Exploiting weak supervision to facilitate segmentation, classification, and analysis of microplastics (<100 µm) using Raman microspectroscopy images.

Reliable quantification and characterization of microplastics are necessary for large-scale and long-term monitoring of their behaviors and evolution in the environment. This is especially true in recent times because of the increase in the production and use of plastics during the pandemic. However, because of the myriad of microplastic morphologies, dynamic environmental forces, and time-consuming and expensive methods to characterize microplastics, it is challenging to understand microplastic transport in the environment. This paper describes a novel approach that compares unsupervised, weakly-supervised, and supervised approaches to facilitate segmentation, classification, and the analysis of <100 µm-sized microplastics without the use of pixel-wise human-labeled data. The secondary aim of this work is to provide insight into what can be accomplished when no human annotations are available, using the segmentation and classification tasks as use cases. In particular, the weakly-supervised segmentation performance surpasses the baseline performance set by the unsupervised approach. Consequently, feature extraction (derived from the segmentation results) provides objective parameters describing microplastic morphologies that will result in better standardization and comparisons of microplastic morphology across future studies. The weakly-supervised performance for microplastic morphology classification (e.g., fiber, spheroid, shard/fragment, irregular) also exceeds the performance of the supervised analogue. Moreover, in contrast to the supervised method, our weakly-supervised approach provides the benefit of pixel-wise detection of microplastic morphology. Pixel-wise detection is used further to improve shape classifications. We also demonstrate a proof-of-concept for distinguishing microplastic particles from non-microplastic particles using verification data from Raman microspectroscopy. As the automation of microplastic monitoring progresses, robust and scalable identification of microplastics based on their morphology may be achievable.

Carbon Nanotube Composites with Bottlebrush Elastomers for Compliant Electrodes.

Wearable electronics and biointerfacing technology require materials that are both compliant and conductive. The typical design strategy exploits polymer composites containing conductive particles, but the addition of a hard filler generally leads to a substantial increase in modulus that is not well-matched to biological tissue. Here, we report a new class of supersoft, conductive composites comprising carbon nanotubes (CNT) embedded in bottlebrush polymer networks. By virtue of the bottlebrush polymer architecture, these materials are several orders of magnitude softer than comparable composites in the literature involving linear polymer networks. For example, a CNT content of 0.25 wt % yields a shear modulus of 66 kPa while maintaining a typical conductivity for a CNT composite (ca. 10-2 S/m). An added benefit of this bottlebrush matrix chemistry is the presence of dynamic polyester bonds that facilitate thermal (re)processing. This unique strategy of designing soft composites provides new opportunities to tailor the structure and properties of sustainable advanced materials.