Research Quick Takes

Apr 16, 2026

Revolutionizing Computing Hardware

Professor Jifeng Liu authored "Atomic Ordering as a New Degree of Freedom for Semiconductor Device Engineering" published in Computer. The paper makes the case for engineering the atomic neighborhood in semiconductor alloys as a way to "leap beyond CMOS" for a new generation of computing hardware. "It is my great honor to introduce our latest research on harnessing atomic ordering in semiconductors to the computer science community. As Jensen Huang pointed out, 'the next wave of AI is physical AI,' and hardware revolutions will play a critical role there," said Liu.

Apr 09, 2026

Top Influencer in AI Energy

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.

Headshots of the four Dartmouth-affiliated authors.

Mar 26, 2026

Custom Crystallization for Flexible Transparent Electronics

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.

Amritha Anup holds a silk cryogel next to a silk-spun cocoon.

Mar 26, 2026

Engineering Silk for the Bone-Tendon Interface

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.

Ian Baker poses while accepting the Sherby Award

Mar 26, 2026

Award for Alloys

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.

Mar 19, 2026

Early Detection of Hidden Internal Bleeding

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.

Mar 12, 2026

New Design Strategy for Solid-State Batteries

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.

Headshots of Zequn Chen and Wesley Marrero

Mar 12, 2026

Fair AI and Optimization in Healthcare

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.

Jiahui "Gary" Luo standing with his research poster

Mar 05, 2026

Better Liver Transplant Decisions

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.

Figure depicting reversible all-liquid conversion path

Feb 26, 2026

Next-Gen Batteries for Grid Storage

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.

Feb 19, 2026

Machine-Learning-Enabled Phototransistors

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.

Research figure depicting transfer learning

Feb 12, 2026

Better Metamaterial Design Via Transfer Learning

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.