Biochemical sensors play an important role in protecting humans with applications ranging from routine health monitoring to detecting biothreats. The objective of this DMREF 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 (RPI) 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 (also in collaboration with AFRL), and sensor fabrication and testing.

The overarching research objective of this DMREF program (Award#2323296) is to create a materials innovation infrastructure (MII) that accelerates the discovery and optimization of biosensing 2D materials through a closed-loop computational and experimental approach. The driving hypothesis in this proposal is that 2D materials can be structurally engineered for on-demand tuning and  enhancement of sensitivity and selectivity to adsorb and detect different biomolecules.

• Tuning molecular specificity/selectivity and sensitivity through TMD functionalization and studying the effect of defects, dopants, single atoms on adsorption energies, electronic band alignment, interfacial charge transfer, and governing processes.
• Develop active learning AI models to accelerate computational modeling through a feedback loop.
• Synthesize the AI-guided functionalized TMDs using scalable methods and investigate multimodal testing with electrochemical sensors for sensitive, selective, and multiplexed detection.
• Create a database for storage and sharing the project outcomes to make digital data findable, accessible, interoperable, reusable, and relevant (FAIR) to the research community.

Through various outreach and educational activities involving underrepresented minority (URM) students and teaming with an industry advisory board (IAB), this project aims to educate the next generation interdisciplinary R&D workforce which is aligned with the Materials Genome Initiatives (MGI).