Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds.

Morphologies of nanoparticles and aggregates play an important role in their properties for a range of applications. In particular, significant synthesis efforts have been directed toward controlling nanoparticle morphology and aggregation behavior in biomedical applications, as their size and shape have a significant impact on cellular uptake. Among several techniques for morphological characterization, transmission electron microscopy (TEM) can provide direct and accurate characterization of nanoparticle/aggregate morphology details. Nevertheless, manually analyzing a large number of TEM images is still a laborious process. Hence, there has been a surge of interest in employing machine learning methods to analyze nanoparticle size and shape. In order to achieve accurate nanoparticle analysis using machine learning methods, reliable and automated nanoparticle segmentation from TEM images is critical, especially when the nanoparticle image contrast is weak and the background is complex. These challenges are particularly pertinent in biomedical applications. In this work, we demonstrate an efficient, robust, and automated nanoparticle image segmentation method suitable for subsequent machine learning analysis. Our method is robust for noisy, low-electron-dose cryo-TEM images and for TEM cell images with complex, strong-contrast background features. Moreover, our method does not require any a priori training datasets, making it efficient and general. The ability to automatically, reliably, and efficiently segment nanoparticle/aggregate images is critical for advancing precise particle/aggregate control in biomedical applications.

On-demand activatable peroxidase-mimicking enzymatic polymer nanocomposite films.

Nanozymes continue to attract considerable attention to minimise the dependence on expensive enzymes in bioassays, particularly in medical diagnostics. While there has been considerable effort directed towards developing different nanozymes, there has been limited progress in fabricating composite materials based on such nanozymes. One of the biggest gaps in the field is the control, tuneability, and on-demand catalytic response. Herein, a nanocomposite nanozymatic film that enables precise tuning of catalytic activity through stretching is demonstrated. In a systematic study, we developed poly(styrene-stat-n-butyl acrylate)/iron oxide-embedded porous silica nanoparticle (FeSiNP) nanocomposite films with controlled, highly tuneable, and on-demand activatable peroxidase-like activity. The polymer/FeSiNP nanocomposite was designed to undergo film formation at ambient temperature yielding a highly flexible and stretchable film, responsible for enabling precise control over the peroxidase-like activity. The fabricated nanocomposite films exhibited a prolonged FeSiNP dose-dependent catalytic response. Interestingly, the optimised composite films with 10 wt% FeSiNP exhibited a drastic change in the enzymatic activity upon stretching, which provides the nanocomposite films with an on-demand performance activation characteristic. This is the first report showing control over the nanozyme activity using a nanocomposite film, which is expected to pave the way for further research in the field leading to the development of system-embedded activatable sensors for diagnostic, food spoilage, and environmental applications.