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Formation kinetics of metal nanoparticles are generally describedviamass transport and thermodynamics‐based models, such as diffusion‐limited growth and classical nucleation theory (CNT). However, metal monomers are commonly assumed as precursors, leaving the identity of molecular intermediates and their contribution to nanoparticle formation unclear. Herein, liquid phase transmission electron microscopy (LPTEM) and reaction kinetic modeling are utilized to establish the nucleation and growth mechanisms and discover molecular intermediates during silver nanoparticle formation. Quantitative LPTEM measurements show that their nucleation rate decreases while growth rate is nearly invariant with electron dose rate. Reaction kinetic simulations show that Ag4and Ag−follow a statistically similar dose rate dependence as the experimentally determined growth rate. We show that experimental growth rates are consistent with diffusion‐limited growthviathe attachment of these species to nanoparticles. The dose rate dependence of nucleation rate is inconsistent with CNT. A reaction‐limited nucleation mechanism is proposed and it is demonstrated that experimental nucleation kinetics are consistent with Ag42+aggregation rates at millisecond time scales. Reaction throughput analysis of the kinetic simulations uncovered formation and decay pathways mediating intermediate concentrations. We demonstrate the power of quantitative LPTEM combined with kinetic modeling for establishing nanoparticle formation mechanisms and principal intermediates.
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Learning the diffusion of nanoparticles in liquid phase TEM via physics-informed generative AI
Abstract The motion of nanoparticles in complex environments can provide us with a detailed understanding of interactions occurring at the molecular level. Liquid phase transmission electron microscopy (LPTEM) enables us to probe and capture the dynamic motion of nanoparticles directly in their native liquid environment, offering real time insights into nanoscale motion and interaction. However, linking motion to interactions to decode the underlying mechanisms of motion and interpret interactive forces at play is challenging, particularly when closed-form Langevin-based equations are not available to model the motion. Herein, we present LEONARDO, a deep generative model that leverages a physics-informed loss function and an attention-based transformer architecture to learn the stochastic motion of nanoparticles in LPTEM. We demonstrate that LEONARDO successfully captures statistical properties suggestive of the heterogeneity and viscoelasticity of the liquid cell environment surrounding the nanoparticles.
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- Award ID(s):
- 2338466
- PAR ID:
- 10614270
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 16
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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