Xi'an Polytechnic University

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higherdimensional optimisation problems.

Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.

Abstract In this study, mechanical vibration is used for hydrogen generation and decomposition of dye molecules, with the help of BiFeO 3 (BFO) square nanosheets. A high hydrogen production rate of ≈124.1 μmol g −1 is achieved under mechanical vibration (100 W) for 1 h at the resonant frequency of the BFO nanosheets. The decomposition ratio of Rhodamine B dye reaches up to ≈94.1 % after mechanical vibration of the BFO catalyst for 50 min. The vibration‐induced catalysis of the BFO square nanosheets may be attributed to the piezocatalytic properties of BFO and the high specific surface area of the nanosheets. The uncompensated piezoelectric charges on the surfaces of BFO nanosheets induced by mechanical vibration result in a built‐in electric field across the nanosheets. Unlike a photocatalyst for water splitting, which requires a proper band edge position for hydrogen evolution, such a requirement is not needed in piezocatalytic water splitting, where the band tilting under the induced piezoelectric field will make the conduction band of BFO more negative than the H 2 /H 2 O redox potential (0 V) for hydrogen generation.

Metasurfaces provide unprecedented routes to manipulations on electromagnetic waves, which can realize many exotic functionalities. Despite the rapid development of metasurfaces in recent years, the design process of metasurface is still time-consuming and computational resource-consuming. Moreover, it is quite complicated for layman users to design metasurfaces as plenty of specialized knowledge is required. In this work, a metasurface design method named REACTIVE is proposed on the basis of deep learning, as deep learning method has shown its natural advantages and superiorities in mining undefined rules automatically in many fields. REACTIVE is capable of calculating metasurface structure directly through a given design target; meanwhile, it also shows the advantage in making the design process automatic, more efficient, less time-consuming, and less computational resource-consuming. Besides, it asks for less professional knowledge, so that engineers are required only to pay attention to the design target. Herein, a triple-band absorber is designed using the REACTIVE method, where a deep learning model computes the metasurface structure automatically through inputting the desired absorption rate. The whole design process is achieved 200 times faster than the conventional one, which convincingly demonstrates the superiority of this design method. REACTIVE is an effective design tool for designers, especially for laymen users and engineers.

A chiral two-dimensional MOF, {[Ca(D-Hpmpc)(H2O)2]·2HO0.5}n (1, D-H3pmpc = D-1-(phosphonomethyl) piperidine-3-carboxylic acid), with intrinsic proton conductivity has been synthesized and characterized. Structure analysis shows that compound 1 possesses protonated tertiary amines as proton carriers and hydrogen-bonding chains served as proton-conducting pathways. Further, MOF–polymer composite membranes have been fabricated via assembling polymer PVP with different contents of rod-like 1 submicrometer crystals. Interestingly, the proton conductivity of this composite membrane containing 50 wt% 1 is rapidly increased, compared with that of pure submicrometer crystals at 298 K and ∼53% RH. Therefore, it is feasible to introduce humidification of PVP into composite membranes to enhance low-humidity proton conductivity; and humidified PVP with adsorbed water molecules plays an important role in proton conduction indicated by the results of water physical sorption and TG/DTG analyses. This study may offer a facile strategy to prepare a variety of solid electrolyte materials with distinctive proton-conducting properties under a low humidity.

Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective flower algorithm can accurately find the Pareto fronts for a set of test functions. We then solve a bi-objective disc brake design problem, which indeed converges quickly.

Abstract 2D materials, such as graphene, transition metal dichalcogenides, and black phosphorus, have become the most potential semiconductor materials in the field of optoelectronic devices due to their extraordinary properties. Owing to the layer‐dependent and appropriately sized bandgaps, photodetectors based on various 2D materials are designed and manufactured rationally. Utilizing the unique properties of 2D materials, many surprising physical phenomena of junctions based on 2D materials can be obtained after different 2D materials are stacked together. This makes heterojunctions more popular than 2D materials themselves, and the design of 2D materials for human beings is easier than ever. In this review, recent progress in optoelectronic applications based on 2D materials and their heterojunctions is summarized and discussed.

Abstract A highly crystalline perylene imide polymer (Urea‐PDI) photocatalyst is successfully constructed. The Urea‐PDI presents a wide spectrum response owing to its large conjugated system. The Urea‐PDI performs so far highest oxygen evolution rate (3223.9 µmol g −1 h −1 ) without cocatalysts under visible light. The performance is over 107.5 times higher than that of the conventional PDI supramolecular photocatalysts. The strong oxidizing ability comes from the deep valence band (+1.52 eV) which is contributed by the covalent‐bonded conjugated molecules. Besides, the high crystallinity and the large molecular dipoles of the Urea‐PDI contribute to a robust built‐in electric field promoting the separation and transportation of photogenerated carriers. Moreover, the Urea‐PDI is very stable and has no performance attenuation after 100 h continuous irradiation. The Urea‐PDI polymer photocatalyst provides with a new platform for the use of photocatalytic water oxidation, which is expected to contribute to clean energy production.

Abstract Hydrogen peroxide (H 2 O 2 ) is a mild but versatile oxidizing agent with extensive applications in bleaching, wastewater purification, medical treatment, and chemical synthesis. The state‐of‐art H 2 O 2 production via anthraquinone oxidation is hardly considered a cost‐efficient and environment‐friendly process because it requires high energy input and generates hazardous organic wastes. Photocatalytic H 2 O 2 production is a green, sustainable, and inexpensive process which only needs water and gaseous dioxygen as the raw materials and sunlight as the power source. Inorganic metal oxide semiconductors are good candidates for photocatalytic H 2 O 2 production due to their abundance in nature, biocompatibility, exceptional stability, and low cost. Progress has been made to enhance the photocatalytic activity toward H 2 O 2 production, however, H 2 O 2 photosynthesis is still in the laboratory research phase since the productivity is far from satisfaction. To inspire innovative ideas for boosting the H 2 O 2 yield in photocatalysis, the most well‐studied metal oxide photocatalysts are selected and the modification strategies to improve their activity are listed. The mechanisms for H 2 O 2 production over modified photocatalysts are discussed to highlight the facilitating role of the modification methods. Besides, methods for the quantification of H 2 O 2 and associated radical intermediates are provided to guide future studies in this field.

Deep learning–based fabric defect detection methods have been widely investigated to improve production efficiency and product quality. Although deep learning–based methods have proved to be powerful tools for classification and segmentation, some key issues remain to be addressed when applied to real applications. Firstly, the actual fabric production conditions of factories necessitate higher real-time performance of methods. Moreover, fabric defects as abnormal samples are very rare compared with normal samples, which results in data imbalance. It makes model training based on deep learning challenging. To solve these problems, an extremely efficient convolutional neural network, Mobile-Unet, is proposed to achieve the end-to-end defect segmentation. The median frequency balancing loss function is used to overcome the challenge of sample imbalance. Additionally, Mobile-Unet introduces depth-wise separable convolution, which dramatically reduces the complexity cost and model size of the network. It comprises two parts: encoder and decoder. The MobileNetV2 feature extractor is used as the encoder, and then five deconvolution layers are added as the decoder. Finally, the softmax layer is used to generate the segmentation mask. The performance of the proposed model has been evaluated by public fabric datasets and self-built fabric datasets. In comparison with other methods, the experimental results demonstrate that segmentation accuracy and detection speed in the proposed method achieve state-of-the-art performance.

Abstract Major challenges encountered when developing manganese-based materials for ozone decomposition are related to the low stability and water inactivation. To solve these problems, a hierarchical structure consisted of graphene encapsulating α-MnO 2 nanofiber was developed. The optimized catalyst exhibited a stable ozone conversion efficiency of 80% and excellent stability over 100 h under a relative humidity (RH) of 20%. Even though the RH increased to 50%, the ozone conversion also reached 70%, well beyond the performance of α-MnO 2 nanofiber. Here, surface graphite carbon was activated by capturing the electron from inner unsaturated Mn atoms. The excellent stability originated from the moderate local work function, which compromised the reaction barriers in the adsorption of ozone molecule and the desorption of the intermediate oxygen species. The hydrophobic graphene shells hindered the chemisorption of water vapour, consequently enhanced its water resistance. This work offered insights for catalyst design and would promote the practical application of manganese-based catalysts in ozone decomposition.

Precisely reducing the size of metal-organic frameworks (MOFs) derivatives is an effective strategy to manipulate their phase engineering owing to size-dependent oxidation; however, the underlying relationship between the size of derivatives and phase engineering has not been clarified so far. Herein, a spatial confined growth strategy is proposed to encapsulate small-size MOFs derivatives into hollow carbon nanocages. It realizes that the hollow cavity shows a significant spatial confinement effect on the size of confined MOFs crystals and subsequently affects the dielectric polarization due to the phase hybridization with tunable coherent interfaces and heterojunctions owing to size-dependent oxidation motion, yielding to satisfied microwave attenuation with an optimal reflection loss of -50.6 dB and effective bandwidth of 6.6 GHz. Meanwhile, the effect of phase hybridization on dielectric polarization is deeply visualized, and the simulated calculation and electron holograms demonstrate that dielectric polarization is shown to be dominant dissipation mechanism in determining microwave absorption. This spatial confined growth strategy provides a versatile methodology for manipulating the size of MOFs derivatives and the understanding of size-dependent oxidation-induced phase hybridization offers a precise inspiration in optimizing dielectric polarization and microwave attenuation in theory.

). Therefore, these MOFs@textiles are promising composites for producing efficient and recyclable out-/indoor air purifiers.

Commercial wearable piezoelectric sensors possess excellent anti-interference stability due to their electronic packaging. However, this packaging renders them barely breathable and compromises human comfort. To address this issue, we develop a PVDF piezoelectric nanoyarns with an ultrahigh strength of 313.3 MPa, weaving them with different yarns to form three-dimensional piezoelectric fabric (3DPF) sensor using the advanced 3D textile technology. The tensile strength (46.0 MPa) of 3DPF exhibits the highest among the reported flexible piezoelectric sensors. The 3DPF features anti-gravity unidirectional liquid transport that allows sweat to move from the inner layer near to the skin to the outer layer in 4 s, resulting in a comfortable and dry environment for the user. It should be noted that sweating does not weaken the piezoelectric properties of 3DPF, but rather enhances. Additionally, the durability and comfortability of 3DPF are similar to those of the commercial cotton T-shirts. This work provides a strategy for developing comfortable flexible wearable electronic devices.

Abstract Although green customer and supplier integration have gained much attention, how it affects green innovation performance is still unclear. This study examines the direct and interaction effects of green customer and supplier integration on green innovation performance and the moderating effect of internal integration, using data from 176 Chinese manufacturing firms. The results reveal that green customer integration, green supplier integration, and their interaction are all positively related to green innovation performance. In addition, internal integration moderates the relationship between green customer integration and green innovation performance, but does not moderate the relationship between green supplier integration and green innovation performance. Further analysis indicate that the effects of interaction term on green innovation performance and the moderating effects of internal integration on the relationship between green supplier integration and green innovation performance are significantly different across different firm sizes, providing useful insights for firms. This study provides novel insights for making environmental policies.

Body temperature is an important indicator of human health. The traditional mercury and medical electronic thermometers have a slow response (≥1 min) and can not be worn for long to achieve continuous temperature monitoring due to their rigidity. In this work, we prepared a skin-core structure polyurethane (PU)/graphene encapsulated poly(3,4-ethylenedioxythiophene)–poly(styrenesulfonate) (PEDOT:PSS) temperature-sensitive fiber in one step by combining wet spinning technology with impregnation technology. The composite fiber has high sensitivity (−1.72%/°C), super-resolution (0.1 °C), fast time response (17 s), antisweat interference, and high linearity (R2 = 0.98) in the temperature sensing range of 30–50 °C. The fiber is strong enough to be braided into the temperature-sensitive fabric with commercial cotton yarns. The fabric with good comfort and durability can be arranged in the armpit position of the cloth to realize real-time body temperature monitoring without interruption during daily activities. Through Bluetooth wireless transmission, body temperature can be monitored in real-time and displayed on mobile phones to the parents or guardians. Overall, the fiber-based temperature sensor will significantly improve the practical applications of wearable temperature sensors in intelligent medical treatment due to its sensing stability, comfort, and durability.

Abstract Wearable tensile strain sensors have aroused substantial attention on account of their exciting applications in rebuilding tactile inputs of human and intelligent robots. Conventional such devices, however, face the dilemma of both sensitive response to pressure and bending stimulations, and poor breathability for wearing comfort. In this paper, a breathable, pressure and bending insensitive strain sensor is reported, which presents fascinating properties including high sensitivity and remarkable linearity (gauge factor of 49.5 in strain 0–100%, R 2 = 99.5%), wide sensing range (up to 200%), as well as superior permeability to moisture, air, and water vapor. On the other hand, it exhibits negligible response to wide‐range pressure (0–100 kPa) and bending (0–75%) inputs. This work provides a new route for achieving wearing comfortable, high‐performance, and anti‐jamming strain sensors.

The LTMN_0.25 + 1 wt% 0.6CuO–0.4B₂O₃ ceramic with low sintering temperature, small density and excellent performance have wide application prospects in 5G devices.

Abstract MXene, a transition metal carbide/nitride, has been prominent as an ideal electrochemical active material for supercapacitors. However, the low MXene load limits its practical applications. As environmental concerns and sustainable development become more widely recognized, it is necessary to explore a greener and cleaner technology to recycle textile by-products such as cotton. The present study proposes an effective 3D fabrication method that uses MXene to fabricate waste denim felt into ultralight and flexible supercapacitors through needling and carbonization. The 3D structure provided more sites for loading MXene onto Z-directional fiber bundles, resulting in more efficient ion exchange between the electrolyte and electrodes. Furthermore, the carbonization process removed the specific adverse groups in MXenes, further improving the specific capacitance, energy density, power density and electrical conductivity of supercapacitors. The electrodes achieve a maximum specific capacitance of 1748.5 mF cm −2 and demonstrate remarkable cycling stability maintaining more than 94% after 15,000 galvanostatic charge/discharge cycles. Besides, the obtained supercapacitors present a maximum specific capacitance of 577.5 mF cm −2 , energy density of 80.2 μWh cm −2 and power density of 3 mW cm −2 , respectively. The resulting supercapacitors can be used to develop smart wearable power devices such as smartwatches, laying the foundation for a novel strategy of utilizing waste cotton in a high-quality manner.