The School of Engineering’s 2026 Hazeltine Innovation Awards, grants to underwrite early-stage faculty research projects with the potential to attract external funding and create a lasting broadbased impact, have been awarded to principal investigators Joy Zeng, Daniel Harris, Marissa Gray, Lucas Caretta, Roberto Zenit and Chixia (Trisha) Tian.
Established in 2023 and presented annually, these awards aim to spur bold and innovative research that is transformative, impactful, and has the potential to significantly advance a field; to lead to creative and revolutionary approaches to STEM education and/or development of a diverse and inclusive workforce; or catalyze new knowledge and discoveries through purchasing or development of new instrumentation or equipment. The goal is to enable faculty and their student researchers in the most exciting and important areas of innovation and discovery.
The Hazeltine Innovation Awards were established by Aneel Bhusri ’88 and named after engineering’s beloved professor emeritus Barrett Hazeltine, who joined the Brown faculty in 1959 and is best known for his classes in management and entrepreneurship that helped to launch countless careers in business and nonprofit leadership. “ENGN 9: Management of Industrial and Nonprofit Organizations,” which Hazeltine taught for more than 40 years, continues to be one of the most popular courses at the University.
The following one-year projects were accepted for funding this year:
Electrochemically Converting Methane into Benzene (Principal Investigator Joy Zeng)
Electrochemistry has the potential to make chemical manufacturing more sustainable by replacing extreme operating conditions, such as toxic reagents, high temperatures, and high pressures, with electrical voltage, which can be sourced from renewables. However, a central challenge is the limited scope of electrocatalytic reactions. For example, aromatic hydrocarbons such as benzene, toluene, and xylene are among the world’s top 20 commodity chemicals, yet there is little to no precedent for their electrochemical synthesis. In this work, Zeng aims to develop electrochemical methods for the synthesis of benzene.
The central hypothesis is that traditional thermochemical catalysts for the synthesis of benzene can be “imported” into electrochemical reactors, and that upon exposure to electrochemistry-suitable conditions, will perform the analogous electrochemical reaction. Specifically, investigators propose that catalysts for thermochemical methane dehydroaromatization (thermo-MDA), which converts methane into benzene and hydrogen gas, can be adapted to operate as electrochemical MDA (electro-MDA) catalysts, converting methane into benzene, protons, and electrons.
This work will focus on an initial demonstration of both the design principle and the target chemistry. Investigators will design and construct a high-temperature (>700 °C) electrochemical reactor capable of delivering gasphase reactants with precise voltage control, develop electronically conductive catalyst systems, and interrogate the underlying reaction mechanisms.
Together, these efforts will establish the feasibility of an unprecedented and challenging electrocatalytic transformation while introducing a new design strategy for expanding electrocatalytic reaction space. Both contributions are significant for ultimately broadening the scope and impact of sustainable chemical manufacturing. The Hazeltine support will allow rapid development of the experimental infrastructure, along with the expertise needed to establish this concept and position it for future external funding.
Autonomous Discovery of Bioreactor Designs via Agentic Multi-Fidelity Bayesian Optimization (Principal Investigator Associate Professor Daniel Harris, with co-Investigators Associate Professor Miguel Bessa and Ph.D. candidate Elvis Aguero)
Traditional animal agriculture is a significant contributor to climate change, environmental degradation, loss of biodiversity, pandemic risk, and human and animal welfare concerns. To address these issues, a radical transformation in our food system is necessary. The emerging technology of cultivated meat, wherein actual animal tissue is grown for food in industrial-scale bioprocesses, is a promising candidate. Central to the production process (and many related biopharmaceutical processes) is a bioreactor, which induces mixing and mass transfer to promote efficient biomass production.
Bioreactor design requires navigating a high-dimensional parameter space where geometry, operating conditions, and fluid mechanics jointly determine oxygen delivery and cell viability. Physics-accurate simulation of this space has recently become tractable for individual designs, but remains prohibitively expensive as a search strategy, leaving the design space largely unexplored. The researchers propose addressing this outstanding challenge with a multi-fidelity Bayesian optimization framework built on the open-source Basilisk solver. Low-fidelity simulations (reduced resolution) will be fused with a sparse set of high-fidelity direct numerical simulations through a nonlinear surrogate that captures cross-fidelity correlations without linearity assumptions, substantially reducing the computational budget required to identify high-performing designs. To further accelerate discovery, investigators also propose an autonomous framework wherein a language-model agent operates between optimization iterations: it interprets surrogate training diagnostics and adjusts network architecture and sampling strategy without human input, accepting changes only when held-out performance does not degrade. This enhancement eliminates the expert oversight that currently prevents multi-fidelity surrogates from running unattended on high-dimensional problems.
The pipeline will be released as an open-source package applicable to any bioreactor geometry where multiphase flow governs mass transfer. The target is a 70 percent reduction in simulation cost relative to single-fidelity Bayesian optimization, prediction accuracy exceeding 95 percent on mass transfer metrics, and at least one novel operating protocol that measurably outperforms current design baselines.
Scaling the “Pathway to Ph.D.” Model: A Cross-Disciplinary, Customizable Framework for Engineering Master’s Student Advancement to Doctoral Study (Principal Investigator Lecturer Marissa Gray, with co-Investigator Assistant Teaching Professor Chixia (Trisha) Tian, and Director of Strategic Master’s Operations and Student Engagement Tina Garfinkel)
Engineering master’s students pursue diverse career pathways, including industry, entrepreneurship, and doctoral study. While universities provide strong support for industry pathways, structured preparation for Ph.D. programs remains limited. This gap is especially significant given the complexity of the application process and the importance of sustained research experience. To address this gap, investigators developed the “Pathway to Ph.D.” program within the Brown University Biomedical Engineering (BME) master’s program. This selective, cohort-based workshop model was designed to increase clarity, confidence, and preparedness for doctoral applications. Initial implementation demonstrated strong engagement and promising outcomes, including increased student confidence and improved understanding of the application process. Of 14 participants, 13 applied to Ph.D. programs and six have committed to programs, with additional decisions pending. Informal participation from students outside of BME further suggests broader applicability across engineering disciplines.
This proposal seeks to expand the Pathway to Ph.D. program into a cross-disciplinary, customizable framework across the School of Engineering. The program will integrate a shared core curriculum, discipline-specific breakout sessions, and individualized mentorship. This content will be delivered through a six-workshop series aligned with the Ph.D. application cycle. A subset of participants will also receive competitive summer research stipends to support sustained research engagement and strengthen application competitiveness. Program effectiveness will be evaluated using quantitative and qualitative measures, including pre/post surveys, tracking of application and admissions outcomes, and participant feedback. This data will inform continuous improvements and identify high-impact program components.
This work will result in a scalable, evidence-based model for preparing engineering master’s students for doctoral study. By combining structured advising with targeted research support, the proposed program addresses both informational and experiential barriers to Ph.D. access and has the potential to be broadly adopted across institutions.
Acquisition of a Potentiostat for Cross-Disciplinary Electrochemical Research (Principal Investigator Assistant Professor Lucas Caretta, with collaborators Assistant Professor Joy Zeng, Associate Professor Feng Lin, and Professor Brian Sheldon)
Understanding electrochemical and chemo-mechanical processes at interfaces is a central challenge in next-generation energy storage, catalysis, and functional materials. Emerging platforms, including thin-film solid-state batteries, geometrically confined energy systems, ferroic–ionic materials, and low-concentration adsorption processes, operate in regimes where currents, charge densities, and reaction volumes are orders of magnitude smaller than in conventional systems. As a result, key physical processes remain inaccessible with standard electrochemical instrumentation, creating a fundamental barrier to discovery.
This proposal seeks to acquire a state-of-the-art, ultrahigh-sensitivity potentiostat, upgradable to high-bandwidth impedance spectroscopy, to enable transformative, cross-disciplinary electrochemical research at Brown University. Integrated within a newly established thin-film synthesis and electrochemistry laboratory, the instrument will serve as a shared resource supporting collaborative efforts across materials science, chemical engineering, and solid mechanics.
The proposed capability will enable quantitative measurements of ionic transport, interfacial kinetics, and degradation in thin-film solid-state batteries, advancing understanding of dendrite formation, interphase evolution, and chemo-mechanical coupling at buried interfaces. It will also unlock new research directions in geometrically confined energy storage systems, including fiber-integrated batteries, where electrochemical processes occur under extreme spatial and mechanical constraints. In parallel, the instrument will enable fundamental studies of interfacial adsorption and electrocatalytic processes in environmentally relevant, low-concentration regimes, including PFAS capture and degradation. As a shared, enabling platform, it will accelerate research, training, and collaboration, positioning Brown as a leader in electrochemical science in confined and emergent material systems.
Smart firefighting with designer fluids (Principal Investigator Professor Roberto Zenit)
Efficient fluid atomization is central to modern firefighting, where rapid heat extraction and flame suppression depend strongly on droplet size distributions and spray dynamics. However, conventional water-based sprays are limited by poor control over droplet formation, leading to suboptimal penetration, excessive runoff, and inefficient resource use. This project aims to transform firefighting atomization strategies through the combined design of viscoelastic fluids and physics-informed machine learning models.
Researchers propose to engineer tunable, environmentally safe viscoelastic additives that modify fluid rheology – particularly shear viscosity and elasticity – to control ligament breakup and stabilize droplet formation under high-shear conditions. Through systematic rheological characterization and high-speed imaging of atomization, investigators will quantify how non-Newtonian properties influence droplet size distributions, and spray structure, generating a comprehensive dataset linking formulation, flow conditions, and performance. Particular emphasis will be placed on conditions relevant to aerial and ground-based wildfire suppression, including crosswinds and long-range spray dispersion.
Building on this foundation, interpretable, physics-based machine learning models that embed governing fluid dynamics into data-driven architectures will be developed. These models will enable inverse design of both fluid formulations and nozzle configurations to maximize efficiency and suppression effectiveness. The integration of rheologically engineered fluids with predictive, generalizable models represents a fundamental departure from traditional trial-and-error spray optimization. For forest wildfires, this approach has the potential to dramatically improve suppression efficiency by producing droplets that resist premature evaporation, enhance canopy penetration, and adhere more effectively to vegetation. This could reduce the volume of fluids required, extend operational reach in aerial and ground firefighting, and improve containment in rapidly spreading fires under extreme conditions.
Beyond firefighting, the proposed framework has broad significance across engineering fields where atomization is critical, including agricultural spraying, thermal management, spray cooling of electronics, fuel injection in combustion systems, additive manufacturing, and pharmaceutical aerosol delivery. By enabling precise, physics-guided control over droplet formation in complex fluids, this work introduces a new paradigm for spray design. Its novelty lies in coupling material design with interpretable machine learning to achieve robust, transferable predictions across regimes. The anticipated impact is a step change in atomization efficiency and adaptability, with implications for sustainability, resource conservation, and performance in diverse industrial and environmental applications.
Distributed Engineering Lab Initiative: Embedding Research-Based Experiential Learning Across Laboratories (Principal Investigator Assistant Teaching Professor Chixia (Trisha) Tian, with co-Investigators Professor Anita Shukla, Associate Professor Vikas Srivastava, Associate Professor Kareen Coulombe, Assistant Professor Theresa Raimondo, Lecturer Marissa Gray, and Senior Research Engineer Zachary Saleeba)
Hands-on experimental learning is essential in engineering education, yet access to advanced instrumentation is often limited to students engaged in research labs. This creates inequities in experiential learning and limits students’ exposure to real-world engineering tools and workflows. At the same time, the School of Engineering hosts a rich ecosystem of research laboratories and instrumentation that remains largely inaccessible for structured educational use.
This project proposes a Distributed Engineering Lab Initiative, a scalable and transformative approach that embeds experiential learning within existing research laboratories. Instead of building centralized teaching labs, this model supports lab-embedded master’s student fellows to translate active research tools and projects into structured educational modules. Each participating lab will develop a module centered on a key instrument or technique, including prelab materials and recorded pre-lab lecture, guided and recorded lab experiences, and post-lab learning activities connected to real biomedical engineering problems and industry practices.
The pilot will involve four laboratories within biomedical engineering, generating a library of reusable modules integrated into existing courses. By embedding educational development within research environments, the model ensures feasibility, sustainability, and continuous updating alongside ongoing research activities. An evaluative component will assess student learning gains, confidence in experimental settings, and engagement with research pathways. Faculty feedback and participation metrics will inform scalability across engineering disciplines.
This initiative represents a novel and scalable paradigm for STEM education, shifting from centralized laboratory models to a distributed, research-integrated learning infrastructure that expands access, supports workforce development, and fosters a more inclusive and practice-ready engineering student population. This project is designed as a pilot that generates both scalable educational infrastructure and evidence-based evaluation data, positioning it for future funding through National Science Foundation’s Improving Undergraduate STEM Education program and dissemination as scholarly contributions to undergraduate STEM education.
