24 Sep 2021

Environmental Life Cycle Assessment on CNTRENE® 1030 Material and CNT Based Sensors

Abstract: This report details a study investigating the environmental impacts associated with the development and manufacturing of carbon nanotube (CNT)–based ink (called CNTRENE 1030 material) and novel CNT temperature, flex, and moisture sensors. Undertaken by a private-public partnership involving Brewer Science (Rolla, Missouri), Jordan Valley In-novation Center of Missouri State University (Springfield, Missouri), and the US Army Engineer Research and Development Center (Vicksburg, Mississippi), this work demonstrates the environmental life cycle assessment (ELCA) methodology as a diagnostic tool to pinpoint the particular processes and materials posing the greatest environmental impact associated with the manufacture of the CNTRENE material and CNT-based sensor devices. Additionally, ELCA tracked the degree to which optimizing the device manufacturing process for full production also changed its predicted marginal environmental impacts.

1 Oct 2020

Method Selection Framework for the Quantitation of Nanocarbon Scientific Operating Procedure Series (SOP-C-3): Selection of Methods for Release Testing and Quantitation of Solids, Suspensions, and Air Samples for Carbon-Based Nanomaterials

Abstract: There is significant concern regarding the health and safety risk of nanocarbon (for example, nanotubes, graphene, fullerene), and the cur-rent capability gap for accurately determining exposure levels encumbers risk assessment, regulatory decisions, and commercialization. Given the various analytical challenges associated with the detection and quantitation of nanocarbon, it is unlikely that a single method or technique will prove effective for all forms of nanocarbon, all exposure scenarios, or all possible environmental systems. The optimal approach, or series of techniques, will likely depend on the nature of the material being measured, its concentration, and the matrix in which it is contained. In this work, a preliminary decision framework is presented that assists the user in deter-mining which analytical methods are best suited for a given sample.