Covalent organic frameworks (COF) possess a robust and porous crystalline structure, making them an appealing candidate for energy storage. Herein, we report an exfoliated polyimide COF composite ...(P‐COF@SWCNT) prepared by an in situ condensation of anhydride and amine on the single‐walled carbon nanotubes as advanced anode for potassium‐ion batteries (PIBs). Numerous active sites exposed on the exfoliated frameworks and the various open pathways promote the highly efficient ion diffusion in the P‐COF@SWCNT while preventing irreversible dissolution in the electrolyte. During the charging/discharging process, K+ is engaged in the carbonyls of imide group and naphthalene rings through the enolization and π‐K+ effect, which is demonstrated by the DFT calculation and XPS, ex‐situ FTIR, Raman. As a result, the prepared P‐COF@SWCNT anode enables an incredibly high reversible specific capacity of 438 mA h g−1 at 0.05 A g−1 and extended stability. The structural advantage of P‐COF@SWCNT enables more insights into the design and versatility of COF as an electrode.
We prepare a polyimide covalent organic framework composite anode by effective in‐situ condensation of anhydride and amine on the surface of single‐walled carbon nanotubes. The construction of the conductive network accelerates the transport of electron. Dual electroactive sites in the framework, carbonyls and aromatic naphthalene rings, could store more potassium ions by the enolization and π‐K+ effect.
Impossible voltage plateau regulation for the cathode materials with fixed active elemental center is a pressing issue hindering the development of Na‐superionic‐conductor (NASICON)‐type ...Na3V2(PO4)2F3 (NVPF) cathodes in sodium‐ion batteries (SIBs). Herein, a high‐entropy substitution strategy, to alter the detailed crystal structure of NVPF without changing the central active V atom, is pioneeringly utilized, achieving simultaneous electronic conductivity enhancement and diffusion barrier reduction for Na+, according to theoretical calculations. The as‐prepared carbon‐free high‐entropy Na3V1.9(Ca,Mg,Al,Cr,Mn)0.1(PO4)2F3 (HE‐NVPF) cathode can deliver higher mean voltage of 3.81 V and more advantageous energy density up to 445.5 Wh kg−1, which is attributed by the diverse transition‐metal elemental substitution in high‐entropy crystalline. More importantly, high‐entropy introduction can help realize disordered rearrangement of Na+ at Na(2) active sites, thereby to refrain from unfavorable discharging behaviors at low‐voltage region, further lifting up the mean working voltage to realize a full Na‐ion storage at the high voltage plateau. Coupling with a hard carbon (HC) anode, HE‐NVPF//HC SIB full cells can deliver high specific energy density of 326.8 Wh kg−1 at 5 C with the power density of 2178.9 W kg−1. This route means the unlikely potential regulation in NASICON‐type crystal with unchangeable active center becomes possible, inspiring new ideas on elevating the mean working voltage for SIB cathodes.
A high‐entropy effect is delicately introduced into fluorophosphate cathode for sodium‐ion batteries by in situ partial substitution of active V central atom, preparing a high‐entropy carbon‐free Na3V1.9(Ca,Mg,Al,Cr,Mn)0.1(PO4)2F3 cathode, suppressing the occurrence of detrimental phase transition process in the low‐voltage region, and further lifting up the mean working voltage of pristine Na3V2(PO4)2F3, enhancing sodium storage behavior, rate capability, and cycle performance.
In this paper, an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict-feedback systems without the requirement of states measurement. The ...stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties (unknown nonlinear functions) and, in the presence of unmodeled dynamics, dynamics disturbances. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique with the stochastic small-gain approach, a new adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that the closed-loop system is input-state-practically stability (ISpS) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance. In addition, the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties.
This paper considers a hybrid-format online retailing supply chain in which a manufacturer sells products to an online retailer and an intermediary with a wholesale contract, the retailer sells them ...through the intermediary by paying a commission fee (i.e. agency selling format), and the intermediary resells products as an e-tailer (i.e. reselling format). We use a theoretical model to answer a key question: whether the intermediary has an incentive to share demand information with others, and if it shares, which strategy is most beneficial to each member? Four information-sharing models are established and the results show that the intermediary always has incentive to share information voluntarily, and the best strategy strongly depends on the channel competition intensity and proportional fee. In addition, the manufacturer (retailer) can obtain profit if the intermediary only shares information with him (her), and all members can achieve a Pareto improvement (i.e. win-win-win situation) when both the manufacturer and retailer are informed. We further examine the impact of platform cost to demonstrate the robustness of results. When manufacturer cooperates with the retailer, the intermediary always intends to share information, whereas it has no incentive to do so if the intermediary and retailer make a coalition.
The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. ...Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.
This paper investigates the tracking and optimization problems for a class of industrial processes by utilizing output feedback fault-tolerant control (FTC) and predictive compensation strategy. At ...device layer, the tracking problem for device layer subsystems which subject to random failures and random network-induced delays is investigated. These two different random processes are modeled as Markovian chains. Device layer controllers are designed to guarantee the tracking performance at H ∞ disturbance attenuation level. At operation layer, a nonlinear model predictive control (NMPC) strategy is proposed to stabilize the upper operation layer system. Then by considering the effect of radial basis function (RBF) performance index and random packet dropout phenomena, a predictive compensator is designed to guarantee the input-to-state practical stability (ISpS) of the resulting system. In addition, networked flotation processes are considered in the simulation part, and the simulation results further demonstrate the effectiveness of the proposed method.
The development of highly active, universal, and stable inexpensive electrocatalysts/cocatalysts for hydrogen evolution reaction (HER) by morphology and structure modulations remains a great ...challenge. Herein, a simple self-template strategy was developed to synthesize hollow Co-based bimetallic sulfide (M x Co3–x S4, M = Zn, Ni, and Cu) polyhedra with superior HER activity and stability. Homogenous bimetallic metal–organic frameworks are transformed to hollow bimetallic sulfides by solvothermal sulfidation and thermal annealing. Electrochemical measurements and density functional theory computations show that the combination of hollow structure and homoincorporation of a second metal significantly enhances the HER activity of Co3S4. Specifically, the homogeneous doping in Co3S4 lattice optimizes the Gibbs free energy for H* adsorption and improves the electrical conductivity. Impressively, hollow Zn0.30Co2.70S4 exhibits electrocatalytic HER activity better than most of the reported nobel-metal-free electrocatalysts over a wide pH range, with overpotentials of 80, 90, and 85 mV at 10 mA cm–2 and 129, 144, and 136 mV at 100 mA cm–2 in 0.5 M H2SO4, 0.1 M phosphate buffer, and 1 M KOH, respectively. It also exhibits photocatalytic HER activity comparable to that of Pt cocatalyst when working with organic photosensitizer (Eosin Y) or semiconductors (TiO2 and C3N4). Furthermore, this catalyst shows excellent stability in the electrochemical and photocatalytic reactions. The strategy developed here, i.e., homogeneous doping and self-templated hollow structure, provides a way to synthesize transition metal sulfides for catalysis and energy conversion.
Abstract
Purpose
To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs).
...Materials and methods
128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model.
Results
For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846.
Conclusion
Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.
The aim of this study was to investigate the effect of natural organic matter (NOM) including humic acid (HA) and fulvic acid (FA), intracellular organic matter (IOM) extracted from Microcystis ...aeruginosa (MA) and Chlorella sp. (CH), and their different molecular weight (MW) fractions on the aerobic denitrification performance of bacterial strain WGX-9 by monitoring nitrogen removal efficiency and testing changes in organic matter with HA, FA, MA-IOM and CH-IOM as the sole carbon source. Strain WGX-9 was identified as Acinetobacter johnsonii and exhibited excellent aerobic denitrification capability. The nitrate removal efficiency with IOM as the sole carbon source was relatively higher than that with NOM as the sole carbon source. The prepared NOM and extracted IOM samples were separated into six fractions with MW cut-offs of 100, 30, 10, 5 and 1 kDa. The fraction of MW > 100 kDa contributed the largest amount to the MW distribution, accounting for 77.11%, 29.00%, 44.97% and 24.81% of HA, FA, MA-IOM, and CH-IOM, respectively. Nitrate removal efficiency was improved with decreasing MW of organic matter. For example, nitrate removal efficiency was 26.50%, 32.41%, 27.88% and 43.89% using HA, FA, MA-IOM, and CH-IOM fractions of MW > 100 kDa as the carbon source, whereas with MW < 1 kDa, it increased to 36.67%, 37.88%, 60.90%, and 68.90%, respectively. This is probably because the smaller MW fraction is more suitable for bacterial growth. These results demonstrate that the strain WGX-9 can utilize lower MW organic matter, which lays the foundations for nitrogen removal in actual drinking water reservoirs.
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•Acinetobacter johnsonii WGX-9 has excellent aerobic denitrification performance.•IOM as carbon source show higher nitrate removal relative to NOM, but lower than sodium acetate.•The nitrate removal efficiency was improved with the MW of organic matter decrease.•The tyrosine/tryptophan-like organic matter are preferentially used by microorganisms.
Spheroid-based three-dimensional (3D) liver culture models, offering a desirable biomimetic microenvironment, are useful for recapitulating liver functions in vitro. However, a user-friendly, robust ...and specially optimized method has not been well developed for a convenient, highly efficient, and safe in situ perfusion culture of spheroid-based 3D liver models. Here, we have developed a biomimetic and reversibly assembled liver-on-a-chip (3D-LOC) platform and presented a proof of concept for long-term perfusion culture of 3D human HepG2/C3A spheroids for building a 3D liver spheroid model. On the basis of a fast and reversible seal of concave microwell-based PDMS-membrane-PDMS sandwich multilayer chips, it enables a high-throughput and parallel perfusion culture of 1080 cell spheroids in a high mass transfer and low fluid shear stress biomimetic microenvironment as well as allowing the convenient collection and analysis of the cell spheroids. In terms of reducing spheroid loss and maintaining cell morphology and viability in long-term perfusion culture, the cell spheroids in the 3D-LOC were more safe and efficient. Notably, the polarisation, liver-specific functions, and metabolic activity of the cell spheroids in 3D-LOC were also remarkably improved and exhibited better long-term maintenance over conventional perfusion methods. Additionally, a robust micromilling method that incorporates secondary PDMS coating techniques (SPCs) for fabricating V-shaped concave microwells was also developed. The V-shaped concave microwell arrays exhibited a higher distribution density and aperture ratio, making it easy to form large-scale and uniform-sized cell spheroids with minimum cell loss. In summary, the proposed 3D-LOC could provide a convenient and robust solution for the long-term safe perfusion culture of hepatic spheroids and be beneficial for a variety of potential applications including development of bio-artificial livers, disease modeling, and drug toxicity screening.
