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

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.

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.

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.

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.

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

Modeling EV charging behavior: The impact of service experience and user heterogeneity

Methylene blue adsorption by synergistic clay-chitosan-alginate hydrogel beads: Batch and fixed-bed column studies with theoretical modeling.

The rapid growth of electric vehicles (EVs) and widespread deployment of fast charging stations greatly strengthened the coupling between coupled transportation and power distribution networks (TN-PDN). However, uncoordinated charging behaviors significant pose operational challenges to PDNs, particularly as regular power demands and EV charging demands peak simultaneously. As such, the approach to leverage EV flexibility through spatiotemporal pricing mechanism to guide orderly charging is of growing interest. The present work addresses this issue by proposing a deep-learning graph-attentive surrogate model to quantify the flexibility of EV charging demands by learning the nonlinear relationships between charging prices and EV charging responses. Specifically, our model seeks to approximate user equilibrium solutions in multi-temporal optimal traffic-power flows by integrating a graph convolutional network to encode the spatial topology of TNs, a bidirectional cross-attention mechanism to fuse global-topological features, and a transformer architecture to capture temporal interactions between traffic flow and environmental parameters. Simulation results validate the high accuracy of the proposed model, yielding an average error of 2.26%. Moreover, the framework effectively exploits EV flexibility to reduce system operating costs by 3.5%, while alleviating traffic congestion and improving the operational security of the PDN.

• Data-model mechanism synergistically optimizes EV load prediction model. • Based on ArcGIS parcel zoning, road network coupling modeling is achieved. • Optimize the travel and charging model for single-unit EV in the region. • The Dijkstra algorithm efficiently solves the shortest time-cost path. • The Bass-GM model captures EV trends for multi-timescale simulation. Spatial-temporal prediction of electric vehicle (EV) charging loads is crucial for the optimization of urban charging station scheduling and layout planning. However, with the rapid growth of EV penetration, the spatial-temporal distribution of charging loads has become increasingly random. This paper proposes a novel model that combines data-driven and model-driven methods for predicting regional EV charging loads across multiple time scales. First, ArcGIS is used to divide urban land parcels into different areas and establish a time-flow model based on road intersection characteristics. Secondly, dynamic Dijkstra algorithms, unit mileage power consumption and other method models are introduced to construct a perfect single EV mobility model. Then, the advantages of the Bass diffusion model and the GM (1,1) model are integrated through a recursive weighting mechanism to establish a hybrid prediction model for EV ownership. Finally, the Monte Carlo method is used to simulate the charging behavior of EV in a region of Lanzhou City, and compared and analyzed with the speed-flow model and the fixed power consumption model. The results indicate that the RMSE of Bass-GM(1, 1) decreased by 28.80 % compared to Bass model, while the prediction accuracy of the MC model for residential evening peak loads improved by 12.02 %. The feasibility and validity of the proposed model are verified, and it can more intuitively reflect the demand distribution of EV charging load short-term and development trend medium- and long-term.

Surface charge behavior of natural and modified adsorbents: Insights into pHzpc, mechanisms, and methodological perspectives

In high-density crossbar array memory architectures, selector devices play a crucial role in suppressing sneak-path currents and ensuring stable operation. In this study, we propose a pure SiO2-based selector fabricated using conventional semiconductor processes and materials and experimentally demonstrate its threshold switching (TS) characteristics. Stable TS behavior is verified with an endurance exceeding 109 cycles under repeatable measurement sequences and pulse-driven operations. Interface structure analysis combined with controlled pulse-based electrical characterization reveals that the formation of oxygen vacancies (VOs), the resulting structural asymmetry, and the dynamic charging behavior of VOs within the oxide are the key mechanisms governing the TS manifestation. Based on these insights, the optimized pulse conditions are established for reliable TS operation. The proposed selector, featuring a simple undoped oxide structure, can be fabricated at temperatures below 300 °C and offers tunable threshold voltage, enabling excellent integration compatibility with advanced memory devices. This study introduces a selector structure that distinguishes itself from conventional chalcogenide- or metal-oxide-based selectors by combining superior process and device compatibility with structural simplicity, offering a promising pathway toward a highly integrated and CMOS-compatible selector.

Dealing with complicated personalized driving behaviors and finding a global energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs) still remains a technical issue. Considering only external driving conditions without integrating driving behavior in EMS often results in energy waste and shortened power source lifespan. To address this issue, this study proposes a Personalized Behavior-Aware Energy Management Optimization (PBA-ECMS) framework based on large-scale real-world user data. Firstly, trip data from 300 PHEVs with relatively long average travel distances between charges are collected via a cloud platform. The Growing Neural Gas (GNG) algorithm is then employed to perform unsupervised clustering on the travel and charging behaviors of users, resulting in four representative user clusters. Secondly, for each cluster, the Equivalent Consumption Minimization Strategy (ECMS) is optimized by calibrating the equivalent factor using the bisection method, in order to reflect user-specific behavioral characteristics. Thirdly, Gamma distribution is fitted to model the driving distance between charges. Based on this model, Monte Carlo simulations are conducted to predict the users’ future travel for strategy evaluation. The results indicate that, compared to the conventional charge depletion-charge sustaining (CD-CS) strategy, the proposed PBA-ECMS significantly reduces energy cost by approximately 5.5% and battery aging cost by over 35% on average. The improvements are particularly evident for users who frequently undertake long-distance travels between charging events, for whom the reductions reach 8.5% in energy cost and 46% in battery aging cost. Furthermore, compared to ECMS with a uniform equivalent factor, the PBA-ECMS demonstrates better adaptability to behavioral diversity and achieves lower energy costs.