More Like this

1. High Production Rate of High Purity, High Fidelity Nafion Nanofibers via Needleless Electrospinning

   https://doi.org/10.1021/acsapm.9b00681  

   Hwang, Monica; Karenson, Muizz O.; Elabd, Yossef A. (August 2019, ACS Applied Polymer Materials)
2. Effective High-Speed via Model Considering Equivalent High-Order-Mode Inductance

   https://doi.org/10.1109/TSIPI.2024.3487539  

   Li, Chaofeng; Cai, Xiao-Ding; Mathew, Manish K; Park, Junyong; Mousavi, Mehdi; Ahmed, Shameem; Sen, Bidyut; Kim, DongHyun (January 2024, IEEE Transactions on Signal and Power Integrity)

   A high-speed via model considering equivalent high-order-mode inductance for frequencies above 70 GHz is proposed in this article. The proposed via model, which considers the equivalent high-order-mode inductance, is a high-accuracy and high-bandwidth via model, improved from the previously proposed mode-decomposition-based equivalent via (MEV) model. The equivalent high-order-mode inductance is an inductance produced by the magnetic fields of high-order modes in the via domain. By including the empirical equivalent high-order-mode inductance, the proposed via model accurately predicts the insertion and return losses of the via with a large antipad size up to the frequency of 150 GHz, which expands the application range of the previously proposed MEV model. In this article, the limitation of the previously proposed MEV model is analyzed by comparing the input impedance extracted from the MEV model and the full-wave simulation. An empirical closed-form formula is proposed to calculate the equivalent high-order-mode inductance at high frequencies to improve the accuracy of the previously proposed model. In addition, the proposed single high-speed via model was expanded to a differential via pair model. The proposed high-speed via model was verified using simulation and measurement results. 

   more » « less
3. Functionally Graded Adhesives via High-Energy Irradiation

   https://doi.org/10.1021/acsapm.0c01137  

   Xia, Weiqing; Najafian, Sara; Cassano, Alessandro G.; Stapleton, Scott E.; Schmidt, Daniel F. (January 2021, ACS Applied Polymer Materials)

   null (Ed.)
4. High-performance symbolic-numerics via multiple dispatch

   https://doi.org/10.1145/3511528.3511535  

   Gowda, Shashi; Ma, Yingbo; Cheli, Alessandro; Gwóźzdź, Maja; Shah, Viral B.; Edelman, Alan; Rackauckas, Christopher (September 2021, ACM Communications in Computer Algebra)

   As mathematical computing becomes more democratized in high-level languages, high-performance symbolic-numeric systems are necessary for domain scientists and engineers to get the best performance out of their machine without deep knowledge of code optimization. Naturally, users need different term types either to have different algebraic properties for them, or to use efficient data structures. To this end, we developed Symbolics.jl, an extendable symbolic system which uses dynamic multiple dispatch to change behavior depending on the domain needs. In this work we detail an underlying abstract term interface which allows for speed without sacrificing generality. We show that by formalizing a generic API on actions independent of implementation, we can retroactively add optimized data structures to our system without changing the pre-existing term rewriters. We showcase how this can be used to optimize term construction and give a 113x acceleration on general symbolic transformations. Further, we show that such a generic API allows for complementary term-rewriting implementations. Exploiting this feature, we demonstrate the ability to swap between classical term-rewriting simplifiers and e-graph-based term-rewriting simplifiers. We illustrate how this symbolic system improves numerical computing tasks by showcasing an e-graph ruleset which minimizes the number of CPU cycles during expression evaluation, and demonstrate how it simplifies a real-world reaction-network simulation to halve the runtime. Additionally, we show a reaction-diffusion partial differential equation solver which is able to be automatically converted into symbolic expressions via multiple dispatch tracing, which is subsequently accelerated and parallelized to give a 157x simulation speedup. Together, this presents Symbolics.jl as a next-generation symbolic-numeric computing environment geared towards modeling and simulation. 

   more » « less
5. Revealing local order via high energy EELS

   https://doi.org/10.1016/j.mtnano.2022.100298  

   Hart, J.L.; Lang, A.C.; Li, Y.; Shahrezaei, S.; Alix-Williams, D.D.; Falk, M.L.; Mathaudhu, S.N.; Frenkel, A.I.; Taheri, M.L. (March 2023, Materials Today Nano)

   Short range order (SRO) is critical in determining the performance of many important engineering materials. However, accurate characterization of SRO with high spatial resolution – which is needed for the study of individual nanoparticles and at material defects and interfaces – is often experimentally inaccessible. Here, we locally quantify SRO via scanning transmission electron microscopy with extended energy loss fine structure analysis. Specifically, we use novel instrumentation to perform electron energy loss spectroscopy out to 12 keV, accessing energies which are conventionally only possible using a synchrotron. Our data is of sufficient energy resolution and signal-to-noise ratio to perform quantitative extended fine structure analysis, which allows determination of local coordination environments. To showcase this technique, we investigate a multicomponent metallic glass nanolaminate and locally quantify the SRO with <10 nm spatial resolution; this measurement would have been impossible with conventional synchrotron or electron microscopy methods. We discuss the nature of SRO within the metallic glass phase, as well as the wider applicability of our approach for determining processing–SRO–property relationships in complex materials. 

   more » « less