Three-Dimensional Graphene Sheet-Carbon Veil Thermoelectric Composite with Microinterfaces for Energy Applications.

Over the years, various processing techniques have been explored to synthesize three-dimensional graphene (3DG) composites with tunable properties for advanced applications. In this work, we have demonstrated a new procedure to join a 3D graphene sheet (3DGS) synthesized by chemical vapor deposition (CVD) with a commercially available carbon veil (CV) via cold rolling to create 3DGS-CV composites. Characterization techniques such as scanning electron microscopy (SEM), Raman mapping, X-ray diffraction (XRD), electrical resistance, tensile strength, and Seebeck coefficient measurements were performed to understand various properties of the 3DGS-CV composite. Extrusion of 3DGS into the pores of CV with multiple microinterfaces between 3DGS and the graphitic fibers of CV was observed, which was facilitated by cold rolling. The extruded 3D graphene revealed pristine-like behavior with no change in the shape of the Raman 2D peak and Seebeck coefficient. Thermoelectric (TE) power generation and photothermoelectric responses have been demonstrated with in-plane TE devices of various designs made of p-type 3DGS and n-type CV couples yielding a Seebeck coefficient of 32.5 µV K-1. Unlike various TE materials, 3DGS, CV, and the 3DGS-CV composite were very stable at high relative humidity. The 3DGS-CV composite revealed a thin, flexible profile, good moisture and thermal stability, and scalability for fabrication. These qualities allowed it to be successfully tested for temperature monitoring of a Li-ion battery during charging cycles and for large-area temperature mapping.

The Factors Associated with the Development of COVID-19 Symptoms among Employees in a U.S. Healthcare Institution.

Healthcare workers have experienced increased occupational health risks caused by COVID-19 disease. The purpose of this project was to examine the relationships between reporting COVID-19 symptoms by employees in a healthcare institution in the United States and employees' demographics, vaccination status, co-morbid conditions, and body mass index (BMI). This project employed a cross-sectional design. It involved the analysis of data on COVID-19 exposure and infection incidents among employees in the healthcare institution. The dataset contained more than 20,000 entries. The results indicate that being female, African American, between 20 and 30 years old, diagnosed with diabetes, diagnosed with chronic obstructive pulmonary disease (COPD), or on immunosuppressive medicines is associated with greater reporting of COVID-19 symptoms by the employees. Furthermore, BMI is associated with reporting COVID-19 symptoms; the higher the BMI, the greater the likelihood of reporting a symptomatic infection. Moreover, having COPD, being 20-30 years old, being 40-50 years old, BMI, and vaccination status were significantly associated with employees reporting symptoms while controlling for other variables associated with reporting the symptoms among the employees. These findings may be applicable to other infectious disease outbreaks or pandemics.