2024 @PT Call for Exploratory Proposals

Scientific Area: Earth Systems: Ocean to near Space

Abstract: The ocean plays a pivotal role in the blue economy. However, the increasing intensity of economic activities exerts significant pressure on the marine environment, thereby threatening ocean health. Sustaining ocean health requires advanced observation systems capable of delivering persistent, wide-area monitoring. Autonomous surface vehicles (ASVs) provide a promising solution; however, single ASVs face limitations in endurance, communication, and data storage. Multi-ASV systems can overcome these barriers, but coordinating their operations poses significant challenges due to underactuated dynamics, poor manoeuvrability, and the risk of collision in confined or dynamic ocean environments.

This project proposes the development of a distributed intelligent ocean monitoring system using multi-ASVs, with the long-term goal of enabling efficient and adaptive ocean observation. In collaboration with Dr. Michael Benjamin at MIT, the study focuses on creating an onboard distributed decision-making framework for optimal path planning. The system will employ multi-objective optimisation methods to ensure complete coverage of designated areas while avoiding collisions and accounting for the kinematic constraints of ASVs. Three custom-built ASVs at CENTEC and eight Heron ASVs at MIT will be deployed to conduct preliminary experimental tests, validating the proposed approach through evaluation of coverage efficiency and real-time navigation performance.

The project outcomes are expected to provide a cost-effective, scalable, and intelligent solution for sustainable ocean observation, with broader benefits for periodic monitoring and offshore inspection. By advancing distributed autonomy in multi-ASV systems, the project directly contributes to the development of measurement technologies aligned with Earth systems research priorities, particularly in ocean monitoring.

PT PIs:
Haitong Xu, Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico

MIT PIs:
Michael Benjamin, Center for Ocean Engineering, Department of Mechanical Engineering, MIT

Scientific Area: Climate Science & Climate Change

Abstract:
Owing to the exponential increase in the quantity of information to be stored and processed, there is a need for lower energy, faster and more cost-effective computing. To achieve this, new computing paradigms are highly sought after. If current trends continue, the global power requirements for computing will reach global primary power production capacity by 2040. The use of complementary metal-oxide semiconductor (CMOS) to mimic brain-inspired architectures requires large memory and frequent off-chip memory access at a tremendous hardware and energy cost. The goal for the field at present is to improve the energy efficiency of computing by more than a million fold. To achieve this goal will require radical departures from von Neumann processing and ultra-scaled CMOS.

At the device level, the PI’s inspiration is the biological synapse, which is an ultra-efficient electrochemical machine working with ions in liquid medium, and combines processing and memory in one unit. These electrochemical devices can be downscaled to achieve high-density, and that can respond at ultrafast time scales under strong driving forces.

This project will advance a novel ionic computing device that we call Electrochemical Ionic Synapse (EIS) that is inspired from how our biological synapses function. An EIS consists of three key functional layers: reservoir, electrolyte, and channel.

In order to reduce the operating voltage while operating at nano-second speed regime, we will explore promising materials based on binary oxides, such as HfO2 and CeO2. Use of a good proton conductor (nano-porous ferroelectric orthorhombic HfO2-based electrolyte), together with a Pr doped CeO2 channel will enable a high-quality interface with low resistance to proton transfer. High proton conductivity of the electrolyte and low interface resistance promise to improve the energy efficiency as well as reduce the operating voltage and improve the endurance of the EIS devices. In addition, while ferroelectric HfO2 is currently being investigated for low-power and ultra-fast in-memory computing and neuromorphic devices, the experimental demonstration of the potential use of these materials as electrolyte in EIS devices was not yet done. Therefore, we will use ferroelectric doped-HfO2 as the electrolyte (as we do in ferroelectric capacitors for memory and energy storage capacitors. Ultimately, this project will advance a novel device technology, to reduce the energy consumption and CO2 emissions of computing, while advancing the abilities of artificial intelligence hardware.

PT PIs: José Pedro Basto da Silva, School of Sciences, University of Minho

MIT PIs: Bilge Yildiz, Department of Materials Science and Engineering, MIT

Scientific Area: Climate Science & Climate Change

Abstract:

PT PIs: João Avo, Associação para a Inovação e Desenvolvimento da FCT (NOVA.ID.FCT)

MIT PIs: Timothy M. Swager, Department of Chemistry, MIT

Scientific Area: Sustainable Cities

Abstract:
The Green Transition is one of the most pressing and complex challenges of our time. Cities are at the frontline of this transformation, facing increasing population density, growing mobility demands, and environmental pressures that threaten infrastructure capacity, air quality, and overall sustainability. At the same time, new mobility paradigms—electric, connected, automated, and data-driven—are reshaping the way people and goods move. The convergence of these dynamics creates not only opportunities, but also significant risks if transition efforts are not carefully designed, measured, and adapted over time.

A key gap lies in the lack of robust indicators and methodologies to evaluate the quality and resilience of Green Transition projects. Too often, initiatives focus narrowly on technology adoption or infrastructure expansion without ensuring long-term adaptability and systemic impact. This project tackles that challenge by providing tools that enable cities to make informed, future-proof decisions.
Our vision is to create a comprehensive framework for Smart Sustainable Cities that integrates multiple domains—mobility, infrastructure, energy, and environment—through a dynamic, data-driven approach. By combining System Dynamics modeling, AI machine learning, and Digital Twin (DT) technology, the project will allow stakeholders to model complex urban systems, test scenarios in real time, and design adaptive strategies that evolve with changing conditions.

The framework will be grounded in quality and resilience indicators that help decision-makers evaluate projects not only by immediate outcomes, but also by their capacity to endure, scale, and deliver benefits over decades. Practical applications include optimizing electric vehicle (EV) charging networks, improving air quality, reducing congestion, and strengthening infrastructure planning. By addressing these challenges holistically, the project will contribute to cities that are not only greener, but also more resilient, inclusive, and equitable.

Ultimately, the project aims to bridge the gap between cutting-edge research and real-world application, empowering cities to make the Green Transition successful, sustainable, and durable.

PT PIs: André Mendes de Carvalho, Associação para a Inovação e Desenvolvimento da FCT (NOVA.ID.FCT)

MIT PIs: Donna H. Rhodes, Sociotechnical Systems Research Center, MIT

Scientific Area: Climate Science & Climate Change

Abstract:
The efficient water use (WU) in agriculture is one of the main challenges under climate change (CC). The growing scarcity of water, combined with CC precipitation anomalies, imposes the need to improve WU accuracy for irrigation. Therefore, the scientific community has been encouraged to develop technologies for the improvement and transferability of irrigation programming based on crop water requirements, with real-time information, enabling a prompt reaction to changing environmental conditions. These developments must consider the socio-economic conditions of different regions of the world. Thus, new solutions need to be low-cost (LC), easily reproducible, and easy to maintain.

Digital tools have contributed to crucial advances in irrigation and solutions are available on the market. However, the associated costs and limited specialized technical support led digital agriculture (DA) to be exclusive to a small fraction of farmers. The objective of this study is to present an innovative contribution to DA that resides in the development of SOFIS (Smart Orchard Fertigation System) defined as a LC intelligent system. SOFIS hardware and software are designed to use LC approaches to control WU in orchards and vineyards, with the aim of establishing and validating a machine learning system for automated real-time data analysis.

The SOFIS hardware consists of a set of devices and sensors that estimate soil water directed to the plant and to the atmosphere. The system acquires sensor data and sends it to a web platform. In the soil, its sensors measure soil water content, evaporation, salinity, and temperature. The plant has sensors that measure sap flow (SF) and atmospheric sensors measure the air temperature and relative humidity inside the canopy. Soil water evaporation is assessed using an innovative automated weighing device (mLy), suitable for continuous use and Peltier cells estimate the latent heat flux in the soil. The present project will improve and validate LC SF and mLy sensors. Regarding data science (SOFIS software), it focuses on the Agriculture 4.0 approach, using AI for the treatment, processing, statistical analysis, and systematization of data in real-time and using a machine learning approach to take decisions about watering, frost, disease and forecasting events (pests). The aim of the project is to develop a LC, customizable, modular digital tool based on proximal sensors, useful in the context of irrigation management, allowing a quick reaction and adaptation to environmental conditions, with improved accuracy. The solution found allows an easy transition to the digital world due to its LC, open access, and specialized customer support.

PT PIs: Maria Teresa Gomes Afonso do Paço, Instituto Superior de Agronomia (ISA)

MIT PIs: Amos G. Winter, Department of Mechanical Engineering, MIT

Scientific Area: Climate Science & Climate Change

Abstract:
The global climate crisis demands innovative solutions to accelerate the transition to renewable energy sources, with photovoltaics (PV) playing a pivotal role. Among emerging technologies, chalcogenide perovskites have shown immense promise as sustainable, high-performance alternatives to lead-based perovskites. BaZr(S,Se)3 (BZSSe), a member of this material family, exhibits strong optical absorption, thermal stability, and bandgap tunability, making it a compelling candidate for next-generation PV applications. However, critical knowledge gaps remain regarding its nanoscale electronic behavior, surface stability, and suitability for device integration, which this project aims to address.

The project leverages advanced thin-film samples of BZSSe provided by MIT, produced using molecular beam epitaxy with precise control of the sulfur-to-selenium (S-to-Se) ratio. These samples enable systematic investigation of the material’s optoelectronic and surface properties. In addition, INL brings expertise in advanced scanning probe microscopy (SPM) techniques such as conductive atomic force microscopy (c-AFM) and Kelvin probe force microscopy (KPFM). These tools will provide high-resolution insights into charge carrier behavior, surface states, and defect characteristics.

The project is organized into two main tasks. First, the bulk properties of BZSSe thin films will be characterized to assess the impact of the S-to-Se ratio and preparation methods on charge transport properties. Second, surface behavior under environmental exposure will be studied to evaluate the effect of air-induced native oxide layers and light-driven processes on charge carrier dynamics. The correlation of surface photovoltage spectroscopy (SPV) with c-AFM will further elucidate the material’s response to light at the nanoscale, offering critical insights into potential challenges for photovoltaic device applications.

The novelty of this work lies in its focus on BZSSe, a chalcogenide perovskite with unique structural and optoelectronic properties, and its comprehensive analysis of both bulk and surface phenomena. The project is expected to advance understanding of BZSSe stability and electronic properties, informing future device engineering strategies. Ultimately, this research seeks to accelerate the development of chalcogenide perovskites as high-performance active layers for PV devices, advancing the global transition to sustainable energy and addressing critical climate challenges.

PT PIs: Sascha Sadewasser, Laboratório Ibérico Internacional de Nanotecnologias (INL)

MIT PIs: Rafael Jaramillo, Department of Materials Science and Engineering, MIT

Scientific Area: Digital Transformation in Manufacturing

Abstract: 
Numerical modeling and simulation tools are indispensable in the design, manufacturing and life-cycle management of modern mechanical systems. These tools enable the evaluation of various physical phenomena, such as structural, thermal, fluid, and electromagnetic effects, by solving domain specific differential equations. However, accurately capturing real-world behavior remains a significant challenge due to factors such as material property variability, geometric deviations introduced during manufacturing, and other uncertainties that often require extensive experimental validation or significant amount of operational data. Furthermore, the integration of manufacturing and assembly processes data into product digital twins is limited, necessitating labor-intensive testing to calibrate numerical models and ensure their reliability.

This project explores and evaluates innovative model updating techniques to enhance the reliability of numerical simulations. By leveraging advanced machine learning approaches, such as Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (vPINNs), the project aims to enable more accurate model calibration and updating. These methods integrate available data with physics-based models developed during the design phases, bridging the gap between theoretical predictions and real-world performance.

Building upon the groundwork laid by the MIT seed project, “Geometric Deep Learning Enhanced Multiphysics Digital Twins for Complex Product Design,” this project aims to foster synergies with TEMA-UA and INEGI, in the emerging fields of digital twins and machine learning for digital manufacturing applications, with a focus on multiphysics analysis. By advancing the precision and fidelity of virtualized multiphysics behaviors, the project is poised to drive transformative innovations across industries such as energy, aeronautics, and automotive, unlocking new possibilities for efficiency, sustainability, and design optimization.

PT PIs: Sérgio Manuel Oliveira Tavares, University of Aveiro

MIT PIs: Faez Ahmed, Department of Mechanical Engineering, MIT

Scientific Area: Climate Science & Climate Change

Abstract:
Climate change is a global challenge that transcends borders, affecting ecosystems, communities, and economies worldwide. It disrupts natural resources, increases vulnerability to extreme weather events, and imposes rising costs on infrastructure and disaster recovery. Human activities—primarily greenhouse gas (GHG) emissions from carbon dioxide (CO₂)—are the main drivers, altering Earth’s climate balance. Cities generate over 70% of global emissions, a share set to rise as urban populations hit 68% by 2050.

Mitigating climate change demands urgent, collective action. Many cities have pledged carbon neutrality, yet achieving this goal is complex. Alongside emission reductions through infrastructure upgrades, strategies must also remove CO₂ already present in the atmosphere. Nature-based solutions (NBS)—approaches inspired and supported by nature—are recognized as cost-effective tools for carbon sequestration. However, their deployment is hindered by the lack of systematic, spatially detailed frameworks that guide selection, allocation, and communication of benefits.
GreenCities seeks to accelerate urban climate neutrality by creating a scalable, evidence-based framework for integrating NBS into city planning. This framework will assist authorities, communities, and stakeholders in promoting large-scale NBS adoption. It builds on three components: (i) Fine-scale spatial analysis of emissions from critical economic sectors; (ii) Scientific evidence of the effectiveness of urban green infrastructure (GI) as NBS for carbon reduction; and (iii) Targeted spatial allocation of NBS interventions to emission hotspots.

GreenCities will generate spatially explicit CO₂ maps from annual databases, identifying hotspots across sectors and prioritizing intervention areas. Urban GI (e.g. parks, forests, and green corridors) offers significant sequestration potential, yet research often overlooks soil carbon storage and urban-specific dynamics. GreenCities will address these gaps by assessing the carbon storage potential of various GI types, in both vegetation and soil reservoirs, and examining soil carbon flux dynamics. Advanced methods, including remote sensing, field data, and machine learning, will support this effort.

The project will deliver actionable recommendations for scaling NBS, focusing on hotspots while considering local socio-economic and physical contexts. By prioritizing suitable sites, GreenCities aims to maximize co-benefits, such as equitable green space access and long-term sustainability.
Using Coimbra, Portugal—a mid-sized European city committed to carbon neutrality—as a case study, GreenCities will validate the framework by tailoring emission reduction strategies and assessing the carbon mitigation impacts of NBS. The project will develop a GIS-based decision-support tool that integrates environmental and socio-economic data to guide stakeholders in optimizing GI planning. This tool will provide spatially explicit recommendations for scaling up GI interventions.

PT PIs: Carla Sofia Santos Ferreira, Instituto Politécnico de Coimbra

MIT PIs: Cong Cong, Department of Urban Studies and Planning, MIT