Electrical Energy Research Articles

From stress to charge: investigating the piezoelectric response of solvate ionic liquid in structural energy storage composites.

Solvate ionic liquids (SILs) are a class of ionic liquids where the liquid-state salt is chelated by a coordinating solvent, and of interest due to their advantageous properties such as low vapour pressure and superb thermal and chemical stability for energy storage applications. The electromechanical and piezoelectric effect were studied in lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) solvated by triethylene glycol dimethyl ether (triglyme, G3), forming [Li-G3]TFSI. These effects were also investigated in full solid polymer electrolyte (SPE) used in energy storage devices, consisting of [Li-G3]TFSI paired with an epoxy-based resin system. The SIL's electromechanical response was first established in isolation, as well as within the SPE. Experimental data demonstrates the effect of a major part of the SPE contributing to the electrical potential generation during application of force and subsequent pressurisation as well as depressurisation, underlined by a direct piezoelectric effect. SPE response to applied load is explored after the recent discovery of liquid-to-crystalline phase transition following pressurisation in pure ionic liquids. This finding has the potential to ameliorate the performance of energy storage composites via additional effects of charging such a device by subjecting it to stress, leading to increased efficiency. Results to date show a bulk potential difference across the SIL of up to 150 mV, while the SPE potential response is scaled down due to a significantly lower volume of SIL at the interface (∼30 mV). Nevertheless, such findings can still significantly affect the performance of carbon fibre (CF)-based structural supercapacitors and batteries that are able to store and release electrical energy whilst simultaneously contributing to load-bearing performance.

A piezoelectric-electromagnetic hybrid energy harvester for rotational motion driven by magnetic repulsion

Harvesting energy from rotational motion is an efficient and widely used technology that provides power support for various electronic devices and systems by converting mechanical kinetic energy into electrical energy. This energy harvesting method shows great potential and advantages in industrial automation, transportation, wind power generation, and smart homes. This study proposes a piezoelectric-electromagnetic hybrid energy harvester (HEH) for rotational motion driven by magnetic repulsion. HEH comprises of two parts: a piezoelectric energy harvester (PEH) and an electromagnetic energy harvester (EMH). HEH utilizes the magnetic drive reciprocating motion to enhance the energy collection efficiency of PEH and EMH. Additionally, by adding the magnetic effect to the ends of bimorph piezoelectric sheets, the spectrum of energy harvesting is expanded. Its parameters are analyzed using theoretical analysis and simulation, and an experimental testbed is established to explore the influence of HEH output performance. The results indicate that the output power reaches its maximum when there are 2 circular magnets on the rotor, the gap distance of magnets is 15 mm, and 2 mass blocks at the end of the bimorph piezoelectric sheet. The PEH and EMH outputs are 173.36 V and 4.81 V, respectively. The maximum output power of HEH is 53.45 mW. The power density can reach 6.818 mW cm−3. Compared with PEH and EMH, the output performance is improved by 46.94% and 174.95%, respectively. When the rotation speed is 500 r min−1, HEH can effortlessly light up 80 LEDs. The experimental results all demonstrate the potential of HEH to power low-power sensors.

Dual photoelectrode-drived Fe–Br rechargeable flow battery for solar energy conversion and storage: A cost-effective approach

The integrated design of solar energy conversion and storage systems has attracted increasing attention, and non-spontaneous redox reactions driven by dual photoelectrodes provide a potential solution to this issue. This study presents a solar rechargeable flow battery (SRFB) that combines dual photoelectrodes (BiVO4 or Mo–BiVO4 as photoanode, polyterthiophene (pTTh) as photocathode) with cost-effective redox pairs (Fe3+/Fe2+ and Br3−/Br−). The system charges under simulated solar illumination (100 mW∙cm−2, AM 1.5G) and releases stored energy controllably as electrical energy. Research indicates the Mo–BiVO4 photoanode and pTTh photocathode achieve an open-circuit voltage of 0.34 V and a short-circuit current density of 0.38 mA cm−2. Using a specially designed Fe3+/Fe2+-Br3−/Br− (Fe–Br) SRFB device made via 3D printing, a charging photocurrent of approximately 1.9 mA cm−2 is attained. In constant current discharge tests, an initial discharge voltage of 0.23 V is observed at 0.1 mA∙cm−2 within the 10 discharge cycles, demonstrating system stability and offering a viable solution for low-cost, large-scale solar energy storage and conversion.

Multibioinspired Hybrid Superwetting Surface for Efficient Fog Collection and Power Generation.

Obtaining water and renewable energy from the atmosphere provides a potential solution to the growing energy shortage. Leveraging the synergistic inspiration from desert beetles, cactus spines, and rice leaves, here, a multibioinspired hybrid wetting rod (HWR) is prepared through simple solution immersion and laser etching, which endows an efficient water collection from the atmosphere. Importantly, benefiting from the bionic asymmetric pattern design and the three-dimensional structure, the HWR possesses an omnidirectional fog collection with a rate of up to 23 g cm-2 h-1. We further show that the HWR could be combined with a droplet-based electricity generator to convert kinetic energy from falling droplets into electrical energy with a maximum output voltage of 200 V and a current of 2.47 μA to light up 28 LEDs. Collectively, this research provides a strategy for synchronous fog collection and power generation, which is promising for environmentally friendly energy production.

A high-efficiency poly-input boost DC–DC converter for energy storage and electric vehicle applications

This research paper introduces an avant-garde poly-input DC–DC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sources—solar energy and fuel cells—with an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 85–90% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 91–95% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDC’s adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DC–DC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures.

An improved data-driven predictive optimal control approach for designing hybrid electric vehicle energy management strategies

This paper proposes an improved “prediction + optimal control” method for energy management in hybrid electric vehicles equipped with planetary gears. A differentiable predictor and a differentiable optimal controller are developed using supervised learning and reinforcement learning approaches, respectively. Three training steps are performed for the initial predictor, the optimal controller, and the final predictor. This method improves the traditional energy management predictive optimal control approach by incorporating an additional step of retraining the differentiable predictor. This adjustment ensures that the predictor does not blindly improve its performance based on evaluation criterion irrelevant to energy management, which was commonly used in previous studies. Instead, it focuses on enhancing the overall performance of energy management under the “prediction + optimal control” framework. The approach introduced in this paper is compared with the globally optimal dynamic programming results and traditional predictive optimal control methods on the Next Generation Simulation (NGSIM) data. Our method outperforms traditional approaches in energy management on both the training dataset and the test dataset. This further illustrates that the conventional practice of presumptuously optimizing predictors in “prediction + optimal control” methods can be improved using the proposed method.

Research on a Metal–Organic Framework (MOF)-Derived Carbon-Coated Metal Cathode for Strengthening Bioelectrochemical Salt Resistance and Norfloxacin Degradation

Microbial fuel cells (MFCs) can realize the conversion of chemical energy to electrical energy in high-salt wastewater, but the easily deactivated cathode seriously affects the performance of MFCs. To enhance the stability and sustainability of MFC in such circumstances, a bimetallic organic framework ZIF-8/ZIF-67 was utilized for the synthesis of a carbon cage-encapsulated metal catalysts in this study. Catalysts with different Co and Ce ratio (Co@C (without the Ce element), CoCe0.25@C, CoCe0.5@C, and CoCe1@C) were employed to modify the activated carbon cathodes of MFCs. The tests demonstrated that the MFCs with the CoCe0.5@C cathode catalyst obtained the highest maximum power density (188.93 mW/m2) and the smaller polarization curve slope, which boosted the electrochemical activity of microorganisms attached to the anode. The appropriate addition of the Ce element was conductive to the stability of the catalyst’s active center, which is beneficial for the stability of catalytic performance. Under the function of the CoCe0.5@C catalyst, the MFCs exhibited superior and stable norfloxacin (NOR) degradation efficiency. Even after three cycles, the NOR degradation rate remained at 68%, a negligible 5.6% lower than the initial stage. Furthermore, based on the analysis of microbial diversity, the abundance of electrogenic microorganisms on a bioanode is relatively high with CoCe0.5@C as the cathode catalyst. This may be because the better cathode oxygen reduction reaction (ORR) performance can strengthen the metabolic activity of anode microorganisms. The electrochemical performance and NOR degradation ability of MFC were enhanced in a high-salt environment. This paper provides an approach to address the challenge of the poor salt tolerance of cathode catalysts in MFC treatment, and presents a new perspective on resource utilization, low carbon emissions, and the sustainable treatment of high-salt wastewater.

Modelling of Lifetime Inverter Batteries and Energy Storage Systems

Lifetime batteries are essential component of energy storage systems. These batteries are designed to store electrical energy and provide power during periods of power outages. Energy storage systems are broader solutions for storing electrical energy which often include inverter batteries (Lithium ion) as a component. Energy storage systems are becoming increasingly important for integrating renewable energy sources like solar ensuring stability and reliability. To maximize battery performance and extend lifespan, precise assessment of the batteries' state of charge, battery impedance, and remaining capacity is necessary. In this paper, Gamma Distribution (G (α, β)) and Weibull Distribution (W (α, β)) were used to model the lifetime batteries and energy storage systems in other to determine the maximum lifetime of an inverter batteries and secondly to compare the results obtained from the two distributions. The two probability distributions (The Gamma and The Weibull) were employed in the analysis of our simulated data because of their flexibility in modeling data longevity. From the results obtained, it was observed that the average lifespan of inverter batteries is seven years (7 years). Weibull distribution demonstrated superior fit when compared with Gamma distribution in handling the modeling of lifetime inverter batteries.

Short‐term electric power and energy balance optimization scheduling based on low‐carbon bilateral demand response mechanism from multiple perspectives

AbstractCarbon emissions limit the output of traditional fuel‐fired generating units, significantly affecting the new power system scheduling mechanism. This paper proposes a short‐term electric power and energy balance optimization scheduling method with low‐carbon bilateral demand response (LCBDR). The LCBDR mechanism framework is constructed by combining the analysis of short‐term electric power and energy balance of the system under a dual perspective, along with the electric‐carbon coupling mechanism of the dynamic scheduling on the source‐load side. Based on the carbon emission flow (CEF) theory, the carbon emission index information of load‐side users is obtained. An optimal scheduling model of LCBDR is established. The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with “ε‐greedy” strategy is proposed to solve this model. Comprehensive case studies from three different perspectives verify that this method can effectively realize the low‐carbon economic operation of the system, with the peak net load reduced by 24.02% and valley net load increased by 20.43%. Compared with a single perspective, the total operational costs can be reduced by 5.27%, and the carbon emissions of users can be reduced by 5.70%.

Estimation and Management of Wind Energy in Ikorodu Lagos Lagoon, Using Weibull Parametric Measurement to Electricity Production in Nigeria

This paper is an approach on the estimation and approximation management of wind energy production around the lagoon axis of Ikorodu, Lagos state, Nigeria. The article concentrates on the availability of renewable resource such as wind to generate electrical energy in the greater Ikorodu metropolis of Lagos state Nigeria. Here, probability distribution function is used to generate the wind data. In this paper, three distinct methods are presented; Data time series analysis, Weibull probability function, and theoretical comparison with analytical concept. This research uses two important parameters for analyzing wind data: shape factor “k” and scale factor “c” from Weibull distribution function. The theoretical uses mathematical equations of popular methods such as: (a) Moment method, (b) Empirical Formula, or statistical standard deviation, (c) Peak likelihood, (d) modified peak likelihood, (e) double modified peak likelihood (f) graphical method or smallest mean square, and (g) energy sequence factor. The results obtained are tested to optimize the value from the Weibull parameters by adopting five techniques: (i) root average square error methodology, X2, power of agreement , MAPE, and RRMSE. The results expatiated on the practical and theoretical techniques design to confront the outcome of wind energy harnessed per 1.5 km2. Here, a differential optimization technique is used to determine the precision report. This serve as the basis of error litmus check existing between the wind energy determined by theory of statistical and mathematical Weibull Parametric function and the practical time-series data analysis in LSTM. Again, the wind data (speed and energy/power) were measured and recorded between January 2020 to December 2023 in the Ijede-Ikorodu Lagoon area of Lagos State. The optimized value for the shape factor k and scale factor c parametric measurement and management for maximizing the output electrical energy are obtained by using a well robust Weibull distribution function techniques and by absolutely determining and selecting the best position and location for installing the wind/wave alternators/generator. These generators come with turbines as a single unit. The measurement of the yearly average wind speed and average wind power are 10.09 ms-1 and 10.1 KWm-2, concurrently.

Carbon‐Based Textile‐Structured Triboelectric Nanogenerators for Smart Wearables

Recent advances in wearable electronics have been propelled by the rapid growth of microelectronics and Internet of Things. The proliferation of electronic devices and sensors relies heavily on power sources, predominantly batteries, with significant implications for the environment. To address this concern and to reduce carbon emissions, there is a growing emphasis on renewable energy harvesting technologies, among which textile‐based triboelectric nanogenerators (T‐TENGs) stand out as an innovative and sustainable solution due to having the interesting characteristics like large contact area, lightweight design, flexibility, comfort, scalability, and breathability. T‐TENGs can harness mechanical energy from human movement and convert it into electric energy. However, one of the challenges is low electric power output, which can be addressed by meticulous selection of material pairs with significant differences in work function and optimizing contact areas. The incorporation of carbon‐based nanomaterials, such as carbon nanotubes and graphene, emerges as a key strategy to enhance output. This review delineates recent progress in T‐TENGs incorporating carbonaceous nanofillers, comprehensively addressing fundamental classification, operational mode, structural design, working performance, and potential challenges that are hindering commercialization. By doing this, this review aims to stimulate future investigations into sustainable, high‐performance smart wearables integrated with T‐TENGs.

6E analysis and particle swarm optimization of a novel ultra-high temperature solar cogeneration system fusing thermochemical energy storage and multistage direct heat transfer

The reshaping of the energy development model depends on the penetration of renewable energy on the input side and the substitution of electrical energy on the supply side. Therefore, this research proposes a novel solar cogeneration system in which the Brayton cycle possesses triple pressure and can be flexibly coupled directly to each subsystem under supercriticality without intermediate exchangers. Firstly, a series of research tasks are executed sequentially, including model establishment, cycle potential comparison, operating parameter analysis, and multi-objective particle swarm optimization. Secondly, the exergy flow rate, consumption rate, and 6E (energy, exergy, exergoeconomic, exergoenvironmental, emergoeconomic, and emergoenvironmental) performances of each subsystem and subcomponent are thoroughly discussed. Lastly, the operating performance, economic benefits, and regional characteristics of each subsystem and the cogeneration system are explored. After optimization, the exergy efficiency, average advanced exergy-emergy factor, and advanced exergy-emergy impact difference of the system are 22.72 %, 56.17 %, and 23.97 %. Compared with similar research, the energy efficiency of the system is improved by 8.2 %, which can repay the capital cost in 8.83 years, and the economic benefit reaches $5,503,086 for the entire service year. The system embodies excellent 6E performance, aligns with the goals of reshaping the energy development model, and provides guidelines for the development of ultra-high temperature solar cogeneration.

Experimental Validation of a Modular All-Electric Power Take-Off Topology for Wave Energy Converter Enabling Marine Renewable Energy Interconnection

Power electronic converters are an enabling technology for the emerging marine energy applications, such as using ocean waves to produce electricity. This paper outlines the power take-off system and its key components used in a wave energy converter offering modularity and scalability to generate power efficiently. The proposed power take-off system was implemented based on a modular multilevel converter and could be deployed to convert any alternating current electrical energy to a different alternating current for interconnection to grid or non-grid applications. Examples of widespread deployment are supplying electricity to coastal communities or producing clean drinking water. The analysis using both the simulation tests and laboratory experiments verified the design objectives and basic functionality of the developed power take-off system. An acceptable response using a field programmable gate array-based controlled laboratory testbench was achieved, complying with guidelines specified in the prevalent industry standards. Seamless operation during steady-state and transients for the studied wave energy converter was achieved as supported by the obtained results. The key findings of this work were experimentally examined under different load conditions, direct current bus voltage fluctuations, and generator speed–torque regulation. The ability of the power take-off system to generate high-power quality of the waveforms, e.g., against adhering to the IEEE 519-2022 standard for total harmonic distortion limits, is also confirmed.

Solar-driven self-sustaining remediation of petroleum-contaminated soil

Petroleum-contaminated soil poses an environmental risk and is costly to remediate. Self-sustaining Treatment for Active Remediation (STAR) is an emerging remediation technique that consists of igniting and propagating a smoldering front through a volume of contaminated soil. While effective, the electrical energy demand required to initiate smoldering can be cost-prohibitive in locations without robust electrical infrastructure. To address this, we investigated the viability of a novel concentrated solar power process as a low-carbon alternative to electrically driven heating for STAR. The solar-driven system uses parabolic reflectors to focus solar energy, then directs it through fiber optic bundles to flexibly transmit it to a soil column. This process is demonstrated on ∼ 750 mL samples of petroleum-contaminated soil and detectable hydrocarbons were reduced in each case. Research evaluated several parameters of interest for effective treatment including thermal receiver material and distance between solar output and thermal receiver. Temperature was measured throughout treatment and typically exceeds 400 °C prior to ignition. Steel and quartz thermal receivers show effective temperature distribution, initiating smoldering, reducing detectable hydrocarbons and maintaining durability. The results indicate that concentrated solar power can efficiently ignite smoldering remediation, showing promise as a low-carbon alternative to current methods.

Response Surface Methodology Approach for Modeling and Performance Optimization of a PEM Fuel Cell

A Polymer Electrolyte Membrane (PEM) fuel cell is a popular green source of electrical energy and is often used in applications like electric vehicles due to its environmentally friendly operation. This type of fuel cell has a low operating temperature, light weight, and negligible emission of greenhouse gases. However, the PEM fuel cell is a complex multivariable system with a large number of input and output factors, and most of the input factors affect output factors directly or indirectly. As a result, it is conventionally quite difficult to determine which input factor has a major effect on a particular output factor. Statistical methods are very popular for finding the individual and interaction effects of input factors on output factors. In this paper, for the first time, a simple and realistic MATLAB SIMULINK model for a PEM fuel cell is presented to conduct various experimental tests. The developed MATLAB SIMULINK model and statistical design of experiments, Response Surface Methodology (RSM), are used to develop metamodels (mathematical models of the simulation model) for the PEM fuel cell to find the individual and interaction effects of various input factors on output factors. The developed metamodels can be used to find the region for optimum operation of the presented fuel cell. The metamodels are validated by conducting four different statistical tests. The optimum point of operation is presented by calculating stationary points from the metamodels.

Is it time to start moving soil microbial fuel cell research out of the lab and into the field?

Soil microbial fuel cells (SMFCs) function as bioelectrochemical energy harvesters that convert electrons stored in soil organic matter into useful electrical energy. Broadly, an SMFC comprises three essential components: an anode buried in the soil (the negative terminal), a colony of exoelectrogenic microorganisms residing on this anode, and a cathode (the positive terminal). As the exoelectrogens respire, they release electrons to the anode, which acts as an external receptor. These released electrons then flow through a load (e.g. a resistor), connecting the anode and cathode. Though minuscule, the electrical power produced by SMFCs has a number of potential applications such as sustaining low-power embedded electronics, pollutant remediation, or as a bio-sensing proxy for soil qualities and microbial activity. This discussion aims to emphasize the potential of SMFCs in addressing real-world environmental issues and to generate interest in the larger scientific community for broad interdisciplinary research efforts, particularly in field deployments.

Towards sustainable water management for Galdieria sulphuraria cultivation

The red microalga Galdieria sulphuraria has emerged as a promising biotechnological platform for large-scale cultivation and production of high-value compounds, such as the blue pigment phycocyanin. However, a large amount of freshwater and a substantial supply of nutrients challenge both the environmental and the economic sustainability of algal cultivation. Additionally, the extremophilic nature of Galdieria sulphuraria requires cultivation in an acidic culture medium that directly leads to strongly acidic wastewater, which in turn generally exceeds legal limits for industrial wastewater discharge. This research aims to address these challenges, by investigating cultivation water reuse as a strategy to reduce the impacts of Galdieria sulphuraria management. The results indicated that a 25 % water reuse may be easily implemented and showed to be effective at the pilot scale, providing no significant changes in microalgae growth (biomass productivity ~0.21 g L−1 d−1) or in phycocyanin accumulation (~ 10.8 % w/w) after three consecutive cultivation cycles in reused water. Moreover, a single cultivation cycle with water reuse percentages of 71 and 98 %, achieved with membrane filtration and with centrifugation, respectively, was also successful (biomass productivity ~0.24 g L−1 d−1). These findings encourage freshwater reuse implementations in the microalgae sector and support further investigations focusing on coupling cultivation and harvesting in continuous, real-scale configurations. Centrifugation and membrane filtration required substantially different specific electrical energy consumption for water reuse and biomass concentration: in real applications, the former technique would roughly span from 1 to 10 kWh m−3 while the latter is expected to fall within the ample range 0.1–100 kWh m−3, strongly dependent on system size. For this reason, the most suitable separation train should be chosen on a case-by-case basis, considering the prevailing flow rate and the target biomass concentration factor targeted by the separation process.

Causes of accidents in electrical distribution networks (using the example of the branch of PJSC Rossetti Volga – Saratov Distribution Networks)

The article is a continuation of a series of studies dedicated to the analysis of power transmission through distribution electric networks of PJSC ROSSETI. The presented material analyzes the functioning of the electrical networks of the branch of PJSC Rosseti Volga – Saratov Distribution Networks. An analytical assessment of the electric energy transport through distribution electric networks was carried out for the period 2018–2022. The structure of the company is considered, the structural characteristics of the main elements of the studied electrical networks are given, and the number of emergencies that occurred during the study period is estimated. A quantitative assessment of the magnitude of the electric energy undersupply caused by power supply interruptions is given. The classification of failures according to the degree of the intensity of undersupply was carried out and a quantitative assessment of each group of failures according to their intensity was made. The influence of the seasonal component on the number of emergencies during the study period is considered. Based on the data from 2022–2023, the analysis of the main causes of damage to the elements of electrical networks was carried out. The classification of failures by groups of failure causes is made, their main characteristics for this period are considered. The percentage ratio of the number of failures number for the most characteristic reasons to the total number of outages during the study period has been established. The information published in the open press was used as the source data. General scientific methods as well as numerical methods of analysis were used in the research. MATLAB graphics editor technologies were used to visualize the obtained analysis results. The results obtained may be of interest to the managers of power grid companies, as well as researchers and engineers engaged in research in the field of reliability of power supply.

Internet of things-based electrical energy control and monitoring in households using spreadsheet datalogger

Today, the demand for electrical energy is paramount in various daily activities. Hence, individuals must be aware of the amount of electrical energy consumed to maintain the quality of electronic devices. Knowing the quality of electronic devices is essential since it can impact the performance and lifespan of electrical equipment. The value of electrical power is determined by the quality of electrical power and the number of hours. Monitoring electrical energy involves collecting or measuring data to assess the current level of energy consumption. The author is interested in researching the use of Datalogger Spreadsheets to monitor and gather real-time information on energy use, which is made possible through integration with internet of things (IoT) and microcontrollers. Through data analysis and observation, solutions to existing problems are sought by comparing and matching data. Monitoring daily energy usage in a home setting produces output data that can be viewed directly and remotely with real-time results. This tool is expected to address current issues.

Hysteresis in radio frequency capacitively coupled CF4 plasmas

Based on experiments and simulations, various plasma parameters are found to undergo a hysteresis as a function of the driving voltage amplitude in capacitively coupled CF4 discharges. Phase Resolved Optical Emission Spectroscopy reveals that the discharge operates in a hybrid combination of the drift-ambipolar and α-mode at low voltage. In this mode, the electric field and mean electron energy are high in the electronegative plasma bulk region. As the cross section for electron attachment is appreciable only at high electron energies, this mode results in strong negative ion production and keeps the electron density low as well as the mode of plasma operation stable, when the voltage is increased moderately. Increasing the driving voltage amplitude further ultimately induces a mode transition into a pure α-mode, once the electron density increases strongly. Decreasing the voltage again results in a reverse mode transition at a lower voltage compared to the previous mode transition, because the electron density is now initially high in the bulk and, thus, the bulk electric field and mean electron energy are low resulting in inefficient generation of negative ions via electron attachment. This keeps the electron density high even at lower driving voltages. This effect leads to the emergence of two steady states of plasma operation within a certain voltage range. The different electron energy distribution functions in these two states result in markedly different generation and density profiles of F atoms, with higher values occurring in the increasing voltage branch of the hysteresis. The ion flux and mean energy at the electrodes also differ. The voltage range, where the hysteresis occurs, is affected by the ion induced secondary electron coefficient (γ). A larger value of γ results in a shift of the hysteresis voltage range towards lower values.