Physical Sciences and Mathematics Commons^™

Enhanced Continual Reinforcement Learning-Based Output Feedback Control Of Heterogeneous Quadrotors Formation, Ehsan Soleimani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a unified framework for the safe and optimal control of heterogeneous quadrotor unmanned aerial vehicles (QUAVs) in formation, enabling multitask missions without requiring precise system dynamics. To address partial state observability, a multilayer neural network (MNN) observer is designed to estimate unmeasured states. Reinforcement learning (RL) is employed for optimal control utilizing an MNN ensuring adaptability. Barrier Lyapunov Functions (BLFs) are integrated into the RL framework to enforce safety by maintaining QUAVs within predefined constraints. An enhanced continual learning (ECL) method is proposed to improve the adaptability of MNNs. This method enables effective multitask learning while mitigating …

Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing presence of unmanned aerial vehicles (UAVs) raises serious security concerns, particularly regarding unauthorized drone operations. Recent U.S. security statistics report a sharp rise in unauthorized UAV activities, with the Federal Aviation Administration (FAA) receiving over 100 monthly reports of illegal drone operations near airports. In 2024 alone, Dedrone records 1.19 million unauthorized drone flights across major U.S. cities, highlighting the need for robust UAV detection and classification systems. In this work, a lightweight Convolutional Neural Network (CNN) model is proposed for RF-based UAV classification under noisy and multipath fading conditions. The proposed CNN consists of multiple convolutional blocks, …

Adaptive Nussbaum Design For Nonholonomic Systems With Asymptotic Stabilization Against False Data Injection, Guilong Liu, Yongliang Yang, Weinan Gao, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article addresses the stabilization challenges of nonholonomic systems under the threat of false data injection (FDI) attacks, which compromise the integrity of state information. A novel adaptive control strategy using Nussbaum-type gains is proposed to ensure the asymptotic stability of the closed-loop system while maintaining signal boundedness. The approach extends conventional Nussbaum designs to handle multiple unknown control directions. It integrates online learning mechanisms to mitigate the impact of FDI attacks. Additionally, adaptive backstepping and fuzzy-logic systems are utilized to approximate and compensate for unknown nonlinear dynamics. The methodology transforms nonholonomic systems into equivalent cascade structures to address inherent …

An Extensive Analysis Of Match-Tracking Methods For Artmap, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper identifies and studies five match-tracking (MT) methods in the adaptive resonance theory (ART) literature and conducts a detailed comparative analysis of these in ARTMAP applications. We focus on model performance for each MT method with respect to time and space efficiency as well as classification accuracy. Experimental results indicate that one MT variant, used in ARTMAP applications for the first time in this work, provides significant improvements in computational efficiency: depending on the ARTMAP variant, it was able to achieve up to one order of magnitude reduction in both time and space requirements, albeit with a compromise in …

Electrochemical Observation And Ph Dependence Of All Three Expected Redox Couples In An Extremophilic Bifurcating Electron Transfer Flavoprotein With Fused Subunits, Debarati Das, Wassim El Housseini, Monica Brachi, Shelley D. Minteer, Anne Frances Miller

Chemistry Faculty Research & Creative Works

Bifurcating enzymes employ energy from a favorable electron transfer to drive unfavorable transfer of a second electron, thereby generating a more reactive product. They are therefore highly desirable in catalytic systems, for example, to drive challenging reactions such as nitrogen fixation. While most bifurcating enzymes contain air-sensitive metal centers, bifurcating electron transfer flavoproteins (bETFs) employ flavins. However, they have not been successfully deployed on electrodes. Herein, we demonstrate immobilization and expected thermodynamic reactivity of a bETF from a hyperthermophilic archaeon, Sulfolobus acidocaldarius (SaETF). SaETF differs from previously biochemically characterized bETFs in being a single protein, representing a concatenation of the …

Scalable Fabrication Of High-Payload Dendrimer-Based Nanoparticles For Targeted Atherosclerosis Therapy, Huari Kou, Lin Qi, Da Huang, Jiandong Wu, Honglan Shi, Hu Yang

Chemistry Faculty Research & Creative Works

Nanoparticle-based therapeutics hold promise for the treatment of atherosclerosis, but challenges such as low drug-loading capacity and a lack of scalable, controllable production hinder their clinical translation. Flash nanoprecipitation, a continuous synthesis method, offers a potential solution for scalable and reproducible nanoparticle production. In this study, we employed a custom-designed multi-inlet vortex mixer to perform cross-linking reaction-enabled flash nanoprecipitation, facilitating controlled and scalable synthesis of cross-linked Poly amidoamine (PAMAM) dendrimer nanoparticles. Notably, this approach allows simultaneous nanoparticle cross-linking and drug loading in a single step. The mannose moiety enabled specific targeting of macrophages via mannose receptors, enhancing the localization of …

Leveraging Deep Learning Models And Social Media Data For Enhanced Situation Awareness In Disaster Management, Ademola Abdulganiyu Adesokan

Doctoral Dissertations

"In recent years, social media has become a crucial source of real-time data for disaster management, supporting emergency responses when traditional channels like 911 are overcrowded and overwhelmed. It offers authorities valuable data for developing effective strategies, especially when swift actions are essential to save lives. However, the informal language, ambiguous meanings, and irrelevant content on social media pose challenges to accurate classification and hinder the efficient extraction of disaster-relevant information, leading to inefficiencies in emergency response efforts.

This research focuses on seven key questions: i) How can we detect, classify, and analyze hate and offensive tweet emotions during large-scale …

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

Enhanced Biofuel Cells Based On A Hybrid Enzymatic/Bimetallic Composite For Complete Lactate Catalytic Electrooxidation, Jefferson Honorio Franco, João Victor Bonaldo, Shelley D. Minteer, Adalgisa R. De Andrade

Chemistry Faculty Research & Creative Works

We describe complete lactate electrooxidation in an enzymatic biofuel cell that combines the catalytic action of the bimetallic composite Ru@Pt-CNT and the enzyme oxalate oxidase (OxOx). The Ru@Pt-CNT/OxOx hybrid electrode was 2.0-fold more catalytically active than the electrode containing the bimetallic composite only. During chronoamperometric experiments, the hybrid electrode achieved a 35% higher maximum current density (2.65 ± 0.15 mA cm^-2) than the Ru@Pt-CNT electrode. Electrochemical impedance spectroscopy showed that the hybrid electrode had lower charge transfer resistance than the Ru@Pt-CNT electrode, confirming that OxOx had a high affinity for lactate during the bioelectrocatalytic reaction on the electrode …

Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses the infinite horizon optimal tracking control problem for partially uncertain control-affine nonlinear discrete-time (DT) systems, where the control input dynamics are known. Multi-layer critic and actor neural networks (MNNs) are utilized for online estimation of the infinite horizon value function and optimal control input. The NN weights are tuned online using a direct temporal difference error (TDE)-driven learning approach, which modifies the singular values of the gradient with respect to the NN weights to accelerate their convergence. The critic NN uses a novel experience replay technique to improve sample efficiency without introducing biased TDEs and guarantee the …

Anti-Jamming Attack Mixed Strategy For Formation Tracking Control Via Game-Theoretical Reinforcement Learning, Lei Xue, Bei Ma, Yongbao Wu, Jian Liu, Chaoxu Mu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Communication plays a role in multi-UAV to perform formation tracking missions. In complex environments, UAV communication is often subject to jamming attacks, affecting the formation process. Therefore, studying the formation tracking control problem in jamming attacks is of great significance. Typically, the actions of the UAV consist of two fundamental modules: mobility strategy and communication strategy. In this paper, we design an anti-jamming attack mixed strategy for formation tracking control of the multi-UAV system. In practical scenarios, multi-UAV systems not only require the accomplishment of formation maneuvers but also necessitate effective mitigation of jamming attacks caused by other UAVs. Therefore, …

Aperiodically Intermittent Dynamic Event-Triggered Control For Predefined-Time Synchronization Of Stochastic Complex Networks, Lei Xue, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the problem of practical predefined-time synchronization in mean square (PTSMS) of stochastic complex networks (SCNs) is investigated through dynamic event-triggered control (E-TC). Different from the existing literature, this paper considers the dynamic E-TC in an a periodically intermittent control framework and employs the average control rate, which makes it easier to satisfy the conditions of the theorem. In comparison to existing finite-time and fixed-time synchronization, by introducing the time-varying function, it can be guaranteed that all states of SCNs achieve the practical PTSMS within a preset time without calculating the convergence time. Combined with stochastic analysis theory, …

Safe Optimal Control Of Quadrotor Formations Using Multilayer Neural Networks And Continual Learning, Ehsan Soleimani, Irfan Ahmad Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents an integral reinforcement learning-based optimal formation tracking scheme for multiple quadrotors unmanned aerial vehicles (QUAVs) experiencing nonlinear coupled dynamics and subject to constraints. We use multilayer neural networks (MNN) within an actor-critic framework where the MNN weights are tuned using singular value decomposition (SVD) of the activation function gradient to approximate optimal control policy via backstepping. Additionally, barrier Lyapunov functions (BLF) are introduced to ensure set invariance, thereby maintaining the quadrotors within a defined safety space due to constraints. A novel weight update law for each layer is derived using the HJB approximation error and control input …

Prescribed-Time Nash Equilibrium Seeking For Pursuit-Evasion Game Under Intermittent Control With Undirected/Directed Graph, Lei Xue, Jianfeng Ye, Yongbao Wu, Jian Liu, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper studies the prescribed-time Nash equilibrium (PTNE) seeking problem of the pursuit-evasion game (PEG) with second-order dynamics under the intermittent control (IC) strategy. To achieve Nash equilibrium (NE) in a user-defined prescribed-time, a time-varying high-gain function is incorporated into the design. The core challenge lies in applying IC to NE seeking, which complicates the convergence analysis and control design. To address this sticking point, we construct an auxiliary function and propose a Lyapunov function considering second-order dynamics to solve the PTNE seeking problem of PEG. Building upon the results for undirected graphs, we further extend our findings to directed …

Metal-Free Photoredox Catalysis For The S-Trifluoromethylation Of Thiols And Investigation Of Ph-Dependent Decontamination Of Dimethyl Chlorophosphate, A Nerve Agent Simulant, Via Hydrolysis With Azoles, Raheemat Rafiu

Doctoral Dissertations

"Organofluorine chemistry plays a critical role in synthetic chemistry due to the unique properties of fluorine. However, the S-trifluoromethylation of thiols remains underexplored, with existing methods often relying on toxic and expensive organometallic catalysts. Given the advantages of S-trifluoromethylated compounds, including enhanced pharmacological activity and improved metabolic stability, we developed a green and cost-effective method for the synthesis of S-CF3 compounds using diacetyl as the organic catalyst and Langloi's reagent as the trifluoromethylating reagent. This reaction, which proceeds under mild conditions using visible light photoredox catalysis, works across various substrate scopes, including aromatic, heteroaromatic, and aliphatic thiols.

Chemical Warfare Agents …

Provenance Of The Uppermost Carboniferous–Lower Triassic Sandstones, Bogda Mountains, Nw China: Implications On The Late Paleozoic Tectonic History Of The Southern Central Asian Orogenic Belt, Sixuan Wu

Doctoral Dissertations

"The Permian-Triassic period represents a significant but insufficiently understood tectonic transition stage in the southern Central Asian Orogenic Belt (CAOB) between the Paleozoic continental amalgamation and Cenozoic orogenic reactivation. This study integrates fieldwork, petrographic, and detrital zircon geochronological data to document the trend of depositional environments, framework grain compositions, and dates of detrital zircon grains of the uppermost Carboniferous–Lower Triassic sandstones from three study areas in Bogda Mountains, the greater Turpan-Junggar basin, NW China, to decipher the spatial and temporal changes in the provenance lithology and area and reconstruct the unroofing history of the southern CAOB.

In the first part …

The Real-Time Detection Infrastructure Of Ligo, Virgo, And Kagra: Data Products, Current Performance, And Future Developments, Sushant Sharma Chaudhary

Doctoral Dissertations

The discovery of the binary neutron star merger event GW170817 marked the dawn of Multi-Messenger Astronomy (MMA) with Gravitational Waves (GWs). Such multi-messenger events are of immense scientific interest due to the wealth of information they provide through joint observations across different messengers. In this rapidly evolving field, prompt identification and timely distribution of alerts is critical for follow-up observations.

This work offers a comprehensive overview of the LIGO-Virgo-KAGRA (LVK) Collaboration’s low-latency analysis pipeline for GW events, covering key stages from calibration and data analysis to the issuance of public alerts. I examine the latency and accuracy of each stage …

Design And Synthesis Of Organocatalyts For Efficient Decontamination Of Organophosphate-Based Nerve Agents And Pesticides, Emmanuel Kingsley Darkwah

Doctoral Dissertations

Exposure to organophosphate-based nerve agents and pesticides poses significant health and security threats to civilians, soldiers, and first responders. Despite extensive efforts to develop chemical detoxification agents for use in topical applications on exposed skin surfaces and for intravenous injections, there remains an unmet need for effective, non-hazardous decontaminating agents. The current state-of-the-art decontaminating agent, Dekon-139 (2,3-butanedione oxime, potassium salt), exhibits adverse effects when applied to the skin.

In this study, we designed and synthesized pharmaceutically relevant aminoguanidine-derived aldimines that are relatively non-toxic and substantially more effective at decontaminating nerve agents and pesticides compared to existing agents, and they act …

A Hybrid Method For Source Direction Finding With Radio Frequency Interference And Gaussian White Noise, Yanming Zhang, Wenchao Xu, Antonios Argyriou, A. Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise …

Utility Of Immobilized Metal Salens As Electrocatalysts: Fuel Cells And Organic Electrosynthesis, Zachary A. Nguyen, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

There have been significant advancements in the electrosynthesis of fuels and organic molecules, making it an increasingly sustainable and cost-effective alternative to traditional chemical redox reagents. Early versions of these systems faced challenges in chemo selectivity due to high applied overpotentials, which have been mitigated with the introduction of molecular electrocatalysts, like metal salens (MSalens). These MSalens reduce the required overpotentials, increase turnover numbers (TON), and have simple modularity within their ligand structure allowing for tunable selectivity. While these MSalen electrocatalysts are typically used homogeneously for engineering simplicity, downstream separations are often costly and time-consuming. Immobilization of MSalens addresses these …

Conditional Cooperation With Longer Memory, Nikoleta E. Glynatsi, Ethan Akin, Martin A. Nowak, Christian Hilbe

Mathematics and Statistics Faculty Research & Creative Works

Direct reciprocity is a wide-spread mechanism for the evolution of cooperation. In repeated interactions, players can condition their behavior on previous outcomes. A well-known approach is given by reactive strategies, which respond to the coplayer's previous move. Here, we extend reactive strategies to longer memories. A reactive-n strategy takes into account the sequence of the last n moves of the coplayer. A reactive-n counting strategy responds to how often the coplayer cooperated during the last n rounds. We derive an algorithm to identify the partner strategies within these strategy sets. Partner strategies are those that ensure mutual cooperation without exploitation. …

Multiple And Nonexistence Of Positive Solutions For A Class Of Fractional Differential Equations With P-Laplacian Operator, Haoran Zhang, Zhaocai Hao, Martin Bohner

Mathematics and Statistics Faculty Research & Creative Works

Research about multiple positive solutions for fractional differential equations is very important. Based on some outstanding results reported in this field, this paper continues the focus on this topic. By using the properties of the Green function and generalized Avery–Henderson fixed point theorem, we derive three positive solutions of a class of fractional differential equations with a p-Laplacian operator. We also study the nonexistence of positive solutions to the eigenvalue problem of the equation. Three examples are given to illustrate our main result.

A New Reduced Basis Method For Parabolic Equations Based On Single-Eigenvalue Acceleration, Qijia Zhai, Qingguo Hong, Xiaoping Xie

Mathematics and Statistics Faculty Research & Creative Works

In this paper, we develop a new reduced basis (RB) method, named as Single Eigenvalue Acceleration Method (SEAM), for second order parabolic equations with homogeneous Dirichlet boundary conditions. The high-fidelity numerical method adopts the backward Euler scheme and conforming simplicial finite elements for the temporal and spatial discretizations, respectively. Under the assumption that the time step size is sufficiently small, and time steps are not very large, we show that the singular value distribution of the high-fidelity solution matrix U is close to that of a rank one matrix. We select the eigenfunction associated to the principal eigenvalue of the …

Geochemical Evidence Of Drying During The 4.2 Ka Event In Sediment Cores From The Yucatán Peninsula, Mexico, Derek K. Gibson, Jonathan Obrist-Farner, Alex Correa-Metrio, Alejandra Rodriguez-Abaunza, Carlos Castañeda-Posadas

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Tropical hydroclimate variability during the Middle and Late Holocene was investigated using geochemical indicators of local-scale precipitation and evaporation preserved in sediment cores from two sites in the Mexican Yucatán Peninsula. Scanning X-Ray fluorescence spectroscopy data show generally decreasing precipitation trends during the Early and Middle Holocene. During the transition between the Middle and Late Holocene, geochemical evidence of reduced watershed erosion and increased evaporation indicate that a centennial-scale drying event impacted the region between 4.3 and 4.0 ka (kilo-anum; thousand years before present). These findings suggest that the 4.2 ka drying event, which has been previously recorded in Europe, …

Decoding Emotions: Unveiling Facial Expressions Through Acoustic Sensing With Contrastive Attention, Guangjing Wang, Juexing Wang, Ce Zhou, Weikang Ding, Huacheng Zeng, Tianxing Li, Qiben Yan

Computer Science Faculty Research & Creative Works

Expression recognition holds great promise for applications such as content recommendation and mental healthcare by accurately detecting users’ emotional states. Traditional methods often rely on cameras or wearable sensors, which raise privacy concerns and add extra device burdens. In addition, existing acoustic-based methods struggle to maintain satisfactory performance when there is a distribution shift between the training dataset and the inference dataset. In this paper, we introduce FacER+, an active acoustic facial expression recognition system, which eliminates the requirement for external microphone arrays. FacER+ extracts facial expression features by analyzing the echoes of near-ultrasound signals emitted between the 3D facial …

Ai Is A Viable Alternative To High Throughput Screening: A 318-Target Study, Izhar Wallach, Denzil Bernard, Kong Nguyen, Gregory Ho, Adrian Morrison, Adrian Stecula, Andreana Rosnik, Ann Marie O'Sullivan, Aram Davtyan, Ben Samudio, Bill Thomas, Brad Worley, Brittany Butler, Christian Laggner

Chemistry Faculty Research & Creative Works

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, …

A Strategic Insight Into The Market For Carbon Management Capacity, Mahelet G. Fikru, Ting Shen, Jennifer Brodmann, Hongyan Ma

Economics Faculty Research & Creative Works

This study presents the market for carbon management capacity via carbon capture, utilization, and storage technologies, identifying demand and supply forces, as well as clarifying the potential impact of market and non-market-based shocks on technology developers versus adopters. The paper addresses a prevailing gap in market analysis, introducing a microeconomic framework and unique dataset to identify key players, market forces, and policy incentives shaping the carbon capture, utilization, and storage landscape. The analysis equips industry stakeholders, policymakers, and investors with valuable insights regarding (1) leaders in the design, development, and manufacture of carbon capture, utilization, and storage technologies (supply), (2) …

Thermal Conductivity Characterization Of High Oleic Vegetable Oils Based Hybrid Nanofluids Formulated Using Gnp, Tio2, Mos2, Al2o3 Nanoparticles For Mql Machining, Anthony Chukwujekwu Okafor, Tobechukwu Kingsley Abor, Saidanvar Esanjonovich Valiev, Ignatius Echezona Ekengwu, Abiodun Saka, Monday Uchenna Okoronkwo

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper presents the results of thermal conductivity characterization of six high oleic soybean oil (HOSO) and four high oleic canola oil (HOCO)-based hybrid nanofluids formulated with four types of nanoparticles (Graphene nanoplatelet (xGnP), TiO2, MoS2, and Al2O3) at nanoparticles wt.% concentration from 1 % to 7 % in 1 % increment using the two-step method for use in MQL machining of difficult-to-cut metals. Thermal conductivity of the formulated hybrid nanofluids were measured using Thermtest Transient Hot Wire Liquid Thermal Conductivity Meter at temperatures from 25 °C to 75 °C in increment of 10 °C. Obtained results showed that thermal …

Experimental Evaluation Of A Recrosslinkable Co2-Resistant Micro-Sized Preformed Particle Gel For Co2 Sweep Efficiency Improvement In Reservoirs With Super-K Channels, Adel Alotibi, Tao Song, Ali Al Brahim, Baojun Bai, Thomas P. Schuman

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

A recross linkable CO₂-resistant branched preformed particle gel (CO₂-BRPPG) was developed for controlling CO₂ injection conformance, particularly in reservoirs with super-permeable channels. Previous work focused on a millimeter-sized CO₂-BRPPG in open fractures, but its performance in high-permeability channels with pore throat networks remained unexplored. This study used a sand pack model to evaluate a micro-sized CO₂-BRPPG under varying conditions of salinity, gel concentration, and pH. at ambient conditions, the equilibrium swelling ratio (ESR) of the gel reached 76 times its original size. This ratio decreased with increasing salinity but remained stable …

Fitting Quality Of Nmr Relaxation Data To Differentiate Asphalt Binders, Rebecca M. Herndon, Kevin Lai, Magdy Abdelrahman, Klaus Woelk

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Asphalt binder performance grades (PGs) are important metrics in designing pavements for effective transportation infrastructure. the PG system relies on the binder's stiffness and is determined through energy- and time-intensive physical testing. Physical properties, like stiffness, can also be determined by spin–lattice NMR relaxometry, a non-destructive chemical method. NMR relaxometry can quantify the molecular mobility of materials by determining relaxation times from exponential decays of excited nuclear magnetization. While relaxation times have been used to determine physical properties of materials, a quantitative relation to the PG grades of asphalt binder is yet to be established. in this study, T1 NMR …