Photoelectrochemical-driven nitrogen reduction to ammonia by a Vo-SnO2/TiO2 composite electrode.

N2 molecules with the NN triple bond structure are difficult to cleave under mild conditions to achieve the nitrogen fixation reaction. Photoelectrochemical (PEC) catalysis technology combining the advantages of photocatalysis and electrocatalysis provides the possibility of the nitrogen reduction reaction under ambient conditions. Herein, an SnO2/TiO2 photoelectrode was first fabricated through depositing SnO2 quantum dots on TiO2 nanorod arrays via a simple hydrothermal method. The oxygen vacancy (Vo) content was then induced in SnO2 through annealing SnO2/TiO2 at high temperature under an inert atmosphere. The heterogeneous structure of Vo-SnO2 quantum dots and TiO2 nanorods boosted the separation of photocarriers. The photoelectrons generated by photoexcitation were transferred from the conduction band of TiO2 to the conduction band of Vo-SnO2 and trapped by Vo. Vo activates N2 molecules adsorbed on the catalyst surface, and reacts with H+ in the electrolyte to generate NH3. The nitrogen fixation yield of PEC catalysis and its faradaic efficiency can reach 19.41 µg cm-2 h-1, and 59.6% at -0.2 V bias potential, respectively. The heterogeneous structure of Vo-SnO2/TiO2, introduction of Vo and synergistic effect between light and electricity greatly promotes the PEC nitrogen reduction to NH3.

Constructing the Polymer Molecules to Regulate the Electrode/Electrolyte Interface to Enhance Lithium-Metal Battery Performance.

Lithium-ion batteries, with high energy density and long cycle life, have become the battery of choice for most vehicles and portable electronic devices; however, energy density, safety and cycle life require further improvements. Single-functional group electrolyte additives are very limited in practical applications, a ternary polymer bifunctional electrolyte additive copolymer (acrylonitrile-butyl hexafluoro methacrylate- poly (ethylene glycol) methacrylate- methyl ether) (PMANHF) was synthesized by free radical polymerization of acrylonitrile, 2, 2, 3, 4, 4, 4-hexafluorobutyl methacrylate and poly (ethylene glycol) methyl ether methacrylate. A series of characterizations show that in Li metal anodes, the preferential reduction of PMANHF is conducive to the formation of a uniform and stable solid electrolyte interphase layer, and Li deposition is uniform and dense. At the NCM811 cathode, a film composed of LiF- and Li3N-rich is formed at the cathode-electrolyte interface, mitigating the side reaction at the interface. At 1.0 mA cm-2, the Li/Li cell can be stabilized for 1000 cycles. In addition, the Li/NCM811 cell can stabilize 200 cycles with a cathode capacity of 153.7 mAh g-1, with the capacity retention of 89.93 %, at a negative/positive capacity ratio of 2.5. This study brings to light essential ideas for the fabrication of additives for lithium-metal batteries.

Integrated analysis of RNA-seq in hepatocellular carcinoma reveals competing endogenous RNA network composed of circRNA, lncRNA, and mRNA.

BACKGROUND: Circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) have been hypothesized to have important roles in the etiology of hepatocellular carcinoma (HCC). However, the synergistic effect of circRNA and lncRNA in the pathogenesis of HCC has rarely been studied. METHODS: In this study, the Gene Expression Omnibus database was used to get the expression profiles of circRNAs, micro RNAs (miRNAs), lncRNAs, and messenger RNAs (mRNAs) in HCC tissues and normal tissues. The accession numbers for this database are GSE101728, GSE155949, and GSE108724. We found 291 differentially overexpressed lncRNAs and 541 differentially overexpressed mRNA in GSE101728, 30 differentially overexpressed circRNA in GSE155949, and 48 significantly downregulated miRNA in GSE198724. Meanwhile, based on Pearson correlation test, we established lncRNA-mRNA networks. We constructed lncRNA/circRNA-miRNA pairs through Starbase database prediction and identified the common miRNAs. The intersection of co-predicted miRNAs and the 48 significantly low expression miRNAs in GSE198724 were included in the following study. miRDB, Targetscan, miRwalk, and lncRNA-related mRNA jointly determined the miRNA-mRNA portion of the circRNA/lncRNA-miRNA-mRNA co-expression network. And, among 55 differentially expressed mRNA in circRNA/lncRNA-miRNA-mRNA network, CPEB3, EFNB3, FATA4, growth hormone receptor, GSTZ1, KLF8, MFAP4, PAIP2B, PHACTR3, PITPNM3, RPS6KA6, RSPO3, SLITRK6, SMOC1, STEAP4, SYT1, TMEM132E, TSPAN11, and ZFPM2 were intimately related to the prognosis of HCC patients in Kaplan-Meier plotter analysis (P < .05). CONCLUSION: We have discovered that the prognosis-related lncRNAs/circRNAs-miRNA-mRNA network plays a significant role in the pathogenesis of HCC. These findings may offer fresh perspectives for further research into the pathogenesis of HCC and the search for novel treatments for HCC.

Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.

The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the l1 -SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer's Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions. The source code can be visited by the url https://github.com/reynard-hu/mbbna.

Brain functional connectivity analysis based on multi-graph fusion.

In this paper, we propose a framework for functional connectivity network (FCN) analysis, which conducts the brain disease diagnosis on the resting state functional magnetic resonance imaging (rs-fMRI) data, aiming at reducing the influence of the noise, the inter-subject variability, and the heterogeneity across subjects. To this end, our proposed framework investigates a multi-graph fusion method to explore both the common and the complementary information between two FCNs, i.e., a fully-connected FCN and a 1 nearest neighbor (1NN) FCN, whereas previous methods only focus on conducting FCN analysis from a single FCN. Specifically, our framework first conducts the graph fusion to produce the representation of the rs-fMRI data with high discriminative ability, and then employs the L1SVM to jointly conduct brain region selection and disease diagnosis. We further evaluate the effectiveness of the proposed framework on various data sets of the neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimers Disease (AD). The experimental results demonstrate that the proposed framework achieves the best diagnosis performance via selecting reasonable brain regions for the classification tasks, compared to state-of-the-art FCN analysis methods.