Electrical Circuit Research Articles

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

An approach to interpreting natural indicators of the state of space weather to assess the effects of its impact on high-latitude power systems

The dynamic exploration and development of the Arctic zone of the Russian Federation is inextricably connected with the need to minimize technospheric risks, including those associated with the space weather effects on power equipment systems operated within the boundaries of the auroral oval. At the same time, accompanying monitoring of space weather parameters and geomagnetic field variations in the Arctic is carried out only through a group of satellites and several dozen magnetic stations located mainly in the United States, Canada, northern and central Europe. Obviously, the current situation practically excludes the possibility of promptly diagnostics of the level of geoinduced currents (GIC) for most of the Arctic zone of the Russian Federation, where in fact the only available indicator of the state of space weather is auroras. In the paper the authors propose an approach to interpreting the manifestation of auroras to assess the effects of space weather on objects and systems of high-latitude infrastructure. Thus, using the example of the “Vykhodnoy” substation of the “Severny Transit” main electrical network, it is shown that when recording auroras in the north, zenith and south, the most probable (averaged over 30 min) GIC level is 0.08, 0.23 and 0.68 A accordingly. In this case, the probability that the average half-hour GIC level will exceed 2 A (in the case of auroras in the north, zenith and south) is ∼6, ∼10 and ∼15%, respectively. In conclusion, ways of modernization and the limits of applicability of the proposed approach are considered.

Power system stability enhancement through optimal PSS design

Low-frequency oscillations (LFO) are generated in interconnected electric networks because of the weak tie lines between the parts of the networks. Such oscillations have been a significant challenge for engineers that may lead to system instability if appropriate measures are not taken. This article proposes two new metaheuristic algorithms, the jellyfish search algorithm (JSA) and the tunicate swarm algorithm (TSA), to design the power system stabilizers (PSS) for single machine infinite bus (SMIB) and multimachine power system (MPS) networks. The proposed method uses the JSA and TSA to dampen out the LFOs by modifying the vital parameters of lead-lag type conventional PSS (CPSS). A damping ratio-based objective function improves both models' system damping. These methods are tested on an SMIB system and two distinct MPS networks exposed to the three-phase fault under various loading conditions. The effectiveness of the suggested approach has been studied by comparing the system eigenvalues and minimum damping ratio (MDR) produced from JSA and TSA-tuned PSS and the CPSS. In addition, time domain simulation results are compared, demonstrating that the new approach outperforms the standard techniques, such as particle swarm optimization (PSO) and backtracking search algorithm (BSA). In the SMIB system, MDR produced by JSA and TSA-based PSS is 2.55 times higher than that of CPSS. Similarly, in comparison to PSO and BSA-based PSS, JSA, and TSA-tuned PSS offer >1.4 times greater MDR in the Two Area Four Machine network and up to 8.4 times larger MDR for the IEEE-39 Bus network. Furthermore, the efficacy of the suggested JSA and TSA approaches' prediction capacity is validated by satisfying the values of statistical performance metrics.

Circuit topology aware GNN-based multi-variable model for DC-DC converters dynamics prediction in CCM and DCM

A regression model based on graph neural network, tailored for electric circuit dynamics prediction is introduced, providing converter performance predictions on converter circuit level and internal parameter variations. Regardless of the number of components or connections present in a converter circuit, the proposed model can be readily scaled to incorporate different converter circuit topologies. Moreover, the model can be used to analyse converter circuits with any number of circuit components and any control parameters variation. To enable the use of machine learning methods and applications, all physical and switching circuit properties such as converter circuits operating in continuous conduction mode or discontinuous conduction mode are accurately mapped to graph representation. Three of the most common converters (Buck, Boost, and Buck-boost) are used as example circuits applied to model and the target is to predict the gain and current ripples in inductor. The model achieves 99.51% on the R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} measure and a mean square error of 0.0263.

Analysis of the results of studying the quality of electric power in the electric networks of cjsc sibgazstroydetal

Analysis of the results of studying the quality of electric power in the electric networks of cjsc sibgazstroydetal

Predicting power and solar energy using neural networks and PCA with meteorological parameters from Diass and Taïba Ndiaye

The excessive reliance on conventional fossil fuel-based resources poses a significant threat to our environment. To mitigate this impact, it has become increasingly crucial to increase the integration of intermittent and non-polluting energy sources into our electrical grids. However, while this higher penetration rate brings benefits such as improved producer satisfaction and reduced fossil fuel consumption, it also presents challenges for traditional non-smart electrical networks. To promote intermittent energy sources effectively and maintain a balance between consumption and production, accurate forecasting of these energy outputs plays a vital role. This research paper focuses on studying the application of artificial neural networks for predicting the power and energy output of the Diass solar power plant in the short and medium term. The proposed approach utilizes not only the meteorological data from the city where the power plant is located but also data from a nearby city with a data acquisition station. Principal component analysis (PCA) is employed to select the relevant variables for the prediction model. Furthermore, the results obtained from our approach are compared to existing literature that solely uses meteorological data from the power plant's location. The comparison shows that our method achieves more satisfactory results, with mean absolute errors and root mean square errors of 0.0223 KWh and 0.003 KWh, respectively, and a prediction accuracy of 94.57% in terms of energy and power. It is worth noting that the computational resource requirements for our approach are higher, with simulation times ranging between 1788 seconds and 2201 seconds. By utilizing a broader range of data sources and employing advanced techniques like artificial neural networks, this research contributes to improving the accuracy of solar power generation forecasts. The findings highlight the potential of incorporating additional data inputs and advanced modeling techniques to enhance the performance of renewable energy systems, paving the way for a more sustainable and efficient energy future.

Effective resistance, random walk and algorithms

Efficiently determining the effective resistance of large-scale electrical networks is crucial for optimizing energy distribution and assessing network robustness. In this paper, we propose a novel approach that combines random walk simulations and advanced linear algebra techniques to compute the effective resistance of complex electrical networks. Our method leverages the concept of random walks to simulate the flow of current through the network, capturing its behavior under various conditions. Subsequently, we employ graph Laplacian-based linear algebra algorithms to analyze the resulting data, enabling accurate computation of the effective resistance. In our model, we aim to deploy these combined algorithm to develop a novel method to deal with such methods.

A Review on Electric Vehicles for Holistic Robust Integration in Cities: History, Legislation, Meta-Analysis of Technology and Grid Impact

Electric vehicle technology is transitioning from mobility based on fossil fuel combustion to one based on vehicle electrification, in which the primary energy is increasingly renewable, and the generation of pollutants and CO2 emissions is being reduced. This paper provides a tour of the key aspects of these systems, reviewing their most important historical, legislative, and grid impact topics. For this purpose, a literature review of publications up to 2022 is conducted. The last decade is the subject of a deeper analysis, shedding light on the essential characteristics of this technology and fundamentally focusing on its integration into electrical distribution networks. This work is carried out based on a review of a selection of articles written by authors worldwide who have researched these topics. We ordered and analyzed the temporal evolution of the defined categories, obtaining their research line direction. A meta-analysis of grid impact was also carried out, prompting clear conclusions about the state of the art and potential future works.

A dynamic reference voltage adjustment strategy for Open-UPQC to increase hosting capacity of electrical distribution networks

Future electrical grids, particularly the distribution networks, may face more severe voltage rises/drops, and in general, more power quality problems in the presence of new loads such as electric vehicle chargers and renewable energy generation units like photovoltaic systems. This necessitates investing in additional high-cost infrastructure to increase the capability of the feeder in hosting higher levels of loads and generation units while the existing capacity is not utilized effectively. In the stated condition, effective voltage stabilization strategies in electrical distribution networks can contribute to hosting capacity improvement and the better utilization of the existing infrastructure. Accordingly, in this paper, the application of Open-UPQC in voltage profile improvement and hosting capacity enhancement is evaluated in low-voltage distribution networks. Furthermore, a dynamic reference voltage adjustment strategy is applied to the device to improve its capabilities in power quality improvement and hosting capacity enhancement. Simulation studies have been implemented to evaluate the capability of Open-UPQC either with static reference voltage or the dynamically-adjusted one in low-voltage networks with real measured data while different cases are assessed regarding the topology and the length of the feeder. The simulation results approved the capability of Open-UPQC especially with the dynamic reference voltage in hosting capacity enhancement while providing the highest level of voltage profile improvement among all the assessed custom power devices in the studied low-voltage networks.

Optimal allocation of EV charging station and capacitors considering reliability using a hybrid optimization approach

This paper proposes a novel approach for strategically placing EV charging stations (EVCS) and capacitors within electrical distribution networks (EDN) to mitigate issues arising from the widespread adoption of Electric Vehicles (EVs). The goal is to maximize the net present value (NPV) by reducing energy losses and minimizing system interruption costs while ensuring reliability. To achieve this, the paper introduces a hybrid optimization technique called HGP_CS, which combines Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Cuckoo Search optimization methods. By employing various compensation coefficients to assess failure rates and identify strategies for enhancing NPV, the approach aims to optimize the placement of EVCS and capacitors within the EDN. Validation is carried out in IEEE 33-bus and 118-bus systems to demonstrate the performance of the proposed approach. It is found that the proposed approach, compared to the base case, reduced the energy loss cost by 34.19% and 31.57% and also the Expected Interruption Costs (EIC) by 17.26% and 13.41%, for IEEE 33-bus and 118-bus system, respectively. Additionally, the proposed method is economically beneficial as it can attain higher profits. In IEEE 33- bus and 118 bus systems, the annual net profit using the proposed approach is 36,805 $/year and 271,670.72 $/year, respectively. Thus, the proposed technique enhances the overall system performance and significantly boosts profitability, making it a compelling choice for optimizing the placement of charging stations and reactive power sources in networks.

Light-Controlled Electric Stimulation with Organic Electrolytic Photocapacitors Achieves Complex Neuronal Network Activation: Semi-Chronic Study in Cortical Cell Culture and Rat Model.

Neurostimulation employing photoactive organic semiconductors offers an appealing alternative to conventional techniques, enabling targeted action and wireless control through light. In this study, organic electrolytic photocapacitors (OEPC) are employed toinvestigate theeffects oflight-controlled electric stimulation onneuronal networks in vitro and in vivo. The interactions between the devices and biological systems are characterized. Stimulation of primary rat cortical neurons results in anelevated expression of c-Fos within a mature neuronal network. OEPC implantation for three weeks and subsequent stimulation of the somatosensory cortex leads to anincrease of c-Fos in neurons at the stimulation site and in connected brainregions (entorhinal cortex, hippocampus), both intheipsi- andcontralateral hemispheres. Reactivity ofglial andimmune cells after semi-chronic implantation of OEPC in the rat brain is comparable tothatofsurgical controls, indicating minimal foreign body response. Device functionality is further substantiated through retained charging dynamics following explantation. OEPC-based, light-controlled electric stimulation has asignificant impact onneural responsiveness. Theabsence ofdetrimental effects onboth the brain and device encourages further use of OEPC as cortical implants. These findings highlight its potential as anovel mode of neurostimulation and instigate further exploration into applications in fundamental neuroscience.

Computer Vision Search System for the Identification of Power Flow Faults in Micro Electric Networks

To improve the safety of microgrid operation, this paper uses a computer vision search system to study the identification of power flow faults in microgrids. Firstly, a power flow fault detection method based on a message transmission network was proposed, and the fault mapping from the microgrid fault to each node was constructed, and the power flow fault of each node in the microgrid was extracted. Then, by migrating the long short-term memory network to identify power flow faults in microgrids, the influence of computer vision search system on power flow fault identification in microgrids was comprehensively analyzed. The simulation results show that the proposed method can effectively improve the accuracy of power flow fault identification in the microgrid to improve the safety of the microgrid operation.

Investigation of the Composite Motor Load in the Presence of Higher Harmonics in the Electrical Network

Investigation of the Composite Motor Load in the Presence of Higher Harmonics in the Electrical Network

Modeling Interdependent Infrastructure System Vulnerability with Imprecise Information Using Two Fuzzy Inference Systems

Infrastructure systems play important roles in economic development and the social quality of life. Interdependencies exist between infrastructure systems: a functional disruption in one system can affect dependent systems, thereby escalating the impacts. It is vital to properly model interdependencies to understand the full impacts of disruptive events on infrastructure systems. Quantitative data on infrastructure interdependency is often difficult to obtain or unavailable for a variety of reasons. To overcome quantitative data scarcity issues, qualitative subject expert knowledge has been used in interdependency analysis, primarily in the form of linguistic responses. Linguistic data is susceptible to uncertainties arising from variations in intended meanings, which may yield inaccurate results. This paper proposes a framework to address this problem using two fuzzy inference systems to model event-specific, network-wide infrastructure failures. The first fuzzy inference system models the damage induced by interdependencies using verbal descriptions. The second fuzzy inference system accounts for synergistic, compounding effects of multiple incidences of indirect damage caused by interdependencies. A case study is conducted to demonstrate the applicability of the proposed methodology using electric and gas distribution networks in the United Kingdom. Sensitivity analyses are performed to show the flexibility of the fuzzy inference systems. The results show that the proposed method can model the interdependency and vulnerability of infrastructure systems using fuzzy inference systems to handle imprecise input. The proposed framework may assist practitioners in better understanding the interdependency and vulnerability of infrastructure systems, and in making more informed decisions to reduce losses resulting from disruptive events.

Dynamometer card generation for pumping units based on CNN and electrical parameters

In the actual production process of oil fields, the real-time and accurate acquisition of dynamic recorder data from the pumping unit is of great significance for the diagnosis of well failures. The traditional method of obtaining the card of the dynamometer usually includes installing a load sensor on the auxiliary head of the pumping unit. However, due to the harsh environment of the oil field production site, these load sensors often suffer from damage, distortion, and aging, resulting in large measurement errors and low reliability. This paper proposes a mixed model of pumping based on motor electrical parameter data and CNN convolutional neural network, which has good consistency with actual data in terms of predictive performance. Thus, the highlights of this paper can be summed up in two points: (1) Based on the mathematical model of the AC motor, the speed of the motor and the torque output of the motor are accurately estimated. (2) The convolutional neural network is introduced to compensate for the errors caused by the defects of the pumping unit mechanism model.

Sizing a Renewable-Based Microgrid to Supply an Electric Vehicle Charging Station: A Design and Modelling Approach

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

Robust Design of Electrical System for Nuclear Plants to Prevent a Nuclear Accident During an Emergency Situation

The Chernobyl accident taught us an important lesson regarding the significance of the emergency diesel generator (EDG), which is crucial for supplying a nuclear facility with emergency alternating-current power. A worst-case scenario would occur if the EDG failed to start functioning. In response to this problem, this research addresses the issue and suggests an optimal design for another alternative electrical source using solar energy. Until now, employing this technology presented a challenge as a solar energy system generates electrical harmonics that are transmitted from the photovoltaic (PV) grid to the electrical network. These harmonics can negatively impact the performance and operation of nuclear facilities. Consequently, this study proposes a comprehensive investigation that tackles this issue by implementing a LCL filter to mitigate the harmonics in the PV network. This paper not only provides an alternative solution in the event of a diesel generator breakdown, but also studies the effect of this solution on the station’s performance and how to improve it. A mathematical model based on the MATLAB program is developed to simulate the application of the LCL filter strategy. The proposed technique is tested through simulation with an actual case, demonstrating its reliability and high-quality outcomes.

Ultrathin flaky FeSiAl and Fe3O4@C core-shell integrating the high-performance microwave absorber with gradient anti-corrosion barriers

Heterogeneous interface engineering and magnetic coupling are the main promising strategies for efficient microwave absorbers, but getting them stable in high-salt and humidity environments remains a challenge. Herein, we developed ultrathin flaky FeSiAl(FSA) integrating with Fe3O4@C core-shell for the high-performance microwave absorber with gradient anti-corrosion barriers. The Fe-Prussian blue analogue (PBA) derived microcubes decorated ultrathin FSA are prepared and transformed into Fe3O4@C core-shell in range of 500 ∼ 700 °C. The introduction of ferric oxide favors to impedance matching. Furthermore, the ultra-thin carbon layer and ferric oxide form a hybrid structure, which is conducive to enhancing electron spin and thereby enhancing microwave loss. This hybrid possess enhanced microwave absorption properties with a minimum reflection loss (RLmin) of −45.5 dB at 9.2 GHz at a thickness of 2.5 mm. Moreover, the maximum effective absorption bandwidth value reaches 5.2 GHz from 12.8 to 18 GHz at 1.6 mm. The gradient carbon layers and Fe3O4 provided the bi-barriers towards corrosive medium. Then corrosion resistance of the electric circuit increased up to 3.56×107 Ω∙cm2 for FSA/Fe3O4@C-600. Meanwhile, the corresponding corrosion current decreased into 2.52×10−8 A∙cm−2. Hence, introducing multi-heterointerfaces and dual-magnetic components opens up a fantastic avenue for designing high-efficient absorbers with gradient anti-corrosion barriers.

Design and on-site alert effect of piezoelectric device with amplified displacement for improving clean-energy collection

The installation of road alert devices leads to increased power consumption and electric network complexity on roads, which can be mitigated by cantilever piezoelectric transducers applicable to self-powered road alert devices. However, currently the cantilever piezoelectric transducer does not match the actual pavement deformation. In view of this, the link-type stroke amplification mechanism for road was designed. Then, the factors influencing the magnification were clarified and the size parameters were optimized. Finally, the performance of the piezoelectric transducer array under the amplification mechanism was investigated, and the traffic alert application effect of the amplified displacement road piezoelectric device was tested. The results showed that: The end connecting rod with short length and small distance and the middle connecting rod with longer length were favorable to the magnification. The theoretical magnification of the fabricated stroke amplification mechanism is 2.0. Under its action, the 0.7 mm excitation displacement could result in piezoelectric transducer array electrical outputs of 48 V, 103.96 mW, and power densities of 32.1 W/m2. And the alert piezoelectric self-powered effect was good based on the actual road test. The results will help improve the level of road alert self-powered, and promote the development of clean energy on the road.

Biofilm Spatial Monitoring By Multiple Electrochemical Techniques Integrated with Optical Microscopy and a Microfluidic Device

Biofilm represents the inherent structure of bacterial populations, consisting of bacteria encapsulated within extracellular polymeric substances they secrete. The natural formation of a biofilm acts as a physical barrier, offering protection against environmental changes and increasing bacterial antibiotic resistance, often up to 1,000 times. To enhance our understanding of these intricate biosystems, electrochemical analytical micro-systems are crucial to mimic the biofilm’s behavior in nature and to monitor responses to diverse treatments. Current electrochemical micro-systems, however, face a fundamental limitation, employing a single electrode to characterize biofilm behavior [1], despite biofilms being inherently three-dimensional and non-uniformly distributed across the electrode. Here, we introduce a novel electrochemical micro-systems that is based on an array of microelectrodes and microfluidics to provide spatial monitoring of the biofilm. Fabricated through conventional photolithography and lift-off techniques, the micro-system comprises five individual gold microelectrode arrays covered with a polydimethylsiloxane microfluidic channel (Fig. 1A) and linked to a reference Ag/AgCl electrode (Fig. 1B). In a proof-of-concept study focusing on monitoring the biofilm formation and functionality, we cultivated Pseudomonas aeruginosa, a pathogenic bacterium secreting electroactive molecules known as phenazines when reaching high cell density. Subsequently, we monitored biofilm formation over 30 hours by repetitively measuring the electrochemical signal using differential pulse voltammetry (DPV). The analysis of anodic (Fig. 1C) and cathodic (Fig. 1D) currents unveiled an increasing oxidation peak at -0.28V vs Ag/AgCl, varying among the five electrodes, attributed to phenazine formation (Fig. 1E). Furthermore, both anodic and cathodic currents decreased at the negative applied potential (-0.8 V vs Ag/AgCl), correlating with bacterial growth and phenazines production (Fig. 1F). Electrochemical impedance spectroscopy (EIS) at different DC potentials (-400, +50, and +400 mV vs Ag/AgCl; Fig. 1G, +400mV) was performed, and the impedance generated was correlated with 10 different equivalent electrical circuits (optimal circuit with the best fitting score is shown in Fig. 1H) [2]. Hourly monitoring of biofilm growth was conducted using microscopy imaging at multiple locations along the microfluidic channel (Fig. 1I, image after 10hr). The experiment's conclusion involved validating biofilm formation through fluorescent staining of the biofilm's sugars (Figs. 1J – 1L, phase image, fluorescent image, and overlay image). By integrating the developed electrochemical micro-system with microscopy imaging, real-time and direct monitoring of biofilm formation becomes achievable. Furthermore, this integrated system can be employed across electrochemical and microscopic platforms to explore biofilm properties in response to diverse conditions. Figure 1. (A) Microfluidic electrochemical array illustration (B) photograph of the experimental setup (C) 30-hours anodic and cathodic (D) voltammograms recorded in the presence of Pseudomonas aeruginosa (PA14). (E) transient oxidation peak current at -0.28V (F) the anodic and cathodic currents at -0.8V (G) transient impedance spectrographs recorded from PA14 (H) raw data and fitted electric circuit. Microscopic imaging of biofilm growth at (I) 10 hr, end of experiment (J) phase image, (K) biofilm polysaccharides fluorescent staining and (L) overlayed image.[1] Estrada-Leypon, O. et. al, Bioelectrochemistry 2015, 105, 56–64.[2] Ben-Yoav, H. et.al, Electrochim. Acta 2011, 56 (23), 7780–7786. Figure 1