Iron-nitrogen on carbon (Fe-N/C) catalysts have emerged as promising nonprecious metal catalysts (NPMCs) for oxygen reduction reaction (ORR) in energy conversion and storage devices. It has been widely suggested that an active site structure for Fe-N/C catalysts contains Fe-N coordination. However, the preparation of high-performance Fe-N/C catalysts mostly involves a high-temperature pyrolysis step, which generates not only catalytically active Fe-N sites, but also less active large iron-based particles. Herein, we report a general "silica-protective-layer-assisted" approach that can preferentially generate the catalytically active Fe-N sites in Fe-N/C catalysts while suppressing the formation of large Fe-based particles. The catalyst preparation consisted of an adsorption of iron porphyrin precursor on carbon nanotube (CNT), silica layer overcoating, high-temperature pyrolysis, and silica layer etching, which yielded CNTs coated with thin layer of porphyrinic carbon (CNT/PC) catalysts. Temperature-controlled in situ X-ray absorption spectroscopy during the preparation of CNT/PC catalyst revealed the coordination of silica layer to stabilize the Fe-N sites. The CNT/PC catalyst contained higher density of active Fe-N sites compared to the CNT/PC prepared without silica coating. The CNT/PC showed very high ORR activity and excellent stability in alkaline media. Importantly, an alkaline anion exchange membrane fuel cell (AEMFC) with a CNT/PC-based cathode exhibited record high current and power densities among NPMC-based AEMFCs. In addition, a CNT/PC-based cathode exhibited a high volumetric current density of 320 A cm in acidic proton exchange membrane fuel cell. We further demonstrated the generality of this synthetic strategy to other carbon supports.

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Background/PurposeAcral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions.MethodsA total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist’s and non-expert’s evaluation.ResultsThe accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert’s evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden’s index like 0.6795, 0.6073, which were similar score with the expert.ConclusionAlthough further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.

The majority of color histogram equalization methods do not yield uniform histogram in gray scale. After converting a color histogram equalized image into gray scale, the contrast of the converted image is worse than that of an 1-D gray scale histogram equalized image. We propose a novel 3-D color histogram equalization method that produces uniform distribution in gray scale histogram by defining a new cumulative probability density function in 3-D color space. Test results with natural and synthetic images are presented to compare and analyze various color histogram equalization algorithms based upon 3-D color histograms. We also present theoretical analysis for nonideal performance of existing methods.