Ruthenium anchored on carbon nanotube electrocatalyst for hydrogen production with enhanced Faradaic efficiency

Developing efficient and stable electrocatalysts is crucial for the electrochemical production of pure and clean hydrogen. For practical applications, an economical and facile method of producing catalysts for the hydrogen evolution reaction (HER) is essential. Here, we report ruthenium (Ru) nanoparticles uniformly deposited on multi-walled carbon nanotubes (MWCNTs) as an efficient HER catalyst. The catalyst exhibits the small overpotentials of 13 and 17 mV at a current density of 10 mA cm(-2) in 0.5M aq. H2SO4 and 1.0M aq. KOH, respectively, surpassing the commercial Pt/C (16 mV and 33 mV). Moreover, the catalyst has excellent stability in both media, showing almost "zeroloss" during cycling. In a real device, the catalyst produces 15.4% more hydrogen per power consumed, and shows a higher Faradaic efficiency (92.28%) than the benchmark Pt/C (85.97%). Density functional theory calculations suggest that Ru-C bonding is the most plausible active site for the HER

Dissociating stable nitrogen molecules under mild conditions by cyclic strain engineering

All quiet on the nitrogen front. The dissociation of stable diatomic nitrogen molecules (N-2) is one of the most challenging tasks in the scientific community and currently requires both high pressure and high temperature. Here, we demonstrate that N-2 can be dissociated under mild conditions by cyclic strain engineering. The method can be performed at a critical reaction pressure of less than 1 bar, and the temperature of the reaction container is only 40 degrees C. When graphite was used as a dissociated N* receptor, the normalized loading of N to C reached as high as 16.3 at/at %. Such efficient nitrogen dissociation is induced by the cyclic loading and unloading mechanical strain, which has the effect of altering the binding energy of N, facilitating adsorption in the strain-free stage and desorption in the compressive strain stage. Our finding may lead to opportunities for the direct synthesis of N-containing compounds from N-2

Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

This study proposes an automated brittle fracture rate (BFR) estimator using deep learning. As the demand for line-pipes increases in various industries, the need for BFR estimation through dropweight tear test (DWTT) increases to evaluate steel's property. Conventional BFR or ductile fracture rate (DFR) estimation methods require an expensive 3D scanner. Alternatively, a rule-based approach is used with a single charge-coupled device (CCD) camera. However, it is sensitive to the hyper-parameter. To solve these problems, we propose an approach based on deep learning that has recently been successful in the fields of computer vision and image processing. The method proposed in this study is the first to use deep learning approach for BFR estimation. The proposed method consists of a VGG-based U-Net (VU-Net) which is inspired by U-Net and fully convolutional network (FCN). VU-Net includes a deep encoder and a decoder. The encoder is adopted from VGG19 and transferred with a pre-trained model with ImageNet. In addition, the structure of the decoder is the same as that of the encoder, and the decoder uses the feature maps of the encoder through concatenation operation to compensate for the reduced spatial information. To analyze the proposed VU-Net, we experimented with different depths of networks and various transfer learning approaches. In terms of accuracy used in real industrial application, we compared the proposed VU-Net with U-Net and FCN to evaluate the performance. The experiments showed that VU-Net was the accuracy of approximately 94.9 %, and was better than the other two, which had the accuracies of about 91.8 % and 93.7 %, respectively.11Ysciescopu

Self‐Assembly Mechanism of Spiky Magnetoplasmonic Supraparticles

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106774/1/adfm201302405.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106774/2/adfm201302405-sup-0001-S1.pd

PharmaKoVariome database for supporting genetic testing

Pharmacogenomics (PGx) provides information about routine precision medicine, based on the patient's genotype. However, many of the available information about human allele frequencies, and about clinical drug-gene interactions, is based on American and European populations. PharmaKoVariome database was constructed to support genetic testing for safe prescription and drug development. It consolidated and stored 2507 diseases, 11 459 drugs and 61 627 drug-target or druggable genes from public databases. PharmaKoVariome precomputed ethnic-specific abundant variants for approximately 120 M single-nucleotide variants of drug-target or druggable genes. A user can search by gene symbol, drug name, disease and reference SNP ID number (rslD) to statistically analyse the frequency of ethnical variations, such as odds ratio and P-values for related genes. In an example study, we observed five Korean-enriched variants in the CYP2B6 and CYP2D6 genes, one of which (rs1065852) is known to be incapable of metabolizing drug. It is also shown that 4-6% of North and East Asians have risk factors for drugs metabolized by the CYP2D6 gene. Therefore, PharmaKoVariome is a useful database for pharmaceutical or diagnostic companies for developing diagnostic technologies that can be applied in the Asian PGx industry

The complete mitochondrial genome of a Dokdo shrimp, Lebbeus groenlandicus

Lebbeus groenlandicus is a shrimp species indigenous to the Dokdo islands in the East Sea of Korea. We report the 17,399 bp mitochondrial genome (mitogenome) of the species that consists of 13 protein-coding genes, 22 transfer RNAs (tRNAs), 2 ribosomal RNAs (rRNAs), and a control region (CR). A maximum-likelihood tree, constructed with 18 prawn and 45 shrimp mitogenomes, confirmed that L. groenlandicus occupies the most basal position within the Caridea infra-order and is closely related to Pandalidae shrimps

Results of immediate loading for implant restoration in partially edentulous patients: a 6-month preliminary prospective study using SinusQuick™ EB implant system

STATEMENT OF PROBLEM. Many dental clinicians are concerned about immediate loading of inserted implants. However, there have been few clinical studies surveying the success rates of immediate loading, based on Korean implant systems. PURPOSE. The aim of this study was to evaluate the outcome of immediate functional loading of the implant (SinusQuickTM EB, Neobiotech Co., Seoul, Korea) in partially edentulous maxilla or mandible. MATERIAL AND METHODS. Total 15 implants were placed. Within 2 weeks after implant insertion, provisional implant-supported fixed partial dentures were delivered to the patients. Quantitatively, marginal bone loss was measured at the time of immediate loading, after 3-months of continued loading and at the last follow-up. The mean follow-up period was 4.8 months. RESULTS. Mean marginal bone loss from implant surgery to early loading, 3-months follow-up and last follow-up was 0.03 ± 0.07 mm, 0.16 ± 0.17 mm and 0.29 ± 0.19 mm. No implant failed up to 6 months after insertion, resulting in a 100% survival rate. CONCLUSION. Immediate loading exhibited high success rate in partial edentulism for up to 6 months. Well-controlled long term clinical studies with large sample size are necessary to confirm this finding