The accumulation of excessive nitrate in the atmosphere not only jeopardizes human health but also disrupts the balance of the nitrogen cycle in the ecosystem. Among various nitrate removal technologies, electrocatalytic nitrate reduction reaction(eNO3RR)has been widely studied for its advantages of being eco-friendly, easy to operate, and controllable under environmental conditions with renewable energy as the driving force. Transition metal-based catalysts(TMCs)have been widely used in electrocatalysis due to their abundant reserves, low costs, easy-to-regulate electronic structure and considerable electrochemical activity. In addition, TMCs have been extensively studied in terms of the kinetics of the nitrate reduction reaction, the moderate adsorption energy of nitrogen-containing species and the active hydrogen supply capacity. Based on this, this review firstly discusses the mechanism as well as analyzes the two main reduction products(N2 and NH3)of eNO3RR, and reveals the basic guidelines for the design of efficient nitrate catalysts from the perspective of the reaction mechanism. Secondly, this review mainly focuses on the recent advances in the direction of eNO3RR with four types of TMCs, Fe, Co, Ni and Cu, and unveils the interfacial modulation strategies of Fe, Co, Ni and Cu catalysts for the activity, reaction pathway and stability. Finally, reasonable suggestions and opportunities are proposed for the challenges and future development of eNO3RR. This review provides far-reaching implications for exploring cost-effective TMCs to replace high-cost noble metal catalysts(NMCs)for eNO3RR.
Textile production has received considerable attention owing to its significance in production value, the complexity of its manufacturing processes and the extensive reach of its supply chains. However, textile industry consumes substantial energy and materials and emits greenhouse gases that severely harm the environment. In addressing this challenge, the concept of sustainable production offers crucial guidance for the sustainable development of the textile industry. Low-carbon manufacturing technologies provide robust technical support for the textile industry to transition to a low-carbon model by optimizing production processes, enhancing energy efficiency and minimizing material waste.Consequently, low-carbon manufacturing technologies have gradually been implemented in sustainable textile production scenarios. However, while research on low-carbon manufacturing technologies for textile production has advanced, these studies predominantly concentrate on theoretical methods, with relatively limited exploration of practical applications. To address this gap, a thorough overview of carbon emission management methods and tools in textile production, as well as the characteristics and influencing factors of carbon emissions in key textile manufacturing processes is presented to identify common issues. Additionally, two new concepts, carbon knowledge graph and carbon traceability, are introduced, offering strategic recommendations and application directions for the low-carbon development of sustainable textile production. Beginning with seven key aspects of sustainable textile production, the characteristics of carbon emissions and their influencing factors in key textile manufacturing process are systematically summarized. The aim is to provide guidance and optimization strategies for future emission reduction efforts by exploring the carbon emission situations and influencing factors at each stage. Furthermore, the potential and challenges of carbon knowledge graph technology are summarized in achieving carbon traceability, and several research ideas and suggestions are proposed.
Highly dispersed bimetallic alloy nanoparticle electrocatalysts have been demonstrated to exhibit exceptional performance in driving the nitrate reduction reaction(NO_3RR)to generate ammonia(NH3). In this study, we prepared mesoporous carbon nanofibers(mCNFs)functionalized with ordered PtFe alloys(O-PtFe-mCNFs)by a composite micelle interface-induced co-assembly method using poly(ethylene oxide)-block-polystyrene(PEO-b-PS)as a template. When employed as electrocatalysts, O-PtFe-mCNFs exhibited superior electrocatalytic performance for the NO_3RR compared to the mCNFs functionalized with disordered PtFe alloys(D-PtFe-mCNFs). Notably, the NH3 production performance was particularly outstanding, with a maximum NH3 yield of up to 959.6 μmol/(h·cm2).Furthermore, the Faraday efficiency(FE)was even 88. 0% at -0.4 V vs. reversible hydrogen electrode(RHE). This finding provides compelling evidence of the potential of ordered PtFe alloy catalysts for the electrocatalytic NO3RR.
This article studies the role of electrochemical parameters in controlling the morphology of oxidized TiO2 nanotubes and the electrochemical performance of modified TiO2 nanotubes. Humidity is a key factor for fabricating TiO2 nanotubes. When the relative humidity belows 70%,the TiO2 nanotubes can be successfully prepared. What's more, by changing the anodization voltage and time, the diameter and the length of TiO2 nanotubes can be adjusted.In addition, the TiO2 nanotubes are modified through electrochemical self-doping and loading Pt metal particles on the surface of the nanotubes, which promotes the performance of the supercapacitor. The sample anodized at 100 V for 3 h has a specific capacity of up to 2.576 mF/cm2 at a scan rate of 100 mV/s after self-doping, and its capacity retention rate still remains at 89.55% after 5 000 cycles, demonstrating excellent cycling stability. The Pt-modified sample has a specific capacity of up to 3.486 mF/cm2 at the same scan rate, exhibiting more outstanding electrochemical performance.
Organic photovoltaic(OPV)devices hold great promise for indoor light harvesting, offering a theoretical upper limit of power conversion efficiency that surpasses that of other photovoltaic technologies. However, the presence of high leakage currents in OPV devices commonly constrains their effective performance under indoor conditions. In this study, we identified that the origin of the high leakage currents in OPV devices lay in pinhole defects present within the active layer(AL). By integrating an automated spin-coating strategy with sequential deposition processes, we achieved the compactness of the AL and minimized the occurrence of pinhole defects therein.Experimental findings demonstrated that with an increase in the number of deposition cycles, the density of pinhole defects in the AL underwent a marked reduction.Consequently, the leakage current experienced a substantial decrease by several orders of magnitude which achieved through well-calibrated AL deposition procedures. This enabled a twofold enhancement in the power conversion efficiency(PCE)of the OPV devices under conditions of indoor illumination.
Polymer matrix types of fiber hybrid composites are key factors to improve ballistic impact damage tolerances. Here we report ballistic penetration damages of Kevlar/ultra-high molecular weight polyethylene(UHMWPE)hybrid composites with thermoplastic polyurethane(PU)matrix. The hybrid composites were penetrated by fragment-simulating projectiles(FSPs)using an air gun impact system. The effects of stacking sequences on the ballistic performance of hybrid composites were analyzed. Two types of specific energy absorption(the energy absorption per unit area density and the energy absorption per unit thickness)were investigated. It was found that the main damage modes of PU hybrid composites were fiber breakage, matrix damage, fiber pullout and interlayer delamination. The instantaneous deformation could not be used as a reference index for evaluating the ballistic performance of the target plate. The energy absorption process of the PU hybrid composites showed a nonlinear pattern. The hybrid structure affected the specific energy absorption of the materials.
Clothing attribute recognition has become an essential technology, which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes. However, existing methods cannot recognize newly added attributes and may fail to capture region-level visual features. To address the aforementioned issues, a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed. This model aligned cropped and segmented images with category and multiple fine-grained attribute texts, achieving the matching of fashion region and corresponding texts through contrastive learning. Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes, and to further improve the accuracy of retrieval, an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced, specifically designed for composed image retrieval. This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism, it realized the fusion and selection of image features and attribute text features. Experimental results show that the proposed model achieves a mean precision of 0. 663 3 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39. 18 for composed image retrieval task, satisfying user needs for freely searching for clothing through images and texts.
Defect detection is vital in the nonwoven material industry, ensuring surface quality before producing finished products. Recently, deep learning and computer vision advancements have revolutionized defect detection, making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model. Using the constructed samples of defects in nonwoven materials as the research objects, transfer learning experiments were conducted based on the Nano DetPlus object detection framework. Within this framework,the Backbone, path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing, with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy. The half-precision quantization method was used to optimize the model after transfer learning experiments, reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO, SSD and other common industrial defect detection algorithms, validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
The silk fabrics were matching dyed with three natural edible pigments(red rice red, ginger yellow and gardenia blue). By investigating the dyeing rates and lifting properties of these pigments, it was observed that their compatibilities were excellent in the dyeing process: dye dosage 2. 5%(omf), mordant alum dosage 2. 0%(omf),dyeing temperature 80 ℃ and dyeing time 40 min. The silk fabrics dyed with secondary colors exhibited vibrant and vivid color owing to the remarkable lightness and chroma of ginger yellow. However, gardenia blue exhibited multiple absorption peaks in the visible light range, resulting in significantly lower lightness and chroma for the silk fabrics dyed with tertiary colors, thus making it suitable only for matte-colored fabrics with low chroma levels. In addition,the silk fabrics dyed with these three pigments had a color fastness that exceeded grade 3 in resistance to perspiration,soap washing and light exposure, indicating acceptable wearing properties. The dyeing process described in this research exhibited a wide range of potential applications in matching dyeing of protein-based textiles with natural colorants.
A hybrid compensation scheme for piezoelectric ceramic actuators(PEAs)is proposed. In the hybrid compensation scheme, the input rate-dependent hysteresis characteristics of the PEAs are compensated. The feedforward controller is a novel input rate-dependent neural network hysteresis inverse model, while the feedback controller is a proportion integration differentiation(PID)controller. In the proposed inverse model, an input ratedependent auxiliary inverse operator(RAIO)and output of the hysteresis construct the expanded input space(EIS)of the inverse model which transforms the hysteresis inverse with multi-valued mapping into single-valued mapping, and the wiping-out, rate-dependent and continuous properties of the RAIO are analyzed in theories. Based on the EIS method, a hysteresis neural network inverse model, namely the dynamic back propagation neural network(DBPNN)model, is established. Moreover, a hybrid compensation scheme for the PEAs is designed to compensate for the hysteresis. Finally, the proposed method, the conventional PID controller and the hybrid controller with the modified input rate-dependent Prandtl-Ishlinskii(MRPI)model are all applied in the experimental platform. Experimental results show that the proposed method has obvious superiorities in the performance of the system.
The theory of quadratic residues plays an important role in cryptography. In 2001, Cocks developed an identity-based encryption(IBE)scheme based on quadratic residues, resolving Shamir's 17-year-old open problem. However, a notable drawback of Cocks' scheme is the significant expansion of the ciphertext, and some of its limitations have been addressed in subsequent research.Recently, Cotan and Teşeleanu highlighted that previous studies on Cocks' scheme relied on a trial-and-error method based on Jacobi symbols to generate the necessary parameters for the encryption process. They enhanced the encryption speed of Cocks' scheme by eliminating this trialand-error method. Based on security analysis, this study concludes that the security of Cotan-Teşeleanu's proposal cannot be directly derived from the security of the original Cocks' scheme. Furthermore, by adopting the CotanTeşeleanu method and introducing an additional variable as a public element, this study develops a similar enhancement scheme that not only accelerates the encryption speed but also provides security equivalent to the original Cocks' scheme.