Bourns College of Engineering

The reliance on fossil fuels for the increasing global energy demand necessitates the advancement of carbon capture and storage technologies to mitigate anthropogenic CO₂ emissions. However, conventional experimental platforms have reached their practical limitations, stagnating further advancements in optimizing carbon capture and storage (CCS) processes. Microfluidic technologies have emerged as promising tools for investigating and optimizing CCS processes at the microscale, offering precise control over gas-liquid interactions, reaction kinetics, and multiphase flow dynamics, which would be very challenging with conventional macroscale platforms. This review uniquely consolidates carbon capture and storage advancements, providing a structured approach that starts from fundamental mechanisms and systematically progresses to advanced microfluidic-assisted CCS strategies. Unlike prior reports, this review demonstrates how microfluidics complements conventional macroscale approaches and outperforms in certain aspects for studying CO₂ capture and storage through specific experimental examples. The carbon capture section explores microfluidic approaches for physical absorption, chemical absorption, and adsorption, highlighting their advantages over conventional methods. The discussion extends to key mass transfer models, microchannel geometries, and flow regimes that dictate CO₂ dissolution rates and interfacial transport phenomena. The carbon storage section examines microfluidic investigations into carbon mineralization and geological sequestration, providing insights into pore-scale trapping mechanisms, mineralization kinetics, and enhanced storage efficiency. The integration of real-time analytical techniques has further facilitated high-resolution quantification of CO₂ transport and reaction dynamics. Despite significant progress, challenges remain in scaling microfluidic findings to field applications, accurately mimicking reservoir conditions, and developing reactive microfluidic platforms for in situ mineralization studies. This review aims to show the pivotal role of microfluidics in accelerating CCS innovation.

Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger causality and dynamic causal modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested alternatives. From resting-state human fMRI, CaLLTiF learns causal connectomes that are highly consistent across individuals, show clear top-down flow of causal effect from attention and default mode to sensorimotor networks, exhibit Euclidean distance dependence in causal interactions, and are highly dominated by contemporaneous effects. Overall, this work takes a major step in enhancing causal discovery from whole-brain fMRI and defines a new standard for future investigations.

Mineral dissolution in porous media coupled with single- and/or multi-phase flows is pervasive in natural and engineering systems. Dissolution modifies the physical, hydrological, and geochemical properties of the solid matrix, resulting in a complex coupling between local dissolution rate and pore-scale flow. The work reports a microfluidic approach that includes 2D reactive porous media and advanced pore flow diagnostics for the study of pore-scale dissolution in porous media with unprecedented details. The 2D microfluidic porous media, called micromodels, were fabricated in calcite by combining photolithography and wet etching, which not only offers precise control over the structural and chemical properties, but also facilitate unobstructed optical access to the pore flow, significantly improving over existing methods. We believe the work represents the first of its kind as it for the first time directly applies photolithography to calcite samples and demonstrates the use of particle image velocimetry to investigate chemical reactions in porous media. The preliminary results have revealed the crucial roles of local concentration gradients in mineral dissolution and call for reconsideration of many assumptions used in the current modeling tools, which paves the way for renewed fundamental understanding of reactive transport and improved modeling tools with better accuracy.

Manta rays use wing-like pectoral fins for intriguing oscillatory swimming. It provides rich inspiration for designing potentially fast, efficient, and maneuverable soft swimming robots, which, however, have yet to be realized. It remains a grand challenge to combine fast speed, high efficiency, and high maneuverability in a single soft swimmer while using simple actuation and control. Here, we report leveraging spontaneous snapping stroke in the monostable flapping wing of a manta-like soft swimmer to address the challenge. The monostable wing is pneumatically actuated to instantaneously snap through to stroke down, and upon deflation, it will spontaneously stroke up by snapping back to its initial state, driven by elastic restoring force, without consuming additional energy. This largely simplifies designs, actuation, and control for achieving a record-high speed of 6.8 body length per second, high energy efficiency, and high maneuverability and collision resilience in navigating through underwater unstructured environments with obstacles by simply tuning single-input actuation frequencies.

Pulmonary air leaks are amongst the most common complications in lung surgery. Lung sealants are applied to the organ surface and need to synchronously stretch with the visceral pleura, the layer of tissue which encompasses the lung parenchymal tissue. These adhesives are commonly tested on pig and rat lungs, but applied to human lungs. However, the unknown mechanics of human lung visceral pleura undermines the clinical translatability of such animal-tested sealants and the absence of how pig and rat lung visceral pleura compare to human tissues is necessary to address. Here we quantify the biaxial planar tensile mechanics of visceral pleura from healthy transplant-eligible and smoker human lungs for the first time, and further compare the material behaviors to pig and rat lung visceral pleura. Initial and final stiffness moduli, maximum stress, low-to-high strain transition, and stress relaxation are analyzed and compared between and within groups, further considering regional and directional dependencies. Visceral pleura tissue from all species behave isotropically, and pig and human visceral pleura exhibits regional heterogeneity (i.e. upper versus lower lobe differences). We find that pig visceral pleura exhibits similar initial stiffness moduli and regional trends compared to human visceral pleura, suggesting pig tissue may serve as a viable animal model candidate for lung sealant testing. The outcomes and mechanical characterization of these scarce tissues enables future development of biomimetic lung sealants for improved surgical applications. STATEMENT OF SIGNIFICANCE: Surgical lung sealants must synchronously deform with the underlying tissue and with each breath to minimize post-operative air leaks, which remain the most frequent complications of pulmonary intervention. These adhesives are often tested on pig and rat lungs, but applied to humans; however, the material properties of human lung visceral pleura were previously unexplored. Here, for the first time, the mechanics of human visceral pleura tissue are investigated, further contrasting rarely acquired donated lungs from healthy and smoking individuals, and additionally, comparing biaxial planar material characterizations to animal models often employed for pulmonary sealant development. This fundamental material characterization addresses key hindrances in the advancement of biomimetic sealants and evaluates the translatability of animal model experiments for clinical applications.

Nonthermal plasmas provide a unique approach to electrically driven heterogeneous catalytic processes. Despite much interest from the community, fundamental activation pathways in these processes remain poorly understood. Here, we investigate how exposure to a nonthermal plasma sustained in an argon nonreactive atmosphere affects the desorption of carbon monoxide (CO) from platinum nanoparticles. Temperature-programmed desorption measurements indicate that the plasma reduces the effective binding energy (BE) of CO to Pt surfaces by as much as ∼0.3 eV, with the reduction in the BE scaling linearly with the plasma density. We find that the effective CO BE is most strongly reduced for under-coordinated sites (steps and edges) compared to well-coordinated sites (terraces). Density functional theory calculations suggest that this is due to plasma-induced charging and electric fields at the catalyst surface, which preferentially affect under-coordinated sites. This study provides direct experimental evidence of plasma-induced nonthermal activation of the adsorbate-catalyst couple.

Abstract: Since Nobel Laureate Arthur Ashkin first introduced the trapping and manipulation of microparticles using light, numerous studies have explored this technique not only for dielectric/metallic particles but also for organic matter. This advancement has significantly expanded the landscape of non-contact and non-invasive micromanipulation at the nanometric scale. However, micromanipulation of particles with a refractive index smaller than the host medium, n p < n m, proves challenging with Gaussian beams. To overcome this obstacle, a force known as thermocapillary, or the Marangoni force, has emerged as a straightforward trapping mechanism for bubbles in liquids. The Marangoni force results from the surface tension of bubbles, induced either thermally or chemically—by creating a temperature gradient or adding surfactants, respectively. The surface tension gradient on the liquid host induces tangential stress on the bubble wall, causing the bubble to move toward the region of lower surface tension, where it faces less opposing force. When the Marangoni force is generated by a laser beam’s temperature gradient, it becomes an exceptionally effective mechanism for the steady-state trapping and three-dimensional manipulation of bubbles, even with low optical power lasers. This force produces both longitudinal and transversal forces, resembling optical forces, creating a three-dimensional potential well capable of handling bubbles with radii of tens to hundreds of microns. This work provides guidance and demonstrates, both experimentally and theoretically, the step-by-step process of quasi-steady-state trapping and three-dimensional manipulation of bubbles through optothermal effects. The bubbles in question are tens of microns in size, significantly larger than those that optical tweezers can trap/manipulate. Furthermore, the study emphasizes the crucial role of the Marangoni force in this process, outlining its various advantages.

Three-dimensional variation in structural components or fiber alignments results in complex mechanical property distribution in tissues and biomaterials. In this paper, we use a physics-informed UNet-based neural network model (El-UNet) to discover the three-dimensional (3D) internal composition and space-dependent material properties of heterogeneous isotropic and transversely isotropic materials without a priori knowledge of the composition. We then show the capabilities of El-UNet by validating against data obtained from finite-element simulations of two soft tissues, namely, brain tissue and articular cartilage, under various loading conditions. We first simulated compressive loading of 3D brain tissue comprising of distinct white matter and gray matter mechanical properties undergoing small strains with isotropic linear elastic behavior, where El-UNet reached mean absolute relative errors under 1.5 % for elastic modulus and Poisson's ratio estimations across the 3D volume. We showed that the 3D solution achieved by El-UNet was superior to relative stiffness mapping by inverse of axial strain and two-dimensional plane stress/plane strain approximations. Additionally, we simulated a transversely isotropic articular cartilage with known fiber orientations undergoing compressive loading, and accurately estimated the spatial distribution of all five material parameters, with mean absolute relative errors under 5 %. Our work demonstrates the application of the computationally efficient physics-informed El-UNet in 3D elasticity imaging and provides methods for translation to experimental 3D characterization of soft tissues and other materials. The proposed El-UNet offers a powerful tool for both in vitro and ex vivo tissue analysis, with potential extensions to in vivo diagnostics. STATEMENT OF SIGNIFICANCE: Elasticity imaging is a technique that reconstructs mechanical properties of tissue using deformation and force measurements. Given the complexity of this reconstruction, most existing methods have mostly focused on 2D problems. Our work is the first implementation of physics-informed UNets to reconstruct three-dimensional material parameter distributions for isotropic and transversely isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using synthetic data generated using finite element models of biological tissues with high bio-fidelity-the brain and articular cartilage. Our method can be implemented in elasticity imaging scenarios for in vitro and ex vivo mechanical characterization of biomaterials and biological tissues, with potential extensions to in vivo diagnostics.

Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of mechanical parameters from strain maps as input images, normal stress boundary conditions, and domain physics information. We show superior performance - both in terms of accuracy and computational cost - by El-UNet compared to fully-connected physics-informed neural networks in estimating unknown parameters and stress distributions for isotropic linear elasticity. We characterize different variations of El-UNet and propose a self-adaptive spatial loss weighting approach. To validate our inversion models, we performed various finite-element simulations of isotropic domains with heterogenous distributions of material parameters to generate synthetic data. El-UNet is faster and more accurate than the fully-connected physics-informed implementation in resolving the distribution of unknown fields. Among the tested models, the self-adaptive spatially weighted models had the most accurate reconstructions in equal computation times. The learned spatial weighting distribution visibly corresponded to regions that the unweighted models were resolving inaccurately. Our work demonstrates a computationally efficient inversion algorithm for elasticity imaging using convolutional neural networks and presents a potential fast framework for three-dimensional inverse elasticity problems that have proven unachievable through previously proposed methods.