2024 Exploratory Research Projects (ERPs)
The 2024 Exploratory Research Projects (ERPs) reflect the joint commitment of the UT Austin Portugal Program and the Portuguese Foundation for Science and Technology (FCT) to foster cutting-edge, collaborative research between Portugal and The University of Texas at Austin.
The projects listed below were selected by an independent international review panel appointed by FCT and address scientific challenges across Nanotechnologies and Advanced Computing, two core areas of the Program in Phase III.
FOMO-HODORFOundation MOdels for HumanOid DOmestic RObots
2025-10-01 – 2026-09-30
CoSpunTexSeamless dressing made of bioactive, co-axial wet-spun fibers for treating diabetic foot ulcers
2025-10-15 – 2026-10-14 2027-01-14
LightCureNew Implantable Device for Breast Cancer Phototherapy
2025-10-19 – 2026-10-18 2027-01-18
PiezoHeartNanostructured piezoelectric biocomposites as advanced scaffolds for cardiovascular tissue engineering market
2025-10-01 – 2026-09-30
ComplexBRAINMechanoluminescent nanotransducers for deep brain sono-optogenetics in Parkinson’s disease treatment
2025-10-01 – 2026-09-30 2026-12-31
DefCom2DDefect engineering in quantum memristors for neuromorphic computing
2025-10-01 – 2026-09-30
TARGETZ-OSRNA-Guided Epigenetic Therapy with Zoledronate for Osteosarcoma
2025-10-15 – 2026-10-14 2027-01-14
NanoBBMTecMultifunctional nano-immunotherapy to regulate STAT3 pathway and adaptive immunity in Breast Cancer Brain Metastases
2025-10-17 – 2026-10-17
FOMO-HODOR: FOundation MOdels for HumanOid DOmestic RObots
- Funding
- PT: 50,000 EUR | UT Austin 100,000 USD
- PIs
- PT — Pedro U. Lima (ISR-Lisboa/IST-ID) | UT — Peter Stone (UT Austin)
- Dates
- 2025-10-01 – 2026-09-30
Summary This project will leverage LLMs in various roles to improve robotics, aiming to harness their full potential within practical general-purpose deployment scenarios, and leveraging their advanced reasoning capabilities for the effective planning of complex tasks. As pre-trained language models have expanded in both model size and data volume, current LLMs can now function as a versatile solution for language-related tasks that involve comprehending instructions and applying the internalized knowledge to solve practical problems and engage in coherent, accurate, and human-aligned dialogues. The field of robotics research can benefit significantly from the use of LLMs. The natural language understanding and commonsense reasoning capabilities of LLMs can significantly enhance a robot’s ability to comprehend contexts and execute commands. Through conversation, natural language instructions can be translated from text prompts into machine-understandable code that triggers corresponding actions, thereby rendering robots more adaptive and flexible. The FOMO-HODOR project will advance robotic task planning grounded in large language models, aiming at the development of a computational framework for large-model-based robot control that is capable of addressing tasks of long duration and elevated complexity, such as those associated to the General-Purpose Service Robot (GPSR) test within RoboCup@Home. We argue that one such framework can constitute a FOundation MOdel for HumanOid DOmestic RObots (FOMO-HODOR), which can potentially be re-used in multiple robot deployments.
Leading Institutes ISR-Lisboa Intelligent Robots and Systems group will bring its expertise on intelligent robot systems and the use of lLMs for task planning and human-robot interaction.
UT Austin will bring its large expertise on artificial intelligence, multiagent systems and machine learning applied to robotics.
INESC-ID Human Language Technologies group will bring its expertise on natural language processing and speech processing.
CoSpunTex: Seamless dressing made of bioactive, co-axial wet-spun fibers for treating diabetic foot ulcers
- Funding
- PT: 49,964 EUR | UT Austin 100,000 USD
- PIs
- PT — Helena Felgueiras (University of Minho) | UT — Jonathan Chen (University of Texas at Austin)
- Dates
- 2025-10-15 –
2026-10-142027-01-14
Summary Diabetic foot ulcers are chronic, non-healing wounds characterized by persistent inflammation, infection, and poor vascularization. Current dressings are largely passive and fail to address these multiple biological barriers simultaneously. There is a technical gap in creating an integrated solution that can actively respond to the complex wound environment. This project addresses that gap by developing a multifunctional, biodegradable dressing capable of releasing therapeutic agents in response to local stimuli (e.g., pH, enzymes), aiming to reduce inflammation, combat infection, and promote tissue regeneration, all within a single, seamless system. The project proposes a seamless dressing composed of co-axial microfluidic-spun fibers that integrate multiple therapeutic agents within a single structure. The outer fiber layer contains carbon nanofibers and chitosan nanoparticles loaded with the CW49 peptide, providing antimicrobial and anti-inflammatory action. The inner core includes sodium alginate and hyaluronic acid for moisture regulation and tissue regeneration. This design enables a stimuli-responsive release of bioactive compounds triggered by the wound’s environment (e.g., pH, enzymes), offering a multifunctional, biodegradable solution that actively promotes healing in complex diabetic foot ulcers. This research introduces a “all-in-one” wound dressing that actively responds to the complex environment of diabetic foot ulcers, combining antimicrobial, anti-inflammatory, and regenerative actions in a single, biodegradable product. It has the potential to reduce treatment costs, lower amputation rates, and improve patients’ quality of life. For the healthcare market, it offers a scalable, high-impact solution for chronic wound care. Scientifically, it opens new research avenues in stimuli-responsive biomaterials, fiber-based drug delivery systems, and advanced medical textiles, with strong potential for translation into other biomedical and therapeutic contexts.
Leading Institutes
The University of Minho brings expertise in bioactive fiber development, textile engineering, semaless technology and biomedical applications.
The University of Texas at Austin contributes advanced knowledge in fiber spinning technologies, yarn processing, and wet-spinning technique.
Together, the partners integrate complementary skills to engineer innovative, multifunctional dressings capable of addressing complex challenges in diabetic wound care.
LightCure: New Implantable Device for Breast Cancer Phototherapy
- Funding
- PT: 50,000 EUR | UT Austin 100,000 USD
- PIs
- PT — Artur Moreira Pinto (LEPABE-FEUP – University of Porto) | UT — Jean Anne Incorvia (The University of Texas at Austin)
- Dates
- 2025-10-19 –
2026-10-18– 2027-01-18
Summary Breast conserving surgery (BCS) presents an ipsilateral breast-tumor recurrence of 2-20%, and the following radiotherapy is a burden both to the patient and to the healthcare system. Also, BCS can lead to significant breast deformity in 30-40% of the patients. Moreover, the most used strategy for breast reconstruction, fat grafting, results in 20–70% volume loss with time, because the lack of revascularization and efficient tissue repair results in tissue death and resorption. Bringing together 5 multidisciplinary institutions (FEUP, i3S, UTAD, TU/e, UTAustin), an implant will be developed, having a core composed of light-emitting diodes (LEDs) and a receiver coil system, which will be encapsulated in silicone rubber. The implant will be triggered through wireless power transfer using an external controller emitter device (smartphone-sized). Using advanced 3D-printing techniques, a PCL/2DnMat scaffold will be assembled encircling the silicone surface, envisioning to match the approximate dimensions of the tissues removed during BCS, preventing volume loss, conferring structural integrity, and a structure for cell proliferation. Moreover, it will be filled with a hydrogel containing 2D-nanomaterials/chemotherapeutics conjugates. A totally new device for more selective and effective cancer treatment and morbidity decrease will be developed. Such strategy aims to introduce additional therapeutics immediately after surgery. This project will revolutionize current approaches to deal with TNBC, by minimizing time to treatment, improving poor BCS aesthetic outcomes, overcoming limited reconstructive surgery solutions, and decreasing recurrence.
All effective developed conjugates, phototherapy approaches, scaffolds, and ultimately the integrated implantable system will be patented, later tested in vivo, and translation to clinics and industry pursued together with our consultants. All findings with potential to be commercialized or translated into industry will be patented.
Leading Institutes
The FEUP/i3S team has been collaborating for 15 years on studying 2D-nanomaterials for biomedical applications, having co-authored 30 articles and 90 conference communications on this subject.
UTAD has been developing LED-systems for the team for 6 years. TU/e has been collaborating with the PI on the 3D-printing of graphene-containing biomaterials for 7 years. UT Austin (Prof. Incorvia) and FEUP/i3S/UTAD collaborate for 4 years on exploring new 2DnMat for cancer phototherapy.
Therefore, the proposed methods and models are implemented in our team and the feasibility of the project is assured. Graphenest S.A. co-founder, Bruno Figueiredo, will be a consultant on market translation.
PiezoHeart: Nanostructured piezoelectric biocomposites as advanced scaffolds for cardiovascular tissue engineering market
- Funding
- PT: 50,000 EUR | UT Austin 100,000 USD
- PIs
- PT — Maxim Ivanov (CICECO-Institute of Materials, Department of Materials and Ceramic Engineering (DEMaC), University of Aveiro) | UT — Aaron B. Baker (Department of Biomedical Engineering, Cockrell School of Engineering, University of Texas at Austin)
- Dates
- 2025-10-01 – 2026-09-30
Summary The scientific problem/technical uncertainty addressed by this project lies in the use of microelectromechanically functionalized biocompatible polymer scaffolds as a new generation of cardiac patches and cardiac pacing devices (pacemakers). A key challenge is understanding how electrical stimulation can be tuned to elicit a specific and desired tissue response. While biomedical studies have investigated the application of electrical potential to cardiac tissues—assessing viability, morphology, and gene expression—they have largely overlooked how the fine physicochemical, mechanical, and transport properties of the materials used for in vitro cell culture influence these observed responses. The solution our research is proposing to address the scientific problem is based on the development of electroactive biopolymers such as poly(vinylidene difluoride), poly(L-lactic acid), cellulose etc. These scaffolds will be utilized to fabricate the composite material in cooperation with nanoparticles of ferroelectric and conductive materials. The final devices will be functionalized macroscopically using a corona discharge polling procedure or locally via an Atomic Force microscopy approach. In-vitro and in-vivo tests will study the developed materials' physicochemical biointerfaces and biomechanical responses from the perspectives of cellular, molecular biomechanics, and transport phenomena. The game-changing potential of our research comes from the perspective of understanding how the electrical stimulus can be tuned for the design of desired cardiac muscle cells (cardiomyocytes) and cardiac tissue responses. In this sense, the proposed microelectromechanically functionalized biocompatible polymer scaffolds will represent a new generation of piezoelectric cardiac patches and cardiac pacing devices (pacemakers) for the cardiovascular tissue engineering market.
Leading Institutes
This project aims to develop biocompatible and microelectromechanically functionalized polymer scaffolds as a novel approach to cardiovascular tissue engineering.
The approach focuses on the precise control of intrinsic and induced electrical charges as well as local mechanical properties in biocompatible polymers, a scientific direction that will be pursued at the University of Aveiro.
Physicochemical biointerface and biomechanical studies from the perspectives of cellular, molecular biomechanics and transport phenomena will be conducted at the University of Texas.
ComplexBRAIN: Mechanoluminescent nanotransducers for deep brain sono-optogenetics in Parkinson’s disease treatment
- Funding
- PT: 45,657 EUR | UT Austin 100,000 USD
- PIs
- PT — Rosemeyre Cordeiro (CERES - Chemical Engineering and Renewable Resources for Sustainability; Centre for Innovative Biomedicine and Biotechnology (CIBB)) | UT — Huiliang Wang (Biomedical Engineering Cockrell School of Engineering – The University of Texas at Austin)
- Dates
- 2025-10-01 –
2026-09-302026-12-31
Summary The scientific problem is how to achieve precise, circuit-specific neuromodulation in deep brain regions without invasive procedures. The main technical uncertainties are whether focused ultrasound–activated mechanoluminescent nanoparticles can generate sufficient, controllable photon emission for optogenetic stimulation, and whether polymer-based non-viral systems can deliver genes to neurons with efficiency comparable to viral vectors. This project proposes a non-invasive sono-optogenetic platform that combines focused ultrasound–activated hydrogen-bonded organic framework (HOF) nanoparticles with polymer-based gene delivery. Boronic acid–functionalized polymers will be designed to enhance neuronal transfection efficiency, enabling the expression of light-sensitive proteins in targeted neurons. HOF nanoparticles will be engineered to encapsulate high loads of chemiluminescent probes, producing robust and controllable photon emission upon ultrasound stimulation. Integrating these technologies will enable precise, circuit-specific neuromodulation in deep brain regions without implanted optical fibers, addressing both the efficiency of non-viral neuronal gene delivery and the reliability of non-invasive light generation. The ComplexBRAIN project aims to deliver a science-to-technology breakthrough by integrating advanced mechanoluminescent HOF-based nanotransducers, sono-optogenetics, and polymer-based non-viral gene delivery for non-invasive, circuit-specific deep brain neuromodulation. This paradigm-shifting approach could establish a new foundation for treating Parkinson’s disease and evolve into a modular toolkit for neurological conditions such as epilepsy, depression, and Alzheimer’s disease. By removing the need for implanted devices, it promises safer, more accessible, and cost-effective treatments.
Leading Institutes
In this partnership, the University of Coimbra (UC) will be responsible for developing the polymer-based non-viral gene delivery system, designing and optimizing cationic polymers for efficient neuronal transfection.
The University of Texas at Austin (UT Austin) will focus on the development of hydrogen-bonded organic framework (HOF) nanoparticles, including their synthesis, characterization, and loading with chemiluminescent probes for focused ultrasound–triggered light emission.
By combining UC’s expertise in gene delivery with UT Austin’s experience in HOF nanomaterials and sono-optogenetics, the collaboration integrates complementary capabilities to create a non-invasive, targeted platform for deep-brain neuromodulation.
DefCom2D: Defect engineering in quantum memristors for neuromorphic computing
- Funding
- PT: 49,975 EUR | UT Austin 100,000 USD
- PIs
- PT — Andrea Capasso (International Iberian Nanotechnology Laboratory) | UT — Deji Akinwande (Microelectronics Research Center)
- Dates
- 2025-10-01 – 2026-09-30
Summary The resistive switching behavior of hexagonal boron nitride (hBN) memristors is strongly influenced by structural defects such as vacancies, grain boundaries, and edge states. However, the precise mechanisms by which these defects affect key performance metrics (ON/OFF ratios, endurance, retention, and switching speed) remain poorly understood. This lack of systematic knowledge limits the ability to design and optimize hBN-based devices for neuromorphic computing. Bridging this gap requires comprehensive experimental and theoretical investigations to correlate defect structures with electronic behavior. DefCom2D addresses the insufficient understanding of how defects, such as vacancies, grain boundaries, and edge states, govern resistive switching in hBN-based memristors. The project proposes a combined experimental and computational approach to study defect-mediated switching in hBN. Controlled defects will be introduced via argon plasma treatments, and both engineered and intrinsic defects will be characterized using techniques such as Raman spectroscopy, XPS, and TEM. These results will be correlated with electrical measurements and supported by first-principles simulations to model defect behavior and transport mechanisms. This integrated methodology will enable the design of optimized, defect-engineered hBN memristors suitable for scalable neuromorphic computing applications. DefCom2D will enable controllable defect engineering in 2D hBN memristors, linking atomic-scale defects to quantum conductance and synaptic behavior. This can unlock neuromorphic hardware with ultra-low energy use and multi-level memory, reducing the power and cost of AI computing. The approach will open new research on defect–function relationships in quantum materials, support scalable fabrication of next-generation in-memory devices, and strengthen European expertise in 2D electronics. Expected returns include more efficient AI hardware, new materials processing methods, and opportunities for spin-off research in quantum transport and low-power edge computing.
Leading Institutes
DefCom2D benefits from a complementary partnership between the International Iberian Nanotechnology Laboratory (INL) and the University of Texas at Austin (UT Austin).
INL contributes deep expertise in materials engineering, nanofabrication, and defect characterization using advanced laboratory infrastructure, such as a highly equipped 1000 m2 cleanroom and advanced microscopy.
UT Austin, through Prof. Akinwande and Dr. Wang’s groups, brings world-leading capabilities in computational modeling, quantum device simulation, and neuromorphic systems.
This synergy allows for an integrated approach combining experimental and theoretical insights, ensuring progress from fundamental materials understanding to functional memristive device prototypes
TARGETZ-OS: Targeted RNA-Guided Epigenetic Therapy with Zoledronate for Osteosarcoma
- Funding
- PT: 49,991 EUR | UT Austin 100,000 USD
- PIs
- PT — Pedro Gomes (REQUIMTE - Rede de Química e Tecnologia; Faculty of Dental Medicine, U. Porto) | UT — Zhengrong Cui (College of Pharmacy - University of Texas at Austin)
- Dates
- 2025-10-15 –
2026-10-14– 2027-01-14
Summary Osteosarcoma is the most common malignant bone tumor in children and adolescents, characterized by aggressive progression, high metastatic potential, and profound genetic and epigenetic heterogeneity. Despite intensive treatment combining surgery and chemotherapy, survival rates for advanced cases remain stagnant below 30%. Current therapies fail to overcome resistance mechanisms and often cause severe side effects, highlighting a critical gap in effective, targeted, and safe treatments. The main scientific challenge is to identify and validate innovative strategies that can overcome tumor heterogeneity, disrupt disease-driving mechanisms, and improve patient outcomes in this devastating cancer. Our project proposes a novel therapeutic platform that combines precision molecular targeting with advanced nanotechnology. Specifically, we will design and deliver small interfering RNA (siRNA) against EZH2, a key epigenetic driver of osteosarcoma progression and therapy resistance. The siRNA will be incorporated into lipid nanoparticles modified with zoledronate, enabling selective localization to bone tumors while also modulating the tumor microenvironment. This strategy will be validated through state-of-the-art preclinical models, including humanized osteosarcoma tumoroids and animal studies, generating critical proof-of-concept data to advance more effective and safer treatments for osteosarcoma. This project has the potential to transform osteosarcoma treatment by introducing a precision therapy that targets the disease at its molecular roots while minimizing systemic toxicity. Success would mark a major step forward for pediatric oncology, improving survival and quality of life for young patients facing limited options. Beyond osteosarcoma, the modular nanoplatform can be adapted to other bone cancers and metastases, opening new therapeutic markets and accelerating translation into clinical practice. Scientifically, it will establish advanced preclinical models and methodologies, catalyzing innovation in nanomedicine, epigenetic therapies, and personalized oncology.
Leading Institutes The UT Austin team contributes strong expertise in RNA-based therapeutics and nanomedicine, focusing on the design, synthesis, and characterization of lipid nanoparticles for siRNA delivery.
They will lead the development of the bone-targeted nanoplatform and perform pharmacokinetics and toxicity studies in vivo.
The University of Porto/REQUIMTE team brings complementary expertise in bone biology, biomaterials, and advanced preclinical models.
They will design and validate the EZH2-targeting siRNA, perform biological testing in osteosarcoma cells, and evaluate therapeutic efficacy in innovative humanized tumoroid models.
Together, both teams integrate molecular therapy, nanotechnology, and translational bone oncology.
NanoBBMTec: Multifunctional nano-immunotherapy to regulate STAT3 pathway and adaptive immunity in Breast Cancer Brain Metastases
- Funding
- PT: 50,000 EUR | UT Austin 100,000 USD
- PIs
- Helena Florindo Roque Ferreira (Faculty of Pharmacy, University of Lisbon) | UT — Nicholas Peppas (The University of Texas at Austin)
- Dates
- 2025-10-17 – 2026-10-17
Summary Breast cancer brain metastases (BBM) remain a major clinical challenge due to late onset, poor prognosis, and limited therapeutic options. Current treatments offer minimal survival benefit, and systemic therapies fail to overcome the blood-brain barrier and immune suppression within the BBM microenvironment. There is a critical need for innovative strategies that effectively target both tumor cells and the immunosuppressive niche. NanoBBMTec proposes a dual nanotechnology-based immunotherapy combining pH-responsive nanoparticles delivering STAT3 inhibitors with dendritic cell-targeting nanoparticles to activate anti-tumor immunity. This approach aims to overcome the blood-brain barrier and immune suppression, enabling a cytotoxic T-cell response against BBM. The strategy will be validated using established preclinical models. NanoBBMTec could revolutionize BBM treatment by introducing a multifunctional immunotherapy capable of penetrating the brain and reprogramming the immune microenvironment. This platform may extend survival, reduce recurrence, and open new avenues for treating other brain metastases. It also holds potential for broader applications in nanomedicine and cancer immunotherapy, benefiting patients and advancing translational research.
Leading Institutes
The University of Lisbon team, led by Dr. Florindo, contributes expertise in immunotherapy and dendritic cell-targeted nanocarriers, including mannosylated nanoparticles that have been shown to control breast cancer brain metastasis (BBM).
UT Austin, led by Dr. Peppas, brings advanced knowledge in bioengineering, nanotechnology, and mathematical modeling to optimize pH-responsive nanoparticles for STAT3 inhibitor delivery. Together, they will develop and evaluate a dual nanotechnology-based immunotherapy targeting both tumor cells and immune suppression in BBM.
Their complementary strengths in material science and immunology will enable rational design and preclinical validation of multifunctional nanocarriers to sensitize BBM to immunotherapy.
2024 Extra Exploratory Research Projects (ERPs)
Scalable and Cost-Effective
2026-01-01 – 2026-12-31
Workload-Intelligent Data Storage for Next-Generation Advanced Computing Centers
2025-09-16 – 2026-12-31
ALMAtlantic Land Monitor
2025-09-01 – 2026-12-31
Space Distributed Ocean Monitorization and Exploration
2025-08-16 – 2026-12-31
PATAPower- and Thermal-aware Management for Energy-Efficient AI Training in HPC Infrastructures
2025-09-16 – 2026-12-31
SusBioASustainable Electrochemical Approach for BioAmine production
2025-12-01 – 2026-12-31
Scalable and Cost-Effective Solar-Powered Water SplittingIntegrating Silicon-Based Photoelectrodes with High-Performance Perovskite Solar Cells
2025-08-18 – 2026-12-31
NanoCatH2Advanced Nanostructured Catalysts for PFAS Destruction via Catalytic Hydrogenation
2025-08-16 – 2026-12-31
ALM: Atlantic Land Monitor
- Funding
- UT Austin 100,000 USD
- PIs
- UT — Jingyi Ann Chen (UT Austin) | PT — Joao Pinelo (Atlantic International Research Centre)
- Dates
- 2025-09-01 - 2026-12-31
Summary The proposed international project, titled "Atlantic Land Monitor (ALM)", aims to create a robust data processing pipeline and prototype of a web-service to continuously monitor and report on land deformation using Interferometric Synthetic Aperture Radar (InSAR) technology. The ALM system will employ the Julia programming language to retrieve, process, analyze, and visualize InSAR data, and to convey information to key stakeholders. Julia's high performance capabilities, combined with its user-friendly syntax, make it an ideal choice for large-scale scientific computing tasks. The ALM web-service will allow users to be notified of land deformation events and interactively explore land deformation patterns, providing valuable insights for various applications such as natural disaster management, infrastructure monitoring, and environmental conservation.
- Develop a NISAR L2 data fetcher.
- Validate our Julia InSAR software package through comparison with existing non-Julia InSAR processing packages.
- Setup software defined object storage and a database to store the interferograms and their metadata.
- Create functionality to process time series data based on a stack of interferograms.
- Create visualization tools.
SusBioA: Sustainable Electrochemical Approach for BioAmine production
- Funding
- UT Austin 100,000 USD
- PIs
- UT — Guihua Yu (UT Austin) | PT — Diana Fernandes (REQUIMTE – Rede de Química e Tecnologia – Associação (REQUIMTE-P)
- Dates
- 2025-12-01 - 2026-12-31
Summary Nitrogen-containing compounds, particularly primary amines, are pivotal building blocks in nature and industry with extensive application in the synthesis of pharmaceuticals, polymer materials and agrochemicals. Still, the majority is industrially produced from fossil fuels using non-environmentally friendly processes. Therefore, the development of effective and greener catalytic processes for the preparation of amines from renewable resources is of utmost importance for a future sustainable society. A potential solution is the application of a new approach based on the emerging concept of Electro-Refinery – Electrochemical Reductive Amination (ERA). The main goals of SusBioA are to: 1. Apply the emerging concept of ERA as a clean, safe, low-cost and scalable catalytic process as an alternative to the conventional CRA using only electrons and protons (avoiding the use of fossil fuel derived molecular H2); 2. Use biomass as a substitute for fossil resources to produce bioamines, and as a substitute of noble metal-based and fossil fuel-based electrocatalysts. In route with this, SusBioA will develop new sustainable and highly efficient electrocatalysts for ERA of two selected biomass-derived platform molecules and establish an optimized ERA procedure with high activity and selectivity for amines production by systematic study of different electrochemical parameters.
This multidisciplinary team with complementary knowledge and facilities and with proven experience in the areas will advance the main goals of this project as below:
- Produce N/BCH-based catalysts from WH and MSW with high specific surface area
- Deliver a set of new sustainable and highly efficient SAECs for ERA of biomass-derived platform molecules FUR and HMF. Each SAECs will be specifically designed considering the target bioamine.
- Establish an optimized ERA procedure with high activity and selectivity for amines production by systematic study of different electrochemical parameters (potential bias, electrodes, electrolyte composition and pH, optimal electrocatalyst).
Scalable and Cost-Effective Solar-Powered Water Splitting: Integrating Silicon-Based Photoelectrodes with High-Performance Perovskite Solar Cells
- Funding
- UT Austin 100,000 USD
- PIs
- UT — Edward Yu (UT Austin) | PT — Luisa Andrade (University of Porto)
- Dates
- 2025-08-18 - 2026-12-31
Summary This project will combine highly scalable silicon-based photoanodes developed at UT Austin using a simple, scalable process involving thin-film reactions in the Al/SiO2/Si system and high-speed, low-cost lithographic processes with advanced perovskite-based solar modules developed and fabricated at the University of Porto to create a complete system that performs solar-powered water splitting for low-cost green hydrogen production. UT Austin researchers will exploit the remarkable thin-film reactions that occur in the Al/SiO2/Si material system combined with a revolutionary technique for extremely rapid nanoparticle self-assembly to enable high-speed, highly scalable nanopatterning of catalysts in metal-insulator-silicon photoanodes. University of Porto researchers will use slot-die coating to fabricate perovskite solar mini-modules with areas of 2 cm2 or larger. The photoelectrodes and perovskite solar mini-modules will be combined into a self-contained solar water splitting system to be demonstrated and characterized at UT Austin.
This project is expected to yield
- Demonstration of micron-scale patterned silicon-based photoelectrodes at wafer scale and with high efficiency and stability.
- Demonstration of a self-contained solar-powered water splitting system for hydrogen generation that integrates silicon-based photoelectrodes with perovskite solar cells, and
- Validation of the scalability, performance, and potential for cost reduction of the targeted approaches for photoelectrode fabrication.
PATA: Power- and Thermal-aware Management for Energy-Efficient AI Training in HPC Infrastructures
- Funding
- UT Austin 100,000 USD
- PIs
- UT — John Cazes (TACC, UT Austin) | PT — Ricardo Macedo (INESC TEC)
- Dates
- 2025-09-16 - 2026-12-31
Summary The growing demand for AI applications has led to an increased reliance on HPC infrastructures to train large-scale models. Although these infrastructures provide the computational power for these workloads, the energy consumption and thermal management challenges associated with AI training remain largely neglected. AI training workloads, including large language models (LLMs) and generative AI (GenAI), exhibit distinct computational and memory access patterns that place unprecedented strain on HPC power and cooling systems. Existing HPC energy management techniques designed for general-purpose workloads fail to address the unique characteristics of AI training.
The PATA project proposes a novel power- and thermal-aware management framework tailored explicitly for AI training in HPC infrastructures. PATA aims to integrate workload-specific power-tuning mechanisms with intelligent thermal-aware job scheduling, PATA's power manager component dynamically monitors energy usage across compute resources and adjusts power draw based on the training phase, preventing energy waste without compromising model convergence time and accuracy. Simultaneously, the thermal manager can identify AI training workloads according to their heat profile and strategically place them within the HPC infrastructure to balance thermal distribution and minimize the cooling system's energy demands.
PATA will produce three deliverables that are measurable indicators of its success:
- A final report detailing the project's research findings, dissemination activities, and key outcomes;
- A proof-of-concept open-source prototype;
- Two scientific papers: the first, detailing initial findings, will be submitted to a major conference/workshop from the systems and HPC communities (e.g., EuroSys, HPDC, ISC); the second, presenting
The core contributions of the project, will be submitted to a major HPC conference (e.g., Supercomputing, EuroSys, IPDPS).
NanoCatH2: Advanced Nanostructured Catalysts for PFAS Destruction via Catalytic Hydrogenation
- Funding
- UT Austin 100,000 USD
- PIs
- UT — Charles Werth (UT Austin) | PT — Salomé Soares (University of Porto)
- Dates
- 2025-08-16 - 2026-12-31
Summary This project will focus on:
- Synthesis of new nanostructured PFAS defluorination catalysts.
- Evaluation of the relationships between catalytic properties and activity to elucidate mechanism of reaction and improve catalyst design.
- Incorporation of catalysts into electrochemical reactors to assess scale-up possibilities.
- A level 1 technoeconomic assessment to compare the new technology to competing alternatives.
Catalysts will be synthesized in different combinations and sizes, and characterized before and after PFAS activity assessment using x-ray diffraction, x-ray photoelectron spectroscopy, scanning and transmission electron microscopy with energy dispersive spectroscopy. PFAS activity assessment will be performed in small batch and flow through reactors, all as a function of solution conditions, including ionic strength, buffer type and capacity, pH, and PFAS composition. The most promising catalysts will be evaluated using a wider range of PFAS compounds, and assessed for capital and operating costs. These costs will be compared to competing technology costs for new technology assessment.
From this project
- Peer-reviewed scientific publication
- 2 conference presentations
- PhD thesis
Workload-Intelligent Data Storage for Next-Generation Advanced Computing Centers
- Funding
- UT Austin 100,000 USD
- PIs
- UT — Amit Ruhela (TACC, UT Austin) | PT — João Paulo (INESC TEC and University of Minho)
- Dates
- 2025-09-16 - 2026-12-31
Summary The WISE project aims to revolutionize data management for advanced computing by developing the first generation of storage systems capable of predicting the intrinsic and dynamic I/O patterns of data-intensive workloads and self-tuning their configurations based on such patterns.
By improving the operational efficiency of supercomputers, this project is set to significantly increase the performance and speed at which advanced computing centers support ground-breaking research. However, two main open research questions make this a high-risk, high-reward proposal: i) how can one accurately and timely predict the relevant set of I/O patterns of thousands of heterogeneous and dynamic workloads running at a given supercomputer? and ii) based on the predicted I/O patterns, how can these be used to automatically fine-tune or even develop tailored optimizations that will improve the overall efficiency of supercomputers?
The project will seek to answer these two questions by leveraging the expertise of INESC TEC’s team on storage and AI systems alongside researchers from TACC and MACC with extensive experience in managing HPC infrastructures. The project’s results will be disseminated through top scientific venues and integrated into an open-source proof-of-concept prototype, laying the groundwork for advanced computing centers’ first workload-aware data storage solution.
As success indicators, WISE will consider a final report detailing the project’s research and dissemination outputs, along with two proof-of-concept open-source prototypes, including the prediction engine and integrated SDS solution.
Moreover, the project aims to submit at least two research papers to peer-reviewed workshops (e.g., REX-IO, HotStorage, FTXS, or PDSW) and to major HPC conferences (e.g., SC, ATC, HPDC, CCGrid, Cluster, IPDPS).
Finally, the project will include both MSc and PhD students contributing to their training and specialization in the HPC and storage systems fields.
Scalable and Cost-Effective
- Funding
- UT Austin 100,000 USD
- PIs
- UT — Fernanda Leite (UT Austin) | PT — Miguel Ângelo Dias Azenha (University of Minho)
- Dates
- 2026-01-01 - 2026-12-31
Summary This project aims to establish an innovative methodology for scalable flood analysis and prediction utilizing advanced airborne data collection techniques such as drone-based photogrammetry and airplane-based LiDAR. By systematically exploring levels of detail (LOD) for hydraulic-relevant features, it seeks to harmonize modeling strategies, propose GeoBIM interoperability frameworks, and validate methodologies through transatlantic case studies in the Gulf of Mexico and Portugal's west coast. The project aims to lay a foundation for scalable, automated, and precise flood modeling applicable globally.
- Create a framework for Levels of Detail (LOD), particularly for hydraulic-relevant infrastructure.
- Establish and propose solutions for GeoBIM interoperability, integrating BIM and GIS for flood modeling.
- Conduct case studies that compare the effects of different LOD and validate our methods.
- Develop and disseminate the latest reality capture datasets of the built environment to support flood modeling studies.
Space Distributed Ocean Monitorization and Exploration
- Funding
- UT Austin 100,000 USD
- PIs
- UT — Thinh Doan (Aerospace Engineering and Engineering Mechanics) | PT — Daniel Silvestre (NOVA University Lisbon)
- Dates
- 2025-08-16 - 2026-12-31
Summary Environmental monitoring - such as assessing ocean health - requires coordinated observations from diverse autonomous platforms, including satellites, high-altitude balloons, and aerial or underwater robots. Integrating these heterogeneous assets presents a fundamental multi-vehicle control challenge: maximizing coverage while ensuring safe operation in increasingly congested orbital and operational environments. In this collaborative UT–Portugal Exploratory Project, we will develop a hierarchical control framework for autonomous vehicles operating in uncertain and dynamic environments. At the high level, we will design reinforcement learning–based path planning algorithms to generate action sequences optimized for given desired tasks in uncertain and dynamic environments. These planned actions will be fed into the low-level control layer, where we will implement a unified Quadratic Program–based architecture combining Control Lyapunov Functions for stability with Control Barrier Functions for safety. The integrated planning and control strategy will be validated on autonomous aerial and ground vehicles in UT’s indoor and outdoor test facilities, paving the way for robust, scalable, and safe environmental monitoring missions.
The expected outcomes of this project include a suite of mathematical tools and techniques for enabling autonomous agents to cooperate effectively in uncertain, dynamic environments, with direct applicability to real-world problems. In addition, the project will establish a strong foundation for long-term collaboration between researchers and students at UT and Portugal, fostering knowledge exchange, joint experimentation, and the development of a shared research agenda in safe autonomy. At the end of this project, the research is expected to produce a conference paper and potentially submit a journal paper.