739 publications from this institution
The processing of Kevlar to certain strengths by hot-drawing can benefit by quantitative understanding of the correlation between structural and mechanical properties during the pre-drawing process. Here, we use a novel continuous dynamic analysis (CDA) to monitor the evolution in storage modulus and loss factor of Kevlar 49 fibers as a function of strain via a quasi-static tensile test. Unlike traditional dynamic mechanical analysis, CDA allows the tracking of strain-dependent mechanical properties until failure. The obtained dynamic viscoelastic properties of Kevlar 49 are correlated with structural data obtained from synchrotron radiation analysis and with Raman scattering frequencies. Rate-dependent stress–strain results from Kevlar are compared to Nomex, spider silk, polyester and rubber, and provide insight into how the mechanical properties of Kevlar originate from its characteristic structural features. We find that as the storage modulus of Kevlar is essentially equal to the Young's modulus, the measured quantitative relationships between storage modulus and strain can provide insights into the tuning of the mechanical properties of aramid materials for specific applications.
In situ transmission electron microscopy (TEM) has enabled researchers to visualize complicated nano- and atomic-scale processes with sub-Angstrom spatial resolution and millisecond time resolution. These processes are often highly dynamical and can be time-consuming to analyze and interpret. Here, we report how variational autoencoders (VAEs), a deep learning algorithm, can provide an artificial intelligence’s interpretation of high-resolution in situ TEM data by condensing and deconvoluting complicated atomic-scale dynamics into a latent space with reduced dimensionality. In this work, we designed a VAEs model with high latent dimensions capable of deconvoluting information from complex high-resolution TEM data. We demonstrate how this model with high latent dimensions trained on atomically resolved TEM images of lead sulfide (PbS) nanocrystals is able to capture movements and perturbations of periodic lattices in both simulated and real in situ TEM data. The VAEs model shows capability of detecting and deconvoluting dynamical nanoscale physical processes, such as the rotation of crystal lattices and intraparticle ripening during the annealing of semiconductor nanocrystals. With the help of the VAEs model, we can identify an in situ observation that can serve as a direct experimental evidence of the existence of intraparticle ripening. The VAEs model provides a potent tool for facilitating the analysis and interpretation of complex in situ TEM data as a part of an autonomous experimental workflow.