2025 Call for Seed Grant Proposals

Abstract

Boiling of refrigerants enables high heat dissipation and is critical in heat pumps, refrigeration, solar power systems, and emerging microsystems. Understanding refrigerant boiling can drive the development of clean technologies for process heat, cooling, and power while reducing greenhouse gas emissions by replacing harmful refrigerants with eco-friendly alternatives. This project aims to enhance boiling heat transfer using next-generation low-global-warming-potential refrigerants. We will conduct pioneering experiments using advanced optical and infrared diagnostics, paired with engineered surfaces, to identify designs that maximize performance. These surfaces will be tested in prototypical conditions, focusing on applications such as refrigeration systems, concentrated solar power and passive, thermally powered cooling. Insights gained will also benefit broader water-based thermal systems, including steam generation and solar cooking.

MIT PI
Matteo Bucci, Associate Professor, Department of Nuclear Science & Engineering

PT PI
Prof. Ana Moita Universidade de Lisboa Instituto Superior Técnico

Abstract

Offshore wind energy leverages the high intensity and consistency of oceanic winds, playing a key role in the transition to renewable energy. As energy demands grow, larger turbines are needed to optimize power generation and reduce costs. However, upscaling introduces structural design and manufacturing challenges. Designing better wind turbines is therefore essential. A key challenge is the time-consuming nature of multiphysics simulations, involving interactions between wind, waves, and ocean currents, limiting exploration of design alternatives. While AI provides a promising way to help alleviate this challenge, most current AI-accelerated models lack the capability to capture multi-physics, leading to untrustworthy or structurally invalid designs. We propose a physics-guided generative design framework that combines Graph Neural Nets (GNNs) and diffusion models, trained on high-fidelity multiphysics simulation data and validated against experimental data from full-scale offshore structures, to design and evaluate offshore structures. This approach enables faster development, improved accuracy, and scalable digital twins for Portugal’s partners in the offshore wind sector.

MIT PI
Faez Ahmed, Associate Professor, Department of Mechanical Engineering

PT PI
Sérgio Tavares (University of Aveiro),
Filipe Magalhães (University of Porto)

Abstract

The motivation for this project is the recent Iberian blackout. We study it by modeling and simulating multi-country interconnected electric power grid affected by this event using modified publicly available data of European Union grid known as PECASE. We will then use extended AC Optimal Power Flow software to assess the most vulnerable parts of the grid, and, to, consequently, reconstruct the events which led to massive loss of electricity service. Of particular interest will be to understand the role of coordinating inter-countries power exchanges, notably between France and Spain for preventing system voltages from collapsing. In parallel, we will introduce adaptive power electronically switched control of intermittent resources for stabilizing voltage and frequency during such extreme events. The study will set a basis for general framework needed in other parts of the world for the same purposes, including US. Results will be demonstrated using Power Digital Twin at MIT.

MIT PI
Marija Ilic, Senior Research Scientist, Department of Electrical Engineering and Computer Science

PT PI
Pedro Carvalho Full Professor, IST Técnico Lisboa, Institute for Systems and Computer Engineering, Research and Development

Abstract

We propose to develop diodes and prototype solar cells using chalcogenide perovskite thin films. We will deposit BaZrS3 thin films, and BaZr(S,Se)3 alloys with tunable band gap, using previouslyestablished methods of molecular beam epitaxy. We will select contact materials, and will develop methods to deposit and evaluate the contact materials as thin films. We will form p-n heterojunction diodes and test their electrical performance. Finally, we will evaluate the photovoltage and solar-cell performance of the top-performing diodes. Our project will build on a new collaboration between MIT and the International Iberian Nanotechnology Laboratory (INL), leveraging world-leading expertise in chalcogenide perovskite deposition (MIT) and in scanning probe microscopy (INL). We will be the first to characterize and optimize chalcogenide perovskite thin-film diodes and photovoltaic performance, which will be a major advance in this growing research field.

MIT PI
Rafael Jaramillo, Associate Professor, Department of Materials Science and Engineering

PT PI
Sascha Sadewasser
Principal Investigator, Laboratory for Nanostructured Solar Cells International Iberian Nanotechnology Laboratory (INL)

Abstract

Tropical forests regulate Earth’s climate, yet deforestation, mega-droughts, windthrows, seed-dispersal collapse and runaway fires threaten to push them beyond tipping points. We will fuse high-resolution remote-sensing data, causal machine-learning and resampling of legacy plots to pinpoint where and why these thresholds are crossed. A “twin-forest paradox” design—Amazonia’s declining sink versus Africa’s stable counterpart (Fig. 1)—isolates the disturbance combinations that flip forests from sink to source. Soil re-cores of 40 Amazon plots, first sampled two decades ago, will be isotopically analysed at INIAV laboratories in Portugal to test whether stressed soils buffer or amplify canopy loss. The project will result in driver-resolved early-warning indicators and a public dashboard ready for climate modeling and REDD+ accounting. Portuguese partners within the TERRA network contribute Europe’s burn-scar atlas, fire-physics labs and Copernicus cloud, ensuring Amazon-derived alerts translate directly into wildfire-resilient management of Mediterranean pine and eucalyptus landscapes.

MIT PI
Cesar Terrer, Associate Professor, Department of Civil and Environmental Engineering

PT PI
José Miguel C. Pereira, Professor, Forest Research Centre, School of Agriculture, University of Lisbon.
Ruben Heleno, Associate Professor, Centre for Functional Ecology, University of Coimbra

Abstract

To answer urgent questions about climate change, food security, and sustainability, it is necessary to mechanistically understand microscale soil processes. From interviews with experts in soil microbial biogeochemistry, it is clear that current soil characterization techniques provide inadequate spatial resolution, analyte variety, and levels of perturbation to study dynamic processes in the challenging soil environment. We propose a novel sensing platform to detect diverse soil analytes in two dimensions on the microscale, composed of a planar matrix housing whole-cell microbial biosensors that contacts the soil through an engineered membrane interface. This project is divided into three stages: (1) designing and modeling the sensor platform; (2) building and validating three sensors to map three diverse soil analytes (a plant metabolite, a microbial electron acceptor, and a contaminant); and (3) testing the sensors by using them to investigate scientific questions of interest for the three model analytes.

MIT PI
Rohit N. Karnik, Professor, Department of Mechanical Engineering

PT PI
Paula Morais
Associate Professor with Habilitation, Department of Life Sciences, Faculty of Sciences and Technology, Laboratory ARISE
University of Coimbra

Abstract

In this project we proposed to develop algorithms and software for coordinated path planning of multi-robot marine robotic platforms. The goal of this work is to enable effective deployments of large numbers of vehicles, both at the surface and underwater, with very little need for operator control. The new algorithms will leverage MIT’s unique software libraries for multi-objective optimization and decentralized decision-making available in MIT’s online moos-ivp.org open-source project. The initial phase of this project will focus on collaborative surface vehicle autonomy, and the second phase will focus on nested swarm clusters of both surface and underwater vehicles. The end-of-project target experiment is deployment of surface and underwater vehicles in Pico Straight in Horta Portugal. MIT will validate Year 1 algorithms in its 100-node sim cluster and using 10 unmanned surface vehicles at the MIT Marine Autonomy Lab on the Charles River. Prof. Haitong Xu from Instituto Superior Técnico, University of Lisbon, will develop complementary algorithms and visit MIT lab for the Spring Semester 2026, co-testing autonomy capabilities.

MIT PI
Michael Benjamin, Principal Research Scientist, Department of Mechanical Engineering

PT PI
Prof. Haitong Xu, Instituto Superior Técnico, University of Lisbon
Prof. Carlos Guedes Soares, Instituto Superior Técnico, University of Lisbon

Abstract

Bispecific antibody drugs provide transformative cures, but they are often refractory to modern bioproduction methods. Here, we will apply large-scale data and AI to integrate manufacturing with early bispecific antibody discovery. We will collect high-throughput manufacturability data and train an AI-based drug design algorithm to enhance drug activity, potency, and product quality. First, we will clone large libraries of bispecific antibody variants into manufacture-ready cell lines and growth conditions. Next, each bispecific antibody-producing cell will be captured emulsion microreactor droplets to analyze functional performance in manufacturing-like conditions. High-throughput sequencing will be used to analyze test results en masse, and AI models will be trained to identify critical features of manufacturable molecules. Finally, we will apply trained AI algorithms to generate new manufacturing-ready bispecific designs and evaluate their improvement related to controls. If successful, we will establish a seamless transition between early discovery and large-scale manufacturing for bispecific antibody drug products.

MIT PI
Brandon DeKosky, Associate Professor, Department of Chemical Engineering

PT PI
Paula Alves, CEO of iBET, Professor at NOVA University of Lisbon, Portugal
Antonio Roldao, Head of Cell-based Vaccines Development Lab, Coordinator of Late-stage R&D and Bioproduction Unit, iBET, Portugal
Jose Escandell, Principal Scientist, Animal Cell Technology Unit iBET, Portugal
Patrícia Alves, Coordinator of Analytical Services Unit, iBET, Portugal

Abstract

Near the sea surface, interactions between small-scale ocean processes, turbulence, and Earth’s climate are not fully resolved by either models or observations. However, we can make decisive progress by fusing models with data through innovative AI techniques. We propose to advance understanding by integrating MIT’s ocean models with satellite data from the Azores ESA Lab. Our focus is on two critical regions for climate change, where well developed AI techniques can help address key questions about small-scale processes. In the Azores region, we will forecast thermal fronts, mesoscale eddies, internal waves, and other mixing indices. Additionally, we will train AI to detect and predict internal waves at the equator during La Ninã seasons, where their interactions with thermal fronts have important implications for climate. In both cases, simulation data from MIT models will be used to train generative AI and fine-tune foundation models, which will then be applied to satellite data.

MIT PI
Gael Forget, Research Scientist, Department of Earth, Atmospheric & Planetary Sciences

PT PI
Dr. Adriana Ferreira (AIR Centre)
Dr. Jorge M. Magalhães (CIIMAR)
M. João Pinelo (AIR Centre)
Pr. José da Silva (FCUP)