Search

Electrochemical heterogeneous catalytic ozonation (E-catazone) is a promising and advanced oxidation technology that uses a titanium dioxide nanoflower (TiO2-NF)-coated porous Ti gas diffuser as an anode material. Our previous study has highlighted that the importance of the TiO2-NF coating layer in enhancing OH production and rapidly degrading O3-resistant drugs. It is well known that the properties of TiO2-NF are closely related to its sintering temperature. However, to date, related research has not been conducted in E-catazone systems. Thus, this study evaluated the effect of the sintering temperature on the degradation of the O3-resistant drug para-chlorobenzoic acid (p-CBA) using both experimental and kinetic modeling and revealed its influence mechanism. The results indicated that the TiO2-NF sintering temperature could influence p-CBA degradation and OH production. TiO2-NF prepared at 450 °C showcased the highest p-CBA removal efficiency (98.5% in 5 min) at a rate of 0.82 min−1, and an OH exposure of 8.41 × 10−10 mol L−1 s. Kinetic modeling results and interface characterization data revealed that the sintering temperature could alter the TiO2 crystallized phase and the content of surface-adsorbed oxygen, thus affecting the two key limiting reactions in the E-catazone process. That is, ≡TiO2 surface reacted with H2O to form TiO2-(OH)2, which then heterogeneously catalyzed O3 to form OH. Consequently, E-catazone with a TiO2-NF anode prepared at 450 °C generated the highest surface reaction rate (5.00 × 10−1 s−1 and 4.00 × 10-3 L mol-1 s−1, respectively), owing to its higher anatase content and adsorbed oxygen. Thus, a rapid O3-TiO2 reaction was achieved, resulting in an enhanced OH formation and a highly effective p-CBA degradation. Overall, this study provides novel baseline data to improve the application of E-catazone technology.

Aqueous K-ion batteries have attracted increasing attention. Yet, their capacity and durability are facing severe challenges. Construction of available electrolyte plays a vital role to overcome these challenges. Herein, a polymer-free CH3COOK (KAc) gel electrolyte is developed with a ultrahigh salt-water mole ratio, which can be high as 1: 1.16 (KAc: H2O). With a glassy carbon electrode, the electrochemical stability window of the gel is up to 4 V (-1.9 ~ 2.1 V vs Ag/AgCl). At room temperature , the ion conductivity is measured to be 10.9 mS cm−1, which increases to 23.5 mS cm−1 at 90 ℃ and is still 3.4 mS cm−1 at -20 ℃, indicating good temperature adaptability. Surprisingly, unlike other high-concentered electrolytes, anti-deliquescence ability of the gel is also rather good. With Raman analysis, it is found that there are almost no free water and cross-link structure among K+, Ac− and water should be formed in the gel, which is further demonstrated by density function theory (DFT) computation. Using this gel electrolyte, FeSe2 can deliver a high and reversible capacity of 250 mAh g−1 at 0.5 A g−1 as anode. Most importantly, stable solid electrolyte interphase (SEI) layer is found to be formed. It is believed that the quasi-solid state of the electrolyte together with the SEI layer can effectively suppress the reaction activity and the solvent ability of water, and hence inhibit mass loss of active materials, even the materials that store charges through phase conversion reactions.

The chemical exploration of Onychium japonicum (Thunb.) O. Kuntze (Pteridaceae) aerial parts afforded two new cyathane diterpene glucosides (1 and 2). The structure elucidation of the new compounds was unambiguously performed based on HRESIMS and 1D and 2D NMR spectroscopic data analyses. These two compounds represent the first two cyathane diterpenes with a glucopyranosyl moiety. Compounds 1 and 2 exhibited potent inhibitory effects against LPS-induced nitric oxide (NO) production in RAW246.7 macrophages with its IC50 values of 1.76 and 2.82 μM, respectively.

Hybrid rice significantly contributes to the food supply worldwide. Backbone parents play important roles in elite hybrid rice breeding systems. In this study, we performed pedigree-based analysis of the elite backbone parent rice variety, namely, Shuhui 527 (SH527, Oryza sativa), to exploit key genome regions during breeding. Twenty-four cultivars (including SH527, its six progenitors and 17 derived cultivars) were collected and analyzed with high-density single nucleotide polymorphism (SNP) array. Scanning all these cultivars with genome-wide SNP data indicated the unique contributions of progenitors to the SH527 genome and identified the key genomic regions of SH527 conserved within all its derivatives. These findings were further supported by known rice yield-related genes or unknown QTLs identified by genome-wide association study. This study reveals several key regions for SH527 and provides insights into hybrid rice breeding.

Aqueous energy storage devices have been widely considered because of their advantages in such aspects as security, cost and ionic conductivity. Yet, current collectors that can be used in water systems are very limited. Herein, a convenient and cost-effective approach to fabricate flexible graphene films (FGFs) that can be used as aqueous available current collectors is developed. Firstly, very thin graphene nanoplatelets are prepared in a very short time (less than 6 h) at room temperature by optimizing the ratio of H2O2 and concentrated H2SO4 using natural graphite flake as raw material. The as-obtained graphene nanoplatelets are subsequently shaped into FGFs through a simple roll pressing technique. Compared with commercial carbon cloth (CC) and carbon paper (CP), the FGF possesses obvious better hydrophilicity and higher conductivity of 2.56 × 105 S/m together with lighter area weight. In alkaline solution, commercial activated carbon loaded on the FGF exhibits better electrochemical performance than that on CC or CP. The FGF fabrication approach developed here is efficient, economical and feasible, making it ideal for bulk production as water available current collectors.

Objective To isolate the phenolic compounds obtained from the dried roots of Polygonum multiflorum and investigate their pharmacological activities. Methods The chemical constituents were isolated and purified by combining them with a macroporous resin (DM-8), MCI gel, and Sephadex LH-20 and by performing ODS column chromatography. Their structures were elucidated by 1D and 2D NMR analyses, as well as mass spectrometry. The isolated compounds were evaluated to determine their hepatoprotective and α-glucosidase inhibitory activities in vitro. Results Two phenolic compounds, namely, polygonimitin E (1) and polygonimitin F (2), were isolated from the dried roots of P. multiflorum. Compound 2 (10 µmol/L) only showed moderate hepatoprotective activity against N-acetyl-p-aminophenol (APAP)-induced HepG2 cell damage. Unfortunately, these two compounds exhibited no α-glucosidase inhibitory activity. Conclusion Compounds 1 and 2 were new compounds. Compound 2 could be one of the potential hepatoprotective constituents of P. multiflorum.

We replicated the rat common carotid artery (CCA) intima hyperplasia model and found the expression of a circular RNA, circRNA_009723 (circDcbld1), was markedly increased in the CCA with intimal hyperplasia. In vitro, the suppression of circDcbld1 in rat vascular smooth muscle cells (VSMCs) led the increase of contractile smooth muscle cell markers and the decrease of cell migration. In vivo, the injection of chemically modified circDcbld1 small interfering RNA (siRNA) lessened the formation of neointima in rat CCA after balloon injury. Further experiments proved that circDcbld1, as a competing endogenous RNA, interacted with miR-145-3p and upregulated the level of neuropilin-1 (Nrp1), thereby regulating the migration of VSMCs. In this study, we demonstrated a new mechanism by which circular RNA promotes intimal hyperplasia. We deem that intervention in the circDcbld1-miR-145-3p/Nrp1 pathway might be a feasible approach to alleviate the post-injury intimal hyperplasia. Keywords: intimal hyperplasia, vascular smooth muscle cells, migration, circular RNA, vascular remodeling

Pure MoO2 and MoO2/Mo are tuned via temperature control in a facile high temperature reduction procedure. Graphene oxide (GO) is then composited into them through physical mixing followed by freeze-drying. When assembled as anodes for Li-ion batteries, samples containing Mo, including MoO2/Mo and MoO2/Mo-GO, possess not only high initial columbic efficiency (ICE) but also high capacity, stability and rate performance. The MoO2/Mo hybrid exhibits a rather high ICE of 97%. The ICE of the MoO2/Mo-GO is still high as 85% with a largely enhanced capacity of 400 mA h g−1, which is gradually increased to 550 mA h g−1 after 150 cycles.

Kidney diseases are serious public problems with high morbidity and mortality in the general population and heavily retard renal function with aging regardless of the cause. Although myriad strategies have been assigned to prevent or harness disease progression, unfortunately, thus far, there is a paucity of effective therapies partly due to an insufficient knowledge of underlying pathological mechanisms, indicating deeper studies are urgently needed. Additionally, natural products are increasingly recognized as an alternative source for disease intervention owing to the potent safety and efficacy, which might be exploited for novel drug discovery. In this review, we primarily expatiate the new advances on mediators that might be amenable to targeting aging kidney and kidney diseases, including nicotinamide adenine dinucleotide phosphate oxidase (NOX), transforming growth factor-β (TGF-β), renin-angiotensin system (RAS), nuclear factor-erythroid 2 related factor 2 (Nrf2), peroxisome proliferator-activated γ receptor (PPARγ), advanced glycation endproducts (AGEs) as well as microRNAs and vitagenes. Of note, we conclude by highlighting some natural products which have the potential to facilitate the development of novel treatment for patients with myriad renal diseases.

In this study, probe/primers of high specificity and sensitivity were selected to analyze donkey-hide gelatin for donkey DNA and to look for horse, ox, and pig DNA as possible adulterants. The mitochondrial CO I genes in donkey, horse, and ox were selected as target sequences for design and synthesis of three pairs of specific probes and primers. In addition, eight pairs of probe/primers were obtained via literature search. Out of these eleven groups of probe/primers, those with the highest specificity and sensitivity were selected, which was fulfilled by the screening firstly with animal hide samples then the hide-glue samples. Other parameters that might affect detection specificity and efficiency-such as the amount of sampling and final concentration of primers-were also optimized. Replication tests were also conducted. The results showed that the selected probe/primers could accurately detect donkey DNA and horse, ox, and pig DNA in gelatin samples with good reproducibility. Analysis of four samples of on-market gelatin using this assay showed that two of the four samples indeed contained only donkey DNA, whereas the other two samples contained both donkey and horse DNA, indicating adulteration of these samples with horse hide. These results indicate that the TaqMan probe real-time PCR method can be used for identifying the purity of donkey DNA in gelatin samples, and can provide technical support for identifying adulterations in the gelatin market.

Mobile personal cloud storage services (PCSSs) have become increasingly common among mobile users in recent years. Prior studies largely focused on understanding users' early adoption of mobile PCSSs, but attention to post-adoption switching behavior is still rare. Based on the push-pull-mooring framework of migration theory, to explore the factors that influence the voluntary switching intentions of mobile PCSS users in the Chinese market, we developed a model around three effects: pushing, pulling, and mooring. The results show that two push factors (privacy risk and security risk), six pull factors (referent network size, complementarity, usefulness, technical compatibility, lifestyle compatibility, and enjoyment), and two mooring factors (habit and switching cost) have significant effects on switching intention. Moreover, referent network size, total network size, complementarity, technical compatibility, lifestyle compatibility and enjoyment are significant to usefulness, and usefulness mediates between them and switching intention. The findings will help practitioners devise proper strategies for retaining current users and attracting new users.

Many species of velvet antler have been used as traditional medicine for thousands of years; however, as medicinal materials, velvet antler derived from different animals have different clinical effects. To distinguish the differences and homologies, ultra-performance liquid chromatography-quadruple-time of flight mass spectrometry (UPLC-QTOF-MS) coupled with principal component analysis (PCA) was developed and applied to identify these antler samples derived from Cervus nippon Temminck, Cervus elaphus Linnaeus and Rangifer tarandus Linnaeus, which were first tested and compared at the molecular level of protein. The UPLC-MS data of the trypsin digested samples were subjected to PCA, and the potential markers based on peptide were depicted to illustrate their differences. With the integrated strategy combining UPLC-QTOF-MS with PCA, the results from this study indicated that the proposed methods could be successfully applied to distinguish reindeer antler from sika deer antler and red deer antler, which were prescribed in the Chinese Pharmacopoeia (2015 edition).

Battery is one of the most important and costly devices in electric vehicles (EVs). Developing an efficient battery management method is of great significance to enhancing vehicle safety and economy. Recently developed big-data and cloud platform computing technologies bring a bright perspective for efficient utilization and protection of vehicle batteries. However, a reliable data transmission network and a high-quality cloud battery dataset are indispensable to enable this benefit. This paper makes the first effort to systematically solve data quality problems in cloud-based vehicle battery monitoring and management by developing a novel integrated battery data cleaning framework. In the first stage, the outlier samples are detected by analyzing the temporal features in the battery data time series. The outlier data in the dataset can be accurately detected to avoid their impacts on battery monitoring and management. Then, the abnormal samples, including the noise polluted data and missing value, are restored by a novel future fusion data restoring model. The real electric bus operation data collected by a cloud-based battery monitoring and management platform are used to verify the performance of the developed data cleaning method. More than 93.3% of outlier samples can be detected, and the data restoring error can be limited to 2.11%, which validates the effectiveness of the developed methods. The proposed data cleaning method provides an effective data quality assessment tool in cloud-based vehicle battery management, which can further boost the practical application of the vehicle big data platform and Internet of vehicle.

Establishing an accurate mathematical model is fundamental to managing, monitoring, and protecting the battery pack in electric vehicles (EVs). The application of the deep learning algorithm-based state estimation method can significantly improve the accuracy and stability of the battery model but is hindered by the great demand for training data. This paper addresses the challenge of health-conscious battery modeling by utilizing multi-source data based on a novel deep transfer learning method. Firstly, a cloud-based battery management framework is designed, which is able to collect and process battery operation data from various EVs and provide a foundation for deploying the transfer learning method. Battery healthy state information in the collected dataset is labeled by a generic perception model, which can be commonly used to quantify the aging state of different battery packs and facilitate the knowledge transfer process. Additionally, a deep transfer learning method is developed to boost the training process of the battery model, where the operation data from different types of EVs can be used for establishing state estimators. The method is verified by the battery operation data collected from two types of electric buses. With the developed healthy state perception model and transfer learning method, battery model error can be limited to 2.43% and 1.27% in the whole life cycle.