arXiv subjects

Todd Emrick

Publications and source records attributed to Todd Emrick.

4 recordsLinked to original sources

arXiv2025

Engineering Nonporous Polymer Hybrids with Suppressed Heat Conduction and Enhanced Flame Retardancy via Molecular and Filler Design

This study presents a new strategy for achieving ultralow thermal conductivity in nonporous polymer/organic filler hybrids by suppressing heat capacity through tailored atomic vibrations to enhance thermal insulation. Unlike conventional polymer/inorganic filler hybrids, these hybrids exhibit interfacial thermal resistance one to three orders of magnitude lower. Combined experiments and simulations uncover thermal transport mechanisms. These hybrids demonstrate enhanced flame retardancy. Please see the abstract in the attached PDF.

physics.app-ph↗

arXiv2022

Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images

In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate the analysis and interpretation of microscopy images. Typically training of machine learning models requires large numbers of images with associated structural labels, however, manual labeling of images requires domain knowledge and is prone to human error and subjectivity. To overcome these limitations, we present a semi-supervised transfer learning approach that uses a small number of labeled microscopy images for training and performs as effectively as methods trained on significantly larger image datasets. Specifically, we train an image encoder with unlabeled images using self-supervised learning methods and use that encoder for transfer learning of different downstream image tasks (classification and segmentation) with a minimal number of labeled images for training. We test the transfer learning ability of two self-supervised learning methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM) images. We demonstrate in detail how this machine learning workflow applied to TEM images of protein nanowires enables automated classification of nanowire morphologies (e.g., single nanowires, nanowire bundles, phase separated) as well as segmentation tasks that can serve as groundwork for quantification of nanowire domain sizes and shape analysis. We also extend the application of the machine learning workflow to classification of nanoparticle morphologies and identification of different type of viruses from TEM images.

cond-mat.mtrl-sci↗

arXiv2022

Shaping Nanoscale Ribbons into Micro-Helices of Controllable Radius and Pitch

We report fabrication of highly flexible micron-sized helices from nanometer-thick ribbons. Building upon the helical coiling of such ultra-thin ribbons mediated by surface tension, we demonstrate that the enhanced creep properties of highly confined materials can be leveraged to shape helices into the desired geometry with full control of the final shape. The helical radius, total length and pitch angle are all freely and independently tunable within a wide range: radius within $\sim$ 1-100 micrometer, length within $\sim$ 100-3000 micrometer, and pitch angle within $\sim$ 0-70{\deg}. This fabrication method is validated for three different materials: poly(methyl methacrylate), poly(dimethylaminoethyl methacrylate), and transition metal chalcogenide quantum dots, each corresponding to a different solid-phase structure: respectively a polymer glass, a crosslinked hydrogel, and a nanoparticle array. This demonstrates excellent versatility with respect to material selection, enabling further control of the helix mechanical properties.

cond-mat.soft↗

arXiv2021

Programmed Wrapping and Assembly of Droplets with Mesoscale Polymers

Nature is remarkably adept at using interfaces to build structures, encapsulate reagents, and regulate biological processes. Inspired by Nature, we describe flexible polymer-based ribbons, termed "mesoscale polymers" (MSPs), to modulate interfacial interactions with liquid droplets. This produces unprecedented hybrid assemblies in the forms of flagellum-like structures and MSP-wrapped droplets. Successful preparation of these hybrid structures hinges on interfacial interactions and tailored MSP compositions, such as MSPs with domains possessing distinctly different affinity for fluid-fluid interfaces as well as mechanical properties. In situ measurements of MSP-droplet interactions confirm that MSPs possess a negligible bending stiffness, allowing interfacial energy to drive mesoscale assembly. By exploiting these interfacial driving forces, mesoscale polymers are demonstrated as a powerful platform that underpins the preparation of sophisticated hybrid structures in fluids.

cond-mat.soft↗