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Creators/Authors contains: "Sun, Jian"

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  1. Controlling the deposition of polymer-wrapped single-walled carbon nanotubes (s-CNTs) onto functionalized substrates can enable the fabrication of s-CNT arrays for semiconductor devices. In this work, we utilize classical atomistic molecular dynamics (MD) simulations to show that a simple descriptor of solvent structure near silica substrates functionalized by a wide variety of self-assembled monolayers (SAMs) can predict trends in the deposition of s-CNTs from toluene. Free energy calculations and experiments indicate that those SAMs that lead to maximum disruption of solvent structure promote deposition to the greatest extent. These findings are consistent with deposition being driven by solvent-mediated interactions that arise from SAM-solvent interactions, rather than direct s-CNT-SAM interactions, and will permit the rapid computational exploration of potential substrate designs for controlling s-CNT deposition and alignment.
    Free, publicly-accessible full text available June 29, 2023
  2. Free, publicly-accessible full text available January 1, 2023
  3. Free, publicly-accessible full text available January 1, 2023
  4. This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.
    Free, publicly-accessible full text available December 1, 2022
  5. Remote sensing observational instruments are critical for better understanding and predicting severe weather. Observational data from such instruments, such as Doppler radar data, for example, are often processed for assimilation into numerical weather prediction models. As such instruments become more sophisticated, the amount of data to be processed grows and requires efficient variational analysis tools. Here we examine the code that implements the popular SAMURAI (Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation) technique for estimating the atmospheric state for a given set of observations. We employ a number of techniques to significantly improve the code’s performance, including porting it to run on standard HPC clusters, analyzing and optimizing its single-node performance, implementing a more efficient nonlinear optimization method, and enabling the use of GPUs via OpenACC. Our efforts thus far have yielded more than 100x improvement over the original code on large test problems of interest to the community.

    Free, publicly-accessible full text available August 28, 2023