Spectroscopically Visualizing the Evolution of Hydrogen-Bonding Interactions.

Understanding chemical bond variations is the soul of chemistry as it is essential for any chemical process. The evolution of hydrogen bonds is one of the most fundamental and emblematic events during proton transfer; however, its experimental visualization remains a formidable challenge because of the transient timescales. Herein, by subtly regulating the proton-donating ability of distinct proton donors (zeolites or tungstophosphoric acid), a series of different hydrogen-bonding configurations were precisely manipulated. Then, an advanced two-dimensional (2D) heteronuclear correlation nuclear magnetic resonance (NMR) spectroscopic technique was utilized to simultaneously monitor the electronic properties of proton donors and acceptors (2-13C-acetone or trimethylphosphine oxide) through chemical shifts. Parabolic 1H-13C NMR relationships combined with single-well and double-well potential energy surfaces derived from theoretical simulations quantitatively identified the hydrogen bond types and allowed the evolution of hydrogen bonds to be visualized in diverse acid-base interaction complexes during proton transfer. Our findings provide a new perspective to reveal the nature and evolution of hydrogen bonds and confirm the superiority of 2D NMR techniques in identifying the subtle distinctions of various hydrogen-bonding configurations.

Hierarchical Ni2P@NiFeAlO x Nanosheet Arrays as Bifunctional Catalysts for Superior Overall Water Splitting.

Bifunctional electrocatalysts based on transition-metal phosphides are appealing for overall water splitting owing to their excellent electrical conductivity, low cost, and high stability. However, these specials are often restricted by some serious drawbacks such as its relatively poor activity for oxygen evolution reaction (OER) and its manufacture, which usually requires one to add additional large numbers of P sources and, consequently, inevitably leads to the release of flammable and detrimental PH3. Herein, we show an effective avenue to overcome these issues. For the first time, the in situ topological transformation of PO43--intercalated NiFeAl-layered double hydroxide nanosheet arrays upon calcination under a H2 atmosphere is developed to fabricate supported nickel phosphide without any additional P source. The resulting phase affords unique Ni2P@NiFeAlO x core-shell nanosheet arrays, which exhibit an excellent performance for OER and hydrogen evolution reaction in 1.0 M KOH, with low overpotentials of 210 and 105 mV at 10 mA cm-2, respectively. Impressively, it can also serve as both a cathode and an anode to drive water splitting in alkaline media, giving 10 and 100 mA cm-2 at cell voltages of only 1.52 and 1.62 V, respectively. This value is better than the commercial criterion of the Pt/C//IrO2 counterpart and also ranks at the top level in all established bifunctional electrocatalysts. The outstanding performance of Ni2P@NiFeAlO x is mainly attributed to the synergistic effect from a highly dispersed Ni2P core and a thin NiFeAlO x shell, as well as the efficient mass transport of a hierarchical nanoarray framework.

Alteration in abundance and compartmentalization of inflammation-related miRNAs in plasma after intracerebral hemorrhage.

BACKGROUND AND PURPOSE: We tested the hypothesis that circulating microRNAs (miRNAs) present in plasma might display a specific signature in patients with intracerebral hemorrhage. METHODS: Global miRNA profiles were determined with the Agilent Human miRNA Microarray platform, and validated by quantitative polymerase chain reaction. RESULTS: A subset of 30 miRNAs were selectively upregulated in both male and female patients with intracerebral hemorrhage. Network analysis revealed that the most significantly overrepresented biological process associated with these miRNAs was inflammation. Unlike the plasma miRNAs in healthy controls, which were detected exclusively in the microvesicle fraction, the deregulated plasma miRNAs after intracerebral hemorrhage were present in both the microvesicle and the supernatant fractions. CONCLUSIONS: Intracerebral hemorrhage altered both the abundance and the compartmentalization of several inflammation-related miRNAs in plasma.

Automatic classification of arrhythmias using multi-branch convolutional neural networks based on channel-based attention and bidirectional LSTM.

Cardiac arrhythmia is an abnormal rhythm of the heartbeat and can be life-threatening Electrocardiogram (ECG) is a technology that uses an electrocardiograph machine to record a graph of the changes in electrical activity produced by the heart at each cardiac cycle. ECG can generally be used to check whether the examinee has arrhythmia, ion channel disease, cardiomyopathy, electrolyte disorder and other diseases. To reduce the workload of doctors and improve the accuracy of ECG signal recognition, a novel and lightweight automatic ECG classification method based on Convolutional Neural Network (CNN) is proposed. The multi-branch network with different receptive fields is used to extract the multi-spatial deep features of heartbeats. The Channel Attention Module (CAM) and Bidirectional Long Short-Term Memory neural network (BLSTM) module are used to filter redundant ECG features. CAM and BLSTM are beneficial for distinguishing different categories of heartbeats. In the experiments, a four-fold cross-validation technique is used to improve the generalization capability of the network, and it shows good performance on the testing set. This method divides heartbeats into five categories according to the American Advancement of Medical Instrumentation (AAMI) criteria, which is validated in the MIT-BIH arrhythmia database. The sensitivity of this method to Ventricular Ectopic Beat (VEB) is 98.5% and the F1 score is 98.2%. The precision of the Supraventricular Ectopic Beat (SVEB) is 91.1%, and the corresponding F1 score is 90.8%. The proposed method has high classification performance and a lightweight feature. In a word, it has broad application prospects in clinical medicine and health testing.

The spillover effect of green finance development on rural revitalization: an empirical analysis based on China's provincial panel data.

With the overall victory of poverty alleviation in China, the focus of rural work has been transformed into rural revitalization. Therefore, based on the panel data of 30 provinces and cities in China spanning 2011 to 2019, this research used the entropy-TOPSIS method to calculate the weights of each index of the two rural revitalization and green finance systems. This research also constructs the spatial Dubin model to empirically analyze the direct effects and spatial spillover effects of green finance development on the level of rural revitalization. Additionally, this research calculates the weight of each indicator of rural revitalization and green finance using entropy-weighted TOPSIS. This research reveals that the current state of green finance is not conducive to increasing local rural revitalization and does not significantly affect all provinces. Further, the number of human resources can improve the local level of rural revitalization, not the entire province. These dynamics benefit the growth of local rural revitalization in the surrounding areas if employment and technology levels are developed domestically. Moreover, this research reveals that education level and air quality have a spatial crowding effect on rural revitalization. Thus, when developing rural revitalization and development policies, it is vital to prioritize the high-quality development of finance to be closely monitored by local governments at the respective levels. Furthermore, the stakeholders must pay critical attention to the connection between supply and demand and between financial institutions and agricultural enterprises in the provinces. Again, the policymakers must also increase policy preference, deepen regional economic cooperation, and improve the supply of essential rural elements to play a more significant role in green finance and support rural revitalization.

Versatile Synthesis of Hollow-Structured Mesoporous Carbons by Enhanced Surface Interaction for High-Performance Lithium-Ion Batteries.

Nanoporous carbons are very attractive for various applications including energy storage. Templating methods with assembled amphiphilic molecules or porous inorganic templates are typically used for the synthesis. Amongst the different members of this family, CMK-5-like structures that are constructed to consist of sub-10 nm amorphous carbon nanotubes and ultrahigh specific surface area due to their thin pore walls, have the best properties in various respects. However, the fabrication of such hollow-structured mesoporous carbons entails elaborately tailoring the surface properties of the template pore walls and selecting specific carbon precursors. Thus, very limited cases are successful. Herein, a versatile and general silanol-assisted surface-casting method to create hollow-structured mesoporous carbons and heteroatom-doped derivatives with numerous organic molecules (e.g., furfuryl alcohol, resol, 2-thiophene methanol, dopamine, tyrosine) and different structural templates is reported. These carbon materials exhibit ultrahigh surface area (2400 m2  g-1 ), large pore volume (4.0 cm3  g-1 ), as well as satisfactory lithium-storage capacity (1460 mAh g-1 at 0.1 A g-1 ), excellent rate capability (320 mAh g-1 at 5 A g-1 ), and very outstanding cycling performance (2000 cycles at 5 A g-1 ).

Structural and Acidic Characteristics of Multiple Zr Defect Sites in UiO-66 Metal-Organic Frameworks.

Although defects are prevalent in metal-organic frameworks (MOFs) and usually play a crucial role in modulating their performance in various applications, detailed structural characterizations of various defects remain a challenging task mainly due to their disordered, heterogeneous, and local nature. In this work, by using solid-state nuclear magnetic resonance spectroscopy (SSNMR) techniques in conjunction with density functional theory (DFT) calculations, it is clearly elucidated that the trimethylphosphine (TMP)-assisted 31P NMR strategy is capable of greatly facilitating the qualitative and quantitative description of the detailed structural and acidic characteristics as well as the evolution process of various Zr defects with subtle distinctions in UiO-66 upon moderate thermal treatment, hence surpassing most conventional analytical techniques. These results offer a fundamental understanding of the defect chemistry in MOFs.

Determination of berberine, palmatine and jatrorrhizine in rabbit plasma by liquid chromatography-electrospray ionization-mass spectrometry.

Incurred rabbit plasmas samples were utilized for method quality assessment in this study, where an optimized protein precipitation method for the preparation of rabbit plasma samples and a rapid and sensitive liquid chromatography-electrospray ionization-mass spectrometry for the simultaneous determination of berberine, palmatine and jatrorrhizine was described. Plasma samples (100 µl) were pretreated by protein precipitation with the mixture of 3% formic acid and 50 ng/ml clozapine (internal standard) in acetonitrile followed by LC analysis using a C(18) column and a mobile phase composed of 0.4% formic acid solution and 0.2% formic acid solution of methanol (60:40, v/v) operated at a flow rate of 0.4 ml/min. The analysis was performed in the multiple reaction monitoring mode via electrospray ionization source operating in the positive ionization mode. The method was linear over the concentration range of 0.1-400 ng/ml for all target components. The lower limits of quantification were 0.1 ng/ml for all analytes, all intra- and inter-day precision values were less than 7.10%, and accuracy (bias, %) was within ±7.11%. The mean absolute recovery was more than 72% for all analytes. The validated method has been successfully applied to the pharmacokinetic study of berberine, palmatine and jatrorrhizine in rabbit plasma after oral administration of San-Huang decoction to rabbits.