Ru-Doped Ultrasmall Cu Nanoparticles Decorated with Carbon for Electroreduction of Nitrate to Ammonia

Electrocatalytic nitrate reduction reaction offers a sustainable approach to treating wastewater and synthesizing high-value ammonia under ambient conditions. However, electrocatalysts with low faradaic efficiency and selectivity severely hinder the development of nitrate-to-ammonia conversion. Herein, Ru-doped ultrasmall copper nanoparticles loaded on a carbon substrate (Cu–Ru@C) were fabricated by the pyrolysis of Cu-BTC metal–organic frameworks (MOFs). The Cu–[email protected] catalyst exhibits a high faradaic efficiency (FE) of 90.4% at −0.6 V (vs RHE) and an ammonia yield rate of 1700.36 μg h–1mgcat.–1 at −0.9 V (vs RHE). Moreover, the nitrate conversion rate is almost 100% over varied pHs (including acid, neutral, and alkaline electrolytes) and different nitrate concentrations. The remarkable performance is attributed to the synergistic effect between Cu and Ru and the excellent conductivity of the carbon substrate. This work will open an exciting avenue to exploring MOF derivatives for ambient ammonia synthesis via selective electrocatalytic nitrate reduction

Distribution and diversity of twelve <i>Curcuma</i> species in China

<p>Genus <i>Curcuma</i> a wild species presents an important source of valuable characters for improving the cultivated <i>Curcuma</i> varieties. Based on the collected germplasms, herbariums, field surveys and other literatures, the ecogeographical diversity of Genus <i>Curcuma</i> and its potential distributions under the present and future climate are analysed by DIVA-GIS. The results indicate Genus <i>Curcuma</i> is distributed over 17 provinces in China, and particularly abundant in Guangxi and Guangdong provinces. The simulated current distributions are close to the actual distribution regions. In the future climate, the suitable areas for four <i>Curcuma</i> species will be extended, while for other three species the regions will be significantly decreased, and thus these valuable resources need protecting.</p

Data_Sheet_1_Identification of important modules and biomarkers in tuberculosis based on WGCNA.docx

BackgroundTuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited.MethodsWe performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers.ResultsBased on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or ConclusionThis study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.</p