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The poor conductivity and deficient active sites of transition metal oxides lead to low energy density of supercapacitors, which limits their wide application. In this work, double transition metal oxide heterojunctions with oxygen vacancy (Vo-ZnO/CoO) nanowires are prepared by effective hydrothermal and thermal treatments. The formation of the heterojunction results in the redistribution of interface charge between ZnO and CoO, generating an internal electric field to accelerate the electron transport. Meanwhile, oxygen vacancies can enhance the redox reaction activity to further improve the electrochemical kinetics of the electrode material. Therefore, the prepared Vo-ZnO/CoO can provide a superior specific capacity of 845 C g−1 (1 A g−1). An asymmetric supercapacitor with the Vo-ZnO/CoO as positive electrode shows a higher energy density of 51.6 Wh kg−1 when the power density reaches 799.9 W kg−1. This work proposes a synergistic combination of defect and heterojunction engineering to improve the electrochemical properties of materials, providing an important guidance for material design in energy-storage field.

In our previous work, the detailed crystal structure, optical and electrical properties of evaporated CdS (hexagonal CdS) and chemical bath deposition CdS (cubic CdS) were discussed. The hexagonal CdS film is more suitable as the buffer layer for Sb2Se3 film solar cells compared with its cubic counterpart. However, in our experiments, the influence of high annealing temperature on hexagonal CdS is difficult to control. Moreover, hexagonal CdS is unsuitable for large-area industrial production of Sb2Se3 solar cells. In this study, an electron beam evaporation-prepared CdS buffer layer (hexagonal CdS) with high quality, uniformity and low-temperature treatment was introduced in Sb2Se3 thin film solar cells. The influence of different substrate temperatures during electron beam evaporation on the crystal structure, surface morphologies, and optical properties of CdS and Sb2Se3 films was investigated. The device performance improvement from 3.52% to 5.89% was obtained after the surface roughness of the CdS film and the heterojunction interface were optimised. Lastly, the efficiency of the Sb2Se3 solar cells was obtained to be 6.71% based on the structure of FTO/EB-CdS/Sb2Se3/MoO3/Au.

In this paper, a computationally efficient multifidelity local search algorithm for aerodynamic design optimization is presented. In this paper’s approach, direct optimization of a computationally ...

Background: Distant metastasis is the major cause of treatment failure in locally advanced rectal cancer (LARC). Adjuvant chemotherapy (AC) is usually used for distant control. However, only certain subgroups of patients could benefit from AC. Our aim was to develop a radiomics model for the prediction of survival and chemotherapeutic benefits using pretreatment multiparameter MR images and clinicopathological features in patients with LARC. Methods: 186 consecutive patients with LARC underwent feature extraction from the whole tumor on T2-weighted (T2w), contrast enhanced T1-weighted (cT1w), and ADC images. Feature selection was based on feature stability and the Boruta algorithm. Radiomics signatures for predicting DFS (disease-free survival) were then generated using the selected features. Combining clinical risk factors, a radiomics nomogram was constructed using Cox proportional hazards regression model. The predictive performance was evaluated by Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis. Results: Four features were selected to construct the radiomics signature, which was significantly associated with DFS (P < 0.001). The radiomics nomogram, incorporating radiomics signature and two clinicopathological variables (pN and tumor differentiation), exhibited better prediction performance for DFS than the clinicopathological model, with C-index of 0.780 (95%CI, 0.718-0.843) and 0.803 (95%CI, 0.717-0.889) in the training and validation cohorts, respectively. The radiomics nomogram-defined high-risk group had a shorter DFS, DMFS, and OS than those in the low-risk group (all P Conclusion: This study demonstrated that the newly developed pretreatment multiparameter MRI-based radiomics model could serve as a powerful predictor of prognosis, and may act as a potential indicator for guiding AC in patients with LARC.

As humidity is one of the most widely demanded environmental parameters, the precision of its detection is significant. An advanced humidity sensor will improve the validity of the humidity monitoring system. In this study, a facile chemical layer-by-layer self-assembly (CLS) method was developed for fabricating graphene oxide (GO)/polyethyleneimine (PEI) multilayer films. Owing to the chemical bonding between the PEI and GO, and the intrinsic stickiness of the PEI, layered films with different numbers of layers were successfully prepared using the CLS method and confirmed through ultraviolet–visible (UV–Vis) spectroscopy and the mass loading of quartz crystal microbalance (QCM). Morphological measurements revealed that the roughness and thickness of the films increased exponentially with the number of bilayers. The GO/PEI films were deposited on QCM electrodes using the CLS method to produce the humidity sensors. The humidity measurement results showed a high sensitivity (37.84 Hz/%RH) and rapid response/recovery (

Polypyrrole (PPy) is a good candidate material for piezoresistive pressure sensors owing to its excellent electrical conductivity and good biocompatibility. However, it remains challenging to fabricate PPy-based flexible piezoresistive pressure sensors with high sensitivity because of the intrinsic rigidity and brittleness of the film composed of dense PPy particles. Here, a rational structure, that is, 3D-conductive and elastic topological film composed of coaxial nanofiber networks, is reported to dramatically improve the sensitivity of flexible PPy-based sensors. The film is prepared through surface modification of electrospun polyvinylidene fluoride (PVDF) nanofibers by polydopamine (PDA), in order to homogeneously deposit PPy particles on the nanofiber networks with strong interfacial adhesion (PVDF/PDA/PPy, PPP). This unique structure has a high surface area and abundant contact sites, leading to superb sensitivity against a subtle pressure. The as-developed piezoresistive pressure sensor delivers a low limit of detection (0.9 Pa), high sensitivity (139.9 kPa

For structures with both random and fuzzy uncertainty, this paper presents a novel method for determining the membership function in fuzzy reliability with the Automatic Updating Extreme Response Surface (AUERS) method. In the proposed method, fuzzy variables are initially converted into a value domain under the given cut level and the extreme point in the domain where the reliability reaches its extreme value is considered. Second, the Particle Swarm Optimization (PSO) algorithm is used to determine the extreme point according to the extreme responses for different sets of random sample inputs. A kriging response surface is subsequently constructed between the random variables and the corresponding extreme points. An automatic updating strategy is then introduced based on the Relative Mean Square Predicted Error (RMSPE) before performing every iteration of reliability analysis. By adding new sample points, the approximate quality of the kriging response surface is improved. Finally, reliability analysis is used to determine the reliability bound under the given cut level. The proposed method assures the accuracy and computation efficiency of the mixed uncertainty reliability analysis results while it prevents the solution from becoming trapped in a local optimum, which occurs in classical optimization methods. Two example analyses are used to demonstrate the validity and advantages of the proposed method.

Excessive or burst production of reactive oxygen species (ROS) brings oxidative stress that leads to various vital diseases such as cancers, Parkinson’s disease and Ischemia-reperfusion damage, etc. However, traditional biological analysis of ROS based on separate procedure of capturing, labelling, and spectrometrically detecting is complicated and time-consuming, the accuracy of which was severely affected by the short lifespan (microseconds to milliseconds) of ROS, and thus, a fast and effective approach that allows for a real-time detection is highly desired. In this work, we have developed a printable CeO2 − x-CNTs-nanocomposite-based paste and fabricated an integrated flexible ROS sensor (IFRS) by directly printing the paste on a flexible polyethylene terephthalate (PET) substrate as both working electrode and sensing material. This IFRS device demonstrated the fast and real-time response to the hydrogen dioxide in lab as well as to total ROS in sweat of an adult, showing excellent current response to hydrogen oxide in the wide range of 0.01 to 10 mM with a limit of detection (LOD) reaching 1.85 µM. The superior sensitivity of the device to hydrogen oxide was found to be 136.59 \(\text{μA∙}{\text{mM}}^{\text{-1}}\text{∙}{\text{cm}}^{\text{-2}}\), 1.5 to 10 times higher than previously reported sensors. This IFRS device distinguished the instant ROS in the sweat of adult men in vitro, with amperometric response increased 8 times after half an hour strenuous exercise, exhibiting good selectivity and excellent stability together with confirmed high biosafety. Overall, our designed IFRS provides an applicable and translational feasibility toward the simple, rapid and real-time detection of ROS in the near future.

We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs. When just few training data are available, it is beneficial to have a compact parametrization in order to ameliorate the ill-posedness of the neural network training problem. By linearly restricting high-dimensional maps to informed reduced bases of the inputs, one can compress high-dimensional maps in a constructive way that can be used to detect appropriate basis ranks, equipped with rigorous error estimates. A scalable neural network learning framework is thus to learn the nonlinear compressed reduced basis mapping. Unlike the reduced basis construction, however, neural network constructions are not guaranteed to reduce errors by adding representation power, making it difficult to achieve good practical performance. Inspired by recent approximation theory that connects ResNets to sequential minimizing flows, we present an adaptive ResNet construction algorithm. This algorithm allows for depth-wise enrichment of the neural network approximation, in a manner that can achieve good practical performance by first training a shallow network and then adapting. We prove universal approximation of the associated neural network class for $L^2_\nu$ functions on compact sets. Our overall framework allows for constructive means to detect appropriate breadth and depth, and related compact parametrizations of neural networks, significantly reducing the need for architectural hyperparameter tuning. Numerical experiments for parametric PDE problems and a 3D CFD wing design optimization parametric map demonstrate that the proposed methodology can achieve remarkably high accuracy for limited training data, and outperformed other neural network strategies we compared against.

Flexible ultrasensitive strain sensors are highly desirable in view of their widespread applications in wearable electronics, health monitoring systems, and smart robots, where subtle strain detection is required. However, traditional fabrication of such sensors was done to prepare sensitive layers on bare or single-sided structural substrates, leading to limited sensitivity. Herein, a stretchable resistive-type strain sensor was demonstrated by self-assembling conductive networks onto a monolithic polydimethylsiloxane substrate with a two-sided topological design, for example, a sinusoid/auxetic binary architecture. The sensitivity of the obtained sensor was greatly improved by 22-fold as compared to the traditional counterpart with a bare substrate. The remarkably good agreement between the experimental results and finite element analysis predictions confirmed that the superior sensitivity is a synergistic effect of local strain enhancement derived from the topological structure on the foreside and an additional strain concentration and a reduced Poisson's ratio from the auxetic arrays on the backside. Furthermore, this sensor can withstand an extreme mechanical force (750 N) because of the shear stiffening characteristic of the auxetic structure. Benefiting from the characteristics of ultrahigh sensitivity (gauge factor ∼1744 at 5%), low detection limit (0.05%), and long-term durability (500 loading cycles), this as-prepared sensor shows promise in practical applications of high-performance wearable electronics.

Cost and fabrication technique, as two important factors affecting the development and practical applications of electronic devices, have attracted extensive attention of researchers. In order to o...

Purpose Model-assisted probability of detection (MAPOD) is an important approach used as part of assessing the reliability of nondestructive testing systems. The purpose of this paper is to apply the polynomial chaos-based Kriging (PCK) metamodeling method to MAPOD for the first time to enable efficient uncertainty propagation, which is currently a major bottleneck when using accurate physics-based models. Design/methodology/approach In this paper, the state-of-the-art Kriging, polynomial chaos expansions (PCE) and PCK are applied to “a^ vs a”-based MAPOD of ultrasonic testing (UT) benchmark problems. In particular, Kriging interpolation matches the observations well, while PCE is capable of capturing the global trend accurately. The proposed UP approach for MAPOD using PCK adopts the PCE bases as the trend function of the universal Kriging model, aiming at combining advantages of both metamodels. Findings To reach a pre-set accuracy threshold, the PCK method requires 50 per cent fewer training points than the PCE method, and around one order of magnitude fewer than Kriging for the test cases considered. The relative differences on the key MAPOD metrics compared with those from the physics-based models are controlled within 1 per cent. Originality/value The contributions of this work are the first application of PCK metamodel for MAPOD analysis, the first comparison between PCK with the current state-of-the-art metamodels for MAPOD and new MAPOD results for the UT benchmark cases.

Developing a facile, cost-saving, and environment-friendly method for fabricating a multifunctional humidity sensor is of great significance to expand its practical applications. However, most humidity sensors involve a complex fabrication process, resulting in their high cost and narrow application fields. Herein, a multifunctional paper-based humidity sensor with many advantages is proposed. This humidity sensor is fabricated using conventional printing paper and flexible conductive adhesive tape by a facile pasting method, in which the paper is used as both the humidity-sensing material and the substrate of the sensor. Owing to the moderate hydrophilicity of the paper and the rational structure design of the paper-based humidity sensor, the sensor exhibits an excellent humidity-sensing response of more than 103 as well as good linearity ( R2 = 0.9549) within the humidity range from 41.1 to 91.5% relative humidity. Furthermore, the paper-based humidity sensor has good flexibility and compatibility, endowing it with multifunctional applications for breath rate, baby diaper wetting, noncontact switch, skin humidity, and spatial localization monitoring. Although the resistance of the paper-based humidity sensor is relatively large, the humidity-sensing response signals of the sensor can be conveniently processed by the designed signal processing system. The readily available starting materials and facile fabrication technique provide useful strategies for the development of multifunctional humidity sensors.

A fast and simple method using co-grinding with Fe powder followed by heat treatment is developed to regulate surface oxygen vacancy content of ZnO nanoparticles. The moderately increased surface oxygen vacancy is conducive to shortening the adsorption and desorption times of NO2 molecules, arising from the enhanced O2 adsorption. Therefore, the response and recovery speeds of ZnO nanoparticle-based sensor under UV irradiation were significantly promoted. Our findings not only offer a feasible strategy to modulate surface oxygen vacancy content of nanostructured oxides, but also provide a novel approach to improve the response and recovery speeds of light-activated NO2 sensor.

Respiration is an important physiological process of human body that can not only act as vital signs for physical health management or “biomarkers” of some diseases, but also as a kind of energy that can be utilized to drive triboelectric nanogenerator (TENG). It is particularly desirable to develop a facile TENG that can be driven by human respiration itself for multifunctional human respiratory monitoring. In this work, a respiration-driven TENG based on Ce-doped ZnO-PANI nanocomposite film that can convert respiratory airflow energy into electric signals was developed for the detection of multiple respiratory parameters including trace-level NH3 concentration, human respiratory flow and respiratory frequency. It was found that the as-designed TENG exhibited excellent ability for respiratory flow monitoring (2–6 L/min), identification of human breathing frequency and recognition of different breathing patterns. Furthermore, output voltage of the self-powered NH3 sensor monotonously decreases with the increase of NH3 concentrations from 0.1 to 25 ppm. Especially, a good NH3-sensing performance was exhibited when exposed to a trace-level NH3 atmosphere ranging from 0.1 to 1 ppm, implying a promising potential in self-powered disease screening related to the detection of exhaled NH3 in human breathing. This research offers a facile method to establish a multifunctional respiratory monitoring system driven by respiration itself, which is of significant importance in self-powered human health monitoring through non-implantable respiratory diagnosis.

To explore the mechanism of superconductivity in lanthanum monoxide (LaO) film, using first-principles calculations we investigated the electronic properties, phonon vibration, and electron-phonon coupling characteristics in LaO with a tetragonally distorted rocksalt structure in accordance with our experiments. It is demonstrated that LaO shows coexisting metallic and ionic characteristics, and the electronic density of states at the Fermi level is primarily from La-$d$ and O-$p$ orbitals. The low-frequency phonons from La atoms dominate 61% of the electron-phonon coupling constant, and the high-frequency phonons derived from O atoms contribute the rest. The calculated ${T}_{\mathrm{c}}$ of about 5.37 K is consistent with the experimental result, indicating that the superconductivity in LaO can be clearly illustrated by the phonon-mediated mechanism and McMillan's formula. The calculated results reveal that comparing LaO with other lanthanum monochalcogenides, the electronic properties vary more significantly than the phonon properties and lead to a stronger electron-phonon coupling in LaO, which is responsible for its higher ${T}_{\mathrm{c}}$. The theoretical and experimental investigations provide deep insights into the mechanism of superconductivity in the LaO system.

Biofouling has been a long-last problem in a variety of marine systems which causes energy waste and device damage. In this study, we present a self-activated anti-biofouling system enabled by electrical double layer disturbance, which could effectively suppress the initial formation of conditioning layers and subsequent microbe attachment. The small and low-frequency alternating electrical fields were produced by a triboelectric nanogenerator under water wave impacts. Systematic analyses confirmed that the anti-biofouling efficacy was directly related to the strength of the electric field and was effective in both fresh lake and sea water environments. An on-site demonstration was implemented at a calm lake shore for three weeks. The water wave-driven anti-biofouling exhibited excellent surface protection, which was significantly superior to several commercial anti-biofouling coatings. This development brings a novel, effective and eco-friendly solution for protecting a broad range of surfaces against biofouling.