Point Of Charge Research Articles

Removal of crystal violet dye from aqueous solutions using Robinia pseudoacacia L. (Fabaceae) Fruits biosorbent

Synthetic dyes are a major source of environmental pollution. In this regard, biosorption is an important treatment method for the removal and detoxification of synthetic dyes from aqueous solutions. Accordingly, the present study was conducted to investigate the potential of Robinia pseudoacacia L. biosorbent (RPF) in the removal of crystal violet (CV) dye from aqueous solutions. To this end, biosorption parameters, including zero charge point, pH, initial dye concentration, biosorbent dose, stirring speed, and temperature, were investigated. Variations in the treated and untreated biosorbent surfaces were characterized using FTIR spectroscopy. The results showed that the RPF biosorbent removed 77% of CV under optimal conditions: pH of 6, initial dye concentration of 10 mg/L, biosorbent dose of 1 g, contact time of 30 min, stirring speed of 150 rpm, and temperature of 298 K. The Dubinin-Radushkevich isotherm (R2= 0.976) and pseudo-second-order kinetic (R2 = 0.995) models were well fitted according to isotherm and kinetic studies. Thermodynamic studies revealed that the process was endothermic according to the ΔG values. Moreover, the phytotoxicity of treated CV solutions was significantly reduced. Thus, the RPF biosorbent was determined to be a low-cost, sustainable, and ecofriendly material for the removal and detoxification of synthetic dyes from aqueous solutions.

Plugging in with neighbours: Defining the social dimension of electric vehicle charging in the Netherlands

Residential public charging points are shared by multiple electric vehicle drivers, often neighbours. Therefore, charging behaviour is embedded in a social context. Behaviours that affect, or are influenced by, other public charging point users have been sparsely studied and lack an overarching and comprehensive definition. Consequently, very few measures are applied in practice to influence charging behaviour. We aim to classify and define the social dimension of charging behaviour from a social-psychological perspective and, using a behaviour change framework, identify and analyse the measures to influence this behaviour. We interviewed 15 experts on residential public charging infrastructure in the Netherlands. We identified 17 charging behaviours rooted in interpersonal interactions between individuals and interactions between individuals and technology. These behaviours can be categorised into prosocial and antisocial charging behaviours. Prosocial charging behaviour provides or enhances the opportunity for other users to charge their vehicle at the public charging point, for instance by charging only when necessary. Antisocial charging behaviour prevents or diminishes this opportunity, for instance by occupying the charging point after charging, intentionally or unintentionally. We then identified 23 measures to influence antisocial and prosocial charging behaviours. These measures can influence behaviour through human–technology interaction, such as providing charging etiquettes to new electric vehicle drivers or charging idle fees, and interpersonal interaction, such as social pressure from other charging point users or facilitating social interactions to exchange requests. Our approach advocates for more attention to the social dimension of charging behaviour.

A Novel Arithmetic Optimization Technique for Electric Vehicle Charging

With the increased usage of Electric vehicles in recent days, the optimal usage of battery both in terms of electrical and economic perspective is very important. Though several battery optimization techniques are evolving day by day, the method to achieve an efficient control of all parameters like battery current, power and power loss has not yet been achieved. This paper proposes a novel approach of implementing an arithmetic optimization scheme for FTP 75 drive cycle source. In this connect, the details of the arithmetic operators are discussed and their relations with electrical parameters are framed from the fundamental equations. A Pseudo-code has been developed and implemented for identifying the optimum charging point from different search spaces. The simulation results obtained for different electrical parameters with respect to different arithmetic operators, emphasizes the enhanced operation of electric vehicles at optimum charging points.

Divergent imaginings: Transitioning to decarbonised mobility in ‘post-coalonial’ County Durham

ABSTRACT What happens when the various parties involved in constructing decarbonised futures’ infrastructure diverge in their imaginaries? Much of the published research on the sociotechnical imaginaries relating to electric vehicles (EVs) describes the creation of the future of decarbonised transport as a process mired in conflict, with various interested parties represented as strenuously disagreeing in their assessment of the most efficacious solution. The aim of the article is to offer an alternative account, based upon data gathered through participant observation, interviews, and grey literature. It describes the sort of personal transportation futures currently being imagined in the United Kingdom. The focus is specifically on the installation of electric vehicle charge points. The author contrasts Whitehall’s national vision for this infrastructure with the ‘post-coalonial’ vision of officers of Durham County Council in North East England have articulated an alternative, a ‘post-coalonial’ vision, and finds that the vision of both the British civil service and Government of the United Kingdom focused on private ownership and commuting, while Durham County Council envisioned publicly accessible charge points that enabled various types of different journeys. Despite the striking differences the conclusion is that contrary to the findings of previous studies the existence of these divergent infrastructural imaginaries led not to conflict but to co-existence.

EV Smart-Charging Strategy for Power Management in Distribution Grid with High Penetration of Distributed Generation

Accelerated environmental impacts are a growing concern in the modern world. Electric mobility and the transition to a cleaner energy matrix have become increasingly discussed topics. In this context, this work presents a framework for controlling an electric vehicle (EV)-charging station integrated into a microgrid application as a basis for creating the infrastructure integrated into a smart grid concept. Considering the electrification of the transportation sector future perspectives, a brief review is conducted on the impacts of EV fleet growth in different countries and how smart-charging technologies are identified as solutions for mitigating the negative effects of energy and power consumption associated with EV-charging stations. An analysis of the technical characteristics and the tools that enable the deployment of a fleet-charging operator are examined, specifically focusing on the communication protocol for EVs, such as the OCPP (Open Charge Point Protocol) parameterization/configuration. A new EV-charging station control method is proposed to manage the impacts of distributed solar photovoltaic generation and mitigate the effects of the duck curve. Finally, an integration architecture via IEC 61850 for these elements is proposed, in a practical implementation for variable power control, considering different strategies to deal with distributed generation impact using EV-fleet-charging power demand dynamic management.

Electric vehicles charging infrastructure framework using internet of things

Electric vehicles (EVs) sales have grown rapidly recently, and more growth is expected over the coming years. A challenging problem arises when managing different battery requirements of moving EVs through reliable Charging Stations (CSs). Current concerns for EV users are long waiting lines at CSs and dropping below a predefined battery capacity limit. For this reason, this paper proposes an Internet of Things (IoT)-based EV charging scheduling system, which with the use of IoT technologies, decides the optimal assignment between EVs and Charging Points (CPs) located at different CSs at given time t. By using cloud computing and real time data such as number of EVs, number of CSs, number of CPs at different CSs … etc; the scheduling controller uses a recursive algorithm to generate all possible scenarios, and then shares the optimal assignment (that minimizes the average waiting time and fulfill battery constraints and charging needs) with all EVs. To test the validity of the IOT based scheduling system, sensitivity analysis by running different scenarios (pertaining to different parameters) was conducted. The different scenarios were compared to a base scenario where the system was not used and real-life random assignment is considered. The different run scenarios show superiority over the base scenario in terms of average waiting time (WT) and battery capacity threshold. For example, in the base scenario, violation of battery capacity threshold occurred 9.1% of the time, making random selection an unreliable choice versus no violations when the IOT scheduling system is used. Also, all tested scenarios under the IOT scheduling system show shorter average WT compared to the base scenario. For instance, scenarios 2 and 3 show more than 35% and 55% decrease in WT compared to the base scenario.

Trustworthy V2G scheduling and energy trading: A blockchain-based framework

The rapid growth of electric vehicles (EVs) and the deployment of vehicle-to-grid (V2G) technology pose significant challenges for distributed power grids, particularly in fostering trust and ensuring effective coordination among stakeholders. Establishing a trustworthy V2G operation environment is crucial for enabling large-scale EV user participation and realizing V2G's potential in real-world applications. In this paper, an integrated scheduling and trading framework is developed to conduct transparent and efficacious coordination in V2G operations. In blockchain implementation, a cyber-physical blockchain architecture is proposed to enhance transaction efficiency and scalability by leveraging smart charging points (SCPs) for rapid transaction validation through a fast-path practical byzantine fault tolerance (fast-path PBFT) consensus mechanism. From the energy dispatching perspective, a game-theoretical pricing strategy is employed and smart contracts are utilized for autonomous decision-making between EVs and operators, aiming to optimize the trading process and maximize economic benefits. Numerical evaluation of blockchain consensus shows the effect of the fast-path PBFT consensus in improving systems scalability with a balanced trade-off in robustness. A case study, utilizing real-world data from the Southern University of Science and Technology (SUSTech), demonstrates significant reductions in EV charging costs and the framework's potential to support auxiliary grid services.

Role of oxide layer on corrosion resistance and surface conductivity of titanium bipolar plates for proton exchange membrane fuel cell

Heat-assisted forming (either hot stamping or multi-step forming with intermediate annealing) has been demonstrated as an effective novel forming process of titanium bipolar plate (Ti-BPP) with fine flow channels and high geometrical accuracy, where an oxide layer can be introduced to Ti-BPPs due to thermal oxidation. Although low-cost coating (e.g., amorphous carbon coating) is normally applied to Ti-BPPs in order to increase their corrosion resistance and surface conductivity, the impact and mechanism of oxide layer on the corrosion resistance and surface conductivity of coated Ti-BPPs remain unclear. This work finds that the oxide layer introduced by thermal oxidation enhances the corrosion resistance of coated Ti-BPPs (cathodic corrosion current density decreased from 1.54 to 0.54 μA/cm2) without reducing surface conductivity. The oxide layer thickens from <10 to 100–150 nm, with its primary composition changing from TiO2 to Ti3O during thermal oxidation and coating process. The presence of Ti3O enhances the resistance of the charge transfer and point defect migration of Ti-BPPs while moderating the capacitive behavior of amorphous carbon coating. Maintaining surface conductivity relies on two key mechanisms: the reduction of concentrated and film resistance by amorphous carbon coating, and the transformation of oxide layer's composition.

Exploring Tenebrio molitor as a source of low-molecular-weight antimicrobial peptides using a n in silico approach: correlation of molecular features and molecular docking.

Yellow mealworm (Tenebrio molitor) larvae are increasingly recognized as a potential source of bioactive peptides due to their high protein content. Antimicrobial peptides from sustainable sources are a research topic of interest. This study aims to characterize the peptidome of T. molitor flour and an Alcalase-derived hydrolysate, and to explore the potential presence of antimicrobial peptides using in silico analyses, including prediction tools, molecular docking and parameter correlations. T. molitor protein was hydrolysed using Alcalase, resulting in a hydrolysate (TMH10A) with a 10% degree of hydrolysis. The peptidome was analysed using LC-TIMS-MS/MS, yielding over 6000 sequences. These sequences were filtered using the PeptideRanker tool, selecting the top 100 sequences with scores >0.8. Bioactivity predictions indicated that specific peptides, particularly WLNSKGGF and GFIPYEPFLKKMMA, showed significant antimicrobial potential, particularly against bacteria, fungi and viruses. Correlations were found between antifungal activity and physicochemical properties such as net charge, hydrophobicity and isoelectric point. The study identified specific T. molitor-derived peptides with strong predicted antimicrobial activity through in silico analysis. These peptides, particularly WLNSKGGF and GFIPYEPFLKKMMA, might offer potential applications in food safety and healthcare. Further experimental validation is required to confirm their efficacy. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Exploiting RSM and ANN modeling methods to optimize phosphate ions removal using LDH/alginate composite beads

In the present work, layered double hydroxide (MgAl) and alginate composite beads (LDH/alginate) were developed and used as low-cost and environmentally friendly adsorbent for phosphate ions removal. The successful incorporation of sodium alginate into the LDH structure was confirmed through SEM analysis. The dried beads exhibited a notably rough surface, which promotes molecular movement and potentially enhances pollutant adsorption. The pH of zero charge point (pHPZC) of the composite was found to be 7.41. This result implies that negative ions have a tendency to draw positive charges on the adsorbent surface via electrostatic interaction forces when the pH is lower than the pHPZC. On the other hand, a surface that has a pH higher than pHPZC mostly has a negative charge. The percentage removal and adsorption capacity were investigated as a function of contact time.Experiments were carried out by simultaneously varying three factors, in order to evaluate and compare the predictive capabilities of the response surface methodology (RSM) and the artificial neural network approach (ANN) for the adsorption process. Both methods demonstrated a strong ability to accurately predict the adsorption process. However, the response surface methodology exhibited a lower prediction error compared to the artificial neural network approach.The central composite design within response surface methodology (CCD-RSM) was employed to optimize the experimental conditions for the adsorption process. The model, which considers three factors: adsorbent dose (A), initial concentration (B), and contact time (C), proved to be significant. Among these, the quadratic term (B2) had the most substantial impact on the phosphate ion adsorption rate. The analysis yielded an R2 value of 0.94, indicating an excellent fit to the data. The findings suggest that increasing contact time and reducing the initial concentration improve phosphate ion removal efficiency, while the adsorbent dose has little to no effect. The Artificial Neural Network (ANN) model effectively predicted the adsorptive remediation of phosphate ions onto LDH/alginate composite beads, achieving a high coefficient of determination (R2 = 0.984) between the model outputs and the experimental data. The study highlights the significant potential of LDH/alginate composite beads as a natural adsorbent for the removal of phosphate ions from aqueous solutions. These results underscore the environmental friendliness and efficiency of the adsorbent used for phosphate adsorption.

Ultrasound Activated Clay: Preliminary Study of PB And as Removal in Aqueous Media

Objective: The objective of this study was to evaluate the removal of Pb and As in aqueous media by activated clay (AC). Theoretical Framework: Heavy metals in water represent a risk to health and ecosystems, their removal through new materials is a challenge for the industry. Method: The methodology adopted for this research comprises in that natural clay (NC) was activated with sodium chloride and phosphoric acid followed by sonication. A synthetic solution of Pb and As was prepared at different pH. Results and Discussion: The zero charge point was found to be 7.10 for AC and 8.02 for NC, the ζ potential was found to be between - 41.15 and - 45.43 mV in the pH range of 4 to 12, with degree of crystallinity of 97.24% and crystal size of 2.85 nm determined by XRD. FTIR analysis allowed the identification of functional groups with chelating capacity. Was observed As removal of 75.51% (110.40 mg/g) and 23.23% (58.13 mg/g) and 97.75% (106.41 mg/g) and 100.0% (206.53 mg/g) for Pb with AC and NC respectively at pH 6 and 8. Research Implications: Los reaultadoe encontrados sugieren que la arcilla activada se convierte en un material alta capacidad de adsorción de metales pesados en sistemas multimetal. Esta cualidad se debe al incremtno de grupos funionales en su superficie y al tamaño nanométrico que incrementa el área de contacto. Originality/Value: Activation of the clay with sodium chloride and weak acid followed by sonication is an environmentally friendly process that produces a material with high heavy metal removal capacity in aqueous media.

Improving electric vehicle charging forecasting: A hybrid deep learning approach for probabilistic predictions

AbstractElectric vehicles (EVs) have gained significant attention recently. Despite their advantages, challenges in the power grid, such as providing necessary information for optimal operation, persist. High‐precision forecasting techniques are essential to address the nonlinear and complex behavior of EV charging. A hybrid structure based on deep learning, called LSTLNet, has been proposed. LSTLNet combines convolutional neural networks (CNN), gated recurrent neural networks (GRU), attention mechanisms (AM), and automatic regression (AR) models. This combination improves the deterministic forecasting model and addresses the weaknesses of CNN and GRU. Deterministic prediction, which determines only one point of consumption charge, is prone to error. Therefore, probabilistic forecasting, represented as a probability distribution function (PDF) containing comprehensive statistical information, is preferred. A smooth band limit maximum likelihood (SBLM) estimator is used to indirectly predict the PDF from the data. Comparative results with conventional shallow and deep methods for similar time series forecasting demonstrate the superiority of the proposed method for both deterministic and probabilistic forecasting.

ADDRESSING INFRASTRUCTURE GAPS IN MUMBAI’S 2-WHEELER EV CHARGING NETWORK: A FOCUS ON ACCESSIBILITY, USABILITY, AND USER FEEDBACK

Electric vehicles (EVs) are transforming transportation in Mumbai, with 2-wheeler EVs gaining significant traction among commuters. However, the insufficient availability and accessibility of charging infrastructure remain critical challenges. This study investigates the experiences of 2- wheeler EV users in Mumbai, focusing on the barriers they encounter when charging their vehicles. Given Mumbai’s dense traffic and urban complexities, limited charging points, long waiting times, and infrastructural constraints can hinder the user experience and discourage further adoption of EVs. The findings provide key insights into optimal charging station locations based on a comprehensive analysis of population density, traffic flow, and urban planning frameworks. Additionally, the study addresses the need for standardized charging protocols, advanced technologies, and user-friendly interfaces to enhance the usability of charging stations. The study emphasizes that addressing these challenges is crucial not only for the user experience but also for policymakers, urban planners, and stakeholders aiming to expand Mumbai’s EV infrastructure. Moreover, the environmental benefits of 2-wheeler EV adoption are examined, including reductions in energy consumption, carbon emissions, and overall environmental impact. By analyzing these factors, the research highlights the positive environmental outcomes of investing in robust charging infrastructure. Key challenges, such as high costs, feasibility concerns, and policy limitations, are also explored, offering a holistic understanding of the practical barriers to improving the EV charging network. The study concludes with actionable recommendations aimed at overcoming these challenges, fostering sustainable growth, and ensuring the efficient deployment of 2-wheeler EV charging infrastructure across Mumbai.

Philosophy within Data Science Ethics Courses

There is wide agreement that ethical considerations are a valuable aspect of a data science curriculum, and to that end, many data science programs offer courses in data science ethics. There are not always, however, explicit connections between data science ethics and the centuries-old work on ethics within the discipline of philosophy. Here, we present a framework for bringing together key data science practices with ethics topics. The ethics topics were collated from 16 data science ethics courses with public-facing syllabi and reading lists. We encourage individuals who are teaching data science ethics to engage with the philosophical literature and its connection to current data science practices, which are rife with potentially morally charged decision points.

Fault characterization and its significance on hydrocarbon migration and accumulation of western Tazhong Uplift, Tarim Basin: Insight from seismic, geochemistry, fluid inclusion and in situ U-Pb dating

Understanding the mechanisms of deep hydrocarbon accumulation is of paramount importance in the field of petroleum geology. Studying deep hydrocarbon accumulation mechanisms contributes to the development of effective reservoir management strategies and efficient extraction techniques. In this study, fluid inclusion observation, homogenization temperatures, in situ calcite U-Pb dating, geochemical analyses and seismic data were applied to investigate the hydrocarbon charging history of the western Tazhong Uplift, and the migration pathways were further analyzed to establish the history of oil and gas accumulation in the study area. The results reveal three distinct episodes of oil and gas charging processes in the western Tazhong Uplift. The first episode, determined by in situ calcite U-Pb dating bearing primary oil inclusions, occurred during the late Caledonian to early Hercynian orogeny. This episode is characterized by yellowish fluoresce color oil inclusions. The second episode involved a complex variation of the thermal maturity of oil. Mature oil, with oil inclusions preferentially fluorescing yellow-green color, was charged into the reservoirs near the Tazhong No.1 fault. Subsequently, the high mature oil characterized by blue-fluorescing oil inclusions was charged. At the same time, the mature oil migrated vertically into the reservoir away from the Tazhong No.1 fault along the strike slip faults. The third charging process is dominant by high mature oil in the Southwest of the western Tazhong Uplift, whereas the gas intrusion occurred in the area near the Shuntuoguole Low Uplift. Furthermore, the strike slip faults and reverse faults were distinguished based on the seismic profiles and coherence diagram. The intersections of strike slip faults and Tazhong No.1 fault developing more fractures served as oil and gas charging points. The oil production revealed that the strike slip faults acted as important hydrocarbon migration pathways. This study provides valuable insights into the processes of hydrocarbon accumulation and offers further evidence for deep oil exploration in the region.

Macadamia nut residue-derived biochar: An eco-friendly solution for β-naphthol and Reactive Black-5 removal

The generation of liquid waste containing dyes and complex organic compounds is a problem in the textile industries. In this work, we evaluated the use of macadamia nut residue as a precursor of a biochar for the treatment of these effluents through the adsorption process. The macadamia nut endocarp undergoes a sequential treatment involving pyrolysis at 600 °C followed by physical activation with CO2 and H2O vapor at 700 °C. Characterization of the resulting macadamia nut residue-derived biochar (MB) is accomplished through various techniques, including X-ray diffractometry (XRD), thermogravimetric analysis (TGA), scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDS), nitrogen physisorption analysis and the pH at the zero charge point (pHZCP). Performance studies reveal that pH exerts minimal influence on the adsorption phenomenon. The Elovich kinetic model effectively describes the adsorption kinetics for both β-naphthol and Reactive Black 5 (RB5), with adjusted coefficients of determination (R2adj) of 0.99 and 0.94, respectively. Further analysis using the intraparticle diffusion model demonstrates that β-naphthol adsorption kinetics involve multiple mechanisms, while RB5 adsorption is predominantly governed by intraparticle diffusion. Equilibrium adsorption data for both adsorbates fit well with the Sips isothermal model, yielding maximum adsorption capacities of qmax β-naphthol = 15.16 mg∙g−1 and qmax RB5 = 3.08 mg∙g−1 at 55 °C. The adsorption process for both compounds is spontaneous and endothermic, based on the enthalpy values, it can be inferred that the β-naphthol adsorption is governed by weak van der Waals forces, indicating physisorption. As for RB5, the enthalpy value suggests that the phenomenon occurs due to electrostatic interactions between the molecule and the surface groups with higher energy involved. This study highlights the potential of macadamia nut residue biomass as a precursor for biochar, demonstrating its favorable attributes for application as an effective adsorbent material in the removal of organic compounds such as β-naphthol and RB5 from wastewater streams within the textile industry.

The accessibility of public electric vehicle (EV) charging infrastructure: Evidence from the cities of Nottingham and Frankfurt

AbstractThe distribution of public electric vehicle (EV) charging infrastructure is a widespread approach for promoting EV adoption and decarbonising transportation. A significant amount of literature explores the distribution of EV charging points at a country scale, but there is a lack of studies focusing on a district scale. This study aims to contribute to this gap by gaining insights into the distribution of EV charging points per district within cities, such as Nottingham and Frankfurt. The study investigates the current distribution of EV charging points across 38 postcode districts in Frankfurt and 9 postcode districts in Nottingham, using geographical data analysis and a linear regression approach. The following factors in response to the number of EV charging points per postcode district (ZIP code) are examined: the percentage of apartment buildings/floor area ratio, the availability of amenities, population, charging capacity (kW), area size, strategic approaches, including policy goals and principles. The results reveal disparities in access to EV charging infrastructure across districts and underscore the importance of expanding EV charging networks not only in districts located near urban centres or those with high availability of amenities but also ensuring that users without home charging options are not left behind.

Enhanced minimum cost consensus model for interval type-2 fuzzy social network group decision making focusing on individual attributes and group attitude

In group decision-making scenarios, consensus reaching is a crucial factor for resolving conflicts of opinion among groups, and social network analysis plays a significant role in fostering group consensus. This paper constructs a social network-driven minimum cost consensus framework for interval type-2 fuzzy group decision-making problems involving different individual attributes. Firstly, this paper proposes a theoretical social network analysis by the implementation of propagation efficiency, propagation reliability and opinion similarity to generate comprehensive trust relationships. The aim is to obtain missing trust relationships and individual centrality. Secondly, a minimum cost consensus model is constructed to give recommendation advice for identified inconsistent decision-makers according to their adjustment willingness. The novelty of the model lies in its capability to consider decision-makers’ individual attributes and group attitude or behavior. Then, this paper proposes an interval type-2 fuzzy Alternative by Alternative Comparison (ABAC) method for ranking multiple alternatives which address the rank reversal problem. Lastly, a case study on the selection of alternative charging point operators illustrates the effectiveness of the proposed method, and comparison and sensitivity analysis show the advantages of the proposed method.

A Novel Charging Management and Security Framework for the Electric Vehicle (EV) Ecosystem

The EV charging network has witnessed significant growth in the UK in the last few years due to the net zero emission target of the government by 2030. The related literature in EV charging management mainly focuses on road-traffic-parameter-based optimization and lacks detail in terms of charging statistics and cyber–security-enabled charging management frameworks. In this context, this paper proposes a novel EV Charging Management and Security (EVCMS) framework using real-time charging statistics and an Open Charge Point Protocol (OCPP). Specifically, a system model for EVCMS is presented considering charging data management and security protocols. An EVCMS framework design is detailed, focusing on charging pricing, optimization, and charging security. The experimental implementation is described in terms of client–server and charge-box-based simulation. The performance of the proposed EVCMS framework is evaluated by considering different charging scenarios and a range of charging-related metrics. An analysis of results and comparative study attest to the benefits of the proposed EVCMS framework for enabling the EV charging ecosystem.

D113 resin in-situ loaded with Fe3+ to develop glyphosate adsorbent with the characteristics of salt-resistance and the remarkable saturated adsorption capacity

There are inorganic salts in glyphosate production liquor and natural water bodies coexisting with glyphosate. It is imperative to develop a salt-tolerant adsorbent for glyphosate in water. Industrial D113 resin undergone two-step transformation to optimize the preparation of D113 in-situ loaded with Fe3+ (D113-Fe3+) as salt-resistance glyphosate adsorbent. The loading amount of Fe3+ on D113-Fe3+ is 3.5 mmol/g. The adsorption mechanism revealed that Fe3+ in D113-Fe3+ formed Fe-O-P bond with the phosphonate group of glyphosate. At 293 K, the maximum complex ratio of the adsorbed glyphosate to Fe3+ in D113-Fe3+ was 2.4:1. At 293 K, the remarkable saturated glyphosate adsorption capacity of D113-Fe3+ reached 1420.2 mg/g. In pK2 state of glyphosate, D113-Fe3+ featured its maximum adsorption capacity at the zero charge point 2.43 of D113-Fe3+ and 293 K. In glyphosate solution coexisting 0–16 % NaCl, D113-Fe3+ exhibited stable glyphosate adsorption capacity and salt-resistance compared with D201, D301 and 330 resin. The endothermic and spontaneous adsorption of glyphosate on D113-Fe3+ can fit Freundlich model and pseudo-second-order model. 2 mol/L NH3·H2O, 2 mol/L FeCl3 and 2 mol/L H2SO4 could all regenerate D113-Fe3+. The characteristics of salt-resistance and the remarkable saturated adsorption capacity made D113-Fe3+ comparable to all reported glyphosate adsorbents.