Atomic-Scale Structure Dynamics of Nanocrystals Revealed By In Situ and Environmental Transmission Electron Microscopy

Nanocrystals are of great importance in material sciences and industry. Engineering nanocrystals with desired structures and properties is no doubt one of the most important challenges in the field, which requires deep insight into atomic-scale dynamics of nanocrystals during the process. The rapid developments of in situ transmission electron microscopy (TEM), especially environmental TEM, reveal insights into nanocrystals to digest. According to the considerable progress based on in situ electron microscopy, a comprehensive review on nanocrystal dynamics from three aspects: nucleation and growth, structure evolution, and dynamics in reaction conditions are given. In the nucleation and growth part, existing nucleation theories and growth pathways are organized based on liquid and gas-solid phases. In the structure evolution part, the focus is on in-depth mechanistic understanding of the evolution, including defects, phase, and disorder/order transitions. In the part of dynamics in reaction conditions, solid-solid and gas-solid interfaces of nanocrystals in atmosphere are discussed and the structure-property relationship is correlated. Even though impressive progress is made, additional efforts are required to develop the integrated and operando TEM methodologies for unveiling nanocrystal dynamics with high spatial, energy, and temporal resolutions.

Chemical and Electronic Investigation of Buried NiO1-δ, PCBM, and PTAA/MAPbI3-xClx Interfaces Using Hard X-ray Photoelectron Spectroscopy and Transmission Electron Microscopy

Identification and profiling of molecular fragments generated over the lifespan of halide perovskite solar cells are needed to overcome the stability issues associated with these devices. Herein, we report the characterization of buried CH₃NH₃PbI_3-xCl_x (HaP)-transport layer (TL) interfaces. By using hard X-ray photoelectron spectroscopy in conjunction with transmission electron microscopy, we reveal that the chemical decomposition of HaP is TL-dependent. With NiO_1-δ, phenyl-C₆₁-butyric acid methyl ester (PCBM), or poly(bis(4-phenyl) (2,4,6-trimethylphenyl)amine) (PTAA) as TLs, probing depth analysis shows that the degradation takes place at the interface (HaP/TL) rather than the HaP bulk area. From core-level data analysis, we identified iodine migration toward the PCBM- and PTAA-TLs. Unexpected diffusion of nitrogen inside NiO_1-δ-TL was also found for the HaP/NiO_1-δ sample. With a HaP/PCBM junction, HaP is dissociated to PbI₂, whereas HaP/PTAA contact favored the formation of CH₃I. The low stability of HaP solar cells in the PTAA-TL system is attributed to the formation of CH₃I and iodide ion vacancies. Improved stability observed with NiO_1-δ-TL is related to weak dissociation of stoichiometric HaP. Here, we provide a new insight to further distinguish different mechanisms of degradation to improve the long-term stability and performance of HaP solar cells.

Waterproof, thin, high-performance pressure sensors-hand drawing for underwater wearable applications

Wearable sensors, especially pressure sensors, have become an indispensable part of life when reflecting human interactions and surroundings. However, the difficulties in technology and production-cost still limit their applicability in the field of human monitoring and healthcare. Herein, we propose a fabrication method with flexible, waterproof, thin, and high-performance circuits - based on hand-drawing for pressure sensors. The shape of the sensor is drawn on the pyralux film without assistance from any designing software and the wet-tissues coated by CNTs act as a sensing layer. Such sensor showed a sensitivity (~0.2 kPa^-1) while ensuring thinness (~0.26 mm) and flexibility for touch detection or breathing monitoring. More especially, our sensor is waterproof for underwater wearable applications, which is a drawback of conventional paper-based sensors. Its outstanding capability is demonstrated in a real application when detecting touch actions to control a phone, while the sensor is dipped underwater. In addition, by leveraging machine learning technology, these touch actions were processed and classified to achieve highly accurate monitoring (up to 94%). The available materials, easy fabrication techniques, and machine learning algorithms are expected to bring significant contributions to the development of hand-drawing sensors in the future.