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Abstract: Sensors and actuators are fundamental building blocks of next-generation human-machine interfaces. This talk presents our recent efforts to establish closed-loop, bidirectional communication and feedback within living systems, with an emphasis on the chemical dimension. The first part of the talk introduces a novel class of flexible, miniaturized probes inspired by biofuel cells for monitoring synaptic release of glutamate in the central nervous system. The resulting sensors can detect real-time changes in glutamate within the biologically relevant concentration range. These advances could aid in basic neuroscience studies and translational engineering, as the sensors provide a diagnostic tool for neurological disorders. The second part of the talk presents our recent work on a bio-integrated gustatory interface, “e-Taste,” which addresses the underrepresented chemical dimension in current VR/AR technologies. This system facilitates remote perception and replication of taste sensations through the coupling of physically separated sensors and actuators with wireless communication modules. Together, these efforts aim to advance the co-design of systems capable of capturing signals and providing feedback, addressing the relatively underexplored chemical aspect in many fields.

Bio: Jinghua Li received her B.S. degree in Biological Sciences from Shandong University, China, in 2011. She earned her Ph.D. from Duke University, United States, in chemistry in 2016. She spent 2016–2019 as a postdoctoral fellow at Northwestern University before joining the Department of Materials Science and Engineering at The Ohio State University as an assistant professor in 2019. Her two focus areas are: 1) fundamental understandings on synthesis chemistry and interfacial properties of thin-film materials as bio-interfaces; and 2) engineering efforts on application of these materials for the next generation wearable/implantable biomedical devices to bridge the gap between rigid machine and soft biology. Her faculty position is funded, in part, by the Discovery Themes Initiative in the area of Chronic Brain Injury, which has promoted faculty hires and support of critical materials needs in the areas of imaging, diagnosis, and treatment of brain injury. Dr. Li supports the Center for Design and Manufacturing Excellence, Nanotech West, and the Center for Electron Microscopy and Analysis with her expertise in the function of biomaterials. Dr. Li has been recognized as the 2025 Alfred P. Sloan Research Fellow, 2024 ACS Materials Au Rising Star, 2024 Nanoscale Emerging Investigator, and 2023 OSU Early Career Innovator of the Year. She also received the DARPA Young Faculty Award, NIH Trailblazer Award, OSU Lumley Research Award and OSU Chronic Brain Injury Program Paper of the Year Award.

Abstract: Synthetic DNA nanotechnology facilitates the design and fabrication of nanoscale particles and devices with diverse applications. Leveraging a growing toolkit of DNA self-assembly methods, it is possible to construct both two- and three-dimensional structures ranging from nanometer to micron scales. The unique biophysical and biochemical properties of DNA—combined with its compatibility with various organic and inorganic nanoparticles and its predictable base-pairing rules—have made it an ideal material for single-molecule studies, photonics, plasmonics, synthetic biology, and healthcare applications. In this work, we present our efforts in developing DNA-based platforms to precisely organize inorganic and organic nanoparticles and biosensors. We investigate how these DNA scaffolds can control the positioning and orientation of nanoparticles to enhance their photophysical properties. Additionally, we explore the behavior of DNA nanostructures when introduced into mammalian cell cytosol, a critical step toward creating biocompatible delivery systems for therapeutic and diagnostic purposes. Finally, we will discuss our recent efforts in building gene-encoded DNA nanoparticles, a promising advancement in the development of targeted delivery systems.

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Organic semiconductors (OSC) are of interest for a wide range of flexible optoelectronics applications, including transistors, solar cells, and sensors, to name a few. Despite their promise, the design and optimization of OSC pose significant challenges due to the complexity of the structures of the molecular building blocks, varied packing configurations of these building blocks in the solid state, which impacts the optical and electronic response, and sensitivity of the solid-state packing to material processing conditions. Accurately predicting the solid-state properties of OSC traditionally requires high-level quantum mechanical methods. These methods, however, can be computationally demanding, particularly for large molecules or when there is interest in extensive material screenings. Overcoming this computational bottleneck is essential to enabling the efficient design of OSC, which would reduce the experimental trial-and-error approach used in material discovery. Moreso, the holy grail of computational study is to be able to accurately and efficiently predict the molecular packing configurations and associated properties of OSC. This dissertation aims to address some of these challenges by developing computational approaches that leverage machine learning (ML) models to accelerate the study of OSC. ML promises to facilitate faster material screening and optimization by offering an alternative to direct quantum mechanical calculations. Specifically, this dissertation describes the development of ML models for intermolecular interactions, including noncovalent interactions (NCI) and electronic couplings (EC). Conventional quantum mechanical methods used to investigate OSC are introduced, and ML approaches are reviewed. The dissertation then discusses the generation of large, high-quality datasets for NCI from symmetry-adapted perturbation theory (SAPT), and the development of ML models to efficiently predict NCI. An active learning approach for the high-throughput derivation of optimal training sets for NCI predictions is then developed, and the training set is used to train new ML models. Finally, ML models to predict EC from three-dimensional (3D) molecular dimer geometries are implemented for the rapid, on-the-fly prediction of ECs across thermally sampled conformations obtained through molecular dynamics (MD) simulations to enable rapid materials characterization during simulation. Ultimately, this dissertation presents a framework that integrates ML with quantum mechanical insights, offering a scalable solution to accelerate OSC discovery and optimization.

KEYWORDS: Organic Semiconductors (OSC), Density Functional Theory (DFT), Symmetry-Adapted Perturbation Theory (SAPT), Noncovalent Interactions (NCI), Electronic Couplings (EC), Machine Learning (ML).

Organic semiconductors (OSC) are of interest for a wide range of flexible optoelectronics applications, including transistors, solar cells, and sensors, to name a few. Despite their promise, the design and optimization of OSC pose significant challenges due to the complexity of the structures of the molecular building blocks, varied packing configurations of these building blocks in the solid state, which impacts the optical and electronic response, and sensitivity of the solid-state packing to material processing conditions. Accurately predicting the solid-state properties of OSC traditionally requires high-level quantum mechanical methods. These methods, however, can be computationally demanding, particularly for large molecules or when there is interest in extensive material screenings. Overcoming this computational bottleneck is essential to enabling the efficient design of OSC, which would reduce the experimental trial-and-error approach used in material discovery. Moreso, the holy grail of computational study is to be able to accurately and efficiently predict the molecular packing configurations and associated properties of OSC. This dissertation aims to address some of these challenges by developing computational approaches that leverage machine learning (ML) models to accelerate the study of OSC. ML promises to facilitate faster material screening and optimization by offering an alternative to direct quantum mechanical calculations. Specifically, this dissertation describes the development of ML models for intermolecular interactions, including noncovalent interactions (NCI) and electronic couplings (EC). Conventional quantum mechanical methods used to investigate OSC are introduced, and ML approaches are reviewed. The dissertation then discusses the generation of large, high-quality datasets for NCI from symmetry-adapted perturbation theory (SAPT), and the development of ML models to efficiently predict NCI. An active learning approach for the high-throughput derivation of optimal training sets for NCI predictions is then developed, and the training set is used to train new ML models. Finally, ML models to predict EC from three-dimensional (3D) molecular dimer geometries are implemented for the rapid, on-the-fly prediction of ECs across thermally sampled conformations obtained through molecular dynamics (MD) simulations to enable rapid materials characterization during simulation. Ultimately, this dissertation presents a framework that integrates ML with quantum mechanical insights, offering a scalable solution to accelerate OSC discovery and optimization.

KEYWORDS: Organic Semiconductors (OSC), Density Functional Theory (DFT), Symmetry-Adapted Perturbation Theory (SAPT), Noncovalent Interactions (NCI), Electronic Couplings (EC), Machine Learning (ML).

Cerebrovasculature refers to the network of blood vessels in the brain, and its coupling with neurons plays a critical role in regulating ion exchange, molecule transport, nutrient and oxygen delivery, and waste removal in the brain. Abnormalities in cerebrovasculature and disruptions of the blood supply are associated with a variety of cerebrovascular and neurodegenerative disorders. Nanocarriers, a nano-sized drug delivery system synthesized from various materials, have been designed to encapsulate therapeutic agents and overcome delivery challenges in crossing the blood-brain barrier (BBB) to achieve targeted and enhanced therapy for these diseases. Unraveling the transport of drugs and nanocarriers in the cerebrovasculature is important for pharmacokinetic and hemodynamic studies but is challenging due to difficulties in detecting these particles within the circulatory system of a live animal. In this dissertation, we developed a technique to achieve real-time in vivo tracking of nanocarriers in the cerebrovasculature using fluorescence correlation spectroscopy (FCS), which has great potential for determining the pharmacokinetics of drugs and nanocarriers, as well as for studying disease-related connections between the cerebrovascular and neurodegenerative diseases.

We utilized novel fluorescent probes composed of DNA-stabilized silver nanocluster (DNA-Ag₁₆NC), that emit in the first near-infrared window (NIR-I) upon two-photon excitation in the second NIR window (NIR-II), encapsulated in liposomes, which were then used to measure cerebral blood flow rates in live mice with high spatiotemporal resolution by two-photon in vivo FCS. Liposome encapsulation concentrated and protected DNA-Ag₁₆NCs from in vivo degradation, enabling the quantification of cerebral blood flow velocity within individual capillaries of a living mouse. We also loaded another DNA-stabilized silver nanocluster (DNA640), which exhibited higher quantum yield and anti-Stokes fluorescence upon upconversion absorption, into cationic mesoporous silica nanoparticles (CMSNs) and successfully coated them with liposomes. The cerebrovasculature was chronically labeled using an adeno-associated viral (AAV) vector encoding Alb-mNG secretion into the bloodstream, combined with FCS under upconversion excitation, enabling real-time observation of the flow velocity and particle number of DNA640-CMSN-liposomes within the capillaries. We applied our proposed techniques to study the cerebrovascular structure and blood flow velocity in Alzheimer's disease mouse models and to explore the effects of disease-related conditions on vasoconstriction and vasodilation.

KEYWORDS: Cerebrovascular, nanocarrier, FCS, NIR fluorescence, DNA-AgNC, in vivo

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Meeting ID: 598 475 5867.

Cerebrovasculature refers to the network of blood vessels in the brain, and its coupling with neurons plays a critical role in regulating ion exchange, molecule transport, nutrient and oxygen delivery, and waste removal in the brain. Abnormalities in cerebrovasculature and disruptions of the blood supply are associated with a variety of cerebrovascular and neurodegenerative disorders. Nanocarriers, a nano-sized drug delivery system synthesized from various materials, have been designed to encapsulate therapeutic agents and overcome delivery challenges in crossing the blood-brain barrier (BBB) to achieve targeted and enhanced therapy for these diseases. Unraveling the transport of drugs and nanocarriers in the cerebrovasculature is important for pharmacokinetic and hemodynamic studies but is challenging due to difficulties in detecting these particles within the circulatory system of a live animal. In this dissertation, we developed a technique to achieve real-time in vivo tracking of nanocarriers in the cerebrovasculature using fluorescence correlation spectroscopy (FCS), which has great potential for determining the pharmacokinetics of drugs and nanocarriers, as well as for studying disease-related connections between the cerebrovascular and neurodegenerative diseases.

We utilized novel fluorescent probes composed of DNA-stabilized silver nanocluster (DNA-Ag₁₆NC), that emit in the first near-infrared window (NIR-I) upon two-photon excitation in the second NIR window (NIR-II), encapsulated in liposomes, which were then used to measure cerebral blood flow rates in live mice with high spatiotemporal resolution by two-photon in vivo FCS. Liposome encapsulation concentrated and protected DNA-Ag₁₆NCs from in vivo degradation, enabling the quantification of cerebral blood flow velocity within individual capillaries of a living mouse. We also loaded another DNA-stabilized silver nanocluster (DNA640), which exhibited higher quantum yield and anti-Stokes fluorescence upon upconversion absorption, into cationic mesoporous silica nanoparticles (CMSNs) and successfully coated them with liposomes. The cerebrovasculature was chronically labeled using an adeno-associated viral (AAV) vector encoding Alb-mNG secretion into the bloodstream, combined with FCS under upconversion excitation, enabling real-time observation of the flow velocity and particle number of DNA640-CMSN-liposomes within the capillaries. We applied our proposed techniques to study the cerebrovascular structure and blood flow velocity in Alzheimer's disease mouse models and to explore the effects of disease-related conditions on vasoconstriction and vasodilation.

KEYWORDS: Cerebrovascular, nanocarrier, FCS, NIR fluorescence, DNA-AgNC, in vivo

Zoom link:

Meeting ID: 598 475 5867.

The health of our communities depends on the effective treatment of both solid and liquid waste to eradicate hazardous pollutants before they can interact with living organisms or contaminate the environment. Daily, society generates solid waste (commonly destined for landfills) and liquid waste, (commonly discharged into wastewater systems) and without proper treatment, these wastes can release hazardous primary secondary pollutants. Industries producing wastewater with high pollutant concentrations, especially those utilizing lignin-based biomass, face complex challenges because each facility may require a tailored treatment approach. In response, this work investigates the use of ozonolysis to transform lignin monomers into smaller, less hazardous components that can be more efficiently managed by public wastewater systems. Furthermore, while conventional wastewater treatment systems are effective for common water quality issues, they can inadvertently allow complex compounds, such pollutants from hospital effluent, to pass through. Under simulated treatment conditions incorporating sunlight and chlorination, a pollutant released from medical facilities is degraded, but this process may also lead to the formation of carcinogenic disinfection by-products (DBPs) that pose direct toxicological risks to nearby communities. The implications extend to solid waste management as well. Chemical phenomena, such as those occurring in poorly understood elevated temperature landfills (ETLFs), can compromise treatment methods and increase community exposure to harmful pollutants. By monitoring hazardous components, such as volatile organic compounds (VOCs), over time, this work aims to elucidate the chemical reactions occurring both during treatment and in the environment thereafter. Ultimately, this research underscores the need for fundamental, innovative approaches to pollution transformation. Bridging the gap between existing practices for solid and liquid waste treatment will be critical to safeguarding environmental and public health.