Transient Plasmonic Imaging of Ion Migration on Single Nanoparticles and Insight for Double Layer Dynamics

Single-Particle Imaging Reveals the Electrical Double-Layer Modulated Ion Dynamics at Crowded Interface

The dynamics of ion transport at the interface is the critical factor for determining the performance of an electrochemical energy storage device. While practical applications are realized in concentrated electrolytes and nanopores, there is a limited understanding of their ion dynamic features. Herein, we studied the interfacial ion dynamics in room-temperature ionic liquids by transient single-particle imaging with microsecond-scale resolution. We observed slowed-down dynamics at lower potential while acceleration was observed at higher potential. Combined with simulation, we found that the microstructure evolution of the electric double layer (EDL) results in potential-dependent kinetics. Then, we established a correspondence between the ion dynamics and interfacial ion composition. Besides, the ordered ion orientation within EDL is also an essential factor for accelerating interfacial ion transport. These results inspire us with a new possibility to optimize electrochemical energy storage through the good control of the rational design of the interfacial ion structures.

Tracking Ion Transport in Nanochannels via Transient Single-Particle Imaging

The transport behavior of ions in the nanopores has an important impact on the performance of the electrochemical devices. Although the classical Transmission-Line (TL) model has long been used to describe ion transport in pores, the boundary conditions for the applicability of the TL model remain controversial. Here, we investigated the transport kinetics of different ions, within nanochannels of different lengths, by using transient single-particle imaging with temporal resolution up to microseconds. We found that the ion transport kinetics within short nanochannels may deviate significantly from the TL model. The reason is that the ion transport under nanoconfinement is composed of multi basic stages, and the kinetics differ much under different stage domination. With the shortening of nanochannels, the electrical double layer (EDL) formation would become the "rate-determining step" and dominate the apparent ion kinetics. Our results imply that using the TL model directly and treating the in-pore mobility as an unchanged parameter to estimate the ion transport kinetics in short nanopores/nanochannels may lead to orders of magnitude bias. These findings may advance the understanding of the nanoconfined ion transport and promote the related applications.

High Confidence Single Particle Analysis with Machine Learning

Single particle analysis can effectively determine the heterogeneity between particles based on the local information on a single particle, which is utilized extensively for monitoring chemical reactions and biological activities. However, the study of obtaining ensemble reaction information at the single particle level, which can obtain both the structural and functional heterogeneity of particles as well as the ensemble reaction information, is challenging because the selection of a single particle mainly depends on experience, which will lead to a certain randomness when analyzing the ensemble reaction with single particles. Using machine learning, it is demonstrated that the proposed intelligent single particle analysis strategy can provide single particle and ensemble analyses with high confidence. Convolutional neural network and Gaussian mixture model were utilized to develop a machine learning model for resonance scattering imaging analysis of plasmonic nanoparticles. It can identify the scattered light of single particles and select representative or diverse particles. When single particle scattering imaging is used to obtain ensemble information on the reaction, the error caused by the selection of individual particles can be significantly reduced by selecting representative particles. In addition, the real situation of the reaction can be better revealed by selecting diverse particles. These results indicate that the intelligent single particle analysis strategy has great potential for imaging analysis and biological sensing.

Formation of Molecular Junctions by Single-Entity Collision Electrochemistry

Controlling and understanding the chemistry of molecular junctions is one of the major themes in various fields ranging from chemistry and nanotechnology to biotechnology and biology. Stochastic single-entity collision electrochemistry (SECE) provides powerful tools to study a single entity, such as single cells, single particles, and even single molecules, in a nanoconfined space. Molecular junctions formed by SECE collision show various potential applications in monitoring molecular dynamics with high spatial resolution and high temporal resolution and in feasible combination with hybrid techniques. This Perspective highlights the new breakthroughs, seminal studies, and trends in the area that have been most recently reported. In addition, future challenges for the study of molecular junction dynamics with SECE are discussed.