Energy Configuration Research Articles

Investigating the photocatalytic efficacy of a porous cuprous gallate (CuGa2O4) thick film

Abstract The study aimed to investigate the photocatalytic activity of porous copper gallate (CuGa2O4) thick film with significant wall dimensions. The thick film was meticulously fabricated using tape casting systems, with CuGa2O4 nanoparticles synthesized by Sol–Gel technique and subsequently transformed into gels for the tape casting process. Rietveld refinements were employed to elucidate the crystal structure and lattice parameters of the CuGa2O4 spinel oxide lattice. Moreover, electronic energy configurations and optical transmittance measurements were acquired through UV-visible spectrophotometry. The correlations between the crystal structure, band gap change and formation of electronic energy levels in relation to the photocatalytic performance of CuGa2O4 were explored. The comprehensive examination provides valuable insights into the complex interaction between material properties and photocatalytic behavior, offering a nuanced understanding of the potential applications of porous CuGa2O4 thick films in various technological fields.

Research on the Possibilities of Expanding the Photovoltaic Installation in the Microgrid Structure of Kielce University of Technology Using Digital Twin Technology

Global challenges related to sustainable development are increasingly focusing on the use of digital twin technology as a universal tool for optimizing and monitoring renewable energy installations. This article discusses digital twin technology as a support for sustainable development based on the analysis of microgrid structures. Digital twins allow the creation of virtual models of physical systems. This capability facilitated the accurate replication of the microgrid model at Kielce University of Technology using ETAP (Electrical Transient Analyzer Program) software (version 22.5). The operational parameters of the microgrid structure were analyzed for the examined power range of the photovoltaic installation to determine the possibilities of expanding the existing installation. The impact of the photovoltaic installation’s power on the operational parameters of the microgrid structure was visualized, and final conclusions were formulated. Moreover, the integration of digital twin technology into renewable energy systems not only enhances operational efficiency but also plays a pivotal role in advancing sustainability objectives. Through real-time monitoring and predictive maintenance, digital twin technology facilitates the optimization of energy production and distribution, thereby reducing waste and contributing to the overall sustainability of energy systems. This technology enables the simulation of various scenarios, such as fluctuations in energy demand or the integration of new renewable sources, which can inform more sustainable decision-making processes. In the context of microgrids, digital twin technology ensures that energy production is closely aligned with consumption patterns, minimizing energy losses and enhancing grid resilience. Furthermore, digital twin technology supports the sustainable expansion of renewable installations by providing detailed insights into potential environmental impacts and the long-term sustainability of various energy configurations. As the demand for clean energy continues to grow, digital twin technology will be indispensable in achieving a balance between energy needs and environmental stewardship, ensuring that the expansion of renewable energy sources contributes positively to global sustainability objectives.

Mixed Thermal and Renewable Energy Generation Optimization in Non-Interconnected Regions via Boolean Mapping

Global efforts aiming to shift towards renewable energy and smart grid configurations require accurate unit commitment schedules to guarantee power balance and ensure feasible operation under different complex constraints. Intelligent systems utilizing hybrid and high-level techniques have arisen as promising solutions to provide optimum exploration–exploitation trade-offs at the expense of computational complexity. To ameliorate this requirement, which is extremely expensive in non-interconnected renewable systems, radically different approaches based on enhanced priority schemes and Boolean encoding/decoding have to take place. This compilation encompasses various mappings that convert multi-valued clausal forms into Boolean expressions with equivalent satisfiability. Avoiding any need to introduce prior parameter settings, the solution utilizes state-of-the-art advancements in the field of artificial intelligence models, namely Boolean mapping. It allows for the efficient identification of the optimal configuration of a non-convex system with binary and discontinuous dynamics in the fewest possible trials, providing impressive performance. In this way, Boolean mapping becomes capable of providing global optimum solutions to unit commitment utilizing fully tractable procedures without deteriorating the computational time. The results, considering a non-interconnected power system, show that the proposed model based on artificial intelligence presents advantageous performance in terms of generating cost and complexity. This is particularly important in isolated networks, where even a-not-so great deviation between production and consumption may reflect as a major disturbance in terms of frequency and voltage.

Advancing Economical and Environmentally Conscious Electrification: A Comprehensive Framework for Microgrid Design in Off‐Grid Regions

AbstractThe design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions.

Geometric parameter control for tunable resonance frequencies in asymmetric nanodots: equilibrium states and dynamic response

Through numerical simulations, we delve into the examination of the equilibrium state and dynamic response of asymmetric nanodots. Within these nanodots, we identify two distinct equilibrium states—single domain and vortex. Our findings reveal a direct correlation between the number and frequencies of peaks and the minimum energy configuration, which is contingent upon the physical parameters of the system. This observation implies the potential to intentionally manipulate a desired set of frequencies by precisely controlling the geometric parameters of the system.

Model and Energy Bounds for a Two-Dimensional System of Electrons Localized in Concentric Rings.

We study a two-dimensional system of interacting electrons confined in equidistant planar circular rings. The electrons are considered spinless and each of them is localized in one ring. While confined to such ring orbits, each electron interacts with the remaining ones by means of a standard Coulomb interaction potential. The classical version of this two-dimensional quantum model can be viewed as representing a system of electrons orbiting planar equidistant concentric rings where the kinetic energy may be discarded when one is searching for the lowest possible energy. Within this framework, the lowest possible energy of the system is the one that minimizes the total Coulomb interaction energy. This is the equilibrium energy that is numerically determined with high accuracy by using the simulated annealing method. This process allows us to obtain both the equilibrium energy and position configuration for different system sizes. The adopted semi-classical approach allows us to provide reliable approximations for the quantum ground state energy of the corresponding quantum system. The model considered in this work represents an interesting problem for studies of low-dimensional systems, with echoes that resonate with developments in nanoscience and nanomaterials.

Energy management with adaptive moving average filter and deep deterministic policy gradient reinforcement learning for fuel cell hybrid electric vehicles

Fuel cell hybrid electric vehicles (FCHEV) with battery (BAT) and supercapacitor (SC) advance in flexible configuration and high energy efficiency. However, the complex coupling relationship among various power sources poses a severe challenge to the design of the energy management system (EMS), including multi-degrees of freedom power allocation, fuel economy, and power sources lifespan of the FCHEV. This paper proposes an EMS based on a dual-layer power distribution structure. In the upper layer, adaptive moving average filter (AMAF) is designed to separate different frequency power, where the energy supply of the SC is managed to attenuate fluctuating power and simplifies the optimization problem and reduces computational costs. The lower layer is constructed by the deep deterministic policy gradient (DDPG) algorithm, where fuel cell system (FCS) hydrogen consumption and degradation rewards are designed to simultaneously enhance fuel efficiency and degradation performance by regulating the FCS real-time power variation. The proposed strategy has been evaluated regarding FCHEV fuel economy and FCS durability under combined driving cycle simulation, which shows AMAF + DDPG strategy reduces fuel consumption by 7.24 % and 1.3 %, also the degradation reduces by 0.04 % and 0.02 % compared with different EMS. Simulation results demonstrate that AMAF + DDPG optimizes the output characteristics of power sources.

Spinel‐Type Structured Phosphor Near‐Infrared‐II Emission: Intervalence Charge Transfer and Hetero‐Valent Chromium Pairs

AbstractNear‐infrared (NIR) emitting phosphors draw much attention because they show great applicability and development prospects in many fields. Herein, a series of inverse spinel‐type structured LiGa5O8 phosphors with a high concentration of Cr3+ activators is reported with a dual emission band covering NIR‐I and II regions. Except for strong ionic exchange interactions such as Cr3+−Cr3+ and Cr3+ clusters, an intervalence charge transfer (IVCT) process between aggregated Cr ion pairs is proposed as the mechanism for the ~1210 nm NIR‐II emission. Comprehensive structural and luminescence characterization points to IVCT between two Cr3+ being induced by structural distortion and further enhanced by irradiation. Construction of the configurational energy level diagram enabled elucidation of this transition within the IVCT process. Therefore, this work provides insight into the emission mechanism within the high Cr3+ concentration system, revealing a new design strategy for NIR‐II emitting phosphors to promote its response.

Electric vehicle power system in intelligent manufacturing based on soft computing optimization

Soft computing technology has attracted extensive attention in computer engineering and automatic control domains because it can deal with uncertainties, fuzziness, and complex practical problems. This study adopts a Genetic Algorithm (GA) in soft computing technology to realize the cooperative optimization of electric vehicle's dynamic and economic performance. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness, and complex practical problems, making GA an effective tool for solving complex optimization problems. Firstly, the electric vehicles' power system structure and energy management strategy are investigated and analyzed. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Thirdly, NSGA-II is used to construct the electric vehicles' power and energy configuration, with power and economic performance as the main optimization objectives. Meanwhile, a fuzzy logic controller is designed to adjust the parameters of GA online, so that the optimization process is closer to the actual operating conditions. Finally, the relevant variables are selected to achieve the optimization goal, the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the optimized electric vehicle's acceleration time is remarkably reduced, the dynamic performance is improved by more than 7 %, and the power loss is reduced by 5 %. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. This study provides a new idea and method for the performance optimization of electric vehicles. Moreover, it offers a valuable reference for the innovation and development of electric vehicle technology in the intelligent manufacturing field. This study indicates that electric vehicles could be more efficient, energy-saving, and environmentally friendly to serve people's travel needs in the future.

Solvent-Regulated Formation of Metal/Metal-Oxo Nodes in Two Indium Metal-Organic Frameworks: Syntheses, Structures, Selective Gas Adsorption and Fluorescence Sensor Properties.

Two water-stable indium metal-organic frameworks, (NH2Me2)3[In3(BTB)4] ⋅ 12DMA ⋅ 4.5H2O (In-MOF-1) and (NH2Me2)9[In9O6(BTB)8(H2O)4(DMSO)4] ⋅ 27DMSO ⋅ 21H2O (In-MOF-2) (BTB=4,4',4''-benzene-1,3,5-tribenzoate) with 3D interpenetrated structure has been constructed by regulating solvents. Structure analysis revealed that In-MOF-1 has a three-dimensional (3D) structure with a single metal core, while In-MOF-2 features an octahedron cage constructed by three kinds of metal clusters to further form a 3D structure. The fluorescence investigations showed that In-MOF-1 and In-MOF-2 are potential MOF-based fluorescent sensors to detect acetone and Fe3+ ions in EtOH or water with high sensitivity, excellent selectivity, recyclability and a low limit of detection. Moreover, the fluorescence mechanisms of In-MOF-1 and In-MOF-2 toward acetone and Fe3+ ions were further explained. In addition, In-MOF-2 has higher thermal and framework stability than In-MOF-1. The activated In-MOF-2 presents a high BET surface area of 998.82 m2g-1 and a pore size distribution of 8 to 16 Å. At the same time, In-MOF-2 exhibits high selective CO2 adsorption for CO2/CH4 and CO2/N2, respectively. Furthermore, the adsorption sites and adsorption isotherms were predicted using grand canonical Monte Carlo (GCMC) simulations, and the adsorption energy of the lowest-energy adsorption configuration was calculated using molecular dynamics (MD) simulations.

Integration of energy storage and determination of optimal solar system size for biomass fast-pyrolysis

This study presents a model and analysis of a heliostat field collector (HFC) system integration with fast-pyrolysis of biomass and determination of the optimal solar system size for this integrated system. Given the intermittent nature of solar energy, an auxiliary heater and a thermochemical energy storage system (TCES) are included. Four cases of HFC integration with the fast-pyrolysis process have been studied: 1) low solar radiation, 2) sufficient solar radiation, 3) high solar radiation, and 4) no solar radiation with available stored energy in TCES. The solar energy system was modeled and calculated using the Engineering Equation Solver (EES) software, while the fast-pyrolysis process and the TCES were simulated using the Aspen Plus software. A thermodynamic and economic analysis has been conducted to estimate the share of solar energy for different process configurations. Economic calculations have been conducted for three different heliostat filed areas: 4000, 8000, and 12000 m2. Solar fraction, investment and operational costs, as well as total cost were calculated for these three heliostat field areas. The results indicate that the optimum heliostat field area for the studied biomass pyrolysis plant is 8000 m2 and the average solar fraction of the required energy in summer is 0.39 and while it is 0.34 for the whole year. Simulation results considering this optimized heliostat filed area indicate that 6.27 t/h of bio-oil is produced from 10 t/h of hybrid poplar biomass. Implementing this solar-assisted system reduces CO2 emissions, increases efficiency of the system and lowers thermal energy requirement for the fast-pyrolysis process from 6MW to 3.99MW.

Techno-financial analysis of 100% renewable electricity for the South West region of the UK by 2050

In the UK, the transition to 100% Renewable Energy Systems is a critical strategy in addressing climate change, aligning with the UK government's 2050 net-zero objective. This study explores the potential for such a transformation within the South West region of the UK by 2050. EnergyPLAN16.2 software was utilised for scenario modelling and the fmincon algorithm in MATLAB for optimisation analysis. The focus was on two scenarios involving hybrid renewable energy systems incorporating onshore wind, solar, offshore wind, and two energy storage systems which are lithium-ion battery energy storage and hydrogen fuel cell energy storage. The optimisation process entailed two stages: determining renewable energy configurations for cost-efficient coverage of annual electricity demand and computing the minimum energy storage capacity to ensure a constant power supply. The results suggest an optimal configuration of 4,460MW onshore wind, 9,880MW solar, 6,982MW offshore wind, and 2,273,662MWh of lithium-ion battery energy storage for 100% hybrid renewable energy systems in the South West region of the UK in 2050. This configuration achieves the lowest annual cost of 3208 M£/year, with a balanced wholesale electricity price ranging from 4.3p/kWh to 6.6p/kWh and a levelized cost of energy of 6.44p/kWh. Furthermore, hydrogen fuel cell storage systems can be another alternative, however, the present study showed it is not economical enough compared to lithium-ion battery storage systems. This research broadens the understanding of hybrid renewable energy systems implementation in large regions, offering valuable insights for sustainable energy planning in the South West region of the UK.

Integrated batch production planning and scheduling with machine-learning based production capacity region considering wind energy system

Continued greenhouse gas emission has led to increased global warming. Towards the goal of carbon emissions reduction and environmental sustainability, replacing high-polluting fossil fuel by clean energy has become a top priority in the decision-making of the current chemical industry. This work presents an optimization framework to address the integration of batch chemical production planning and scheduling considering energy consumption, which is a vital operational problem in chemical production. Assisting the linkage between planning layer and scheduling layer, a novel linear support vector machine based production capacity region calculation method is proposed to identify feasible regions of the scheduling layer. Uncertain factors in both the production system and the energy system are considered, including demand volatility of chemical products, energy supply fluctuations and time-varying energy market prices. To evaluate each strategy and the proposed model, simulation experiments are performed in three representative case studies. The results reveal that the integrated model considering energy consumption shows superior performance in different energy configurations, with or without wind energy generation and battery storage systems.

Massive Assessment of the Geometries of Atmospheric Molecular Clusters.

Atmospheric molecular clusters are important for the formation of new aerosol particles in the air. However, current experimental techniques are not able to yield direct insight into the cluster geometries. This implies that to date there is limited information about how accurately the applied computational methods depict the cluster structures. Here we massively benchmark the molecular geometries of atmospheric molecular clusters. We initially assessed how well different DF-MP2 approaches reproduce the geometries of 45 dimer clusters obtained at a high DF-CCSD(T)-F12b/cc-pVDZ-F12 level of theory. Based on the results, we find that the DF-MP2/aug-cc-pVQZ level of theory best resembles the DF-CCSD(T)-F12b/cc-pVDZ-F12 reference level. We subsequently optimized 1283 acid-base cluster structures (up to tetramers) at the DF-MP2/aug-cc-pVQZ level of theory and assessed how more approximate methods reproduce the geometries. Out of the tested semiempirical methods, we find that the newly parametrized atmospheric molecular cluster extended tight binding method (AMC-xTB) is most reliable for locating the correct lowest energy configuration and yields the lowest root mean square deviation (RMSD) compared to the reference level. In addition, we find that the DFT-3c methods show similar performance as the usually employed ωB97X-D/6-31++G(d,p) level of theory at a potentially reduced computational cost. This suggests that these methods could prove to be valuable for large-scale screening of cluster structures in the future.

Extracting the partonic structure of colorless exchanges at the Electron Ion Collider

We investigate the determination of the partonic structure of colorless exchanges in deep inelastic diffractive ep scattering at the Electron Ion Collider (EIC), using the standard decomposition into Pomeron and Reggeon contributions. We perform fits to simulated diffractive cross section pseudodata in four variables, including the momentum transfer t, to estimate the achievable precision on the Pomeron and Reggeon quark and gluon distributions. We analyze the influence of different cuts in the kinematic variables, beam energy configurations, and luminosities, including a “first year” scenario. We conclude that the EIC will be able to constrain the partonic structure of the subleading Reggeon exchange with a precision comparable to that of the leading Pomeron exchange. Published by the American Physical Society 2024

Mechanism of Fatigue-Life Extension Due to Dynamic Strain Aging in Low-Carbon Steel at High Temperature.

An enhancement in fatigue life for ferrite-pearlite low-carbon steel (LCS) at high temperature (HT) has been discovered, where it increased from 190,873 cycles at room temperature (RT) to 10,000,000 cycles at 400 °C under the same stress conditions. To understand the mechanism behind this phenomenon, the evolution of microstructure and dislocation density during fatigue tests was comprehensively investigated. High-power X-ray diffraction (XRD) was employed to analyze the evolution of total dislocation density, while Electron Backscatter Diffraction (EBSD) and High-Resolution EBSD (HR-EBSD) were conducted to reveal the evolutions of kernel average misorientation (KAM), geometrically necessary dislocations (GND) and elastic strains. Results indicate that the enhancement was attributed to the dynamic strain aging (DSA) effect above the upper temperature limit, where serration and jerky flow disappeared but hindrance of dislocations persisted. Due to the DSA effect, periods of increase and decrease in the total dislocations were observed during HT fatigue tests, and the fraction of screw dislocations increased continuously, caused by viscous movement of the screw dislocations. Furthermore, the increased fraction of screw dislocations resulted in a lower energy configuration, reducing slip traces on sample surfaces and preventing fatigue-crack initiation.

Exploration of modified β-cyclodextrin compounds as enhanced eco-friendly and easily accessible corrosion inhibitors for carbon steel in hydrochloric acid

β-CD was esterified independently with two distinct ionic liquids to evaluate their potential as corrosion inhibitors for carbon steel in a 1.0 M HCl solution. Experimental and computational methods were used to assess the inhibitors’ efficiency regarding temperature and concentration effects and to elucidate the adsorption mechanism. Both inhibitors acted as barriers, with a maximum inhibition efficiency of about 94 % at 200 ppm. There was a direct relationship between the inhibitor's concentration and their efficiency, while increasing the temperature reduced the inhibitors’ effectiveness. The thermodynamic and kinetic parameters were evaluated and studied. DFT calculation revealed the electronic structure and reactivity of the inhibitors. Molecular dynamic simulation provided insight into the optimal configuration and binding energies of inhibitors on the Fe (110) surface.

Evaluating the Viability and Optimization of Plasma Pilot Plants

Nuclear fusion stands as a promising avenue for achieving a sustainable and abundant energy source. Central to this endeavor is the development of effective pilot plants capable of demonstrating the feasibility of fusion power on a commercial scale. This paper provides a comprehensive evaluation of three leading magnetic confinement fusion configurations: the Advanced Tokamak (AT), Spherical Tokamak (ST), and Compact Stellarator (CS).The Tokamak, characterized by its toroidal shape and strong magnetic fields, has been the most researched and developed fusion device. The analysis focuses on the challenges related to maintaining stability and minimizing disruptions. Furthermore, the optimization strategies involving advanced materials, superconducting magnets, and innovative plasma control techniques are discussed to enhance the Tokamak's viability. In contrast, the Spherical Tokamak, a variant with a more compact and spherical design, promises improved current advancements including the handling of higher heat loads and magnetic field configurations. The Stellarator, with its complex, twisted magnetic field structure, eliminates the need for continuous external current drive, addressing some intrinsic issues of the Tokamak. By comparing these configurations, the paper identifies the relative strengths and weaknesses of each approach in terms of confinement efficiency, operational stability, and engineering feasibility. The evaluation is supported by recent experimental data, simulation results, and technological advancements. Finally, the paper proposes a roadmap for the future development of fusion pilot plants, highlighting the need for an integrated approach that leverages the strengths of each configuration while addressing their individual challenges. The synthesis of this evaluation underscores the importance of continued research, cross-collaboration, and investment in advanced technologies to realize the goal of practical and economically viable fusion energy. This comparative analysis aims to provide a strategic framework for policymakers, researchers, and engineers in the fusion community, fostering informed decisions and prioritizing research efforts towards the most promising fusion energy configurations.

On the Strong Binding Affinity of Gold-Graphene Heterostructures with Heavy Metal Ions in Water: A Theoretical and Experimental Investigation.

Minimum energy configurations in 2D material-based heterostructures can enable interactions with external chemical species that are not observable for their monolithic counterparts. Density functional theory (DFT) calculations reveal that the binding energy of divalent toxic metal ions of Cd, Pb, and Hg on graphene-gold heterointerfaces is negative, in contrast to the positive value associated with free-standing graphene. The theoretical predictions are confirmed experimentally by Surface Plasmon Resonance (SPR) spectroscopy, where a strong binding affinity is measured for all the heavy metal ions in water. The results indicate the formation of a film of heavy metal ions on the graphene-gold (Gr/Au) heterointerfaces, where the adsorption of the ions follows a Langmuir isotherm model. The highest thermodynamic affinity constant K = 3.1 × 107 L mol-1 is observed for Hg2+@Gr/Au heterostructures, compared to 1.1 × 107 L mol-1 and 8.5 × 106 L mol-1 for Pb2+@Gr/Au and Cd2+@Gr/Au, respectively. In the case of Hg2+ ions, it was observed a sensitivity of about 0.01°/ppb and a detection limit of 0.7 ppb (∼3 nmol L-1). The combined X-ray photoelectron spectroscopy (XPS) and SPR analysis suggests a permanent interaction of all of the HMIs with the Gr/Au heterointerfaces. The correlation between the theoretical and experimental results indicates that the electron transfer from the graphene-gold heterostructures to the heavy metal ions is the key for correct interpretation of the enhanced sensitivity of the SPR sensors in water.

Microscopic insights into effects of sulfolane additive on Li–S battery electrolyte

Lithium–sulfur (Li–S) batteries, which have a high theoretical capacity and can be prepared using low-cost activation materials, are a promising alternative for realizing high-energy-density battery systems. However, the stability of the electrolyte and the shuttle effect caused by dissolved lithium polysulfides in the electrolyte prevent their practical application. Due to its non-flammability and high oxidation stability, sulfolane shows good promise for improving the oxidation stability and weakening the shuttle effect of Li–S electrolytes. In this work, the effects of a sulfolane additive on a Li–S battery electrolyte were revealed by density functional theory calculations and molecular dynamics simulations. The molecular orbitals, thermodynamics, and dynamics of the oxidation reaction were analyzed from the electron level. The addition of sulfolane lowers the HOMO energy level and the configuration energy difference, which leads to improved oxidation stability. Furthermore, sulfolane shows affinity to the S atoms close to Li, which correspond to low-order polysulfides. This affinity is confirmed by binding energy data and indicates that the addition of sulfolane inhibits the diffusion of Li2S4, Li2S6 and Li2S8. This work provides an analysis of the effects of sulfolane as an additive from the electron and molecular levels, which will promote a better understanding of Li–S battery systems and enable the design of improved electrolytes.