• Energy Research
• 2016-2025close
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SnO2 is one of the most studied materials in gas sensing and is often used as a benchmark for other metal oxide-based gas sensors. To optimize its structural and functional features, the fine tuning of the morphology in nanoparticles, nanowires, nanosheets and their eventual hierarchical organization has become an active field of research. In this paper, the different SnO2 morphologies reported in literature in the last five years are systematically compared in terms of response amplitude through a statistical approach. To have a dataset as homogeneous as possible, which is necessary for a reliable comparison, the analysis is carried out on sensors based on pure SnO2, focusing on ethanol detection in a dry air background as case study. Concerning the central performances of each morphology, results indicate that none clearly outperform the others, while a few individual materials emerge as remarkable outliers with respect to the whole dataset. The observed central performances and outliers may represent a suitable reference for future research activities in the field.

Coastal plant communities are being transformed or lost because of sea level rise (SLR) and land-use change. In conjunction with SLR, the Florida Everglades ecosystem has undergone large-scale drainage and restoration, altering coastal vegetation throughout south Florida. To understand how coastal plant communities are changing over time, accurate mapping techniques are needed that can define plant communities at a fine-enough resolution to detect fine-scale changes. We explored using bi-seasonal versus single-season WorldView-2 satellite data to map three mangrove and four adjacent plant communities, including the buttonwood/glycophyte community that harbors the federally-endangered plant Chromolaena frustrata. Bi-seasonal data were more effective than single-season to differentiate all communities of interest. Bi-seasonal data combined with Light Detection and Ranging (LiDAR) elevation data were used to map coastal plant communities of a coastal stretch within Everglades National Park (ENP). Overall map accuracy was 86%. Black and red mangroves were the dominant communities and covered 50% of the study site. All the remaining communities had ≤10% cover, including the buttonwood/glycophyte community. ENP harbors 21 rare coastal species threatened by SLR. The spatially explicit, quantitative data provided by our map provides a fine-scale baseline for monitoring future change in these species’ habitats. Our results also offer a method to monitor vegetation change in other threatened habitats.

Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure.

Disconnect switch and circuit breakers operations in gas insulated switchgear (GIS) systems may produce very fast transient overvoltage (VFTO). Detecting VFTO is the first step for researchers to reduce the damage to other equipment of the substation caused by VFTO. Most of the existing sensors used for VFTO are generally bulky, complex to install, and require modification of the GIS structure. In this paper, a miniaturized measurement system that uses capacitive voltage divider and differentiating–integrating circuit is proposed. A special sensor structure and optimized differentiating–integrating circuit components arrangement were designed to increase the bandwidth of the measurement system. The frequency-domain, time-domain and voltage divide calibration experiment was performed, and a comparison experiment with an internal VFTO sensor was conducted. The measurement system was applied in the 500 kV GIS substation, and the VFTO measurement under specific conditions was carried out. The measured time domain and frequency domain waveforms conformed to the definition of standard VFTO according to IEC 60,071. It was found that the proposed measurement system meets VFTO measurement requirements and can be applied to actual VFTO measurements.

Galloping of overhead transmission lines (OHTLs) may induce conductor breakage and tower collapse, and there is no effective method for long distance distribution on-line galloping monitoring. To overcome the drawbacks of the conventional galloping monitoring systems, such as sensitivity to electromagnetic interference, the need for onsite power, and short lifetimes, a novel optical remote passive measuring system is proposed in the paper. Firstly, to solve the hysteresis and eccentric load problem in tension sensing, and to extent the dynamic response range, an ‘S’ type elastic element structure with flanges was proposed. Then, a tension experiment was carried out to demonstrate the dynamic response characteristics. Moreover, the designed tension sensor was stretched continuously for 30 min to observe its long time stability. Last but not the least, the sensor was mounted on a 70 m conductor model, and the conductor was oscillated at different frequencies to investigate the dynamic performance of the sensor. The experimental results demonstrate the sensor is suitable for the OHTL galloping detection. Compared with the conventional sensors for OHTL monitoring, the system has many advantages, such as easy installation, no flashover risk, distribution monitoring, better bandwidth, improved accuracy and higher reliability.

Due to the growing impact of the information and communications technology (ICT) sector on electricity usage and greenhouse gas emissions, telecommunication networks require new solutions which will enable the improvement of the energy efficiency of networks. Access networks, which are responsible for the last mile of connectivity and also for one of the largest shares in network energy consumption, are viable candidates for the implementation of new protocols, models and methods which will contribute to the reduction of the energy consumption of such networks. Among the different types of access networks, hybrid fiber–wireless (FiWi) networks are a type of network that combines the capacity and reliability of optical networks with the flexibility and availability of wireless networks, and as such, FiWi networks have begun to be extensively used in modern access networks. However, due to the advent of high-bandwidth applications and Internet of Things networks, the increased energy consumption of FiWi networks has become one of the most concerning challenges required to be addressed. This paper provides a comprehensive overview of the progress in approaches for improving the energy efficiency (EE) of different types of FiWi networks, which include the radio-and-fiber (R&F) networks, the radio-over-fiber networks (RoF), the FiWi networks based on multi-access edge computing (MEC) and the software-defined network (SDN)-based FiWi networks. It also discusses future directions for improving the EE in the FiWi networks.