Novel energy management and harnessing wind power for sustainable electric mobility of multi-micro grid system

To systematically compare the effects of different introduction methods on the direct-current (DC) electrical performance of polyethylene insulation, low-density polyethylene (LDPE) modified with an identical content of trans,trans-dibenzalacetone (TTD) was prepared via blending and grafting approaches. The DC breakdown strength, electrical conductivity, space charge behavior, surface potential decay, and trap distribution characteristics of the modified LDPE were comparatively investigated. The results indicate that both blending and grafting modification can effectively regulate the DC electrical behavior of LDPE and improve its insulation performance to varying extents. Compared with the blended system, the grafted samples exhibit a significantly higher DC breakdown strength, increasing from 353.20 kV/mm to 403.31 kV/mm, accompanied by a pronounced reduction in electrical conductivity and a markedly lower average volume charge density under a DC electric field of 40 kV/mm, indicating more effective suppression of space charge accumulation. Meanwhile, a slower surface potential decay process is observed in the grafted samples, revealing pronounced differences in charge trapping characteristics within the materials. Combined with quantum chemical calculations and trap formation analysis, it is suggested that the different introduction methods alter the existence state of functional molecules in the polymer matrix, thereby affecting the stability and effectiveness of charge trap structures. Chemical grafting is favorable for stably transforming the electron-capturing capability of TTD into intrinsic charge trapping structures within LDPE, enabling sustained and effective regulation of charge transport under DC electric fields. This study provides useful insights into the molecular design and optimization of polyethylene-based insulation materials for high voltage DC applications.

As electric vehicles (EVs) gain widespread adoption, interactions between EVs and charging infrastructure are increasing, driving the need for secure and efficient authentication methods. However, existing authentication approaches are inadequate to address the unique challenges of dynamic EV charging scenarios. Moreover, they often suffer from static credentials, high computational overhead, and limited adaptability to dynamic user behavior and environmental variability. To address these challenges, this paper proposed PufCB-Auth, a lightweight multi-factor authentication scheme that integrates hardware-level Physical Unclonable Functions (PUFs) with charging behavior features to generate a multi-modal digital fingerprint. To alleviate the negative effects rooting from EV user’s charging behavior drift and PUF response fluctuations, the paper also proposed Enhanced PufCB-Auth by incorporating a fingerprint update mechanism. The proposed scheme achieves lightweight design, strong robustness, and continuous authentication capability, making it well-suited for dynamic and resource-constrained EV charging environments. Simulation results validate its effectiveness in improving authentication accuracy and robustness, with minimal system overhead, enabling practical deployment in real-world ChaoJi charging pile–EV interaction environments.

1,3]Oxazine-based NIR molecular switches: Hydrochromic behavior, viscosity sensing, and targeted cell imaging.

With the increasing use of antimicrobial peptides (AMPs) as alternatives to conventional antibiotics, understanding the structural and physicochemical determinants underlying their activity has become essential for the development of effective therapeutic agents. This review provides a state-of-the-art overview of how residue-specific modifications, particularly through amino acid scanning approaches, contribute to the elucidation of structure-activity relationships in AMPs. Different scanning strategies are discussed, highlighting how systematic substitutions reveal the role of individual residues in modulating antimicrobial activity, membrane interaction, and structural stability. Particular emphasis is given to how variations in charge, hydrophobicity, and conformational flexibility influence peptide behavior, including the identification of residues critical for membrane binding, insertion, and disruption. In addition, the impact of specific amino acids on peptide function is analyzed in the context of targeted modifications that enhance activity while maintaining selectivity. Finally, the integration of data derived from these approaches with computational tools and peptide databases is discussed to support rational design strategies. Together, these advances provide a framework for the strategic optimization of antimicrobial peptides, contributing to the development of more effective and selective antimicrobial agents.

Impact of time-of-use tariff policies on electric vehicle charging behavior and power system operation in Beijing

We recently introduced a model for decomposing the global charge transfer (CT) excitation indexes proposed by Le Bahers, Adamo, and Ciofini (Le Bahers, T. J. Chem. Theory Comput. 2011, 7, 2498-2506) into contributions from molecular subdomains (Gatti, C. J. Phys. Chem. A 2022, 126, 6314-6328), together with a software tool, DOCTRINE (atomic group Decomposition Of the Charge TRansfer INdExes), which implements this approach. DOCTRINE has been successfully applied to several excited states (ESs) of a push-pull compound in different solvent environments. In this work, we extend our previous model to spin-polarized systems by introducing, in addition to the global CT excitation indexes, their analogous electron spin transfer (ST) indexes. These can also be decomposed into chemically significant contributions from molecular subdomains. This extension provides a set of related CT and ST descriptors, enabling a visual and quantitative differentiation of the behavior of electronic charge and spin transfers. The updated DOCTRINE_SPIN version of the software now includes computation of ST indexes and their associated descriptors, broadening the applicability of the method to spin-resolved electronic excitations. Our CT and ST decomposition model is applicable to any partitioning of real space, whether fuzzy or disjoint and exhaustive. However, we apply it in terms of chemically relevant molecular subdomains based on the Atoms in Molecules (AIM) Bader's basins, taking advantage of associating intra- and inter-subdomain contributions with rigorously defined quantum objects that retain clear chemical meaning. The model allows for a quantitative evaluation of subdomain contributions to the CT, the ST, and their excitation lengths, and to the charge- and spin-transfer dipole moments. Although these global indexes can be derived either from electron and spin density increments or from their depletions upon excitation, the subdomain contributions obtained from the two distributions generally differ. This distinction helps to determine whether a given property's contribution from a subdomain is dominated by one of the distributions or whether both play a significant role. As an initial application of our spin-polarized model extension, we selected a π-conjugated (acceptor-donor-acceptor) compound (TMTQ), composed of a central 1,6-methano[10]annulene (M10A) and 5-dicyanomethyl-thiophene (DT) peripheries in an exo geometry. TMTQ exhibits a singlet-triplet energy gap of only 4.9 kcal/mol, with the singlet state being more stable than the triplet. This small energy gap arises from the different weights of nearly degenerate mesomeric structures with distinct electron delocalization patterns. The electronic charge (and spin) transfers occurring upon excitation of the singlet and triplet ground states (GS) (S0 and T1) to their first five excited states (S1-S5 and T2-T6) are characterized and compared, highlighting their distinct features, the role of ST on CT when both transfers are possible, and the resulting effects on electron and spin delocalizations.

The study aims to investigate the adsorption of silver ions (Ag+) on iron oxide (Fe2O3) and the hydroxyapatite/iron oxide (Hap/Fe2O3) composite to evaluate its potential for water purification applications. To achieve this goal, Fe2O3 and Hap/Fe2O3 were synthesized by a co-precipitation method and characterized using X-ray diffraction (XRD), scanning electron microscopy, porosimetry, surface charge density, and zeta potential measurements. Batch adsorption experiments were then conducted to assess the effects of contact time, initial Ag+ concentration, pH, and temperature on adsorption efficiency. The results show that both Fe2O3 and Hap/Fe2O3 surfaces exhibit needle-like morphologies with numerous pores, while XRD confirmed the formation of hydroxyapatite and iron oxide phases. The zeta potential of the composite ranged from -25 to -7 mV, indicating surface charge behavior relevant to adsorption. The Hap/Fe2O3 composite demonstrated superior adsorption properties compared to pure Fe2O3, attributed to the high specific surface area and ion-exchange capacity of hydroxyapatite combined with the adsorption contribution of iron oxide. Kinetic analysis revealed that the adsorption followed the pseudo-second-order model, suggesting chemisorption as the dominant mechanism. Isotherm studies indicated that the Freundlich model best described the adsorption, reflecting the heterogeneous nature of the composite surface. Overall, the Hap/Fe2O3 composite exhibited high affinity for Ag+ ions, with adsorption efficiency strongly influenced by pH and contact time, confirming its potential as a promising material for removing heavy metals from aqueous solutions.

Large-scale electric vehicle integration poses significant challenges to power grid operation, demanding high-fidelity and diversified datasets for in-depth research. To address this need, we introduce MP-EVData, a comprehensive dataset of station-level charging load profiles from a major Chinese metropolis in 2024. The core value of MP-EVData is providing charging load data for 10 stations in the same geographical location during the same time period, representing five distinct prototypes: taxi demonstration stations, bus depots, residential charging stations, battery swapping stations and heavy-duty truck stations. This unique structure eliminates the disturbances of external variables such as geographical, climatic, and policy, enabling controlled comparative analysis of their load characteristics. Furthermore, the dataset is augmented with a parallel, high-fidelity synthetic dataset generated using advanced generative AI models to support data-intensive research. Technical validation reveals highly distinct daily, weekly, and annual temporal patterns across prototypes and demonstrates clear price-responsive charging behavior under time-of-use pricing. MP-EVData provides a crucial benchmark for advancing researches in load forecasting, smart charging algorithms and urban infrastructure planning.

Electric vehicles (EVs) are increasingly recognized as an environmentally sustainable transportation option that supports reduced emissions and improved energy utilization. However, integrating EV charging infrastructure into existing power distribution networks presents technical challenges because EV chargers employ power electronic converters that behave as non-linear loads. This study investigates the influence of EV charging on a low-voltage three-phase distribution network (LV3PDN), focusing on key power quality indicators such as voltage deviation, current distortion, and total harmonic distortion (THD) at the point of common coupling (PCC). A three-phase LV network based on a 25-kVA, 415 V/415 V, 1:1 isolation transformer is modelled in the MATLAB Simulink environment, incorporating representative residential and commercial load conditions. To represent EV charging behaviour, a two-wheeler onboard charger model is adopted, selected because its input current harmonic profile lies between those reported for commonly used electric two-wheelers operating in Wardha city, Maharashtra, India. The analysis evaluates three operating conditions: EV charging location along the feeder, battery state of charge (SoC), and DC–DC converter operation mode (average and switched). Simulation results show that EV integration increases feeder current by about 50% and raises THD; distortion grows downstream, while voltage THD stays below 0.1%.

This study develops an integrated, hourly-resolved, multi-vector energy system model to assess pathways for aligning Italy's transport sector with the European Union (EU) 2030 and Fit for 55 decarbonisation targets. The model simultaneously represents electricity generation, grid constraints, storage dynamics, hydrogen production, and heterogeneous vehicle charging behaviours. Three scenarios are analysed: Battery Electric Vehicles (BEV)-dominant, Hydrogen-dominant, and Hybrid. Results indicate that the BEV-dominant pathway requires approximately 52 TWh of electricity for vehicle charging, leading to a national peak load of 72 GW and total system costs of €144 billion, while achieving 21.5 Mt CO2 emissions. The Hydrogen-dominant scenario shifts demand toward electrolysis, consuming 45 TWh for hydrogen production (≈1.35 Mt H2 yr−1), reducing emissions to 19.7 Mt CO2 but increasing system costs to€168 billion. The Hybrid scenario balances 38 TWh of direct electricity use with 22 TWh for hydrogen generation, achieving the lowest emissions (17.8 Mt CO2) and moderate costs (€156 billion). Renewable curtailment decreases from 8.3 % (BEV) to 4.5% (Hybrid), highlighting improved flexibility and resource utilization. Overall, the Hybrid configuration demonstrates the most cost-effective and environmentally coherent pathway, integrating both electricity and hydrogen infrastructures. The findings provide quantitative insights for policymakers and system planners seeking to align Italian mobility decarbonisation strategies with EU climate goals while maintaining energy security and affordability.

Car exhaust emissions significantly contribute to the depletion of the ozone layer. Electric vehicles (EVs) present a sustainable alternative to mitigate this environmental issue. However, the large-scale adoption of EVs introduces challenges for the power grid, primarily due to irregular and uncoordinated charging patterns. This study proposes a comprehensive two-stage framework for optimizing electric vehicle (EV) charging patterns and reactive power dispatch within power distribution systems. In Stage 1, two types of EV charging schedules are developed and compared: day-ahead charging and real-time charging. Day-ahead charging involves planning EV charging over a 24-hour horizon with the objective of minimizing load variance, energy cost, active power losses, and voltage drop, while simultaneously maximizing voltage stability. Real-time charging dynamically adjusts charging behavior based on immediate grid conditions to minimize load variance and charging costs. Stage 2 focuses on optimal real-time reactive power dispatch, utilizing the reactive power capabilities of EV inverters to further reduce the active and reactive power losses. Additionally, the study analyzes EV behavior in response to sudden load changes, providing critical insights for enhancing grid performance. Different optimization algorithms are implemented to efficiently solve the proposed models, including particle swarm optimization, dandelion optimization, wild horse optimization, and slime mould optimization. The optimization is formulated as a multi-objective problem to consider both grid constraints and customer satisfaction. The proposed framework is applied and tested on a 33-bus radial distribution system with 984 electric vehicles using MATLAB M-files, while power flow calculations are performed using the MATPOWER toolbox. Simulation results demonstrate the effectiveness of the proposed framework. Daily active power losses are reduced from 4.04 MWh to 2.55 MWh and 2.77 MWh under day-ahead and real-time planning strategies-representing reductions of 36.8% and 31.4%, respectively. Similarly, EV charging costs drop from 552.31 USD to 394.19 USD and 363.68 USD, achieving cost savings of 28.63% and 34.15%. Furthermore, voltage profiles are maintained within the acceptable operational limit of 0.95 p.u. These outcomes highlight the significant advantages of the proposed methodology in enhancing grid efficiency while ensuring user satisfaction.

Precisely deciphering the intrinsic origin of the high overpotential for oxygen evolution reaction (OER), even on the most active RuO2 catalysts, remains a long-standing challenge in electrocatalysis. Herein, by meticulously elucidating the electrode charging behavior, oxygenated surface phases and interfacial double-layer structures under OER-relevant potentials on RuO2(110), together with their impact on reaction pathways and elementary-step energetics through ab-initio molecular dynamics simulations, we reveal that the high overpotential jointly arises from the pronounced surface negative charge, due to the unusually high potential of zero charge, and the excessive protonation of surface-active *O at coordinatively unsaturated Ru sites (*OCUS) at low potentials (<1.60V). This, on one hand, severely depletes active *OCUS intermediate, thereby suppressing the rate-determining step (RDS) of oxide pathway mechanism (OPM), necessarily involving surface O─O coupling between two *OCUS via Langmuir-Hinshelwood mechanism. On the other hand, it induces the dense, strongly hydrogen-bonded interfacial water layer that, together with electrostatic repulsion, obstructs the essential water reorientation and approach for the RDS of adsorbate evolution mechanism (AEM), featuring incoming interfacial water to reorient and react with *OCUS via Eley-Rideal-like mechanism. Furthermore, a potential-dependent mechanistic switching between AEM and OPM is identified, dictated by their distinct RDS natures and kinetic sensitivities.

ABSTRACT Precisely deciphering the intrinsic origin of the high overpotential for oxygen evolution reaction (OER), even on the most active RuO 2 catalysts, remains a long‐standing challenge in electrocatalysis. Herein, by meticulously elucidating the electrode charging behavior, oxygenated surface phases and interfacial double‐layer structures under OER‐relevant potentials on RuO 2 (110), together with their impact on reaction pathways and elementary‐step energetics through ab‐initio molecular dynamics simulations, we reveal that the high overpotential jointly arises from the pronounced surface negative charge, due to the unusually high potential of zero charge, and the excessive protonation of surface‐active *O at coordinatively unsaturated Ru sites (*O CUS ) at low potentials (&lt;1.60 V). This, on one hand, severely depletes active *O CUS intermediate, thereby suppressing the rate‐determining step (RDS) of oxide pathway mechanism (OPM), necessarily involving surface O─O coupling between two *O CUS via Langmuir‐Hinshelwood mechanism. On the other hand, it induces the dense, strongly hydrogen‐bonded interfacial water layer that, together with electrostatic repulsion, obstructs the essential water reorientation and approach for the RDS of adsorbate evolution mechanism (AEM), featuring incoming interfacial water to reorient and react with *O CUS via Eley‐Rideal‐like mechanism. Furthermore, a potential‐dependent mechanistic switching between AEM and OPM is identified, dictated by their distinct RDS natures and kinetic sensitivities.

Understanding electric vehicle charging behavior: A multidisciplinary review and conceptual framework

Limited prior knowledge and uncertainty of electric vehicle (EV) charging behavior present significant challenges for effective EV charging control. This study presents a novel framework for joint prediction and control of EV charging by integrating mixture density networks (MDNs) with model predictive control (MPC). The MDN-MPC framework uses MDNs to stochastically model EV charging behavior as a set of probability distributions. These models are learned from historical EV transaction data using an autoregressive distribution estimation (ADE) approach and are integrated into a closed-loop MPC controller. The proposed control framework is evaluated through a case study at the University of Twente, Netherlands, demonstrating its capability to manage uncertainties in system dynamics, PV generation, and EV charging behavior, while achieving user satisfaction and operational profitability.

Structural color formation in soft colloidal systems represents a promising approach toward stimuli-responsive photonic materials. In this study, the assembly of poly(N-isopropylacrylamide) (PNIPAm)-based microgels incorporating varying percentages of anionic (methacrylic acid), neutral (acrylamide), and cationic (2-(dimethylamino)ethyl methacrylate) comonomers to create multifunctional colloidal building blocks capable of forming dynamic structural colors was systematically investigated. Through optimization of synthesis conditions, including comonomer selection, their concentration, and surfactant-mediated size control, libraries of copolymer microgels with precisely tailored particle diameter, surface charge, and stimuli-responsive swelling behavior were obtained. Structural color tuning across the visible spectrum was generated by thermal colloidal assembly. The interparticle spacing and consequently the reflected wavelengths were modulated by microgel concentration and their stimuli-responsive behavior: Finely tuned responsiveness of the microgels allowed for precise shifts in the reflected color upon environmental stimuli (temperature or pH). Such changes of the structural colors upon stimuli were clearly observable in the colloidal crystal assemblies of anionic and neutral microgels, while the amorphous assembly of the cationic microgels limited the detection of small shifts. Our findings provide key insights into the interplay between microgel responsiveness, compressibility, and the resulting structural color formation that are expected to support advanced multifunctional colloidal sensors, coatings, and optical tags.

The rapid growth of electric vehicles (EVs) demands intelligent, cost-effective, and sustainable charging solutions. This paper introduces a smart EV charging station system that integrates machine learning (ML) with pressure pad–based energy harvesting. The system forecasts energy demand, predicts vehicle types and slot needs, and recommends optimal charging times using real-time data such as state of charge (SoC), battery health, and user behavior patterns. ML models such as long short-term memory (LSTM) and random forest are employed to ensure accurate scheduling and forecasting. A smart display, the display slot indicator (DSI), powered by sensors and station data, guides users with live cost, time, and slot availability, including alternate suggestions during peak demand. The pressure pad not only contributes to energy recovery but also aids in real-time vehicle detection and traffic regulation within the station. With scalable capacity and intelligent automation, this system can support more than 400 EVs per day, minimizing operational load and energy waste while maximizing convenience and sustainability.

Facile fabrication of chitin-glucan nanofibers from four edible mushroom species using deep eutectic solvent and their adsorption characterization.

A fully passive seasonal multi-cylinder ice storage system: Dynamic charging behavior and entropy generation analysis