Dual-functional gum arabic binder for silicon anodes in lithium ion batteries

Si has attracted enormous research and manufacturing attention as an anode material for
lithium ion batteries (LIBs) because of its high specific capacity. The lack of a low cost and
effective mechanism to prevent the pulverization of Si electrodes during the lithiation/
delithiation process has been a major barrier in the mass production of Si anodes. Naturally
abundant gum arabic (GA), composed of polysaccharides and glycoproteins, is applied as a dualfunction
binder to address this dilemma. Firstly, the hydroxyl groups of the polysaccharide in GA
are crucial in ensuring strong binding to Si. Secondly, similar to the function of fiber in fiberreinforced
concrete (FRC), the long chain glycoproteins provide further mechanical tolerance
to dramatic volume expansion by Si nanoparticles. The resultant Si anodes present an
outstanding capacity of ca. 2000 mAh/g at a 1 C rate and 1000 mAh/g at 2 C rate, respectively,
throughout 500 cycles. Excellent long-term stability is demonstrated by the maintenance of
1000 mAh/g specific capacity at 1 C rate for over 1000 cycles. This low cost, naturally abundant
and environmentally benign polymer is a promising binder for LIBs in the future.

Stochastic discrete meso-scale simulations of concrete fracture: Comparison to experimental data

Fracture analysis of fiber reinforced concrete structures in the micropolar peridynamic analysis framework

Meshless modeling framework for fiber reinforced concrete structures

Modeling of fiber-reinforced cement composites: Discrete representation of fiber pullout

Multiscale modeling of strain-hardening cementitious composites

This paper presents novel developments in the lattice modeling of fiber reinforced cement composites, in which the individual fibers are explicitly represented. Attention is given to the modeling of strain-hardening cement composites under tensile loading. The pullout forces of fibers are derived from micromechanical bases and distributed along the fiber embedment lengths. This spatial description of the fiber bridging forces provides realistic representations of stress transfer between the fiber and matrix, which is essential for simulating crack openings and crack spacing. Stress transfer is modified to account for multiple cracks intersecting individual fibers, which is typical for materials with closely spaced cracks. Multiple cracking generates islands of material interconnected by fiber bridges, which places extraordinary demands on the solution procedure. An event-by-event solution strategy is developed for this reason. Comparisons with test results demonstrate the model’s ability to simulate global and crack-local features. In particular, simulated histograms of crack opening displacement compare favorably with those measured experimentally.

Event-based lattice modeling of strain-hardening cementitious composites

Reconstructing dynamic, time-varying scenes with computed tomography (4D-CT) is a challenging and ill-posed problem common to industrial and medical settings. Existing 4D-CT reconstructions are designed for sparse sampling schemes that require fast CT scanners to capture multiple, rapid revolutions around the scene in order to generate high quality results. However, if the scene is moving too fast, then the sampling occurs along a limited view and is difficult to reconstruct due to spatiotemporal ambiguities. In this work, we design a reconstruction pipeline using implicit neural representations coupled with a novel parametric motion field warping to perform limited view 4D-CT reconstruction of rapidly deforming scenes. Importantly, we utilize a differentiable analysis-by-synthesis approach to compare with captured x-ray sinogram data in a self-supervised fashion. Thus, our resulting optimization method requires no training data to reconstruct the scene. We demonstrate that our proposed system robustly reconstructs scenes containing deformable and periodic motion and validate against state-of-the-art baselines. Further, we demonstrate an ability to reconstruct continuous spatiotemporal representations of our scenes and upsample them to arbitrary volumes and frame rates post-optimization. This research opens a new avenue for implicit neural representations in computed tomography reconstruction in general.

/pdf/dynamic-ct-reconstruction-from-limited-views-with-implicit-501bfdt7ij.pdf

Dynamic CT Reconstruction From Limited Views With Implicit Neural Representations and Parametric Motion Fields

Short fiber reinforcement is added to concrete materials to improve a variety of performance measures related to structural safety, serviceability, and health diagnosis. Mesoscale models have been developed to understand the individual and collective actions of these fibers on various material properties. Those modeling efforts have predominately focussed on the mechanical (i.e., stiffness and strength) contributions of fibers. This paper introduces computationally efficient, semi-discrete representations of fibers within coupled mechanical and transport processes in cement-based matrices. Basic simulations are done to study: the use of conductive fibers for self-sensing; and the influences of fibers on early-age plastic settlement. It is found that the models can account for directional bias on fiber orientation, as may occur during material casting. With respect to plastic settlement, fibers may play competing roles: mechanical restraint offered by the fibers reduces settlement, whereas enhanced hydraulic conductivity along the fiber–matrix interface may increase settlement by facilitating the bleeding process.

https://www.mdpi.com/2076-3417/11/1/184/pdf

Jingu Kang

Papers

Modeling of fiber-reinforced cement composites: Discrete representation of fiber pullout

Multiscale modeling of strain-hardening cementitious composites

Event-based lattice modeling of strain-hardening cementitious composites

Dynamic CT Reconstruction From Limited Views With Implicit Neural Representations and Parametric Motion Fields

Multi-Field Models of Fiber Reinforced Concrete for Structural Applications