Thermochemical conversion of waste printed circuit boards: Thermal behavior,reaction kinetics,pollutant evolution and corresponding controlling strategies

With the rapid development of the global electronics industry, waste printed circuit boards (WPCBs) has become one of the world's fastest growing waste streams. Exploring an environmentally sound treatment for this abundant and multi-component waste is critical to its sustainable development. This study has been aimed to cover thermochemical conversion of WPCBs (combustion, pyrolysis, gasification and hydrothermal process), focusing on thermal behavior, reaction kinetics, pollutant evolution and corresponding controlling strategies, with the aim of promoting circular economic development and building a sustainable future for the electronics industry.     

The detection mechanism for false data injection attack via the ellipsoidal set-membership approach

The false data injection (FDI) attack detection problem in cyber-physical systems (CPSs) is investigated in this paper. A novel attack detection algorithm is proposed based on the ellipsoidal set-membership approach. In comparison to the existing FDI attack detection methods, the developed attack detection approach in this paper neither requires predefined thresholds nor specific statistical characteristics of the attacks. In order to guarantee that the estimation ellipsoid contains normal states despite the unknown but bounded (UBB) process and measurement noises, the one-step ellipsoidal set-membership estimation method is put forward. In addition, a convex optimization algorithm is introduced to calculate the gain matrix of the observer recursively. Moreover, with the help of the state estimation ellipsoid, the residual ellipsoid can be obtained for attack detection. Whether a detector can detect the FDI attack depends on the relationship between the residual value and residual ellipsoidal set. Finally, the effectiveness of the proposed method is demonstrated by a numerical simulation example.     

Attention-based vector quantisation variational autoencoder for colour-patterned fabrics defect detection

Defect detection is an essential link in the fabric production process. Due to the diversity of patterns and scarcity of defect samples for colour-patterned fabrics, reconstruction-based unsupervised deep learning algorithms have received extensive attention in the field of fabric defect detection. Among them, unsupervised reconstruction models based on variational autoencoders (VAEs) have been shown to be effective. However, there is a problem of posterior collapse in the process of modelling parametric distributions of continuous variables by VAEs. Therefore, VAE-based defect detection methods for colour-patterned fabrics usually produce ambiguous reconstruction results, thereby affecting the defect detection performance. In this article, an attention-based vector quantisation variational autoencoder (AVQ-VAE) is proposed for colour-patterned fabric defect detection. The method adopts autoregressive modelling of discrete variables to avoid the posterior collapse problem of traditional VAEs, and utilises attention mechanism to enhance the feature representation ability of the model. AVQ-VAE consists of encoder, embedding space, decoder and attention mechanism. The encoder is used to map the input image into multiple feature vectors. Vector quantisation in embedding space is used for discretisation and autoregressive modelling of feature vectors. A decoder is used to decode discrete variables into images of the same size as the original input. Furthermore, an attention mechanism is used to capture channel and spatial correlations, which help the model focus on important information by adaptively recalibrating feature maps. Experimental results on public datasets demonstrate that the proposed method is robust and effective for colour-patterned fabric defect detection.     

Structures and hydrogen storage properties of AeVH3 (Ae = Be,Mg, Ca,Sr) perovskite hydrides by DFT calculations

The capability of hydrogen to be an energy source has made the hydrogen storage as one of the most investigated research fields during the recent years, and novel perovskite materials have become the current focus for hydrogen storage applications. Here we study the AeVH₃ (Ae = Be, Mg, Ca, Sr) perovskite-type hydrides to explorer their potential for hydrogen storage applications using the density functional theory (DFT) implemented CASTEP code along with exchange correlation potential. The study examines the electronic structure, optical properties, elastic features and mechanical stability of the materials. The crystal structure of AeVH₃ compounds is found to be cubic with lattice constant as 3.66, 3.48, 3.76 and 3.83 for Ae = Be, Mg, Ca and Sr compounds, respectively. The calculated electronic structures of these compounds show ionic bonding and no energy bandgap. The mechanical characteristics of compounds are also investigated as to meet the Born stability criterion, these compounds should be mechanically stable. The Cauchy pressure and Pugh criteria revealed that these materials have a brittle character and rather hard. In low energy range, all optical properties are found to be suitable as needed for storing the hydrogen. Furthermore, the gravimetric ratios suggested that all the compounds are suitable for hydrogen storage as a fuel for a longer time and may provide remarkable contributions in diversity of power and transportation applications.     

A novel ethanol sensor with high response based on one-dimensional YFeO3 nanorods

As the cognition of metal oxide semiconductor becomes deeper and deeper, their excellent sensing ability has also been demonstrated. The gas sensors with metal oxide semiconductor as basis materials have become a hot topic at present. Enhancing the sensitivity and reducing the test limit of the sensor are exceedingly important topic. It is crucial to regulate the morphology of metal oxide semiconductor materials to improve the gas sensing performance. Low-dimensional materials such as quantum dots, one-dimensional nanowires and nanorods usually show the excellent gas-sensitive properties. In this work, one-dimensional YFeO₃ nanorods were synthesized by electrospinning technology. The one-dimensional rod-like structure enables more active sites to be exposed on the surface of materials, which can effectively promote the adsorption process of the YFeO₃ nanorods to the test gases, so as to improve the gas sensing performance. Found by testing the gas sensitivity, YFeO₃ nanorods responds far better to ethanol than other tested gases. The response and recovery time of YFeO₃ nanorods to 100 ppm ethanol at 350 °C was approximately 19 s and 9 s, respectively. It indicates that the response and recovery ability of YFeO₃ nanorods to ethanol were excellent. The study can provide technical reference for subsequent preparation of remarkable performance ethanol sensor and enrich the materials category of gas sensor fields.     

Fabrication of Neodymium (Nd), Cadmium (Cd) and Nd:Cd doped hybrid copper oxide nanocomposites: Evaluation of their antibacterial activity and cytotoxicity against human L132 cell line

In the present study, Neodymium (Nd), Cadmium (Cd), and the various molar ratios of Nd: Cd doped copper oxide nanocomposites (CuO NCs) were prepared by the co-precipitation method. The as-synthesized Nd, Cd, and Nd:Cd doped CuO NCs [Cu_1-x + yNd_xCd_yO, (x:y = 0.006:0.00, 0.00:0.006, 0.005:0.001, 0.004:0.002, 0.003:0.003, M)] were characterized through various instrumentation techniques such as TGA, UV–Vis–NIR, FTIR, Raman, FL, XRD, ZP, FE-SEM with EDAX elemental mapping, HR-TEM and XPS analyses. Further, the antibacterial activity of doped CuO NCs was tested against Staphylococcus aureus and Escherichia coli. An equal molar ratio of Nd and Cd doped CuO NCs showed excellent antibacterial activity mainly due to the synergistic effect of sufficient Nd³⁺, Cd²⁺, and Cu²⁺ ions releasing ability. Interestingly, the doping effect enhances surface defects and decreases the ability to scavenge free radicals compared to pure CuO nanomaterials. At the same time, the cytotoxicity of NCs was evaluated on the human lung epithelial L132 cell line. Evidently, 75 μg/ml concentration of Nd:Cd doped CuO NCs samples shows 80% viability, which confirms their negligible cytotoxic effect. The Nd:Cd doped CuO NCs (94:3:3) had a high aspect ratio shape, remarkable ion-releasing ability, and biocompatibility while being thermally stable. Because of these qualities, they are well suited for treating bacterial infections in the biomedical area.