• Energy Research
• 7. Clean energy close
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N-doped hollow carbon spheres (N-HCSs) are promising candidates as electrode material for supercapacitor application. In this work, we report a facile one-step synthesis of discrete and highly dispersible N-HCSs with dopamine (DA) as a carbon precursor and TEOS as a structure-assistant agent in a mixture containing water, ethanol, and ammonia. The architectures of resultant N-HCSs, including yolk-shell hollow carbon spheres (YS-HCSs), single-shell hollow carbon spheres (SS-HCSs), and double-shells hollow carbon spheres (DS-HCSs), can be efficiently controlled through the adjustment of the amount of ammonia. To explain the relation and formation mechanism of these hollow carbon structures, the samples during the different synthetic steps, including polymer/silica spheres, carbon/silica spheres and silica spheres by combustion in air, were characterized by TEM. Electrochemical measurements performed on YS-HCSs, SS-HCSs, and DS-HCSs showed high capacitance with 215, 280, and 381 F g(-1), respectively. Moreover, all the nitrogen-doped hollow carbon nanospheres showed a good cycling stability 97.0% capacitive retention after 3000 cycles. Notably, the highest capacitance of DS-HCSs up to 381 F g(-1) is higher than the capacitance reported so far for many carbon-based materials, which may be attributed to the high surface area, hollow structure, nitrogen functionalization, and double-shell architecture. These kinds of N-doped hollow-structured carbon spheres may show promising prospects as advanced energy storage materials and catalyst supports.

Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

Biomechanical energy harvesting from human motion presents a promising clean alternative to electrical power supplied by batteries for portable electronic devices and for computerized and motorized prosthetics. We present the theory of energy harvesting from the human body and describe the amount of energy that can be harvested from body heat and from motions of various parts of the body during walking, such as heel strike; ankle, knee, hip, shoulder, and elbow joint motion; and center of mass vertical motion.We evaluated major motions performed during walking and identified the amount of work the body expends and the portion of recoverable energy. During walking, there are phases of the motion at the joints where muscles act as brakes and energy is lost to the surroundings. During those phases of motion, the required braking force or torque can be replaced by an electrical generator, allowing energy to be harvested at the cost of only minimal additional effort. The amount of energy that can be harvested was estimated experimentally and from literature data. Recommendations for future directions are made on the basis of our results in combination with a review of state-of-the-art biomechanical energy harvesting devices and energy conversion methods.For a device that uses center of mass motion, the maximum amount of energy that can be harvested is approximately 1 W per kilogram of device weight. For a person weighing 80 kg and walking at approximately 4 km/h, the power generation from the heel strike is approximately 2 W. For a joint-mounted device based on generative braking, the joints generating the most power are the knees (34 W) and the ankles (20 W).Our theoretical calculations align well with current device performance data. Our results suggest that the most energy can be harvested from the lower limb joints, but to do so efficiently, an innovative and light-weight mechanical design is needed. We also compared the option of carrying batteries to the metabolic cost of harvesting the energy, and examined the advantages of methods for conversion of mechanical energy into electrical energy.

Holding a handrail or using a cane may decrease the energy cost of walking in stroke survivors. However, the factors underlying this decrease have not yet been previously identified. The purpose of the current study was to fill this void by investigating the effect of physical support (through handrail hold) and/or somatosensory input (through light touch contact with a handrail) on energy cost and accompanying changes in both step parameters and neuromuscular activity. Elucidating these aspects may provide useful insights into gait recovery post stroke.Fifteen stroke survivors participated in this study. Participants walked on a treadmill under three conditions: no handrail contact, light touch of the handrail, and firm handrail hold. During the trials we recorded oxygen consumption, center of pressure profiles, and bilateral activation of eight lower limb muscles. Effects of the three conditions on energy cost, step parameters and neuromuscular activation were compared statistically using conventional ANOVAs with repeated measures. In order to examine to which extent energy cost and step parameters/muscle activity are associated, we further employed a partial least squares regression analysis.Handrail hold resulted in a significant reduction in energy cost, whereas light touch contact did not. With handrail hold subjects took longer steps with smaller step width and improved step length symmetry, whereas light touch contact only resulted in a small but significant decrease in step width. The EMG analysis indicated a global drop in muscle activity, accompanied by an increased constancy in the timing of this activity, and a decreased co-activation with handrail hold, but not with light touch. The regression analysis revealed that increased stride time and length, improved step length symmetry, and decreased muscle activity were closely associated with the decreased energy cost during handrail hold.Handrail hold, but not light touch, altered step parameters and was accompanied by a global reduction in muscle activity, with improved timing constancy. This suggests that the use of a handrail allows for a more economic step pattern that requires less muscular activation without resulting in substantial neuromuscular re-organization. Handrail use may thus have beneficial effects on gait economy after stroke, which cannot be accomplished through enhanced somatosensory input alone.

Abstract The classification of various power quality events during the integration of renewable energy sources were proposed in this article. This approach provides a data mining tool linear discrimination analysis for the feature extraction in the expert system where an analytical hierarchy process is used in the knowledge base with the various levels of power quality events. Based on the hierarchy levels the data were compared using the group data handling method and thus the power quality events were classified. To validate the results a real time sample data were used.

Indole is a high-toxic refractory nitrogen-containing compound that could cause serious harm to the human and ecosystem. It has been a challenge to develop economical and efficient technology for degrading indole. Microbial fuel cell (MFC) has great potential in the removal of organic pollutants utilizing microorganisms as catalysts to degrade organic matter into the nutrients. Herein, a novel anode of Fe2O3-polyaniline-dopamine hybrid composite modified carbon felt (Fe2O3-PDHC/CF) was prepared by electrochemical deposition. The degradation efficiency of indole by the MFC loading Fe2O3-PDHC/CF anode was up to 90.3 % in 120 h operation, while that of the MFC loading CF anode was only 44.0 %. The maximum power density of the MFC loading Fe2O3-PDHC/CF anode was 3184.4 mW·m-2, increasing 113 % compared to the MFC loading CF anode. The superior performances of the MFC with Fe2O3-PDHC surface-modified anode owned to the synergistic effect of high conductive Fe2O3 and admirably biocompatible polyaniline-dopamine. MFC with the Fe2O3-PDHC/CF anode could produce considerable electricity and effectively degrade indole in water, which demonstrated a practical approach for the efficient degradation of refractory organic compounds in wastewater.

The state of charge (SOC) is one of the main indexes of the lithium-ion battery, which affects the practice range of new energy vehicles and the safety of the battery. Nevertheless, the value of SOC cannot be measured directly. At present, the algorithm for estimating the state of charge is not very satisfactory. The multilayer perceptron algorithm designed during this paper encompasses a sensible impact on state estimation. During this paper, the multilayer network is designed to estimate the charged state of lithium batteries from the three-layer artificial neural network to the eleven-layer artificial neural network. After preprocessing the dataset and comparing several activation functions, the ten-layer fully connected neural network is the most efficient to estimate the SOC. In order to prevent over-fitting of the multilayer perceptron algorithm, the two techniques of the BatchNormalization layer and Dropout layer work together to inhibit over-fitting. At the same time, the accuracy of extended Kalman filter, long and short memory network, and recurrent neural network are compared. The multilayer perceptron network designed during this paper has the highest accuracy. Finally, in the open dataset, both the training and test errors achieve good results. The algorithm developed in this paper has made some progress in SOC estimation.

When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent randomness and volatility. Consequently, predicting PV power generation becomes necessary for reasonable power distribution scheduling. A hybrid model based on an improved bird swarm algorithm (IBSA) with extreme learning machine (ELM) algorithm, i.e., IBSAELM, was developed in this study for better prediction of the short‐term PV output power. The IBSA model was initially used to optimize the hidden layer threshold and input weight of the ELM model. Further, the obtained optimal parameters were input into the ELM model for predicting short‐term PV power. The results revealed that the IBSAELM model is superior in terms of the prediction accuracy compared to existing methods, such as support vector machine (SVM), back propagation neural network (BP), Gaussian process regression (GPR), and bird swarm algorithm with extreme learning machine (BSAELM) models. Accordingly, it achieved great benefits in terms of the utilization efficiency of whole power generation. Furthermore, the stability of the power grid was well maintained, resulting in balanced power generation, transmission, and electricity consumption.