Phase Transformation Guided Single-Layer β‑Co(OH)₂ Nanosheets for Pseudocapacitive Electrodes

It is known that Co(OH)₂ can be crystallized into a layered structure with two polymorphs: α and β. The single-layer α-Co(OH)₂ nanosheet has been prepared by exfoliating directly α phase layered Co(OH)₂. However, due to theoretical barriers, a single-layer β-Co(OH)₂ nanosheet has not been achieved so far. In this article, phase transformation during exfoliation of layered Co(OH)₂ from α to β is observed and a single-layer β-Co(OH)₂ nanosheet with a thickness of ∼1.1 nm is prepared through phase transition of layered α-Co(OH)₂ nanocones in a mild wet chemical process for the first time, with a nearly 100% yield. The as-prepared single-layer β-Co(OH)₂ nanosheets are assembled with graphene oxide to form an all-two-dimensional materials-based composite for use as an electrode for the pseudocapacitor. The reduced graphene oxide/β-Co(OH)₂ composite exhibits a high specific capacitance up to 2080 F/g scaled to the total mass of the electrode or 3355 F/g scaled to the active mass of β-Co(OH)₂ nanosheets at the current density of 1 A/g. The electrode also demonstrates the excellent rate performance and long cycle life

Presentation_1_Esophageal cancer detection via non-contrast CT and deep learning.PPTX

BackgroundEsophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images.MethodsIn this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted.ResultsIn this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/ConclusionThe DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.</p

A Comparative Study of Composition and Morphology Effect of Ni_<i>x</i>Co_1–<i>x</i>(OH)₂ on Oxygen Evolution/Reduction Reaction

Oxygen electrochemistry has been intensely studied in the pursuit of sustainable and efficient energy conversion and storage solutions. Over the years, developing oxygen electrode catalysts with high activity and low cost remains a great challenge, despite tremendous efforts. Here, Ni_<i>x</i>Co_1–<i>x</i>(OH)₂ is used as a bifunctional electrocatalyst for both oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). The effect of its compositions (<i>x</i> = 1, 0.55, 0) and morphologies (including both multilayer and single-layer Ni_<i>x</i>Co_1–<i>x</i>(OH)₂) on catalytic activity is studied systematically in order to optimize the oxygen-electrochemical performance of 3d-M (M = Ni and Co) metal hydroxides. Our results show that the compositions of Ni_<i>x</i>Co_1–<i>x</i>(OH)₂ has a great influence on overpotentials by comparing multilayer Co­(OH)₂, Ni_0.55Co_0.45(OH)₂, and Ni­(OH)₂ for OER. Multilayer Ni­(OH)₂ exhibits the lowest overpotential of 324 mV at the current density of 5 mA/cm². Moreover, the overpotential could be greatly lowered by using single-layer Ni_<i>x</i>Co_1–<i>x</i>(OH)₂. Single-layer Ni­(OH)₂ nanosheet manifests 71 mV overpotential decrease (5 mA/cm²) and a factor of 14 turnover frequency increase as compared to multilayer Co­(OH)₂ for OER. As for ORR, multilayer Co­(OH)₂ shows the best activity among multilayer Ni_<i>x</i>Co_1–<i>x</i>(OH)₂. Similar to OER, single-layer Ni_<i>x</i>Co_1–<i>x</i>(OH)₂ demonstrates enhanced ORR activity over multilayer Ni_<i>x</i>Co_1–<i>x</i>(OH)₂. Single-layer Co­(OH)₂ exhibits the best catalytic activity and 3.7 electrons are transferred during oxygen reduction process. The successful identification of the composition and morphology effect of 3d metal hydroxides on electrocatalytic performance provides the foundation for rational design of active sites for high-performance catalyst for both OER and ORR