Research Quick Takes

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.

Feb 12, 2026

Diagnosing Non-Melanoma Skin Cancer

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).

Feb 05, 2026

Top Biophotonics Device

Professors Petr Brůža and Brian Pogue—along with former Thayer professor and now CTO of DoseOptics, Venkat Krishnaswamy—attended the Prism Awards Celebration at SPIE's Photonics West conference where DoseOptics' Clinical Cherenkov Imaging was listed in the top three biophotonics devices of 2025. (See Pogue's recent Commercialization Report published in Biophotonics Discovery) "What a treat to be at the world's largest biomedical optics conference with my brilliant colleagues, designers of the BeamSite Cherenkov Imaging camera. In only one year, with over 40 installations in place and planned worldwide, this technology will help keep radiation therapy advances safe for all patients," said Pogue.

Priyanshu Alluri, Zequn Chen, and Wesley Marrero

Jan 29, 2026

A Socially-Fair Framework for Measuring Student Well-Being

Priyanshu Alluri '26, PhD student Zequn Chen, and Professor Wesley Marrero presented a socially-fair framework, published in Journal of the American Medical Informatics Association - Open, that ensures homogeneous clustering performance across demographic groups while minimizing within-cluster variability. "The study integrates fairness considerations into clustering algorithms to reduce discrepancies in risk stratification and provides insights into socioeconomic drivers of student well-being," said Marrero.

Jan 29, 2026

Making Skin Stick

Hixon Lab PhD candidate Adelaide Cagle is first-author on "Keratin Additive for Cellular Adhesion in Transcutaneous Prosthetics," with co-authors including NH BioMade trainee Lois Szulc, Jack Flaggert '26, Yowis Arias '24 Th'25, Annika Nikhar '26, and Professor Katie Hixon. Published in Journal of Tissue Engineering and Regenerative Medicine, the paper explores how incorporating hydrolyzed keratin into electrospun and cryogel scaffolds enhances cell adhesion and proliferation. "This study highlights a promising biomaterials strategy to improve dermal integration at skin-implant interfaces," said Hixon.

Jan 22, 2026

Converting Carbon Emissions into Fuels for Net Neutrality

PhD students Huilin Qing and Baiheng Li, and Professor Weiyang "Fiona" Li co-authored "Protonation pathway for CO2 reduction mediated by coordinated H2O on active sites" published in Nature Communications. This work, presented by Qing, was nominated for "Best Poster Award" at the 2025 Materials Research Society Fall Meeting which drew over 6,000 attendees from 50+ countries.

Jan 15, 2026

More Profitable Strategies

PhD student Bruno Miranda Henrique and Professor Gene Santos co-authored "Cryptocurrencies trading using Parrondo's Paradox" published in the International Review of Economics & Finance. The paper applies a concept from game theory and physics known as Parrondo's Paradox, in which two losing strategies can be combined to produce a winning outcome. "The paper shows that by systematically switching between three cryptocurrencies according to simple, predefined rules, investors can often achieve higher returns than a traditional buy‑and‑hold strategy. Against the backdrop of recent cryptocurrency price swings and heightened market uncertainty, the research adds to ongoing discussions about whether systematic trading strategies can offer an edge over passive investment in digital assets," said Santos.

Jan 08, 2026

Cryosphere Science Lecture

Professor Hélène Seroussi was selected to give the John F. Nye Lecture at the Cryosphere section reception of the AGU Fall Meeting. The award recognizes recent accomplishments and outstanding ability to communicate scientific research. "My talk was about 'Preparing for Sea-Level Rise: Are ice sheet models up to the challenge?' which discussed current capabilities and challenges of ice sheet models to help improve predictions of sea-level rise," said Seroussi.

Jan 08, 2026

Better Airline Crew Recovery Plans

Professor Vikrant Vaze co-authored "Large-Scale Airline Crew Recovery Using Mixed-Integer Optimization and Supervised Machine Learning" published in Transportation Science. Based on work by Vaze's co-advisee at MIT, Ahmet Esat Hizir (pictured), this research won the "Best Innovation" award at AGIFORS' 2024 Crew Management Study Group Meeting. "By teaching a computer to learn from past disruption recovery attempts and then guiding a powerful optimizer with those lessons, we have built a fast, flexible tool that helps airlines get their crews back on schedule more efficiently, cut costs dramatically, and reduce the ripple effects on passengers," said Vaze.

Dec 18, 2025

LISP Lab at NeurIPS

Three members of Professor Peter Chin's LISP Lab—PhD students Mai Pham and Junyan Cheng, and post-doc Xavier Cadet—presented at the Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025) which drew a record-breaking 26,000 attendees. Their presentations addressed optimal auction design, multi-agent cooperation, and language models for autonomous scientific discovery.