Seed grants are awarded with funding provided by the 黑料视频 Road Map through the Provost's Office and the Division of Research.
The goal of these seed grants is to encourage faculty to develop collaborative projects that stimulate the advancement of new ideas that can build 黑料视频's expertise toward a national reputation in the broad area of smart energy. This competitive, peer-reviewed program is providing initial support for proposed long-term programs of collaborative research that have strong potential to attract external funding.
Information on how to apply for seed grant funding for the 2025鈥2026 academic year can be found on the TAE Seed Grant Program landing page. The deadline for request for proposals (RFP) is February 14, 2025; forthcoming, stay tuned.
is December 16, 2024.
The Transdisciplinary Area of Excellence (TAE) invites letters of intent (LOI) for proposals for the 2025-2026 TAE Seed Grant program. All proposals with a requested budget exceeding the common $15,000 limit must submit a letter of intent (LOI). It is strongly recommended that all teams submit an LOI regardless of the budget amount.
Completed LOI packages must be submitted electronically to the at this link:
For the 2024-25 academic year, the following seed grants were awarded:
forthcoming
For the 2023-2024 academic year, the following seed grant was awarded:
Advanced manufacturing of all-solid-state batteries
Hao Liu, chemistry; Scott Schiffres, mechanical engineering; Changhong Ke, mechanical engineering
All-solid-state batteries are a next-generation technology for electrochemical energy storage. The proposed work focuses on a novel additive manufacturing approach to address the key obstacles in the manufacturing and operation of all-solid-state batteries. We propose to use laser powder bed fusion to embed nanotube materials in the solid electrolyte and cathode materials to make robust solid-solid interfaces that can withstand the high-temperature materials processing conditions and the electrochemical stress during battery operation. This project will demonstrate the feasibility of this novel approach and provide preliminary results for external grants applications.
Deep Reinforcement Learning for Power System Stability Enhancement
Kenneth Chiu, computer science; Ziang Zhang, electrical and computer engineering
This project will seek to develop deep reinforcement learning models for power system stability control of complex renewable energy power grids, via multi-agent deep Q-learning. A simulator will be used to model a complex, real-world power grid consisting of 100s or even 1000s of nodes, each node representing a small renewable source, such as PV array. The resulting problem is a partially-observable Markov decision process. Deep reinforcement learning models will then be developed via multi-agent reinforcement learning. Each node will have only local observable state and can only control the parameters of the local node. The simulator will simulate the complete grid, however, thus the reward function will be based on the stability and other characteristics of the entire grid.
Lead-free halide double perovskites for solar cells
Mengen Wang, electrical and computer engineering; Manuel Smeu, physics; Tara Dhakal, electrical and computer engineering
Lead-based halide perovskites (APbX3) with properties including tunable bandgap and efficient charge generation have attracted great attention for solar cell applications. The fast optimization of device performance has illustrated rapid increase in power conversion efficiency exceeding 25%. However, the use of lead raises environmental concerns. To address this issue, halide double perovskites (HDP) with Pb being replaced by one or two non-toxic elements have been proposed as lead-free alternatives. HDP has high composition flexibility and tunable bandgap, which is a potential candidate for applications in perovskite solar cells. Despite the potential, the power conversion efficiency of HDP-based solar cells is still limited by the low thermodynamic stability and low defect tolerance of HDP. We will use an integrated computational-experimental approach for the design of materials with better performance. We will perform first-principles computation to propose HDP material compositions and synthesizing conditions for better light absorption capability, thermodynamic stability, and defect tolerance (Dr. Wang and Dr. Smeu). The theoretical predictions will be validated and further improved by experimental synthesis and device tests (Dr. Dhakal). This project will provide valuable computational-experimental collaboration opportunities for students and allow the PIs to pursue external funding to expand related materials research.
Web-based, Interactive Decision Support Tool to Navigate NY's Energy System Tradeoffs
Neha Patankar, systems science and industrial engineering; Yong Wang, systems science and industrial engineering; Madhusudhan Govindaraju, computer science; Ziang Zhang, electrical and computer engineering
The journey towards a zero-carbon system involves multiple actors with often conflicting objectives. These conflicting objectives, if not resolved swiftly, can derail the efforts of rapid decarbonization. Current electricity system models prioritize monetary objectives, but to ensure a successful transition to net-zero systems, these models must consider non-monetary objectives and their tradeoffs. In this project, we will develop a detailed power system capacity expansion modeling framework to explore various solar and wind siting tradeoffs in New York state. We will conduct Modeling To Generate Alternatives analysis to get the initial set of cost-effective sites, which will be used to identify and interview key stakeholders to get their insights and perspectives on the development feasibility of the site. Insights from the interviews will be used to create quantifiable objectives for the stakeholders. At the end of this exercise, we will have a set of quantified stakeholder objectives, cost-effective but unsuitable sites, and the impacts of objectives on the remaining system composition. The results of this study will be made available to other researchers at 黑料视频 and local stakeholders via an interactive GIS story map, facilitating communication and community engagement in the transition to a zero-carbon energy system.
For the 2022-2023 academic year, the following seed grant was awarded:
A Reusable and Energy-Efficient Sensor to Detect 鈥淔orever Chemicals鈥 Produced During
Huiyan Guo, chemistry; Manuel Smeu, physics and materials science and engineering; and Ana Laura Elias, physics
Per- and polyfluoroalkyl substances (PFAS) are a large group of emerging environmental contaminants that are often called 鈥渇orever chemicals鈥 due to their persistence in the environment. They are widely used in the manufacture of industrial and consumer products, including Li-ion batteries. With strong chemical bonds (C-F), they are highly persistent in the environment and living organisms, including human bodies. They are of high environmental safety concern due to a variety of adverse health outcomes after exposure. Monitoring their environmental level is key to preventing environmental contamination and human exposure. However, current methods that rely on bench-top instruments are expensive and energy-inefficient. We propose to develop a reusable, energy-efficient and portable sensor to detect PFAS. This sensor will be made of two-dimensional transition metal dichalcogenides decorated with metal atoms, and integrated with a surface-enhanced Raman spectrometer (SERS). To our knowledge, this is the first time that a portable, reusable and sensitive sensor is developed to monitor PFAS in the environment. Our bold and truly innovative strategy will revolutionize the traditional ways to monitor PFAS, allowing us to observe trace levels of targets in seconds in the field with less cost of materials and energy.
For the 2020鈥2021 academic year, the following seed grants were awarded:
Machine-Learning-Driven Battery Energy Storage Operations Control
Soongeol Kwon, systems science and industrial engineering; and Ziang Zhang, electrical and computer engineering
Along with the modernization of power grids and the expansion of renewable energy, battery storage is expected to play a key role for the efficient and sustainable operations of electric power systems. In particular, battery storage has been regarded as a promising solution to stabilize the intermittent renewable energy generation and fluctuated electricity demand load. Hence, there is an urgent need for introducing a systematic approach designed to find proper controls for the better use of battery storage. Meanwhile, machine learning has been recently revitalized and actively studied coupled with increasing interests and popularity of artificial intelligence; and there have been considerable studies on the application of machine learning to a wide range of domains with noticeable outcomes. Given this context, the main objective of this research proposal is to develop a machine-learning-driven optimal control policy uniquely designed to control battery storage operations. In particular, the PIs intend to leverage the capability of recurrent neural networks to find underlying patterns existing in optimal battery storage operations to predict a sequence of charging and discharging controls. The outcome will, in turn, be a strong preliminary study used to validate the idea of the proposed approach for seeking external funding.
Guided Tour through the Operation of a Li-Ion Battery
Louis Piper, materials science and engineering; and G枚khan Ersan, art and design
We propose to develop a novel education module that links hands-on experimental training on cutting-edge battery research to graphical design and Virtual Reality (VR) learning to showcase 黑料视频鈥檚 achievements in the rechargeable battery field. Dr. Piper will develop the hands-on component using rhenium oxide as an easily accessible model system that was only recently demonstrated to be viable for rechargeable lithium batteries in 2018. In tandem with the hands-on component, Dr. Ersan will develop graphic elements that will be incorporated into a guided tour through an operating battery in a virtual reality framework. We propose to work towards implementing the module into the Freshman Research Immersion: Smart Energy stream by Spring, 2021 as an initial test of this innovative educational approach. In addition, the graphical design elements and VR framework developed by Drs. Piper and Ersan will be incorporated into published research and press releases as well as highlights for 黑料视频鈥檚 Materials Science and Engineering (MSE) program.
in prior years, the following seed grants were awarded:
Two-electron redox with earth-abundant silicates for next-generation batteries
Hao Liu, chemistry; and Manuel Smeu, physics
The increasing demand for high-energy density batteries calls for the development of novel charge storage mechanisms that promise a higher capacity using cheap, earth-abundant materials. Our project will focus on the study of a sodium transition metal silicate, which consists of earth-abundant elements, as a promising cathode material for rechargeable batteries. The appeal of this material derives from its potential to electrochemically store two sodium ions per transition metal, which doubles the charge storage capacity of the current lithium-ion battery technology. However, early experimental studies of this material have shown underachieved performance with little mechanistic understanding of the charge/discharge process, hence impeding further development. We will use an integrated experimental and computational approach to decipher the charge/discharge process of this material and develop strategies to improve its performance.
Electricity Generation from Gut Pathogens
Choi, Seokheun Choi, electrical and computer engineering; and Laura Cook, biological sciences
There is a growing appreciation of the role of the gut microbiota in all aspects of human health and disease including metabolism, immunity, and brain functions. Ingestible electronics are fast emerging as a critical technology that can revolutionize gut microbiome research, enabling real-time in-vivo monitoring from within the body. However, even the latest ingestible bacteria-electronic systems for monitoring gastrointestinal health suffer from finite energy budgets available from batteries, hampering long-term operational capabilities. The overall goal of this project is to provide an in-depth understanding of gut microbial electrogenic potential for energy harvesting and its capability for a real-time electrical biosensing platform. The electrogenic potential of gut microbiota will be harnessed to act as a biocatalyst in a microbial fuel cell (MFC) and produce a sufficient electrical current for practical applications. The ability of multi-species bacterial biofilms to generate electricity will be explored. Furthermore, we will establish the conditions in which the bacterial electrogenicity can be maximized to be used in a novel biosensor.