Mechanical Engineering Commons^™

Automated Guided Wave Characterization Of Marcelling: In-Plane Fiber Waviness In Thermoplastic Composites, Sourav Banerjee, Ana Tudor, Corey Leydig, Thomas Ferguson, Tally Bovender, Darun Barazanchy, Josh Widosky, Paul Ziehl

Faculty Publications

Thermoplastic composite (TPC) materials often develop in-plane fiber waviness during manufacturing, a defect known as marcelling. Marcelling negatively impacts the strength and overall performance of the composite and are hard to control. While it may appear as a surface imperfection, it can also extend partially through the thickness of the material. Currently, there is no established nondestructive evaluation (NDE) method to reliably detect and quantify marcelling in composites after manufacturing. As marcelling is not a local defect but spans across the surface in in-plane dimensions of the structure, traditional pulse-echo and phased-array ultrasonic NDE are ineffective. Automated multi-directional guided …

Tunable Shock And Vibration Damping In Metal Laser Powder Bed Fusion Components Via Controlled Post-Print Compaction Of Particle Damper Cavities, Joud N. Stame, Yanzhou Fu, Samuel Roberts, Austin Downey, Tianyu Zhang, Lang Yuan, Daniel Kiracofe

Faculty Publications

Components manufactured by Laser Powder Bed Fusion (LPBF) can incorporate particle dampers (PDS) by retaining unfused powder within internal pockets, providing inherent vibration suppression without added mass or separate damping components. This study investigates the damping performance of such LPBF-integrated PDs through two complementary approaches: (1) quantifying the effect of post-printing volume compression on energy absorption by mechanically indenting the damper pocket, and (2) evaluating how variations in particle packing density influence the dynamic response under both sinusoidal and transient impulse excitation. Experimental results from shaker and shock testing demonstrate that increased packing density, whether by compression or tighter confinement, …

Roadmap On Artificial Intelligence-Augmented Additive Manufacturing, Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei-Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C.L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano-Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan

Faculty Publications

Artificial intelligence-augmented additive manufacturing (AI2AM) represents a transformative frontier in digital fabrication, where artificial intelligence (AI) is embedded not as a peripheral tool, but as a central framework driving intelligent, adaptive, and autonomous additive manufacturing (AM) systems. The objective of this Roadmap is to present a comprehensive vision of the state-of-the-art developments in AI2AM while charting the future trajectory of this rapidly emerging field. As AM applications continue to expand across diverse sectors, conventional design and control strategies face growing limitations in scalability, quality assurance, and material complexity. AI uses tools like computer vision, generative design, and large language models …

Quantum Spin-Like Behavior Of Elastic Guided Waves: Quantized Geometric Phase For Nde And Shm, Md Ebrahim Khalil Bhuiyan

Theses and Dissertations

Ultrasonic guided waves are widely used in nondestructive evaluation (NDE) and structural health monitoring (SHM) for detecting defects and damage in engineering materials and structures. Conventional diagnostic features, such as attenuation of wave amplitude, shifts in time-of-flight, and frequency variations are widely used for damage identification. However, these metrics are inherently sensitive to environmental and operational variations, including temperature, pressure, humidity, sensor coupling conditions, boundary constraints, and external loading. Such sensitivities introduce significant uncertainty in signal interpretation, often obscuring damage-related signatures. Recent advances in wave physics have revealed that elastic guided waves exhibit intrinsic spin angular momentum (SAM), arising from …

Development And Optimization Of Aluminum-Water Reactors For Efficient And Sustainable Hydrogen Production, Guru Prasad Dhungel

Theses and Dissertations

Hydrogen is a promising clean energy carrier; however, safe, controllable, and on- demand hydrogen generation remains a significant engineering challenge. Aluminum– water reactions offer high theoretical hydrogen yield, but practical implementation is limited by oxide passivation and difficulty in maintaining stable thermal and pressure conditions.

This study investigates hydrogen generation from aluminum foam–steam reactions under controlled temperature and pressure using engineered reactor configurations. Three reactor designs, horizontal, vertical, and cylindrical—were experimentally optimized, developed and evaluated to enhance hydrogen production rate and pressure stability without chemical additives. The experiments focused on heat transfer behavior, steam management, pressure buildup, and reaction stability. …

Strain-Induced Oxygen Vacancy Effect On Thermoelectric Properties In Oxide Thin Films, Ebenezer Seesi

Theses and Dissertations

The development of highly efficient and cost-effective thermoelectric (TE) materials is critical for converting waste heat into electricity in advanced power generators. For high-temperature TE applications, transition metal oxides (TMOs) have attracted significant interest because they are comparatively safe, earth-abundant, and stable under oxidizing conditions where many conventional heavy-metal and chalcogenide TE materials degrade. However, the TE performance of TMOs is limited by the intrinsic trade-off between the Seebeck coefficient (S) and electrical conductivity (σ), both of which depend inversely on charge carrier concentration. This constraint limits the achievable power factor (PF = S²∙σ) and remains a central obstacle for …

Plasma Dynamics And Chemistry In Atmospheric Pressure, Ayoub Al Wahaibi

Theses and Dissertations

Atmospheric-pressure plasmas are attractive for practical chemical and energy applications because they can be generated without vacuum systems and can produce reactive environments under relatively mild bulk conditions. However, these discharges are often non-uniform and highly sensitive to operating conditions, especially when water or multiphase flows are introduced. This dissertation investigates how atmospheric- pressure plasma behavior and plasma-driven chemistry change in three challenging but application-relevant regimes: water-mist dielectric barrier discharges (DBDs), multiphase plasma-assisted fuel reforming, and striated AC glow discharges. The work combines optical and electrical diagnostics, imaging, chemical analysis, and time-dependent analysis to connect discharge characteristics with measurable chemical …

Deep Learning Models For Anomaly Detection In Industrial Inspection, Jiawei Guo

Theses and Dissertations

Anomaly detection plays a pivotal role in ensuring the longevity and safety of engineered systems, particularly in domains where defects can have significant operational or economic consequences. From internal damage in structural parts to subtle visual defects in manufactured components, identifying abnormal patterns requires models that are both computationally efficient and suitable for real-world deployment. This dissertation explores deep learning-based solutions designed to address these challenges, with a focus on Structural Health Monitoring (SHM) and industrial defect inspection under real-world scenarios.

This dissertation first addresses the problem of efficient and reliable component- level perception with limited supervision and strict hardware …

Laser-Based Wideband Guided Ultrasonic Wave Inspection Of Isotropic And Anisotropic Materials, Andrew Philip Campbell

Theses and Dissertations

Non-destructive evaluation (NDE) and structural health monitoring (SHM) have experienced significant growth in both capability and utility as material evaluation and damage detection methods for structural components throughout various industries over the past two decades. Evaluating a structure throughout its life cycle or on demand as needed can identify critical weaknesses that impact safe operation and may prevent failures. Various NDE/SHM methods have been shown to be effective; among them, guided ultrasonic waves (GUW), particularly Lamb wave-based methods, have been intensively studied due to their capability to interrogate internal defects and their relatively larger coverage. With the advancements in new …

Mechanical Performance Of Equilateral Triangular Lattices: The Role Of Nodal Fillets, Fakhreddin Emami, Andrew J. Gross

Faculty Publications

Triangular lattices are widely employed for their high strength to weight ratios, yet their mechanical performance is sensitive to geometric features, particularly the nodal geometry. This study investigates the influence of nodal geometry on the mechanical behavior of equilateral triangular lattices across a broad range of relative densities using high fidelity finite element simulations. We characterize the elastic properties, and strength limits as functions of fillet radius. Our results confirm the expected trend that, in stretching-dominated lattices with low relative density, the introduction of fillets reduces both stiffness and buckling resistance. In contrast, at higher relative densities, filleted nodes can …

Roadmap: Integrating Artificial Intelligence In Structural Health Monitoring Systems, Simon Laflamme, Erik Blasch, Flippo Ubertini, Zheng Liu, John Wertz, Christine Knott, Matthew Cherry, Eric Lindgren, Fu-Kuo Chang, Amrita Kumar, Jack Poole, Keith Worden, Austin Downey, Jie Wei, Patrick F. Musgrave, Adrian S. Wong, Guiseppe Quaranta, Marco Martino Rosso, Giuseppe Carlo Marano, Yu Chen, Et. Al.

Faculty Publications

Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to …

Towards Sustainable Energy Storage: Evaluating The Performance Of Three Polymer Electrolytes For Zinc-Ion Batteries, Roya Rajabi, Shichen Sun, Buke Wu, Jamil A. Khan, Kevin Huang

Faculty Publications

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO4)2 and Zn(OTf)2, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The presence of carboxyl groups in …

Towards Sustainable Energy Storage: Evaluating The Performance Of Three Polymer Electrolytes For Zinc-Ion Batteries, Roya Rajabi, Shichen Sun, Buke Sun, Jamil A. Khan, Kevin Huang

Faculty Publications

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO₄)₂ and Zn(OTf)₂, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The …

Influence Of Pulse Width On Energy Deposition And Temperature In Nanosecond-Pulsed Discharges, Christopher B. Reuter, Joshua B. Sinrud, Tanvir I. Farouk, Nicholas S. Dewey, Dmitri Kaganovich

Faculty Publications

Nanosecond-pulsed discharges are a promising method to enhance combustion but can generate significant levels of electromagnetic interference (EMI). Modifying the discharge pulse width is an unexplored option to reduce EMI, but few studies have examined how changing the pulse width affects discharge parameters such as energy and temperature. This study addresses this issue by systematically investigating how the pulse width affects the energy per pulse, breakdown time, rotational temperature, and vibrational temperature in air across different frequencies, flow velocities, and gap distances in a plasma-assisted flow tube. It is observed that the pulse width has a substantial impact on the …

Bridging 2d And 3d Computational Modeling Of Vacuum Arc Remelting: Capturing Rotating Arc Dynamics In Axisymmetric Simulations, Zilong Zhang, Elaheh Dorari, Ramesh Minisandram, Shakarjee Krishnamoorthi, Lang Yuan

Faculty Publications

Computational modeling of the Vacuum Arc Remelting (VAR) process has been developed to provide a deeper physical and metallurgical understanding and assist in the manufacture of defect-free ingots. While fully 3D, time-resolved arc models capture arc–melt interactions with high fidelity, their high computational cost makes 2D steady arc models still the preferred option in industrial applications. In this study, a multi-physics VAR model, which accounts for magnetohydrodynamics, heat transfer, fluid dynamics, and melting/solidification, was established in ANSYS Fluent for Alloy 718 in 3D with a rotating arc. It was validated against measured melt pool morphology. A new 2D axisymmetric oscillating …

Rapid Single-Cell Measurement Of Transient Transmembrane Water Flow Under Osmotic Gradient, Hong Jiang, Jinnawat Jongkhumkrong, Y. J. Chao, Qian Wang, Guiren Wang

Faculty Publications

Aquaporins (AQPs) are critical for transmembrane water transport in response to osmotic gradients, but their gating and regulatory mechanisms remain poorly understood. A central challenge is the lack of methods to measure water flow across AQPs from individual cells with the spatiotemporal resolution and sensitivity equivalent to patch-clamp recordings of ion fluxes—a limitation stemming from the electrically silent nature of water flow. Here, we present a novel optical technique—Flow-Induced Fluorescence Increase Velocimetry (FIFIV) based on Laser-Induced Fluorescence Photobleaching Anemometry (LIFPA)—that enables direct, real-time monitoring of cytoplasmic flow induced by transmembrane water transport under osmotic pressure gradients. Using small molecular fluorescent …

A New Highly Oxygen-Deficient And Cubic Pr3Zro8-Δ For Intermediate-Temperature Thermochemical Production Of Oxygen And Hydrogen, Jiaxin Lu, Yongliang Zhang, Luhong Chen, Yan Chen, Ke An, Yasser Shoukry, Xinfang Jin, Zhi-Hao Wang, Sai Mu, Kevin Huang

Faculty Publications

Two-step thermochemical cycles offer a clean route for hydrogen and oxygen production but are typically limited to high temperatures exceeding 1500 °C. Lowering operating temperatures would enable the use of alternative heat sources such as industrial waste heat. Here, we report Pr₃ZrO₈ as a new enabling material for efficient intermediate-temperature redox cycling, with thermal reduction at 900 °C in argon and steam oxidation at 400 °C. Pr₃ZrO₈ adopts a face-centered cubic structure similar to CeO₂ but exhibits significantly greater oxygen deficiency, achieving average oxygen and hydrogen fluxes of 331.7 and 70.3 µmol·g-1, respectively, …

Digital Twin Enabled Robot Collision Detection Using Time Series Forecasting, Fadi El Kalach, Mojaba A. Farahani, Philip Samaha, Thorsten Wuest, Ramy Harik

Faculty Publications

The advent of Industry 4.0 has reshaped modern manufacturing, driven by breakthroughs in cutting-edge technologies. A key example is the widespread deployment of sensors, which capture and transmit large volumes of operational data. This data surge has fueled the development of advanced Artificial Intelligence (AI) applications, enhancing manufacturing intelligence and efficiency. A key enabler of such intelligence is Time-Series Forecasting (TSF), which leverages historical data to predict future trends and events, thereby providing actionable insights for proactive decision-making. In parallel, Digital Twin (DT) technology has gained significant prominence due to its capacity for bidirectional communication with physical manufacturing systems, enabling …

Approaching Lower Bound Of Lattice Thermal Conductivity By Simultaneously Suppressing Diagonal And Off-Diagonal Phonon Contributions, Alejandro David Rodriguez, Riccardo Rurali, Changpeng Lin, Joshua Ojih, Mohammed Al-Fahdi, G. Jeffrey Snyder, Ming Hu

Faculty Publications

Pushing the intrinsic lattice thermal conductivity (LTC) in crystalline materials to lower bounds is crucial for fundamental materials research towards emerging technologies including thermoelectric energy conversion and thermal management in both hypersonic aircraft and next-generation turbine systems. However, in the ultralow LTC regime ( <  1 Wm-1K-1), the competition between propagative (particle-like) and coherent phonons—arising from off-diagonal components—poses a significant challenge in further reducing LTC. We perform quantitative analysis of 4700 materials using density functional theory (DFT), spanning all crystallographic groups, to elucidate the interplay between diagonal and off-diagonal phonon contributions. We identify a critical balance between these transport mechanisms, where intermediate phonon lifetimes ( ~ 1 ps) and slow group velocities ( ~ 1 km/s) collectively suppress both contributions, enabling ultralow LTC. Results from a large dataset of 31,058 structures by machine learning models strongly resemble the DFT trends of two-channel phonon transport. Leveraging these models, we screen 25,882 additional materials and confirm their properties with DFT, identifying 12 candidates with ultralow room-temperature LTC—including a record-low value of 0.132 Wm-1K-1. Our large-scale analysis reveals fundamental insights into dual-channel phonon transport, enabling rational design of ultralow LTC materials and accelerating the discovery of advanced phononic crystals with tailored thermal transport properties.

Uav-Deployable Open-Source Sensor Nodes For Spatial And Temporal In Situ Water Quality Monitoring And Mapping, Matthew Burnett, Mohamed Abdelwahab, Joud N. Satme, Austin Downey, Gabriel Barahona Smith, Antonio Fonce, Jasim Imran

Faculty Publications

Cost efficient, spatially resolved water quality monitoring is essential for managing pollution and protecting aquatic ecosystems. This study presents a low-cost (approximately USD 200), open-source, unmanned aerial vehicle (UAV)-deployable in situ sensor node for real-time assessment of surface-water conditions. The system integrates sensors for pH, turbidity, temperature, and total dissolved solids (TDSs), with onboard data logging and real-time clock (RTC) synchronization. Bench validation of the sensor package yielded mean absolute percentage errors of 1.34% for pH, 5.23% for TDS, and 0.81% for temperature, and the device operated continuously for 42 h. Field deployment demonstrated its ability to resolve spatial gradients, …

Striation Characteristics In Atmospheric Pressure Ac-Driven Glow Discharge Operating In Monoatomic Gas, Ayuob K. Alwahaibi, Sang Hee Won, Tanvir Farouk

Faculty Publications

Striation in an atmospheric pressure AC-driven helium glow discharge operating at 25 kHz is experimentally characterized. Striation behavior is characterized by the visualization of discharges and voltage–current measurements over a range of plasma power. Voltage–current characteris tics reveal that striations form in the “normal” glow regime of operation, with a higher number of strata at low current, diminishing as dis charge current increases. The relative differences and similarities in the striation patterns during the positive and negative cycles are identified. High-speed imaging shows the striations to be traveling in time—“moving striations.” The spatial luminosity of the striations is tracked over …

Crystal Structures, Optical Behavior, And Magnetic Properties In Hydrated Lanthanide Iron Sulfates, Chole Jones, Silu Huang, Tyler L. Spano, Eric A. Gabilondo, Mohammed Al-Fahdi, Kara Trim, Mary Douglas, Rongying Jin, Andrew Miskowiec, P. Shiv Halasyamani, Ming Hu, Jie Ling

Faculty Publications

Single crystals of LnFe(SO₄)₃(H₂O)₂ (Ln = La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er, Tm; compounds 1–11) and LnFe(SO₄)₃(H₂O) (Ln = Tm, Yb, Lu; compounds 12–14) were synthesized under hydrothermal conditions. Single-crystal X-ray diffraction (SCXRD) analysis revealed that the dihydrated compounds (1–11) crystallize in centrosymmetric (CS) structures, with the lanthanide ions adopting eight-coordinate geometries. In contrast, the monohydrated compounds (12–14) exhibit noncentrosymmetric (NCS) structures, where the lanthanide ions are seven-coordinated. Vibrating sample magnetometry (VSM) confirmed that compounds 2, …

Fatigue Crack Length Estimation Using Acoustic Emissions Technique-Based Convolutional Neural Networks, Asaad Migot, Ahmed Saaudi, Roshan Joseph, Victor Giurgiutiu

Faculty Publications

Fatigue crack propagation is a critical failure mechanism in engineering structures, requiring meticulous monitoring for timely maintenance. This research introduces a deep learning framework for estimating fatigue fracture length in metallic plates through acoustic emission (AE) signals. AE waveforms recorded during crack growth are transformed into time-frequency images using the Choi–Williams distribution. First, a clustering system is developed to analyze the distribution of the AE image-based dataset. This system employs a CNN-based model to extract features from the input images. The AE dataset is then divided into three categories according to fatigue lengths using the K-means algorithm. Principal Component Analysis …

Tuning Oxygen Reduction Kinetics In Lasrcoo4 With Strained Epitaxial Thin Films And Wrinkled Freestanding Membranes, Habib Rostaghi Chalaki, Ebenezer Sessi, Mohammad El Loubani, Dongkyu Lee

Faculty Publications

Sluggish oxygen reduction reaction (ORR) remains a critical barrier to advancing intermediate-temperature electrochemical energy devices. Here, we demonstrate that strain engineering in two platforms, epitaxial thin films and freestanding membranes, systematically tunes ORR kinetics in Ruddlesden-Popper LaSrCoO4. In epitaxial films, film thickness is varied to control in-plane tensile strain, whereas in freestanding membranes strain relaxation during the release step using water-soluble sacrificial layers produces flat or wrinkled architectures. Electrochemical impedance spectroscopy analysis reveals more than an order of magnitude increase in the oxygen surface exchange coefficient for tensile-strained films relative to relaxed films, together with a larger oxygen vacancy concentration. …

Reformulation Of The Protein Databank For Real-Time Search Of Geometrical Attributes Of Protein Structures, Musa Azeem, Christopher Lee, Aaron Hein, Christopher Ott, Homayoun Valafar

Faculty Publications

Introduction:

In this study, we introduce the design and implementation of PDBMine, a large-scale, queryable platform for mining sequence-structure statistics from the Protein Data Bank (PDB). PDBMine enables rapid analysis of local conformational trends across proteins by extracting dihedral angles and sequence patterns at scale. In addition to the design and implementation of PDBMine, we also present results validating its ability to return structurally meaningful information.

Methods:

We first assess the accuracy of its dihedral angle distributions by comparing them to established Ramachandran space and verifying expected behaviors of residues such as glycine and proline. We then use PDBMine to …

Stoichiometry-Controlled Surface Reconstructions In Epitaxial Abo3 Perovskites For Sustainable Energy Applications, Habib Rostaghi Chalaki, Ebenezer Seesi, Gene Yang, Mohammad El Loubani, Dongkyu Lee

Faculty Publications

ABO₃ perovskite oxides are a versatile class of materials whose surfaces and interfaces play essential roles in sustainable energy technologies, including catalysis, solid oxide fuel and electrolysis cells, thermoelectrics, and energy-relevant oxide electronics. The interplay between point defects and surface reconstructions strongly affects interfacial stability, charge transport, and catalytic activity under operating conditions. This review summarizes recent progress in understanding how oxygen vacancies, cation nonstoichiometry, and electronic defects couple to atomic-scale surface rearrangements in representative perovskite systems. We first revisit Tasker’s classification of ionic surfaces and clarify how defect chemistry provides compensation mechanisms that stabilize otherwise polar or metastable …

A Gas-Tight Bonding/Sealing Of Different Bulk Ceramics For Robust Oxygen Separation At Ultra-High Temperatures, Guoan Wang, Xingjian Xue

Faculty Publications

Oxygen transport membrane (OTM) is an economic technology for oxygen separation and high-purity oxygen production. To mitigate various issues induced by high temperatures, intermediate temperature OTM technology has been pursued in recent years. However, in certain circumstances, high operating temperatures are unavoidable, such as in situ oxygen production using OTMs for direct oxy-combustion. Nevertheless, the lack of reliable high temperature gas-tight sealing imposes great challenges on OTM technology for such applications. Herein, a novel sealing strategy is developed to obtain a gas-tight bonding/sealing of two different bulk ceramics using ceramic slurry in combination with phase inversion process. The sealing strategy …

Online Cyber-Physical Neural Network Model For Real-Time Hybrid Simulation, Faisal Nissar Malik, Liang Cao, James Ricles, Austin Downey

Faculty Publications

Real-time hybrid simulation (RTHS) is an experimental testing methodology that divides a structural system into an analyticaland an experimental substructure. The analytical substructure is modeled numerically, and the experimental substructure ismodeled physically in the laboratory. The two substructures are kinematically linked together at their interface degrees of freedom,and the coupled equations of motion are solved in real-time to obtain the response of the complete system. A key challenge inapplying RTHS to large or complex structures is the limited availability of physical devices, which makes it difficult to representall required experimental components simultaneously. The present study addresses this challenge by introducing …

A New Low-Rate Stable Hydrogel Cathode For Aqueous Zn-Ion Batteries, Roya Rajabi, Shichen Sun, Jamil A. Khan, Morgan Stefik, Kevin Huang

Faculty Publications

Aqueous Zn-ion batteries (ZIBs) are attractive candidates for large-scale energy storage owing to the abundance, low cost, and intrinsic safety of Zn metal. However, their practical application is hindered by poor cycle stability, especially at low current densities, due to cathode dissolution and limited electrochemically active sites (EAS). Herein, a hydrogel-based cathode comprising ammonium vanadate, carbon black, and a Zn-ion-conducting carboxymethyl chitosan–acrylamide hydrogel matrix doped with Zn(ClO4)2 is reported. This design establishes a continuous Zn-ion-conducting network, thereby maximizing EAS density throughout the electrode volume. The ZIB with the hydrogel cathode exhibits outstanding cycling stability, with 77% capacity retention after 2000 …

A Comprehensive Assessment And Benchmark Studyof Large Atomistic Foundation Models For Phonons, Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al-Fahdi, Lingyu Kong, Victor Fung, Ming Hu

Faculty Publications

The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs—EquiformerV2, MatterSim, MACE, and CHGNet—on 2429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong …