Significantly Enhanced Oxidation Resistance and Electrochemical Performance of Hydrothermal Ti3C2Tx MXene and Tannic Acid Composite for High-Performance Flexible Supercapacitors.

The electrochemical performances of Ti3C2Tx MXene are severely restricted by the easy oxidation and restacking. Herein, tannic acid (TA) is introduced into Ti3C2Tx dispersion, and the mixed dispersion is further subjected to a simple hydrothermal treatment to prepare the hydrothermal Ti3C2Tx and TA composite (h-Ti3C2Tx@h-TA). Due to the decomposition of TA into gallic acid (GA), hydrothermal TA (h-TA) is a mixture of TA and GA. The strong interaction between h-TA and MXene mainly involves chemical interaction between the hydroxyl groups in h-TA and the surface/edge Ti atoms, along with numerous hydrogen bonds. The h-TA intercalation weakens MXene restacking and increases interlayer spacing, thereby improving ion transport pathways and accessibility. The chemical interaction between the hydroxyl groups of GA and the Ti atoms significantly enhances oxidation resistance and pseudocapacitive active sites. Therefore, the h-Ti3C2Tx@h-TA film electrode shows significantly enhanced capacitance (848 F·g-1 at 1 A g-1) and cycling stability (100% retention after 20 000 cycles). Moreover, flexible sandwiched supercapacitors with symmetrical h-Ti3C2Tx@h-TA electrodes exhibit a high energy density of 30.1 Wh kg-1 at a high power density of 300 W kg-1, outperforming those of Ti3C2Tx-based film electrodes and sandwiched supercapacitors reported so far. The extrusion-printed microsupercapacitors with h-Ti3C2Tx@h-TA electrodes demonstrate high areal capacitance (135 mF cm-2 at 5 mV s-1) along with energy storage performance (6.74 µWh cm-2 at 506 µW cm-2) and cycling stability (98.8% retention after 41 460 cycles), all while maintaining excellent flexibility. These impressive results indicate the great application potential of the hydrothermal Ti3C2Tx MXene and tannic acid composite in flexible energy storage devices.

Electronic, Optical, Mechanical, and Electronic Transport Properties of SrCu2O2: A First-Principles Study.

The structural, electronic, optical, mechanical, lattice dynamics, and electronic transport properties of SrCu2O2 crystals were studied using first-principles calculations. The calculated band gap of SrCu2O2 using the HSE hybrid functional is about 3.33 eV, which is well consistent with the experimental value. The calculated optical parameters show a relatively strong response to the visible light region for SrCu2O2. The calculated elastic constants and phonon dispersion indicate that SrCu2O2 has strong stability in mechanical and lattice dynamics. The deep analysis of calculated mobilities of electrons and holes with their effective masses proves the high separation and low recombination efficiency of photoinduced carriers in SrCu2O2.

Computational and Experimental Investigation of the Selective Adsorption of Indium/Iron Ions by the Epigallocatechin Gallate Monomer.

Selective recovery of indium has been widely studied to improve the resource efficiency of critical metals. However, the interaction and selective adsorption mechanism of indium/iron ions with tannin-based adsorbents is still unclear and hinders further optimization of their selective adsorption performance. In this study, the epigallocatechin gallate (EGCG) monomer, which is the key functional unit of persimmon tannin, was chosen to explore the ability and mechanism of selective separation/extraction of indium from indium-iron mixture solutions. The density functional theory calculation results indicated that the deprotonated EGCG was easier to combine with indium/iron cations than those of un-deprotonated EGCG. Moreover, the interaction of the EGCG-Fe(III) complex was dominated by chelation and electrostatic interaction, while that of the EGCG-In(III) complex was controlled by electrostatic interactions and aromatic ring stacking effects. Furthermore, the calculation of binding energy verified that EGCG exhibited a stronger affinity for Fe(III) than that for In(III) and preferentially adsorbed iron ions in acidic or neutral solutions. Further experimental results were consistent with the theoretical study, which showed that the Freundlich equilibrium isotherm fit the In(III) and Fe(III) adsorption behavior very well, and the Fe(III) adsorption processes followed a pseudo-second-order model. Thermodynamics data revealed that the adsorption of In(III) and Fe(III) onto EGCG was feasible, spontaneous, and endothermic. The adsorption rate of the EGCG monomer for Fe(III) in neutral solution (1:1 mixed solution, pH = 3.0) was 45.7%, 4.3 times that of In(III) (10.7%). This study provides an in-depth understanding of the relationship between the structure of EGCG and the selective adsorption capacity at the molecular level and provides theoretical guidance for further optimization of the selective adsorption performance of structurally similar tannin-based adsorbents.

Surfactant-Modified CdS/CdCO3 Composite Photocatalyst Morphology Enhances Visible-Light-Driven Cr(VI) Reduction Performance.

The surfactant modification of catalyst morphology is considered as an effective method to improve photocatalytic performance. In this work, the visible-light-driven composite photocatalyst was obtained by growing CdS nanoparticles in the cubic crystal structure of CdCO3, which, after surfactant modification, led to the formation of CdCO3 elliptical spheres. This reasonable composite-structure-modification design effectively increased the specific surface area, fully exposing the catalytic-activity check point. Cd2+ from CdCO3 can enter the CdS crystal structure to generate lattice distortion and form hole traps, which productively promoted the separation and transfer of CdS photogenerated electron-hole pairs. The prepared 5-CdS/CdCO3@SDS exhibited excellent Cr(VI) photocatalytic activity with a reduction efficiency of 86.9% within 30 min, and the reduction rate was 0.0675 min-1, which was 15.57 and 14.46 times that of CdS and CdCO3, respectively. Finally, the main active substances during the reduction process, the photogenerated charge transfer pathways related to heterojunctions and the catalytic mechanism were proposed and analyzed.

One-Step Hydrothermal Synthesis of Nanostructured MgBi2O6/TiO2 Composites for Enhanced Hydrogen Production.

A highly efficient MgBi2O6 (MBO)/TiO2 heterostructured photocatalyst for the evolution of H2 was successfully prepared using a one-step hydrothermal method. The phase structure, microstructure and optical properties of the MBO/TiO2 composites were investigated by various experimental techniques. A series of H2 production experiments were performed under visible light. The measured results indicated that the nanostructured MBO/TiO2 composite, with a stoichiometric molar ratio of MBO:TiO2 = 0.2%, displayed the best H2 production rate of 3413 µmol h-1 g-1. The excellent photocatalytic performance of the obtained composite material was due to the heterojunction formed between MBO and TiO2, which reduced the charge transfer resistance and accelerated the separation efficiency of the photogenerated electron-hole pairs. The reaction mechanism was also discussed in detail.

Computational Analysis on Antioxidant Activity of Four Characteristic Structural Units from Persimmon Tannin.

Antioxidants are molecules that can prevent the harmful effects of oxygen, help capture and neutralize free radicals, and thus eliminate the damage of free radicals to the human body. Persimmon tannin (PT) has excellent antioxidant activity, which is closely related to its molecular structure. We report here a comparative study of four characteristic structural units from PT (epicatechin gallate (ECG), epigallocatechin gallate (EGCG), A-type linked ECG dimer (A-ECG dimer), A-type linked EGCG dimer (A-EGCG dimer)) to explore the structure-activity relationship by using the density functional theory. Based on the antioxidation mechanism of hydrogen atom transfer, the most favorable active site for each molecule exerts antioxidant activity is determined. The structural parameters, molecular electrostatic potential, and frontier molecular orbital indicate that the key active sites are located on the phenolic hydroxyl group of the B ring for ECG and EGCG monomers, and the key active sites of the two dimers are located on the phenolic hydroxyl groups of the A and D' rings. The natural bond orbital and bond dissociation energy of the phenolic hydroxyl hydrogen atom show that the C11-OH in the ECG monomer and the C12-OH in the EGCG monomer are the most preferential sites, respectively. The most active site of the two A-linked dimers is likely located on the D' ring C20' phenolic hydroxyl group. Based on computational analysis of quantum chemical parameters, the A-ECG dimer is a more potent antioxidant than the A-EGCG dimer, ECG, and EGCG. This computational analysis provides the structure-activity relationship of the four characteristic units which will contribute to the development of the application of PT antioxidants in the future.

Effective Deep Attributed Network Representation Learning With Topology Adapted Smoothing.

Attributed networks are ubiquitous in the real world, such as social networks. Therefore, many researchers take the node attributes into consideration in the network representation learning to improve the downstream task performance. In this article, we mainly focus on an untouched "oversmoothing" problem in the research of the attributed network representation learning. Although the Laplacian smoothing has been applied by the state-of-the-art works to learn a more robust node representation, these works cannot adapt to the topological characteristics of different networks, thereby causing the new oversmoothing problem and reducing the performance on some networks. In contrast, we adopt a smoothing parameter that is evaluated from the topological characteristics of a specified network, such as small worldness or node convergency and, thus, can smooth the nodes' attribute and structure information adaptively and derive both robust and distinguishable node features for different networks. Moreover, we develop an integrated autoencoder to learn the node representation by reconstructing the combination of the smoothed structure and attribute information. By observation of extensive experiments, our approach can preserve the intrinsical information of networks more effectively than the state-of-the-art works on a number of benchmark datasets with very different topological characteristics.

Multiview Spectral Clustering via Robust Subspace Segmentation.

Multiview clustering refers to partition data according to its multiple views, where information from different perspectives can be jointly used in some certain complementary manner to produce more sensible clusters. It is believed that most of the existing multiview clustering methods technically suffer from possibly corrupted data, resulting in a dramatically decreased clustering performance. To overcome this challenge, we propose a multiview spectral clustering method based on robust subspace segmentation in this article. Our proposed algorithm is composed of three modules, that is: 1) the construction of multiple feature matrices from all views; 2) the formulation of a shared low-rank latent matrix by a low rank and sparse decomposition; and 3) the use of the Markov-chain-based spectral clustering method for producing the final clusters. To solve the optimization problem for a low rank and sparse decomposition, we develop an optimization procedure based on the scheme of the augmented Lagrangian method of multipliers. The experimental results on several benchmark datasets indicate that the proposed method outperforms favorably compared to several state-of-the-art multiview clustering techniques.

Hydrogen Transportation Behaviour of V-Ni Solid Solution: A First-Principles Investigation.

Hydrogen embrittlement causes deterioration of materials used in metal-hydrogen systems. Alloying is a good option for overcoming this issue. In the present work, first-principles calculations were performed to systematically study the effects of adding Ni on the stability, dissolution, trapping, and diffusion behaviour of interstitial/vacancy H atoms of pure V. The results of lattice dynamics and solution energy analyses showed that the V-Ni solid solutions are dynamically and thermodynamically stable, and adding Ni to pure V can reduce the structural stability of various VHx phases and enhance their resistance to H embrittlement. H atoms preferentially occupy the characteristic tetrahedral interstitial site (TIS) and the octahedral interstitial site (OIS), which are composed by different metal atoms, and rapidly diffuse along both the energetically favourable TIS → TIS and OIS → OIS paths. The trapping energy of monovacancy H atoms revealed that Ni addition could help minimise the H trapping ability of the vacancies and suppress the retention of H in V. Monovacancy defects block the diffusion of H atoms more than the interstitials, as determined from the calculated H-diffusion barrier energy data, whereas Ni doping contributes negligibly toward improving the H-diffusion coefficient.

Stochastic configuration network ensembles with selective base models.

Studies have demonstrated that stochastic configuration networks (SCNs) have good potential for rapid data modeling because of their sufficient adequate learning power, which is theoretically guaranteed. Empirical studies have verified that the learner models produced by SCNs can usually achieve favorable test performance in practice but more in-depth theoretical analysis of their generalization power would be useful for constructing SCN-based ensemble models with enhanced generalization capacities. In particular, given a collection of independently developed SCN-based learner models, it is useful to select certain base learners that can potentially obtain preferable test results rather than considering all of the base models together, before simply taking their average in order to build an effective ensemble model. In this study, we propose a novel framework for building SCN ensembles by exploring key factors that might potentially affect the generalization performance of the base model. Under a mild assumption, we provide a comprehensive theoretical framework for examining a learner model's generalization error, as well as formulating a novel indicator that contains measurement information for the training errors, output weights, and a hidden layer output matrix, which can be used by our proposed algorithm to find a subset of appropriate base models from a pool of randomized learner models. A toy example of one-dimensional function approximation, a case study for developing a predictive model for forecasting student learning performance, and two large-scale data sets were used in our experiments. The experimental results indicate that our proposed method has some remarkable advantages for building ensemble models.

2-D Stochastic Configuration Networks for Image Data Analytics.

Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in data analytics due to its universal approximation capability and fast modeling property. The technical essence lies in stochastically configuring the hidden nodes (or basis functions) based on a supervisory mechanism rather than data-independent randomization as usually adopted for building randomized neural networks. Given image data modeling tasks, the use of 1-D SCNs potentially demolishes the spatial information of images, and may result in undesirable performance. This paper extends the original SCNs to a 2-D version, called 2DSCNs, for fast building randomized learners with matrix inputs. Some theoretical analysis on the goodness of 2DSCNs against SCNs, including the complexity of the random parameter space and the superiority of generalization, are presented. Empirical results over one regression example, four benchmark handwritten digit classification tasks, two human face recognition datasets, as well as one natural image database, demonstrate that the proposed 2DSCNs perform favorably and show good potential for image data analytics.

Characterisation of the temperature-dependent M1 to R phase transition in W-doped VO2 nanorod aggregates by Rietveld refinement and theoretical modelling.

Understanding the mechanism of the insulator-metal transition (IMT) in VO2 is a necessary step in optimising this material's properties for a range of functional applications. Here, Rietveld refinement of synchrotron X-ray powder diffraction patterns is performed on thermochromic V1-xWxO2 (0.0 ≤ x ≤ 0.02) nanorod aggregates over the temperature range 100 ≤ T ≤ 400 K to examine the effect of doping on the structure and properties of the insulating monoclinic (M1) phase and metallic rutile (R) phase. Precise measurement of the lattice constants of the M1 and R phases enabled the onset (Ton) and endset (Tend) temperatures of the IMT to be determined accurately for different dopant levels. First-principles calculations reveal that the observed decrease in both Ton and Tend with increasing W content is a result of Peierls type V-O-V dimers being replaced by linear W-O-V dimers with a narrowing of the band gap. The results are interpreted in terms of the bandwidth-controlled Mott-Hubbard IMT model, providing a more detailed understanding of the underlying physical mechanisms driving the IMT as well as a guide to optimising properties of VO2-based materials for specific applications.

Facile One-Step Hydrothermal Fabrication of (Sr0.6Bi0.305)2Bi2O7/SnO2 Heterojunction with Excellent Photocatalytic Activity.

The pyrochlore-type (Sr0.6Bi0.305)2Bi2O7 (SBO), containing Bi3+ and Bi5+ mixed valent states, was recently found to be used as a new visible light responsive photocatalyst. Novel SBO/SnO2 heterostructured composites were synthesized through a facile one-step hydrothermal method. The phase structure, morphology, chemical composition, and optical properties of the obtained samples were characterized by XRD, SEM, TEM, XPS, and UV-vis DRS. Compared to pure SBO and SnO2, the synthesized SBO/SnO2 composites exhibited significantly enhanced photocatalytic efficiency. The results indicated that the photoinduced holes and superoxide radicals play a dominant role and are the main reactive species during the degradation of Methylene Blue (MB) solution under visible light irradiation. Heterojunctions, formed in samples, directly contribute to the improvement of photocatalytic efficiency of SBO/SnO2 composites, since it not only broadens the light response range, but also accelerates the separation of photogenerated carriers.

Observation of Non-FCC Copper in Alkynyl-Protected Cu53 Nanoclusters.

The only feasible access to non-face-centered cubic (FCC) copper was by physical vapor deposition under high vacuum. Now, non-FCC copper is observed in a series of alkynyl-protected Cu53 nanoclusters (NCs) obtained from solution-phase synthesis. Determined by single-crystal X-ray crystallography, the structures of Cu53 (C≡CPhPh)9 (dppp)6 Cl3 (NO3 )9 and its two derivatives reveal an ABABC stacking sequence involving 41 Cu atoms. It can be regarded as a mixed FCC and HCP structure, which gives strong evidence that Cu can be arranged in non-FCC lattice at ambient conditions when proper ligands are provided. Characterization methods including X-ray absorption fine structure, XPS, ESI-MS, UV/Vis, Auger spectroscopy, and DFT calculations were carried out. CuII was shown to successively coordinate with introduced ligands and changed to CuI after bonding with phosphine. The following addition of NaBH4 and the aging step further reduced it to the Cu53 NC.

Investigation of adsorption, dissociation, and diffusion properties of hydrogen on the V (1 0 0) surface and in the bulk: A first-principles calculation.

To investigate the H2 purification mechanism of V membranes, we studied the adsorption, dissociation, and diffusion properties of H in V, an attractive candidate for H2 separation materials. Our results revealed that the most stable site on the V (1 0 0) surface is the hollow site (HS) for both adsorbed H atoms and molecules. As the coverage range increases, the adsorption energy of H2 molecules first decreases and then increases, while that of H atoms remains unchanged. The preferred diffusion path of atoms on the surface, surface to first subsurface, and first subsurface to second subsurface is HS → bridge site (BS) → HS, BS → BS, and BS → tetrahedral interstitial site (TIS) → BS, respectively. In the V bulk, H atoms occupy the energetically favourable TIS, and diffuse along the TIS → TIS path, which has a lower energy barrier. This study facilitates the understanding of the interaction between H and metals and the design of novel V-based alloy membranes.

Effects of Mo alloying on stability and diffusion of hydrogen in the Nb16H phase: a first-principles investigation.

First-principles calculations and the method of climbing-image nudged elastic band were used to investigate the effects of Mo alloying on the structural stability, mechanical properties, and hydrogen-diffusion behavior in the Nb16H phase. The Nb12Mo4H phase (26.5 at% Mo) was found to be the most thermodynamically stable structure, with a low ΔH f value (-0.26 eV) and high elastic modulus. Calculations revealed that the tetrahedral interstitial site (TIS) was the predominant location of H in both Nb16H and Nb12Mo4H phases. The calculated H-diffusion energy barrier and the diffusion coefficient of the Nb12Mo4H phase were 0.153 eV and 5.65 × 10-6 cm2 s-1 (300 K), respectively, which suggest that the addition of Mo would lead to a lower energy barrier and high diffusion coefficients for the Nb16H phase, thus improving the hydrogen-permeation properties of Nb metal.

Higher non-HDL-cholesterol to HDL-cholesterol ratio linked with increased nonalcoholic steatohepatitis.

BACKGROUND: Non-HDL-cholesterol to HDL-cholesterol (non-HDL-c/HDL-c) ratio is a feasible predictor for coronary heart disease, metabolic syndrome, and insulin resistance. Patients with nonalcoholic steatohepatitis (NASH) have an increased risk of developing cardiovascular problems and type 2 diabetes. However, the predictive role of non-HDL-c/HDL-c ratio in NASH hasn't been investigated yet. METHODS: We conducted a retrospective cohort study. A total of 3489 eligible subjects were selected in the present study. Prevalence and characteristics of NASH were demonstrated. Conditional logistic regression was used to analyze the association between non-HDL-c/HDL-c ratio and risks of NASH. Associations between non-HDL-c/HDL-c ratio and serum aminotransferase levels were also investigated. RESULTS: The overall prevalence of NASH was 6.13%, higher in male (6.89%) than that in female (5.04%). Interestingly, the prevalence of NASH showed a positive correlation with the elevation of non-HDL-c/HDL-c ratio (Pearson's Chi-squared test, linear trend 0.010, p <  0.05). The risk of NASH increased approximately 1.8-fold among subjects with higher non-HDL-c/HDL-c ratio. After adjustment for confounding factors, higher non-HDL-c/HDL-c ratio was still associated with a 54.4% increased risk of NASH. Male had higher risk of NASH than female when their non-HDL-c/HDL-c ratio increased. The risk of NASH in subjects with BMI more than 24 was 3 times higher than that in subjects with BMI less than 24. Every one unit increase in Non-HDL-c/HDL-c ratio was associated with 64.5% increase in ALT/AST level (p <  0.05) after adjustment for confounding factors. CONCLUSIONS: Our study provided strong evidence that subjects with higher non-HDL-c/HDL-c ratio had a higher risk of NASH, which suggested that non-HDL-c/HDL-c ratio might be a feasible predictor for NASH.

Nonlinear Decoupling Control With ANFIS-Based Unmodeled Dynamics Compensation for a Class of Complex Industrial Processes.

Complex industrial processes are multivariable and generally exhibit strong coupling among their control loops with heavy nonlinear nature. These make it very difficult to obtain an accurate model. As a result, the conventional and data-driven control methods are difficult to apply. Using a twin-tank level control system as an example, a novel multivariable decoupling control algorithm with adaptive neural-fuzzy inference system (ANFIS)-based unmodeled dynamics (UD) compensation is proposed in this paper for a class of complex industrial processes. At first, a nonlinear multivariable decoupling controller with UD compensation is introduced. Different from the existing methods, the decomposition estimation algorithm using ANFIS is employed to estimate the UD, and the desired estimating and decoupling control effects are achieved. Second, the proposed method does not require the complicated switching mechanism which has been commonly used in the literature. This significantly simplifies the obtained decoupling algorithm and its realization. Third, based on some new lemmas and theorems, the conditions on the stability and convergence of the closed-loop system are analyzed to show the uniform boundedness of all the variables. This is then followed by the summary on experimental tests on a heavily coupled nonlinear twin-tank system that demonstrates the effectiveness and the practicability of the proposed method.

Bayesian Random Vector Functional-Link Networks for Robust Data Modeling.

Random vector functional-link (RVFL) networks are randomized multilayer perceptrons with a single hidden layer and a linear output layer, which can be trained by solving a linear modeling problem. In particular, they are generally trained using a closed-form solution of the (regularized) least-squares approach. This paper introduces several alternative strategies for performing full Bayesian inference (BI) of RVFL networks. Distinct from standard or classical approaches, our proposed Bayesian training algorithms allow to derive an entire probability distribution over the optimal output weights of the network, instead of a single pointwise estimate according to some given criterion (e.g., least-squares). This provides several known advantages, including the possibility of introducing additional prior knowledge in the training process, the availability of an uncertainty measure during the test phase, and the capability of automatically inferring hyper-parameters from given data. In this paper, two BI algorithms for regression are first proposed that, under some practical assumptions, can be implemented by a simple iterative process with closed-form computations. Simulation results show that one of the proposed algorithms, Bayesian RVFL, is able to outperform standard training algorithms for RVFL networks with a proper regularization factor selected carefully via a line search procedure. A general strategy based on variational inference is also presented, with an application to data modeling problems with noisy outputs or outliers. As we discuss in this paper, using recent advances in automatic differentiation this strategy can be applied to a wide range of additional situations in an immediate fashion.

Stochastic Configuration Networks: Fundamentals and Algorithms.

This paper contributes to the development of randomized methods for neural networks. The proposed learner model is generated incrementally by stochastic configuration (SC) algorithms, termed SC networks (SCNs). In contrast to the existing randomized learning algorithms for single layer feed-forward networks, we randomly assign the input weights and biases of the hidden nodes in the light of a supervisory mechanism, and the output weights are analytically evaluated in either a constructive or selective manner. As fundamentals of SCN-based data modeling techniques, we establish some theoretical results on the universal approximation property. Three versions of SC algorithms are presented for data regression and classification problems in this paper. Simulation results concerning both data regression and classification indicate some remarkable merits of our proposed SCNs in terms of less human intervention on the network size setting, the scope adaptation of random parameters, fast learning, and sound generalization.