Leveraging Machine Learning for Size and Shape Analysis of Nanoparticles: A Shortcut to Electron Microscopy

Abstract

Characterizing nanoparticles (NPs) is crucial in nanoscience due to the direct influence of their physiochemical properties on their behavior. Various experimental techniques exist to analyze the size and shape of NPs, each with advantages, limitations, proneness to uncertainty, and resource requirements. One of them is electron microscopy (EM), often considered the gold standard, which offers visualization of the primary particles. However, despite its advantages, EM can be expensive, less accessible, and difficult to apply during dynamic processes. Therefore, using EM for specific experimental conditions, such as observing dynamic processes or visualizing low-contrast particles, is challenging. This study showcases the potential of machine learning in deriving EM parameters by utilizing cost-effective and dynamic techniques such as dynamic light scattering (DLS) and UV−vis spectroscopy. Our developed model successfully predicts the size and shape parameters of gold NPs based on DLS and UV−vis results. Furthermore, we demonstrate the practicality of our model in situations in which conducting EM measurements presents a challenge: Tracking in situ the synthesis of 100 nm gold NPs.

Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV–Vis Extinction Spectra

“…Shiratori et al reported a similar prediction accuracy level for the nanorod sizes. This work has the advantage over ref because here size characterization is achieved only from UV–vis E (λ) spectra, without requiring DLS data as input. Moreover, to the best of our knowledge, the current work exceeds the maximum plasmonic NP radius prediction range of 110 nm previously reported by Tan et al by at least 40 nm.…”

“…Using computationally derived data to train ML algorithms has also drawn attention. , ML tools such as deep neural networks (DNNs) have been proven to be exceptionally powerful . Due to the wide functionality of DNNs, they have found many scientific applications in biomedicine, materials science, detection of chemical materials, and many more fields, including design of NP colloids, and assisting in their synthesis. , Early attempts at using multilayer feedforward DNNs to analyze particle sizes focused on the microparticle size range. − In later years, both DNNs and other ML techniques were used to investigate size distributions of particles in the nanoscale, including more complex shapes such as rod-like NP, , but their success was limited at best. Essentially, determining the size distribution from an E (λ) spectrum is an inverse modeling problem.…”

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC C , the special issue papers describe ML and other data science techniques utilized in the scope of nanoparticles and nanostructures; surface and interface processes; electron, ion, and thermal transport; optic, electronic, and optoelectronic materials; and catalysts and catalysis; as well as energy conversion and storage materials and processes. As in the Parts A and B, a number of articles directly address either the development of new methods or the use of ML methods in new ways. − Several contributions relate to methods in the use or development of so-called machine learning potentials (MLP) or machine learning interaction potentials (MLIP), including a Perspective on improving these models authored by Maxson et al, as well as other contributions. − Interfaces and related phenomena are addressed in several articles. − Nanomaterials and their properties are also prominently featured. − Another large category in JPC C includes studies that address the calculation of properties of materials for wide-ranging applications. − …”