Editor’s Choice Articles

Gas sensors are devices that convert a gas volume fraction into electrical signals, and they are widely used in many fields such as environmental monitoring. Graphene is a new type of two-dimensional crystal material that has many excellent properties including large specific surface area, high conductivity, and high Young’s modulus. These features make it ideally suitable for application for gas sensors. In this paper, the main characteristics of gas sensor are firstly introduced, followed by the preparation methods and properties of graphene. In addition, the development process and the state of graphene gas sensors are introduced emphatically in terms of structure and performance of the sensor. The emergence of new candidates including graphene, polymer and metal/metal oxide composite enhances the performance of gas detection significantly. Finally, the clear direction of graphene gas sensors for the future is provided according to the latest research results and trends. It provides direction and ideas for future research. Full article

Metal nanoparticles are extensively studied due to their unique chemical and physical properties, which differ from the properties of their respective bulk materials. Likewise, the properties of heterogeneous bimetallic structures are far more attractive than those of single-component nanoparticles. For example, the incorporation of a second metal into a nanoparticle structure influences and can potentially enhance the optical/plasmonic and magnetic properties of the material. This review focuses on the enhanced optical/plasmonic and magnetic properties offered by bimetallic nanoparticles and their corresponding impact on biological applications. In this review, we summarize the predominant structures of bimetallic nanoparticles, outline their synthesis methods, and highlight their use in biological applications, both diagnostic and therapeutic, which are dictated by their various optical/plasmonic and magnetic properties. Full article

In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted from signals using time domain, frequency domain, and time–frequency domain analyses, and which are then fused. However, the process of selecting and fusing features for the HI is very complex and labor-intensive. We propose a novel time–frequency image feature to construct HI and predict the RUL. To convert the one-dimensional vibration signals to a two-dimensional (2-D) image, the continuous wavelet transform (CWT) extracts the time–frequency image features, i.e., the wavelet power spectrum. Then, the obtained image features are fed into a 2-D convolutional neural network (CNN) to construct the HI. The estimated HI from the proposed model is used for the RUL prediction. The accuracy of the RUL prediction is improved by using the image features. The proposed method compresses the complex process including feature extraction, selection, and fusion into a single algorithm by adopting a deep learning approach. The proposed method is validated using a bearing dataset provided by PRONOSTIA. The results demonstrate that the proposed method is superior to related studies using the same dataset. Full article

Following high profile, life changing long term mental illnesses and fatalities in sports such as skiing, cricket and American football—sports injuries feature regularly in national and international news. A mismatch between equipment certification tests, user expectations and infield falls and collisions is thought to affect risk perception, increasing the prevalence and severity of injuries. Auxetic foams, structures and textiles have been suggested for application to sporting goods, particularly protective equipment, due to their unique form-fitting deformation and curvature, high energy absorption and high indentation resistance. The purpose of this critical review is to communicate how auxetics could be useful to sports equipment (with a focus on injury prevention), and clearly lay out the steps required to realise their expected benefits. Initial overviews of auxetic materials and sporting protective equipment are followed by a description of common auxetic materials and structures, and how to produce them in foams, textiles and Additively Manufactured structures. Beneficial characteristics, limitations and commercial prospects are discussed, leading to a consideration of possible further work required to realise potential uses (such as in personal protective equipment and highly conformable garments). Full article

Research on nanotechnology application in the oil and gas industry has been growing rapidly in the past decade, as evidenced by the number of scientific articles published in the field. With oil and gas reserves harder to find, access, and produce, the pursuit of more game-changing technologies that can address the challenges of the industry has stimulated this growth. Nanotechnology has the potential to revolutionize the petroleum industry both upstream and downstream, including exploration, drilling, production, and enhanced oil recovery (EOR), as well as refinery processes. It provides a wide range of alternatives for technologies and materials to be utilized in the petroleum industry. Nanoscale materials in various forms such as solid composites, complex fluids, and functional nanoparticle-fluid combinations are key to the new technological advances. This paper aims to provide a state-of-the-art review on the application of nanoparticles and technology in the petroleum industry, and focuses on enhanced oil recovery. We briefly summarize nanotechnology application in exploration and reservoir characterization, drilling and completion, production and stimulation, and refinery. Thereafter, this paper focuses on the application of nanoparticles in EOR. The different types of nanomaterials, e.g., silica, aluminum oxides, iron oxide, nickel oxide, titanium oxide, zinc oxide, zirconium oxide, polymers, and carbon nanotubes that have been studied in EOR are discussed with respect to their properties, their performance, advantages, and disadvantages. We then elaborate upon the parameters that will affect the performance of nanoparticles in EOR, and guidelines for promising recovery factors are emphasized. The mechanisms of the nanoparticles in the EOR processes are then underlined, such as wettability alteration, interfacial tension reduction, disjoining pressure, and viscosity control. The objective of this review is to present a wide range of knowledge and expertise related to the nanotechnology application in the petroleum industry in general, and the EOR process in particular. The challenges and future research directions for nano-EOR are pinpointed. Full article

The PASCAL VOC Challenge performance has been significantly boosted by the prevalently CNN-based pipelines like Faster R-CNN. However, directly applying the Faster R-CNN to the small remote sensing objects usually renders poor performance. To address this issue, this paper investigates on how to modify Faster R-CNN for the task of small object detection in optical remote sensing images. First of all, we not only modify the RPN stage of Faster R-CNN by setting appropriate anchors but also leverage a single high-level feature map of a fine resolution by designing a similar architecture adopting top-down and skip connections. In addition, we incorporate context information to further boost small remote sensing object detection performance while we apply a simple sampling strategy to solve the issue about the imbalanced numbers of images between different classes. At last, we introduce a simple yet effective data augmentation method named ‘random rotation’ during training. Experimental results show that our modified Faster R-CNN algorithm improves the mean average precision by a large margin on detecting small remote sensing objects. Full article

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time. Full article

There is growing attention and rapid development on flexible electronic devices with electronic materials and sensing technology innovations. In particular, strain sensors with high elasticity and stretchability are needed for several potential applications including human entertainment technology, human–machine interface, personal healthcare, and sports performance monitoring, etc. This article presents recent advancements in the development of polydimethylsiloxane (PDMS)-based flexible resistive strain sensors for wearable applications. First of all, the article shows that PDMS-based stretchable resistive strain sensors are successfully fabricated by different methods, such as the filtration method, printing technology, micromolding method, coating techniques, and liquid phase mixing. Next, strain sensing performances including stretchability, gauge factor, linearity, and durability are comprehensively demonstrated and compared. Finally, potential applications of PDMS-based flexible resistive strain sensors are also discussed. This review indicates that the era of wearable intelligent electronic systems has arrived. Full article

It is becoming increasingly more important to provide a low-cost point-of-care diagnostic device with the ability to detect and monitor various biological and chemical compounds. Traditional laboratories can be time-consuming and very costly. Through the combination of well-established materials and fabrication methods, it is possible to produce devices that meet the needs of many patients, healthcare and medical professionals, and environmental specialists. Existing research has demonstrated that inkjet-printed and paper-based electrochemical sensors are suitable for this application due to advantages provided by the carefully selected materials and fabrication method. Inkjet printing provides a low cost fabrication method with incredible control over the material deposition process, while paper-based substrates enable pump-free microfluidic devices due to their natural wicking ability. Furthermore, electrochemical sensing is incredibly selective and provides accurate and repeatable quantitative results without expensive measurement equipment. By merging each of these favorable techniques and materials and continuing to innovate, the production of low-cost point-of-care sensors is certainly within reach. Full article

The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges of 441–948 nm (Spectral range 1) and 975–1646 nm (Spectral range 2) were extracted. K nearest neighbors (KNN), support vector machine (SVM) and CNN models were built using different number of training samples (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000). KNN, SVM and CNN models in the Spectral range 2 performed slightly better than those in the Spectral range 1. The model performances improved with the increase in the number of training samples. The improvements were not significant when the number of training samples was large. CNN model performed better than the corresponding KNN and SVM models in most cases, which indicated the effectiveness of using CNN to analyze spectral data. The results of this study showed that CNN could be adopted in spectral data analysis with promising results. More varieties of rice need to be studied in future research to extend the use of CNNs in spectral data analysis. Full article

An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learning (DRL)-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules. Furthermore, a DRL-based online learning architecture is presented. It is significant for applying the DRL algorithm in HEV energy management under different driving conditions. Simulation experiments have been conducted using MATLAB and Advanced Vehicle Simulator (ADVISOR) co-simulation. Experimental results validate the effectiveness of the DRL-based EMS compared with the rule-based EMS in terms of fuel economy. The online learning architecture is also proved to be effective. The proposed method ensures the optimality, as well as real-time applicability, in HEVs. Full article