Synthesis of crystalline mesoporous transition oxides is very challenging considering the crystallization of oxides is usually accompanied by the growth of crystalline grains that commonly results in pore collapse before crystallization. Here, we have developed a versatile template-free method and successfully realized general preparation of six mesoporous transition oxides such as CuO, CoO, and spinel MCo 2 O 4 (M = Co, Cu, Mn, and Zn). In addition, the mesoporous Co 3 O 4 is identified as surface-defect rich, which endows it with excellent catalaselike activity.

Optical microscopy is a simple, yet essential, imaging technology. Conventional laboratory-grade optical microscopes are bulky and costly, confining their use to within laboratory settings and restricting their accessibility in regions of limited resources. With the aim of overcoming these limitations, we have realized a portable, low-cost, and highly automated optical microscope that integrates mass-manufactured components, including light-emitting diodes, a web camera, optical disk drives, and a microcontroller. Our implementation is capable of bright-field and fluorescence imaging with micrometer-scale resolution and controlled mechanical actuation of both the lens and sample. We interface the lighting, image capture, and mechanical actuators of the microscope into a single software environment, enabling automation of common microscope operations, such as image focusing and large-area sample visualization. Combination of mechanical actuation and software automation into a compact, low-cost microscope system is an important initial step toward the goal of making optical microscopy universally accessible, portable, and easy to use.

Increasing sulfur mass loading and minimizing electrolyte amount remains a major challenge for the development of high energy density Li‐S batteries, which needs to be tackled with combined efforts of materials development and mechanistic analysis. In this work, following our most recent identification of the potential‐limiting step of Li‐S batteries under lean electrolyte conditions, we seek to advance the understanding by extending it to a new catalyst and into the region of high sulfur mass loading. We integrate CeOx nanostructures into cotton‐derived carbon to develop a multifunctional 3D network that can host a large amount of active material, facilitate electron transport, and catalyze the sulfur lithiation reaction. The resulting S/CeOx/C electrode can deliver a stable areal capacity of 9 mAh cm−2 with a high sulfur loading of 14 mg cm−2 at a low electrolyte/sulfur ratio of 5 μL mg−1. We discover that Li||S/CeOx/C cells usually fail during a charging step at high current density, as a consequence of local short circuiting caused by electrochemically deposited Li dendrites penetrating through the separator, a previously overlooked failure pattern distinctive to cells operating under lean electrolyte conditions. This work highlights the importance of developing new material structures and analyzing failure mechanisms in the advancement of Li‐S batteries.This article is protected by copyright. All rights reserved

In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g., lifetime) has become the major technical bottleneck. Here, we present a solution called the DefectTrack, the first dedicated deep learning-based one-shot multi-object tracking (MOT) model capable of tracking cascade-induced defect clusters in in-situ TEM videos in real-time. DefectTrack has achieved a Multi-Object Tracking Accuracy (MOTA) of 66.43% and a Mostly Tracked (MT) of 67.81% on the test set, which are comparable to state-of-the-art MOT algorithms. We discuss the MOT framework, model selection, training, and evaluation strategies for in-situ TEM applications. Further, we compare the DefectTrack with four human experts in quantifying defect cluster lifetime distributions using statistical tests and discuss the relationship between the material science domain metrics and MOT metrics. Our statistical evaluations on the defect lifetime distribution suggest that the DefectTrack outperforms human experts in accuracy and speed.

One limitation of using compact disks (CDs) and optical disk drives for sensing and imaging of analytes placed on a CD is the fluctuations in the voltage signal from the disk drive generated while reading the data on the CD. In this study, we develop a simple, low-cost strategy for sensing and identification using CDs and optical disk drives that spectrally separates contributions to the voltage signal caused by an analyte intentionally placed onto the CD and that caused by the underlying data on the CD. Analytes are printed onto a CD surface with fixed spatial periodicity. As the laser beam in an optical disk drive scans over the section of the CD containing the analyte pattern, the intensity of the laser beam incident onto the photodiode integrated into the disk drive is modulated at a frequency dependent on the spatial periodicity of the analyte pattern and the speed of the optical-disk-drive motor. Fourier transformation of the voltage signal from the optical disk drive yields peaks in the frequency spectrum with amplitudes and locations that enable analyte sensing and identification, respectively. We study the influence of analyte area coverage, pattern periodicity, and CD rotational frequency on the peaks in the frequency spectrum associated with the patterned analyte. We apply this technique to discriminate differently-colored analytes, perform trigger-free detection of multiple analytes distributed on a single CD, and detect at least two different, overlapped analyte patterns on a single CD. The extension of this technique for sensing and identification of colorimetric chemical reagents is discussed.