DMREF: Computationally-Driven Discovery of Designer 2D Materials for Biosensing

Non-technical Description:Biochemical sensors play an important role in protecting humans with applications ranging from routine health monitoring to detecting biothreats. The objective of this project is to create a materials innovation infrastructure that accelerates the discovery and optimization of biosensor materials through a closed-loop computational and experimental approach. This project builds on recent discoveries by the PIs at the Pennsylvania State University and the Rensselaer Polytechnic Institute to develop theory-informed intelligent models that guide the engineering of two-dimensional (2D) materials (specifically transition metal dichalcogenides, TMDs) as core biosensing films. Owing to high surface-to-volume ratio and tunable electronic properties, 2D materials have found unique electrochemical applications. However, sensing applications mainly rely on trial and error when making material choices. This project proposes to address this gap using a feedback loop of computational modeling, artificial intelligence (AI) modeling, scalable TMD synthesis methods, and sensor fabrication and testing. The proposed research is integrated with outreach and educational activities that target a broad range of age groups, with active participation from underrepresented minorities (URM). Networking with industry advisory board (IAB) members will provide students with internship and employment opportunities, strengthening the future interdisciplinary R&D workforce.Technical Description:The discovery of sensor materials is a complex technical challenge that requires the convergence of multiple complementary expertise. The number of candidate materials for various bioanalytes is enormous, making it virtually impossible to search the entire materials space using only theoretical calculations and/or experiments. The aims of this project are to (1) Understand the effect of TMD functionalization and doping on nanoscale interactions with biomolecules and governing processes. (2) Develop active learning AI models to accelerate computational modeling in a closed-loop fashion. (3) Synthesize new and improved sensing materials and establish process-property-performance relationships using various characterization techniques. (4) Synthesize TMDs using scalable methods, followed by sensor fabrication and multimodal screening to determine material sensitivity and specificity to molecules through an iterative feedback loop with computational studies. (5) Create a cloud database for storage and sharing the project outcomes with the research community. While the framework will be developed and benchmarked using a set of stress biomolecules and neurotransmitters, the knowledge base and methodologies can be extended to the intelligent design of functional materials for other molecules, such as those linked to food safety, water monitoring, biodefense, and pharmaceutical agents. The research outcomes are expected to have a transformative impact on AI-guided design of materials for electrochemical sensing that may branch out to other areas, including smart coating, gas sensing, and catalysis. The proposed cloud database can have a lasting impact on academia, government organizations, materials manufacturing industries, and health diagnostic industries.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.