Haotian Wen

In recent years, high‐entropy dielectrics have demonstrated superior performance in capacitive energy storage devices. However, the impressive energy storage density of these materials typically necessitates ultrahigh external electric fields, which restricts the range of their practical applications. In this work, an interphase strain engineering strategy is developed, i.e., through the modulation of the deposition temperatures and post‐deposition cooling rates, an appropriate amount of pyrochlore nanocolumns is introduced into high‐entropy oxide epitaxial films, exerting a nontrivial level of interphase strain on adjacent perovskite lattices. This interphase strain transforms the dispersive polar nanodomains into a compact polar slush state, enabling a delicate balance between spontaneous polarization and breakdown strength. Ultimately, the (Bi _0.5 Na _0.5 )(Ti _0.2 Sn _0.2 Hf _0.2 Fe _0.2 Nb _0.2 )O ₃ (BNTSHFN) high‐entropy oxide thin film in this work exhibits a giant recoverable energy density of 93 J cm ⁻³ and a high efficiency of 83% under a moderate electric field of 3.6 MV cm ⁻¹ . This work provides an innovative idea for designing high‐entropy capacitive energy storage devices with promising potential in real‐world scenarios.

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.

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.

The shape of nanoparticles is a key performance parameter for many applications, ranging from nanophotonics to nanomedicines.

Measuring NV Centers in Diamond Nanoparticles using Electron Energy Loss Spectroscopy - Volume 27 Issue S1