Multistep optimization for the electrodeposited mixed perovskite FA1-yCsyPbBrxI3-xsolar cells.

The electrodeposition method has recently been developed for the fabrication of perovskite solar cells due to its potential advantages in commercial preparation. However, there is few studies on the preparation of perovskite solar cells by the electrodeposition method, especially on the perovskite FAPbI3-based solar cells. Herein, we fabricated the mixed perovskite FA1-yCsyPbBrxI3-xsolar cells by an optimized electrodeposition method, in which the electrodeposited PbO2reacts directly with FAI and an appropriate amount of CsBr dopants. The corresponding solar cells display the best PCE of 4.97%. By regulating the growth temperature in the reaction between PbO2and FAI/CsBr, the efficiency of the mixed perovskite solar cells can be promoted to 10.18%. These results illustrate that the element doping and growth environment regulation can optimize the quality of the perovskite films, thus promoting the efficiency of the perovskite solar cells. With further optimizing the growth process in the electrodeposition method, it is expected to open up a new commercial preparation route for the perovskite solar cells in the near future.

A strategy to explore the quality markers of Ziziphi Spinosae semen by combining metabolic in vivo study with network pharmacology.

Ziziphi Spinosae semen (ZSS), the dried and ripe seed of Ziziphus jujube Mill. var. spinosa (Bunge) Hu ex H. F. Chou, has been used as a sedative in China and other Asian countries for over a millennium. However, its quality markers (Q-markers) are not completely clear. In this study, Q-markers selected by a metabolic in vivo study combined with network pharmacology are proposed for ZSS quality control. An UHPLC (ultra-high-performance liquid chromatography)-Q-Orbitrap-MS method was developed to identify or tentatively assign 48 components including 21 flavonoid C-glycosides, 2 flavonoid O-glycosides, 11 dammarane triterpenoid saponins, 13 alkaloids, and 1 other, using a diagnostic product ion filtering strategy in ZSS. Subsequently, 147 metabolites detected from serum, urine, bile, and feces samples of para-chlorophenylalanine-induced insomnia rats treated with ZSS aqueous extracts could be linked to their respective parent compounds, including 27 prototypes. Meanwhile, three metabolic networks of flavonoids, saponins, and alkaloids are preliminarily established and potential metabolic pathways are investigated under the insomnia condition. Finally, 12 key bioactive components against insomnia including magnoflorine, caaverine, coclaurine, norisocorydine, genkwanin, juzinrine, apigenin, jujubogenin, kaempferol-3-O-rutinoside, jujuboside A, jujuboside B, and spinosin with the highest degree values in component-target-pathways network were selected as Q-markers for the quality control of ZSS.

Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics.

The diverse characteristics and large number of entities make metabolite separation challenging in metabolomics. To date, there is not a singular instrument capable of analyzing all types of metabolites. In order to achieve a better separation for higher peak capacity and accurate metabolite identification and quantification, we integrated GC × GC-MS and parallel 2DLC-MS for analysis of polar metabolites. To test the performance of the developed system, 13 rats were fed different diets to form two animal groups. Polar metabolites extracted from rat livers were analyzed by GC × GC-MS, parallel 2DLC-MS (-) and parallel 2DLC-MS (+), respectively. By integrating all data together, 58 metabolites were detected with significant change in their abundance levels between groups (p≤ 0.05). Of the 58 metabolites, three metabolites were detected in two platforms and two in all three platforms. Manual examination showed that discrepancy of metabolite regulation measured by different platforms was mainly caused by the poor shape of chromatographic peaks resulting from low instrument response. Pathway analysis demonstrated that integrating the results from multiple platforms increased the confidence of metabolic pathway assignment.

[1H NMR based metabonomics study on the antidepressant effect of genipin in rat hippocampus].

The purpose of this study is to explore depression metabolic markers in rat hippocampus and to investigate the anti-depressant effect of genipin and its mechanisms using nuclear magnetic resonance (NMR) metabonomics. Chronic unpredictable mild stress (CUMS) procedure was conducted to establish the depressive rat model. At the beginning of the third week, genipin low dose (25 mg x kg(-1)), middle dose (50 mg x kg(-1)), high dose (100 mg x kg(-1)), and venlafaxine (50 mg x kg(-1)) were given to the CUMS rats separately once daily for two weeks except control and model groups. Rat hippocampus was analyzed by 1H NMR based metabonomics after drug administration for 2 weeks. Significant differences in the metabolic profile of rat hippocampus of the CUMS treated group and the control group were observed with metabolic effects of CUMS including decreasing in glycine and N-acetylaspartate, increasing in inositol, glutamate, lactate, glutamine, taurine and alanine. Genipin showed ideal antidepressive effects at a dose of 50 mg x kg(-1) in rats, decrease of inositol, glutamate, lactate, alanine were observed, while glycine and N-acetylaspartate were increased. Important influence has been found on normal nervous system function of these significant changed metabolites, which suggests that the antidepressant effect of genipin may be played by enhancing the activity of neurons in hippocampus, repairing and improving the function of the neuron. The metabonomics approach is an effective tool for the investigation of the anti-depressant effect and pharmacologic mechanisms of genipin.

Controllable Fabrication of Vertical Graphene with Tunable Growth Nature by Remote Plasma-Enhanced Chemical Vapor Deposition.

As an important member of the graphene family, vertical graphene (VG) has broad applications like field emission, energy storage, and sensors owing to its fascinating physical and chemical properties. Among various fabrication methods for VG, plasma enhanced chemical vapor deposition (PECVD) is most employed because of the fast growth rate at relatively low temperature for the high-quality VG. However, to date, relations between growth manner of VG and growth parameters such as growth temperature, dosage of gaseous carbon source, and electric power to generate plasma are still less known, which in turn hinder the massive production of VG for further applications. In this study, the growth behavior of VG was studied as functions of temperature, plasma power, and gas composition (or chamber pressure). It was found that the growth behavior of VG is sensitive to the growth conditions mentioned above. Although conditions with high growth temperature, large flow rate of mixed gas of methane and carrier gases, and high plasma power may be helpful for the fast growth of VG, brunching of VG is simultaneously enhanced, which in turn decreases the vertical growth nature of VG. High-quality VG can be achieved by optimizing the growth parameters. It was revealed that the vertical growth nature of VG is governed by the electric field at the interfacial layer between VG and the substrate, for which its strength is influenced by the density of plasma. These findings are important for the general understanding of the VG growth and provided a feasible way for the controllable fabrication of VG using the remote PECVD method which is usually believed to be unsuitable for the fabrication of VG.

Constructing a Surface Multi-cationic Heterojunction for CsPbI1.5Br1.5 Perovskite Solar Cells with Efficiency beyond 14.

All-inorganic CsPbI1.5Br1.5 perovskite solar cells are considered as top cell candidates for tandem cells as a result of their excellent thermal stability and photoelectric performance. However, their power conversion efficiencies (PCEs) are still low and far below the theoretical limit mainly as a result of the severe non-radiative recombination and optical loss. Herein, we introduce an versatile method to construct a surface multi-cationic heterojunction to achieve an efficient and stable CsPbI1.5Br1.5 perovskite solar cell. By precisely controlling the content of FA+ and MA+ on PbBr2-rich perovskite films, a high-quality heterojunction layer is formed to help effectively passivate the surface defects and reduce the optical loss of the CsPbI1.5Br1.5 perovskite. In addition, the incorporation of a heterojunction layer can also improve energy-level alignment and reduce interfacial charge recombination loss. As a result, the champion device with the incorporation of SMH exhibits a PCE of 14.11%, which presents the highest reported efficiency for inorganic CsPbI1.5Br1.5 solar cells thus far while retaining 85% of the initial efficiency after 1000 h of storage without encapsulation.

A unique insight for energy metabolism disorders in depression based on chronic unpredictable mild stress rats using stable isotope-resolved metabolomics.

Depression is currently the main disease which endangers human health and emotion. However, the existing antidepressants have the disadvantages of slowly taking effect and high recurrence rate. Numerous studies have shown that depression causes disorders in energy metabolism, but the specific metabolic pathways remain unclear. The stable isotope-resolved metabolomics (SIRM) can clarify the specific metabolic pathways of energy metabolism disorders in depression and provide a strong basis for the pathogenesis of depression and new targets for the development of new antidepressants. We applied this method to the chronic unpredictable mild stress (CUMS) model, and the metabolites related to energy metabolism were comprehensively analyzed on HILIC and T3 columns through LC-MS. Conventional untargeted metabolomics found 50 differential metabolites, and most of them were focused on the energy metabolism pathway. SIRM exhibited that 78 metabolites related to energy metabolism were labeled, and 28 of them were observed significantly changed in the model group compared with control. Our results revealed depression inhibited TCA cycle and activated gluconeogenesis pathway, and abnormally increased pyruvic acid may participate in pyrimidine biosynthesis, phospholipid synthesis, and amino acid metabolism pathways. Pyruvate carboxylase (PC), pyruvate dehydrogenase (PDH), aspartate aminotransferase (AST) and phosphoenolpyruvate carboxykinase (PEPC) may be the new targets for depression. We established a SIRM method at animal level. To the best of our knowledge, it is the first time to apply the method for the study of depression. The method is of great significance and value for further explaining the pathogenesis of depression and the development of antidepressants.

An Ensemble Feature Selection Method for Biomarker Discovery.

Feature selection in Liquid Chromatography-Mass Spectrometry (LC-MS)-based metabolomics data (biomarker discovery) have become an important topic for machine learning researchers. High dimensionality and small sample size of LC-MS data make feature selection a challenging task. The goal of biomarker discovery is to select the few most discriminative features among a large number of irreverent ones. To improve the reliability of the discovered biomarkers, we use an ensemble-based approach. Ensemble learning can improve the accuracy of feature selection by combining multiple algorithms that have complementary information. In this paper, we propose an ensemble approach to combine the results of filter-based feature selection methods. To evaluate the proposed approach, we compared it to two commonly used methods, t-test and PLS-DA, using a real data set.

Analysis of stable isotope assisted metabolomics data acquired by GC-MS.

Stable isotope assisted metabolomics (SIAM) measures the abundance levels of metabolites in a particular pathway using stable isotope tracers (e.g., 13C, 18O and/or 15N). We report a method termed signature ion approach for analysis of SIAM data acquired on a GC-MS system equipped with an electron ionization (EI) ion source. The signature ion is a fragment ion in EI mass spectrum of a derivatized metabolite that contains all atoms of the underivatized metabolite, except the hydrogen atoms lost during derivatization. In this approach, GC-MS data of metabolite standards were used to recognize the signature ion from the EI mass spectra acquired from stable isotope labeled samples, and a linear regression model was used to deconvolute the intensity of overlapping isotopologues. A mixture score function was also employed for cross-sample chromatographic peak list alignment to recognize the chromatographic peaks generated by the same metabolite in different samples, by simultaneously evaluating the similarity of retention time and EI mass spectrum of two chromatographic peaks. Analysis of a mixture of 16 13C-labeled and 16 unlabeled amino acids showed that the signature ion approach accurately identified and quantified all isotopologues. Analysis of polar metabolite extracts from cells respectively fed with uniform 13C-glucose and 13C-glutamine further demonstrated that this method can also be used to analyze the complex data acquired from biological samples.

EIder: A compound identification tool for gas chromatography mass spectrometry data.

We report software entitled EIder (EI mass spectrum identifier) that provides users with eight literature reported spectrum matching algorithms for compound identification from gas chromatography mass spectrometry (GC-MS) data. EIder calculates retention index according to experimental conditions categorized by column class, column type and data type, where 9 empirical distribution functions of the absolute retention index deviation to its mean value were constructed using the National Institute of Standards and Technology (NIST) 2011 retention index database to improve the accuracy of compound identification. EIder filters compound candidates based on elementary composition and derivatization reagent, and automatically adds the molecular information of the native compound to each derivatized compound using a manually created database. When multiple samples are analyzed together, EIder performs cross-sample alignment and provides an option of using an average mass spectrum for compound identification. Furthermore, a suite of graphical user interfaces are implemented in EIder to allow users to both manually and automatically modify the identification results using experimental information at various analysis stages. Analysis of three types of GC-MS datasets indicates that the developed EIder software can improve the accuracy of compound identification.

(1)H NMR-based metabolic profiling of liver in chronic unpredictable mild stress rats with genipin treatment.

Genipin, a hydrolyzed metabolite of geniposide extracted from the fruit of Gardenia jasminoides Ellis, has shown promise in alleviating depressive symptoms, however, the antidepressant mechanism of genipin remains unclear and incomprehensive. In this study, the metabolic profiles of aqueous and lipophilic extracts in liver of the chronic unpredictable mild stress (CUMS)-induced rat with genipin treatment were investigated using proton nuclear magnetic resonance ((1)H NMR) spectroscopy coupled with multivariate data analysis. Significant differences in the metabolic profiles of rats in the CUMS model group (MS) and the control group (NS) were observed with metabolic effects including decreasing in choline, glycerol and glycogen, increasing in lactate, alanine and succinate, and a disordered lipid metabolism, while the moderate dose (50mg/kg) of genipin could significantly regulate the concentrations of glycerol, lactate, alanine, succinate and the lipid to their normal levels. These biomakers were involved in metabolism pathways such as glycolysis/gluconeogensis, tricarboxylic acid (TCA) cycle and lipid metabolism, which may be helpful for understanding of antidepressant mechanism of genipin.

Binding preference of carbon nanotube over proline-rich motif ligand on SH3-domain: a comparison with different force fields.

With the widespread applications of nanomaterials such as carbon nanotubes, there is a growing concern on the biosafety of these engineered nanoparticles, in particular their interactions with proteins. In molecular simulations of nanoparticle-protein interactions, the choice of empirical parameters (force fields) plays a decisive role, and thus is of great importance and should be examined carefully before wider applications. Here we compare three commonly used force fields, CHARMM, OPLSAA, and AMBER in study of the competitive binding of a single wall carbon nanotube (SWCNT) with a native proline-rich motif (PRM) ligand on its target protein SH3 domain, a ubiquitous protein-protein interaction mediator involved in signaling and regulatory pathways. We find that the SWCNT displays a general preference over the PRM in binding with SH3 domain in all the three force fields examined, although the degree of preference can be somewhat different, with the AMBER force field showing the highest preference. The SWCNT prevents the ligand from reaching its native binding pocket by (i) occupying the binding pocket directly, and (ii) binding with the ligand itself and then being trapped together onto some off-sites. The π-π stacking interactions between the SWCNT and aromatic residues are found to play a significant role in its binding to the SH3 domain in all the three force fields. Further analyses show that even the SWCNT-ligand binding can also be relatively more stable than the native ligand-protein binding, indicating a serious potential disruption to the protein SH3 function.

A ¹H-NMR plasma metabonomic study of acute and chronic stress models of depression in rats.

To investigate and compare the metabonomic profiles of three stress-based models of depression, the effects of acute and chronic stress on the production of systemic endogenous metabolites were investigated. Such metabonomic analysis may provide researchers a new way of selecting appropriate animal models for the study of depression and antidepressants. Rats were subjected to one of three stress-based models: CUMS, FST-1d, or FST-14d. Endogenous metabolites excreted in plasma were analyzed using NMR in conjunction with multivariate and statistical techniques. The metabonomic study indicated that the concentration of different plasma metabolites could be used to differentiate among depression models: TMA, aspartic acid, glutamate, AcAc, NAc, alanine, lactate, Leu/Ile, lipids increased and proline, ß-HB, valine decreased in the CUMS model; TMA decreased in the FST-1d model; α-glucose, ß-glucose, ß-HB, valine and lipids increased in the FST-14d model. The results suggested that metabonomics is a potentially appropriate method for evaluating depression models. According to the metabonomics study, CUMS model was more suitable and sensitive than the acute FST-1d model and predictable FST-14d model. The CUMS model was more appropriate for investigating both the efficacy of antidepressants and their mechanisms of action, while the FST-14d model should only be used for evaluating the efficacy of treatment.

1H-NMR-based metabonomic studies on the anti-depressant effect of genipin in the chronic unpredictable mild stress rat model.

The purpose of this work was to investigate the anti-depressant effect of genipin and its mechanisms using (1)H-NMR spectroscopy and multivariate data analysis on a chronic unpredictable mild stress (CUMS) rat model. Rat serum and urine were analyzed by nuclear magnetic resonance (NMR)-based metabonomics after oral administration of either genipin or saline for 2 weeks. Significant differences in the metabolic profile of the CUMS-treated group and the control group were observed, which were consistent with the results of behavioral tests. Metabolic effects of CUMS included decreases in serum trimetlylamine oxide (TMAO) and ß-hydroxybutyric acid (ß-HB), and increases in lipid, lactate, alanine and N-acetyl-glycoproteins. In urine, decreases in creatinine and betaine were observed, while citrate, trimethylamine (TMA) and dimethylamine were increased. These changes suggest that depression may be associated with gut microbes, energy metabolism and glycometabolism. Genipin showed the best anti-depressive effects at a dose of 100 mg/kg in rats. These results indicate that metabonomic approaches could be powerful tools for the investigation of the biochemical changes in pathological conditions or drug treatment.

Coherent microscopic picture for urea-induced denaturation of proteins.

In a previous study, we explored the mechanism of urea-induced denaturation of proteins by performing molecular dynamics (MD) simulations of hen lysozyme in 8 M urea and supported the "direct interaction mechanism" whereby urea denatures protein via dispersion interaction (Hua, L.; Zhou, R. H.; Thirumalai, D.; Berne, B. J. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 16928). Here we perform large scale MD simulations of five representative protein/peptide systems in aqueous urea to investigate if the above mechanism is common to other proteins. In all cases, accumulations of urea around proteins/peptide are observed, suggesting that urea denatures proteins by directly attacking protein backbones and side chains rather than indirectly disrupting water structure as a "water breaker". Consistent with our previous case study of lysozyme, the current energetic analyses with five protein/peptide systems reveal that urea's preferential binding to proteins mainly comes from urea's stronger dispersion interactions with proteins than with bulk solution, whereas the electrostatic (hydrogen-bonded) interactions only play a relatively minor (even negative) role during this denaturation process. Furthermore, the simulations of the peptide system at different urea concentrations (8 and 4.5 M), and with different force fields (CHARMM and OPLSAA) suggest that the above mechanism is robust, independent of the urea concentration and force field used. Last, we emphasize the importance of periodic boundary conditions in pairwise energetic analyses. This article provides a comprehensive study on the physical mechanism of urea-induced protein denaturation and suggests that the "dispersion-interaction-driven" mechanism should be general.

Dewetting transitions in the self-assembly of two amyloidogenic ß-sheets and the importance of matching surfaces.

We use molecular dynamics simulations to investigate the water-mediated self-assembly of two amyloidogenic ß-sheets of hIAPP(22-27) peptides (NFGAIL). The initial configurations of ß-sheet pairs are packed with two different modes, forming a tube-like nanoscale channel and a slab-like 2-D confinement, respectively. For both packing modes, we observe strong water drying transitions occurring in the intersheet region with high occurrence possibilities, suggesting that the "dewetting transition"-induced collapse may play an important role in promoting the amyloid fibrils formation. However, contrary to general dewetting theory prediction, the slab-like confinement (2-D) shows stronger dewetting phenomenon than the tube-like channel (1-D). This unexpected observation is attributed to the different surface roughness caused by different packing modes. Furthermore, we demonstrated the profound influence of internal surface topology of ß-sheet pairs on the dewetting phenomenon through an in silico mutagenesis study. The present study highlights the important role of packing modes (i.e., surface roughness) in the assembly process of ß-sheets, which improves our understanding toward the molecular mechanism of the amyloid fibrils formation. In addition, our study also suggests a potential route to regulate controllably the self-assembly process of ß-sheets through mutations, which may have future applications in nanotechnology and biotechnology.