Point Of Charge Research Articles

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.

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.

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.

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.

Methylene Blue and Rhodamine B Dyes' Efficient Removal Using Biocarbons Developed from Waste.

The preparation of biocarbons from cellulose fibres utilised in the production of baby nappy mats (sourced from Feniks Recycling company, Poland) for the removal of methylene blue and rhodamine B dyes has been documented. A Brunauer, Emmett and Teller analysis revealed a surface area within the range of 384 to 450 m2/g. The objective of this study was to investigate the removal efficiency of dyes from aqueous solutions by biocarbons, with a particular focus on the influence of various parameters, including pH, dye concentration, adsorbent dosage, shaking speed, contact time, and temperature. The maximum adsorption capacity of the dyes onto the biocarbons was found to be 85 mg/g for methylene blue and 48 mg/g for rhodamine B, respectively. The Langmuir equation proved to be the most suitable for interpreting the sorption of organic dyes. The adsorption process was found to exhibit a chemisorption mechanism, effectively mirroring the pseudo-second-order kinetics. Furthermore, the adsorption of dyes was observed to be endothermic (the enthalpy change was positive, 9.1-62.6 kJ/mol) and spontaneous under the tested operating conditions. The findings of this study indicate that biocarbons represent a cost-effective option for the removal of methylene blue and rhodamine B. The adsorption method was observed to be an effective and straightforward approach for the removal of these dyes. The results of the Boehm titration analysis and zero charge point value indicated that the synthesised biomaterials exhibited a slightly basic surface character.

Development of a Cost-Driven, Real-Time Management Strategy for e-Mobility Hubs Including Islanded Operation

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes.

Optimal design of sizing and allocations for highway electric vehicle charging stations based on a PV system

The world’s demand for fossil fuels has recently increased significantly for both transportation and electric power generating sectors. Using these resources not only results in high costs and depletion of them but also increases greenhouse gas emissions and pollution. The electric vehicle (EV) market is growing globally, aiming to face climate change due to greenhouse gas (GHG) emissions and to reduce reliance on fossil fuels. However, the long charging time of EVs and the shortage of charging outlets limits the global adoption of EVs, especially on highways where the problem of accessibility to the electricity distribution grid appears. These issues can be faced by the good planning of charging infrastructure. However, this planning is a multidisciplinary field that includes electricity generation, transportation networks, EVs’ characteristics, and driver behavior. A methodology to provide the optimal locations and sizing of electric vehicle charging stations with their own electricity generation and storage using photovoltaic (PV) and energy storage systems on highways considering different factors is proposed in this paper. This paper takes a section of the western desert highway in Egypt connecting Assiut and Cairo cities as a case study. Four scenarios are proposed for the design of EV charging stations’ locations and sizing which are centralized charging stations, two-way charging stations, utilizing oil stations’ locations, and distributed fixed sizing charging points with a comparison between them. The work also discusses the potential effects of highway slope, wind speed, and number of passengers on the location problem. The results can be used to optimize the design of EV charging stations along highways for a completely sustainable system.

Super-efficient removal of yellow 161 anionic dye from water by activated carbon based on coffee grounds

The study evaluates the effectiveness of activated carbon derived from coffee grounds (ACcg) in removing Reactive Yellow 161, a pollutant from the textile industry. The properties of ACcg are analyzed by various techniques such as FTIR, SEM, EDS and X-ray diffraction. In addition, physico-chemical analyses, such as moisture content, ash content, volatile matter, iodine value and zero charge point (pHpzc), were carried out on the prepared ACcg. The Box-Behnken Design (BBD) scheme combined with the Response Surface Method (RSM) is used to optimize adsorption variables. Results show high microporosity of ACcg, adsorption kinetics fitted to pseudo-first order and Langmuir isotherm consistent with experimental data. Thermodynamic parameters indicate spontaneous and exothermic adsorption behavior. Residue analysis and ANOVA confirm a high adsorption capacity of ACcg (97.67 %) at an initial concentration of 20 ppm Reactive Yellow, with an ACcg mass of 1 g/L and a pH of 3. The chosen model is validated for this application.

Preparation of LMO@FC catalysts and degradation of tetracycline by catalytic ozonation

Developing efficient catalysts with good stability holds great significance for better applying catalytic ozone oxidation in advanced wastewater treatment. Therefore, we designed a novel catalyst of LaMnO3-loaded foam ceramics (LMO@FCs) for catalytic ozonation of tetracycline hydrochloride (TCH) in water. The LaMnO3 (LMO) active components were uniformly loaded on the foam ceramics (FCs) to form an LMO@FC catalyst with mesoporous structure. The total organic carbon (TOC) removal rate by ozonation with LMO@FCs was 2.13 times that of ozone alone. After five repeated uses, the removal rate of TCH and TOC reached more than 99 % and 51.3 %, respectively. The conversion of ozone to reactive oxygen species (ROS) increased with the growth of initial pH. When pH reached zero point of charge (pHpzc), the ozone adsorption on the catalyst surface decreased, leading to the worst degradation efficiency. The Mn3+/Mn4+ and lattice oxygen (OL2−) and surface hydroxyl radical (·OH) were distributed on the catalyst surface. ·OH, superoxide anion (·O2−), and singlet oxygen (1O2) coexisted in the process of ozonation catalyzed by LMO@FCs. The surface ·OH of the catalyst was the catalytic active site. The electrostatic balance between Mn3+ and Mn4+ and the oxidation-reduction between O2− and O2 also played a crucial role in catalytic activity. The TCH experienced a series of reactions in the O3/LMO@FCs process, such as hydroxylation, demethylation, ring-opening oxidation and defunctionalization, and finally was mineralized into CO2, H2O, and NH4+. These results demonstrated that LMO@FCs are promising catalysts for catalytic ozonation of pharmaceutical wastewater treatment.

Poly(allyl alcohol-co-vinyl acetate)-grafted concrete waste for adsorptive removal of As(III)

This study focused on the synthesis of concrete waste grafted with a polymer of allyl alcohol and vinyl acetate as a novel nano adsorbent and the investigation of its ability in the adsorptive removal of Arsenic (As(III)) from aqueous solutions. The prepared nano adsorbent was characterized using X-ray diffraction, thermogravimetric analysis, field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Fourier-transform infrared spectroscopy. Various parameters were optimized by response surface methodology and central composite design model to achieve the highest As(III) removal efficiency, including the solution's temperature, contact time, and pH. The model predicted a maximum removal efficiency of 92 % under the optimal conditions (temperature = 25 °C, contact time = 24 min, and pH = 7), resulting in a sorption capacity of 6.81 mg g−1. The zero point of charge for the nano adsorbent was obtained at about 6.5. The experimental data for As(III) sorption onto the nano adsorbent was well-fitted by the Freundlich isotherm and pseudo-1st-order kinetic models. This suggests that the adsorption process involves the formation of a multilayer of As(III) on the adsorbent surface, and it occurs through physical interactions rather than chemical reactions. The reusability of the nano adsorbent was evaluated, demonstrating a removal efficiency exceeding 72 % even after seven recycling cycles. The presence of interfering cations influenced the removal efficiency in the following order: Pb2+>Mn2+>Cu2+>Zn2+>Ni2+. The prepared nano adsorbent showed significant efficiency in removing As(III) from well water and river water samples, with a repeatability range of 1.09–2.51 %.