such as the face," said Pétusseau who developed the imager called, Pressure-Enhanced Sensing of Tissue Oxygenation (PRESTO).

PhD students Xiangbei Liu, Ya Tang, and Huan Zhao, and Professor Yan Li, are co-authors of "A transfer learning–enabled framework for rapid property prediction toward scalable and data-efficient metamaterial design" published in Results in Engineering. When faced with new requirements, conventional machine-learning approaches require substantial new datasets for retraining—basically starting from scratch. Transfer learning can significantly reduce the required amount of training data while maintaining high accuracy and stability. "This approach provides a foundation for building a scalable, data-efficient knowledge base for future applications," said Li.