Facile Synthesis of PtCuNi Nanohexapods as Active Electrocatalysts for Methanol Oxidation and Ethanol Oxidation

The design of Pt-based alloy nanocatalysts with sufficient accessible surface areas and abundant active sites is important for their application in direct methanol fuel cells (DMFCs) and direct ethanol fuel cells (DEFCs). Here, we report a simple one-pot method for the preparation of PtCuNi hexapod nanocrystals. The conversion mechanism of the nanoparticles from rhombic dodecahedra to hexapods was investigated by time evolution experiments. Electrochemical tests demonstrated that the PtCuNi nanohexapods exhibited greatly elevated catalytic performance for methanol oxidation reaction (MOR) and ethanol oxidation reaction (EOR) under acidic conditions. Specifically, the PtCuNi nanohexapods have mass activities of 1.73 and 2.31 A mgPt–1 for MOR and EOR, which are 2.9 and 2.3 times higher than those of the commercial Pt/C catalyst, respectively. Moreover, the PtCuNi nanohexapods also exhibit enhanced electrocatalytic stability compared to Pt/C. This work provides an effective strategy for the rational construction of nanocrystals with desired morphologies and structures in the application of catalysis, energy, sensors, and other fields

Additional file 2: Table S2. of Actionable gene-based classification toward precision medicine in gastric cancer

Detail of the 435-gene panel. (XLSX 16 kb

Additional file 3: Figures S1-S4. of Actionable gene-based classification toward precision medicine in gastric cancer

Figure S1. Definition of somatic copy number alteration (SCNA) status. Figure S2. Clinicopathological characteristics of TCGA molecular subtype in Japanese and TCGA gastric cancers (GCs). Figure S3. Cluster of 435-gene co-mutation patterns. Figure S4. Distribution of Lauren classification and TCGA molecular subtypes by presence or absence of actionable gene alterations. (PDF 725 kb

Additional file 1: Table S1. of Actionable gene-based classification toward precision medicine in gastric cancer

Clinicopathological characteristics of 207 Japanese gastric cancers. (XLSX 38 kb

Additional file 1: Figure S1. of Genomic landscape of colorectal cancer in Japan: clinical implications of comprehensive genomic sequencing for precision medicine

Location of genetic aberrations for Japanese and US patients, and TCGA samples. Mutations in (A) APC, (B) ERBB2, (C) TP53, (D) NRAS, and (E) KRAS for Japanese patients (n = 201), US patients (n = 108), and TCGA samples (n = 224) were aligned to protein domains. The number of mutations at each given amino acid were plotted in corresponding pie graphs. As shown, KRAS G12 were the highest frequency mutations. Patient samples were further plotted by mutation status (F) KRAS-hypermutated and (G) KRAS-non-hypermutated. Figure S2. Correlation of RNF43 mutations with MMR. (A) The frequencies of APC and RNF43 mutations were determined by MMR phenotype. Statistical significance was determined by Fisher’s exact test. (B) Mutation mapper analysis identified G659 as most frequently altered in MMR-D cases. Figure S3. Gene-based statistical analysis for clinical information. Genes were filtered based on Fisher’s exact test (p < 0.05). Cell values are log odds ratios colored from blue to red. Dendrograms were created by Euclidean distance and Ward’s method. Less (blue) or more (red) aggressive factors of seven clinical variables are shown: lymphatic invasion (ly), vascular invasion (v), histopathological grade (G), TNM classifications (T, N, and M), and tumor stage. Figure S4. Cluster of 61-gene co-mutation patterns. (A) Cluster analysis was performed on non-hypermutated Japanese CRC samples (n = 184 tumors) by using Euclidean distance and Ward’s clustering method (closest distance to common mutated genes are colored yellow to blue). (B) Co-mutated gene patterns of the 61-gene set with statistical analysis. Mutation rate in each group is shown as a bar graph in the middle panel. Group-based mean values for age and tumor diameter are shown (left) with cluster colors and fraction for clinical information (right). Dark bars indicate significant difference (p < 0.05, two-tailed Fisher’s exact test) to the distribution of all other non-hypermutated donors, light bars are non-significant (**p < 0.01, *p < 0.05). Figure S5. Data complementary to Fig. 3. (A) Cluster analysis was performed on non-hypermutated Japanese CRC samples (n = 184 tumors) by using Euclidean distance and Ward’s clustering method (closest distance to common mutated genes are colored yellow to blue). (B) Kaplan–Meier survival estimates according to genomic subgroups. Overall survival was analyzed in 102 patients with Stage IV CRC treated with anti-EGFR therapies. The patients were divided to “All WT (wild type)” (Cluster 1; n = 25) or “Mutated” (Clusters 2–8; n = 77) based on the cluster analysis with targeted therapy-related 26 genes. Table S1. The 415-gene list for the CGS platform. Table S2. BRAF mutation and tumor location (J-CRC, n = 201). Table S3. Raw data for gene-based statistical analysis for clinical information. Table S4. Clinicopathological characteristics of 201 CRC patients. (PDF 1435 kb