University research seed funding awarded to Engineering faculty

Forty-four Brown researchers, including 11 from the School of Engineering, are receiving University research awards through 21 Research Seed grants, totaling $1.4M. This annual program is aimed at helping early-stage research projects grow and help them build collaborations and find other funding sources. Proposals were solicited this fall and reviewed by panels of faculty.

Research seed funding is competitively awarded annually by the Office of the Vice President for Research and helps faculty more successfully advance competitive research proposals by supporting the generation of preliminary data, pursuing new directions or collaborations in research, and other endeavors. Competitive proposals for sizable projects are expected to be submitted to an external funding organization within a year of the completion of the research seed fund period.

Projects from the School of Engineering include: Kyung-Suk Kim, who is “Probing quantum phase transitions in space- and time-domain via quantum-dot local messengers”; Gabriel Taubin for his project titled “Sampling CSG Models with Articulations and Additional Degrees of Freedom”; Miguel Bessa who is researching “Data-driven high-order accurate fail-safe neural topology optimization for plastic deformation and fracture”; Lucas Caretta and Gang Xiao for the project titled Antiferromagnetic Quantum Oxide Tunnel Junctions for Beyond-CMOS Electronics”; Katherine Manz, who is researchingDrinking Water Per- and Polyfluoroalkyl Substances (PFAS) Concentrations in Jackson, Mississippi and Children’s Health”; Kareen Coulombe for a project titled Efficacy of a Novel Reinforced Engineered Cardiac Tissue for Heart Regeneration”; and Nitin Padture, Rod Beresford, Yue Qi, and Shouheng Sun, who are proposing a first of its kind Workshop on Sustainable Energy at Brown University under the auspices of the new Initiative for Sustainable Energy (ISE).

Probing quantum phase transitions in space- and time-domain via quantum-dot local messengers
Quantum phase transitions, headlined by strongly correlated electrons, have been a major stream in modern physical sciences and play a critical role in emerging information and energy technologies. Despite their realizations in 2D materials, experimental results often display measurement-to-measurement variabilities. Sample disorders likely contribute to experimental variations. However, the local static and dynamic information of 2D strongly correlated states currently remains elusive, since commonly-exploited transport methods characterize correlated states via device resistance; local information is thus averaged out. The PIs propose exploiting quantum dots (QDs) on 2D twistronics and using emissive QDs as local messengers to deliver the static and dynamic information of 2D correlated states, with tens-of-nm spatial and ps-to-ms temporal resolutions. The combination of expertise in 2D devices and spectroscopy/microscopy in the Bai Lab, nanocrystal and its high-order-architecture synthesis in the Chen Lab, and nano-mechanical modeling and high-precision characterizations in the Kim Lab is unique and coherent. Optical interrogations of low-dimensional correlated phases are in advent and currently remain empty at Brown. This present interdisciplinary effort will establish groundwork in the optical approach to study quantum systems in situ with spatial-temporal precisions, strengthening Brown’s position in quantum research. Notably, the major focus here tackles some grand challenges in quantum phases of matter, a potential foundation for next-generation information and energy technologies.
PI: Yusong Bai, Assistant Professor of Chemistry
Co-PIs: Ou Chen, Associate Professor of Chemistry and Kyung-Suk Kim, Professor of Engineering

Sampling CSG Models with Articulations and Additional Degrees of Freedom
Point Clouds are one of the primary representations for 3D objects in Computer Graphics and in 3D Computer Vision.  Most existing Deep Learning algorithms to recognize and estimate the pose of 3D objects in complex scenes represented as point clouds can only handle rigid objects.  Parameterized objects, which includes articulated objects, is an emerging area of research.  Vast data sets are required to train these algorithms, but generating such training datasets using 3D sensors and real physical objects is usually not feasible. Generating training datasets by simulation is a well established methodology in Machine Learning. We propose to develop algorithms to generate these data sets by simulating the sampling processes associated with 3D sensors. Important applications include industrial inspection, as well as robot navigation and manipulation.  Constructive Solid Geometry, or CSG for short, is a popular way of representing solids in Computer Aided Design (CAD), particularly in manufacturing, and can also be considered a design methodology. A CSG solid is constructed from a few primitives defined by implicit inequalities (such as planes, spheres, cylinders, cones, torii, etc.) with Boolean operators (i.e., set union, intersection and difference). A CSG solid may be parameterized by a finite number of parameters. While some parameters may represent articulation angles or relative translations of subparts, other parameters may describe shape features such as radii or lengths of subparts.  The proposed formulation, based on sampling CSG objects, will generalize algorithms introduced by the PI years ago to rasterize algebraic curves and surfaces by space subdivision. PI: Gabriel Taubin, Professor of Engineering, Professor of Computer Science

Data-driven high-order accurate fail-safe neural topology optimization for plastic deformation and fracture
Some of the most dramatic progress in materials science and mechanics is rising from exploring extreme phenomena such as unstable behavior (buckling), violent energy absorption through controlled plasticity, and ultra-fatigue resistant and self-healing materials. However, inverse design of materials and structures in these regimes is not possible because (1) the properties of interest are not differentiable, and (2) data generation for the problem of interest is too slow (computationally and experimentally). This project addresses these challenges by exploring for the first time the concept of neural network reparameterization of topology for derivative-free properties of interest and by creating a new entropy-based optimization method that significantly decreases the computational time of the design predictions. These synergistic contributions are believed to open new avenues such that inverse design for extreme conditions becomes feasible, unlocking future explorations of uncharted design spaces to discover materials and structures with unprecedented performance. We aim to use the results developed through this seed award to secure long-term funding from the DOE and DARPA to offer unprecedented solutions to extreme-scale, fail-safe, and risk-averse optimal design via this novel inverse design strategy involving artificial intelligence.
PI: Brendan Keith, Assistant Professor of Applied Mathematics
Co-PI: Miguel Bessa, Associate Professor of Engineering

Antiferromagnetic Quantum Oxide Tunnel Junctions for Beyond-CMOS Electronics
This proposal seeks to design, develop, and characterize a revolutionary antiferromagnetic tunneling junction based on epitaxial quantum oxide thin films to improve the efficiency, scalability, functionality, and bandwidth of beyond-CMOS electronics and magnetic sensors. Most modern spin-based electronic devices, such as magnetic tunnel junctions, use elemental ferromagnetic metals as active materials (e.g. Co, Fe, Ni, and their alloys). Although well-studied, these simple materials systems suffer from numerous inherent limitations. These include slow and lossy precessional dynamics, large stray fields which prevent device scaling and disturbance immunity, sizable power consumption, and poor signal to noise ratios. We aim to integrate quantum antiferromagnetic oxides into spintronics with the development of a model antiferromagnetic tunnel junction to overcome these challenges. This can only be enabled by our cross-disciplinary approach which combines atomically precise synthesis techniques and device physics. Such a device will enable high bandwidth (THz) operations, low energy (attojoule) control, high sensitivity (femtoTelsa), and high on-off device ratios (>500%) suitable for beyond-CMOS technologies. Such high sensitivity, material stability, and scalability can also enable non-invasive imaging, detection, and sensing of minute electromagnetic signals in biological systems and energy storage/conversion devices. This work will strengthen Brown University's relevance in quantum oxide materials synthesis and applications.
PI: Lucas Caretta, Assistant Professor of Engineering
Co-PI: Gang Xiao, Ford Foundation Professor of Physics, Professor of Engineering

Drinking Water Per- and Polyfluoroalkyl Substances (PFAS) Concentrations in Jackson, Mississippi and Children’s Health
The water crisis in Jackson, Mississippi, has made national headlines as a major environmental catastrophe, impacting the public health and well-being of residents.  The Community Noise Lab at the Brown University School of Public Health has been on the front lines of this water crisis, working with faculty and students at The Piney Wood School, a historically black, private, co-educational boarding high school in Greater Jackson. Together, we have implemented The Greater Jackson Water Watch (GJWW) and have operated a mobile tap water testing laboratory, traveling to different locations in the city, testing tap water quality on-site for pH, dissolved oxygen, and turbidity. Residents are able to view summarized tap water sample levels by city and zip code and all residents who had their tap water tested received a summary of their results.  Recently, we quantified concentrations of forty per- and polyfluoroalkyl substances (PFAS) in 49 random samples collected by the GJWW.  In these samples, we detected twenty-eight PFAS species, including several above the EPA Health Advisory levels. This Seed Grant proposal centers on two specific aims, (1) Conduct an exposure assessment to characterize the PFAS levels from tap water in homes across the City of Jackson, Mississippi, (2) Collect biological and self-report health data from children living at these homes to examine the relationship between water quality and pediatric health. The data collected will provide pilot data, which will be leveraged to apply for an NIH R01.
PI: Erica Walker, RGSS Assistant Professor of Epidemiology
Co-PI: Katherine Manz, Assistant Professor of Engineering (Research); Joseph Braun, Associate Professor of Epidemiology

Efficacy of a Novel Reinforced Engineered Cardiac Tissue for Heart Regeneration
Many patients who survive a heart attack experience loss of heart function over time that often leads to heart failure, and there are no therapies that mechanically support and restore the heart to reverse this life-threatening condition. The mission of the Coulombe lab is to advance heart health and regeneration by leveraging technologies in cardiac tissue engineering, stem cell biology, biomaterials, and regenerative medicine to improve the heart’s electromechanical function after injury or disease onset. Our proposed Seed Award project is rooted in the biomechanics of tissue and scaffold engineering and stems from our ongoing work to develop a robust regenerative therapy for the heart after injury caused by a heart attack, or myocardial infarction (MI). We aim (1) to enhance the mechanical support and strength of our implantable engineered cardiac tissue using computational-experimental iteration and rapid prototyping of customized scaffolds, and (2) to evaluate cardiac function and remodeling with implantation of our mechanically reinforced engineered cardiac tissue in an ischemia/reperfusion MI model. We leverage our background with architected fibrous composite biomaterials to direct the orientation-dependent deformation to optimize support computationally, biomanufacture a scaffold, and evaluate its efficacy in vivo. With successful completion of this proposal, we will be able to advance a mechanically robust engineered cardiac tissue therapy for translational applications.
PI: Kareen Coulombe, Associate Professor of Engineering, Director of Biomedical Engineering

Workshop on Sustainable Energy
We propose a first of its kind Workshop on Sustainable Energy at Brown University under the auspices of the new Initiative for Sustainable Energy (ISE). ISE is one of the signature initiatives under the Operational Plan for Growing the Research Enterprise, and it has three elements: (i) Research/Innovation; (ii) Education/Training; and (iii) Translation/Practice/Outreach. The proposed Workshop will focus on the Research/Innovation part of the ISE, and it has three thrust areas: (a) Renewable Energy; (b) Sustainable Fuels/Materials; and (c) Energy Efficiency. These interdependent areas are the most critical for fighting climate change by achieving and maintaining a zero-carbon energy global infrastructure over the next century. These are also the areas of distinct strengths with ‘critical mass’ at Brown, and are primed for elevation to the next level. The proposed Workshop will bring together Brown researchers (faculty, postdocs, students) interested in these areas. This will be augmented by invited distinguished visitors from outside of Brown. The aim of the Workshop is to coalesce around research strengths in these areas, and identify gaps that need to be filled. In addition to community building, the Workshop will create themes for large block-grant proposals where Brown would be competitive. The Workshop will include keynote lectures; thematic invited talks; panel discussions; breakout sessions; and a poster session where students and postdocs will showcase their sustainable energy-related research. A team-building excursion is also envisioned.
PI: Nitin Padture, Otis E. Randall University Professor of Engineering
Co-PIs:  Rod Beresford, Professor of Engineering; Yue Qi, Joan Wernig Sorensen Professor of Engineering; Brad Marston, Professor of Physics; Shouheng Sun, Vernon K. Krieble Professor of Chemistry, Professor of Engineering.