Search for: All records

In this study, we consider three different machine‐learning methods—a three‐hidden‐layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine‐learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus on both the accuracy of the machine‐learning methods and the performance on one GPU including memory consumption. Although it is arguably the least sophisticated machine‐learning model we considered, support vector regression performed very well in our tests. 

Microscale continuous thin films or patterned conductive structures find applications in thin film electronics, energy generation and functional sensor systems. An emerging alternative to conventional vacuum based deposition of such structures is the additive deposition and sintering of conductive nanoparticles, to enable low temperature, low-cost and low energy fabrication. While significant work has gone into additive deposition of nanoparticles the realization of the above potential needs nanoparticle sintering methods that are equally low-cost, in-situ, ambient condition and desktop-sized in nature. This work demonstrates the integration of non-laser based, low-cost and small footprint optical energy sources for ambient condition sintering of conductive nanoparticles, with wide-area aerosol jet based additive printing of nanoparticle inks. The nanoparticle sintering is characterized by quantifying the sintering temperatures, sintered material conductivity, crystallinity, optical properties, thickness and microscale morphology in terms of the sintering parameters. It is shown that such optical sintering sources can be further integrated with inkjet printing as well, and the implications on new paradigms for hybrid additive-