321 research outputs found
Table1_Porous single-crystalline molybdenum nitride enhances electroreduction of nitrogen to ammonia.DOCX
The industrial ammonia synthesis reaction has the disadvantage of large energy consumption; thus, the electrochemical reduction method of ammonia synthesis characterized by its clean nature and environmental protectiveness has received extensive attention. Molybdenum nitride is a commonly used electrocatalyst for ammonia synthesis, and its Faraday efficiency is low, which may be due to many internal grain boundaries and few active sites. In this work, we grow microscale porous Mo2N single crystals and polycrystalline Mo2N from non-porous MoO3 single crystals. Porous molybdenum nitride materials facilitate charge transport in grain boundaries due to their single-crystal nature and enhance the catalytic properties of ammonia synthesis reactions. Compared with polycrystalline Mo2N, the porous Mo2N single crystal shows better performance, with a high NH4+ yield of 272.56 μg h−1 mg−1 and a Faradaic efficiency of 7.3%. In addition, the porous Mo2N single crystal exhibits superior long-term stability with little attenuation after 16 h electrolysis reaction. It provides a new method for the catalyst of ammonia synthesis.</p
Effects of T-Type Calcium Channel Blockers on Renal Function and Aldosterone in Patients with Hypertension: A Systematic Review and Meta-Analysis
<div><p>Background</p><p>High blood pressure can cause kidney damage, which can increase blood pressure, leading to a vicious cycle. It is not clear whether the protective effects of T-type calcium channel blockers (T-type CCBs) on renal function are better than those of L-type CCBs or renin-angiotensin system (RAS) antagonists in patients with hypertension.</p><p>Methods and Findings</p><p>PUBMED, MEDLINE, EMBASE, OVID, Web of Science, Cochrane, CNKI, MEDCH, VIP, and WANFANG databases were searched for clinical trials published in English or Chinese from January 1, 1990, to December 31, 2013. The weighted mean difference (WMD) and 95% confidence interval (<i>CI</i>) were calculated and reported. A total of 1494 reports were collected, of which 24 studies with 1,696 participants (including 809 reports comparing T-type CCBs versus L-type CCBs and 887 reports comparing T-type CCB versus RAS antagonists) met the inclusion criteria. Compared with L-type CCBs, T-type CCBs resulted in a significant decline in aldosterone (mean difference = −15.19, 95% <i>CI</i> −19.65–−10.72, p<1×10<sup>−5</sup>), proteinuria (mean difference = −0.73, 95% <i>CI</i> −0.88–−0.57, p<1×10<sup>−5</sup>), protein to creatinine ratio (mean difference = −0.22, 95% <i>CI</i> −0.41–−0.03, p = 0.02), and urinary albumin to creatinine ratio (mean difference = −55.38, 95% <i>CI</i> −86.67–<i>−</i>24.09, p = 0.0005); no significant difference was noted for systolic blood pressure (SBP) (p = 0.76) and diastolic blood pressure (DBP) (p = 0.16). The effects of T-type CCBs did not significantly differ from those of RAS antagonists for SBP (p = 0.98), DBP (p = 0.86), glomerular filtration rate (p = 0.93), albuminuria (p = 0.97), creatinine clearance rate (p = 0.24), and serum creatinine (p = 0.27) in patients with hypertension.</p><p>Conclusion</p><p>In a pooled analysis of data from 24 studies measuring the effects of T-type CCBs on renal function and aldosterone, the protective effects of T-type CCBs on renal function were enhanced compared with L-type CCBs but did not differ from RAS antagonists. Their protective effects on renal function were independent of blood pressure.</p></div
Use of Polyetheretherketone as a Material for Solid Phase Extraction of Hydroxylated Metabolites of Polycyclic Aromatic Hydrocarbons in Human Urine
In
this study, a novel application of polyetheretherketone (PEEK) tubing
for solid phase extraction (SPE) of urinary hydroxylated metabolites
of polycyclic aromatic hydrocarbons (OH-PAHs) is presented. The use
of PEEK tubing for extracting nine OH-PAHs (2–5 rings) from
different matrixes (e.g., urine, acid/enzymatic hydrolysis solution)
was demonstrated; a facile method for fast (<2 min) quantification
of urinary 1-hydroxypyrene (1-OHPyr) was also developed by the use
of PEEK tubing extraction coupled to electrospray ionization tandem
mass spectrometry (ESI-MS/MS). Although no optimization was performed
for the extraction process, a limit of detection (LOD) as low as 0.01
μg
L<sup>–1</sup> was obtained, and the ratio of signal intensity
of 1-OHPyr to that of 1-OHPyr-d9 (internal standard) was linearly
related with the 1-OHPyr concentration over the range of 0.05–5.00
μg L<sup>–1</sup> (<i>R</i><sup>2</sup> = 0.9995).
Satisfactory recoveries (87–91%) were achieved, and less than
2 min was required to carry out the whole analytical procedure including
sample pretreatment and mass spectrometric detection. In a biomonitoring
study, the PEEK tubing extraction based method was successfully applied
to the quantification of 1-OHPyr in eight human urine samples, further
confirming the potential of PEEK tubing for SPE of organic compounds
Mean differences and 95% <i>CIs</i> of included studies and pooled data for T-type CCBs versus L-type CCBs.
<p>(A) Systolic blood pressure (SBP). (B) Diastolic blood pressure (DBP). (C) Glomerular filtration rate (GFR). (D) Serum creatinine (SCr). (E) Aldosterone. (F) Proteinuria in hypertensive patients with CKD. (G) The urinary protein to creatinine ratio in hypertensive patients with CKD. (H) The urinary albumin to creatinine ratio in hypertensive patients with diabetic nephropathy.</p
Characteristics of twenty-four studies included in the meta-analysis.
<p>CCBs: Calcium Channel Blockers; RAS: Renin-angiotensin system.</p><p>*Some patients were lost to follow-up or withdrew, and the rate of lost to follow-up was not significantly different between the two groups.</p><p>GRADE Working Group grades of evidence. High quality: Further research is very unlikely to change our confidence in the estimate of effect. Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate. Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate. Very low quality: We are very uncertain about the estimate.</p><p>The risk of bias assessment is done using RevMan. Low risk of bias: Plausible bias unlikely to seriously alter the results, low risk of bias for all key domains (within a study), and most information is from studies at low risk of bias (across studies). Unclear risk of bias: That raises some doubt about the results, unclear risk of bias for one or more key domains (within a study), and most information is from studies at low or unclear risk of bias (across studies). High risk of bias: Plausible bias that seriously weakens confidence in the results, high risk of bias for one or more key domains (within a study), the proportion of information from studies at high risk of bias is sufficient to affect the interpretation of results (across studies).</p><p>Characteristics of twenty-four studies included in the meta-analysis.</p
Mean differences and 95% <i>CIs</i> of included studies and pooled data for T-type CCBs versus RAS antagonists.
<p>(A) Systolic blood pressure (SBP). (B) Diastolic blood pressure (DBP). (C) The glomerular filtration rate (GFR) in hypertensive patients with proteinuria. (D) Albuminuria in hypertensive patients with proteinuria. (E) The creatinine clearance rate (CCr) in hypertensive patients with proteinuria. (F) Serum creatinine (SCr) in hypertensive patients with proteinuria. (G) Proteinuria.</p
Table2_A series fault arc detection method based on denoising autoencoder and deep residual network.XLSX
Given the problem that the existing series arc fault identification methods use existing features such as the time-frequency domain of the current signal as the basis for identification, resulting in relatively limited arc detection solutions, and that the methods of directly extracting current signal features using deep learning algorithms have insufficient feature extraction, a new series arc fault detection method based on denoising autoencoder (DAE) and deep residual network (ResNet) is proposed. First, a large number of training samples are obtained through sliding window and data normalization methods, and then high-dimensional abstract feature data are obtained from the fault and normal samples collected in the experiment through denoising autoencoders, converted into grayscale images, and processed in pseudo-color. The single-channel grayscale images are mapped into three-channel color values, and finally, the three-channel values are input into the constructed deep residual network for deep learning training. In the 152 super high-level ResNet, the arc fault recognition rate can reach 99.7%. For loads that have not participated in ResNet network training, the recognition rate can also reach 97.6%.</p
A schematic diagram of the search strategy for published reports.
<p>A schematic diagram of the search strategy for published reports.</p
Table3_A series fault arc detection method based on denoising autoencoder and deep residual network.XLSX
Given the problem that the existing series arc fault identification methods use existing features such as the time-frequency domain of the current signal as the basis for identification, resulting in relatively limited arc detection solutions, and that the methods of directly extracting current signal features using deep learning algorithms have insufficient feature extraction, a new series arc fault detection method based on denoising autoencoder (DAE) and deep residual network (ResNet) is proposed. First, a large number of training samples are obtained through sliding window and data normalization methods, and then high-dimensional abstract feature data are obtained from the fault and normal samples collected in the experiment through denoising autoencoders, converted into grayscale images, and processed in pseudo-color. The single-channel grayscale images are mapped into three-channel color values, and finally, the three-channel values are input into the constructed deep residual network for deep learning training. In the 152 super high-level ResNet, the arc fault recognition rate can reach 99.7%. For loads that have not participated in ResNet network training, the recognition rate can also reach 97.6%.</p
Theoretical Study on DBU-Catalyzed Insertion of Isatins into Aryl Difluoronitromethyl Ketones: A Case for Predicting Chemoselectivity Using Electrophilic Parr Function
The possible mechanisms
of 1,8-diazabicyclo[5.4.0]Âundec-7-ene (DBU)-catalyzed chemoselective
insertion of <i>N</i>-methyl isatin into aryl difluoronitromethyl
ketone to synthesize 3,3-disubstituted and 2,2-disubstituted oxindoles
have been studied in this work. As revealed by calculated results,
the reaction occurs via two competing paths, including α and
β carbonyl paths, and each path contains five steps, that is,
nucleophilic addition of DBU to ketone, C–C bond cleavage affording
difluoromethylnitrate anion and phenylcarbonyl–DBU cation,
nucleophilic addition of difluoromethylnitrate anion to carbonyl carbon
of <i>N</i>-methyl isatin, acyl transfer process, and dissociation
of DBU and product. The computational results suggest that nucleophilic
additions on different carbonyl carbons of <i>N</i>-methyl
isatin via α and β carbonyl paths would lead to different
products in the third step, and β carbonyl path associated with
the main product 3,3-disubstituted oxindole is more energetically
favorable, which is consistent with the experimental observations.
Noteworthy, electrophilic Parr function can be successfully applied
for exactly predicting the activity of reaction site and reasonably
explaining the chemoselectivity. In addition, the distortion/interaction
and noncovalent interaction analyses show that much more hydrogen
bond interactions should be responsible for the lower energy of the
transition state associated with β carbonyl path. The obtained
insights would be valuable for the rational design of efficient organocatalysts
for this kind of reactions with high selectivities
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