| 1. |
- Fan, Ke, et al.
(författare)
-
Direct Observation of Structural Evolution of Metal Chalcogenide in Electrocatalytic Water Oxidation
- 2018
-
Ingår i: ACS Nano. - : American Chemical Society (ACS). - 1936-0851 .- 1936-086X. ; 12:12, s. 12369-12379
-
Tidskriftsartikel (refereegranskat)abstract
- As one of the most remarkable oxygen evolution reaction (OER) electrocatalysts, metal chalcogenides have been intensively reported during the past few decades because of their high OER activities. It has been reported that electron-chemical conversion of metal OER chalcogenides into oxides/hydroxides would take place after the OER. However, the transition mechanism of such unstable structures, as well as the real active sites and catalytic activity during the OER for these electrocatalysts, has not been understood yet; therefore a direct observation for the electrocatalytic water oxidation process, especially at nano or even angstrom scale, is urgently needed. In this research, by employing advanced Cs-corrected transmission electron microscopy (TEM), a step by step oxidational evolution of amorphous electrocatalyst CoSx into crystallized CoOOH in the OER has been in situ captured: irreversible conversion of CoSx to crystallized CoOOH is initiated on the surface of the electrocatalysts with a morphology change via Co(OH)(2) intermediate during the OER measurement, where CoOOH is confirmed as the real active species. Besides, this transition process has also been confirmed by multiple applications of X-ray photoelectron spectroscopy (XPS), in situ Fourier-transform infrared spectroscopy (FTIR), and other ex situ technologies. Moreover, on the basis of this discovery, a high-efficiency electrocatalyst of a nitrogen-doped graphene foam (NGF) coated by CoSx has been explored through a thorough structure transformation of CoOOH. We believe this in situ and in-depth observation of structural evolution in the OER measurement can provide insights into the fundamental understanding of the mechanism for the OER catalysts, thus enabling the more rational design of low-cost and high-efficient electrocatalysts for water splitting.
|
|
| 2. |
- Fan, Ke, et al.
(författare)
-
Hollow Iron-Vanadium Composite Spheres : A Highly Efficient Iron-Based Water Oxidation Electrocatalyst without the Need for Nickel or Cobalt
- 2017
-
Ingår i: Angewandte Chemie International Edition. - : WILEY-V C H VERLAG GMBH. - 1433-7851 .- 1521-3773. ; 56:12, s. 3289-3293
-
Tidskriftsartikel (refereegranskat)abstract
- Noble-metal-free bimetal-based electrocatalysts have shown high efficiency for water oxidation. Ni and/or Co in these electrocatalysts are essential to provide a conductive, high-surface area and a chemically stable host. However, the necessity of Ni or Co limits the scope of low-cost electrocatalysts. Herein, we report a hierarchical hollow FeV composite, which is Ni- and Co-free and highly efficient for electrocatalytic water oxidation with low overpotential 390 mV (10 mA cm(-2) catalytic current density), low Tafel slope of 36.7 mV dec(-1), and a considerable durability. This work provides a novel and efficient catalyst, and greatly expands the scope of low-cost Fe-based electrocatalysts for water splitting without need of Ni or Co.
|
|
| 3. |
- Qiao, Sibo, et al.
(författare)
-
HCMMNet : Hierarchical Conv-MLP-Mixed Network for Medical Image Segmentation in Metaverse for Consumer Health
- 2024
-
Ingår i: IEEE transactions on consumer electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0098-3063 .- 1558-4127. ; 70:1, s. 2078-2089
-
Tidskriftsartikel (refereegranskat)abstract
- In the burgeoning metaverse for consumer health (MCH), medical image segmentation methods with high accuracy and generalization capability are essential to drive personalized healthcare solutions and enhance the patient experience. To address the inherent challenges of capturing complex structures and features in medical image segmentation, we propose a convolutional neural network (CNN) and multi-layer-perceptron (MLP) mixed module named HCMM, which hierarchically incorporates local priors of CNN into fully-connected (FC) layers, ingeniously capturing specific details and a broader range of contextual information of the focused object from diverse perspectives. Then, we propose an MLP-based information fusion module (MIF) designed to dynamically merge feature maps of varying levels from different pathways, enhancing feature expression and discriminative power. Based on the above-proposed modules, we design a novel segmentation model, HCMMNet, which can adeptly capture feature maps from input medical images at different scales and perspectives. Through comparative experiments, we demonstrate the outstanding performance of the HCMMNet for medical image segmentation on three publicly available datasets and one self-organized dataset. Notably, our HCMMNet showcases remarkable efficacy while maintaining an extraordinarily lightweight profile, weighing in at a mere 3M, rendering it ideal for MCH application.
|
|
| 4. |
- Royce, Thomas E., et al.
(författare)
-
Extrapolating traditional DNA microarray statistics to tiling and protein microarray technologies
- 2006
-
Ingår i: Methods in Enzymology. - 0076-6879 .- 1557-7988. ; 411, s. 282-311
-
Tidskriftsartikel (refereegranskat)abstract
- A credit to microarray technology is its broad application. Two experiments--the tiling microarray experiment and the protein microarray experiment--are exemplars of the versatility of the microarrays. With the technology's expanding list of uses, the corresponding bioinformatics must evolve in step. There currently exists a rich literature developing statistical techniques for analyzing traditional gene-centric DNA microarrays, so the first challenge in analyzing the advanced technologies is to identify which of the existing statistical protocols are relevant and where and when revised methods are needed. A second challenge is making these often very technical ideas accessible to the broader microarray community. The aim of this chapter is to present some of the most widely used statistical techniques for normalizing and scoring traditional microarray data and indicate their potential utility for analyzing the newer protein and tiling microarray experiments. In so doing, we will assume little or no prior training in statistics of the reader. Areas covered include background correction, intensity normalization, spatial normalization, and the testing of statistical significance.
|
|
