Search Results (7,137)

The manuscript describes an optical sensing microplate for the high-throughput screening of imidazolinone herbicides in soil extracts. As far as we know, imprinted proteins (IPs) specific to imidazolinone herbicides have not been synthesized and used as a recognition element for their solid-phase extraction before. Imprinted bovine serum albumin (BSA) and glucose oxidase (GOx) were synthesized in the presence of imazamox as a template and then these IPs were immobilized at the bottom of microplate wells. The sorption capacity (Q) of aminated silica nanoparticles modified by IPs (IP–BIS) was 6.38 mg g⁻¹ while the imprinting factor ($IF$) equaled 2.6. The concentration of imazamox was determined by a “turn-off” solid-phase assay using alloyed CdZnSeS/ZnS quantum dots (QDs) as a component of fluorescent substrate. Alloyed CdZnSeS/ZnS QDs were stabilized in an aqueous phase by positively charged cysteamine that, as far we know, had not been used as this type of ligand before. Our method allows for determining the concentration of imazamox in the range of 0.5–9.2 μg mL⁻¹, with a limit of quantification limit of quantitation (LOQ) equal to 0.45 μg mL⁻¹ The sensing microplate enables parallel detection of up to 96 samples containing herbicides using standard fluorescence microplate readers or smartphones. The paper describes how such sensing microplates can be used for the analysis of artificially contaminated soil samples. The proposed approach combines pre-concentration of analyte at the IPs with its subsequent determination on a single analytical platform, thus allowing for both highly sensitive determination in laboratory conditions and mass screening in the field. Full article

The transportation sector is a major contributor to global greenhouse gas emissions, making electric vehicle (EV) adoption critical for decarbonization. This study investigates EV adoption determinants in Turkey using explainable machine learning, focusing on economic, infrastructure, and attitudinal factors while exploring driver behavior and fuel-efficiency awareness. Data from 304 participants were collected; after excluding undecided responses, the final analytical sample comprised 232 participants. Multiple algorithms (Random Forest, XGBoost, Logistic Regression, and SVM) were evaluated, addressing class imbalance via SMOTETomek. SHAP analysis identified policy-relevant predictors. Results reveal that EV adoption intentions are primarily driven by perceived cost impact, EV knowledge, and charging infrastructure accessibility, showing substantially stronger effects than driver behavior. Exploratory analysis indicates that aggressive driving correlates with lower fuel-efficiency awareness, whereas maintenance and eco-driving support higher awareness. The best-performing Random Forest model achieved 89.36% accuracy and a 0.9348 F1-score. Rather than claiming novelty in ML application, this study contributes an interpretable framework and emerging-market evidence contrasting economic/infrastructure factors against behavioral variables. Findings provide actionable insights for policy, highlighting cost-focused incentives, infrastructure deployment, and targeted awareness campaigns. Full article

This paper proposes a novel magnetic coupler, a segmented bipolar pad (SBP) that outperforms the conventional bipolar pad (BP) with symmetrical geometrical dimensions. The performance parameters: mutual inductance (M_TR), coupling coefficient (k), output power (P_O), and DC-DC efficiency (η). The performance evaluation of the proposed pad is compared with the conventional pad under cases: (1) lateral misalignment (ΔY), and (2) interoperability with non-polarized pad (NPP) and polarized pad (PP). A 4.7 kW inductive power transfer (IPT) system is designed with an inductor–capacitor–capacitor-series (LCC-S) compensation network. For case 1, the M_TR of the SBP at ΔY = ±90 mm is the same as the M_TR of BP at ΔY = 0 mm, ensuring better misalignment tolerance capability of SBP. The maximum η of SBP is 93.64%, which is 4.96% greater than the highest η of BP. For case 2, the M_TR of the SBP with NPP is 22–24% and with PP is 20–25% higher than the BP interoperable performance. The obtained η shows maximum improvement of 2.46% for SBP with NPP, and 3.7% for SBP with PP when compared to the interoperable results of BP. SBP gives enhanced performance for both cases compared to the conventional pad at no additional pad design cost. The proposed work is validated through an experimental setup. Full article

This study presents the use of a Battery Energy Storage System (BESS) and a thermal power plant to enhance Primary Frequency Regulation (PFR) in a power system. This integration seeks to mitigate operational challenges, such as the reduction in system inertia and frequency regulation, which are heightened when increasing renewable energy use in power grids with high hydroelectric generation. The proposed solution enables thermal generators to operate at optimal capacity, while the BESS provides a rapid frequency response, thereby enhancing operational efficiency and compliance with national standards. The process was structured in five stages: criteria definition, analysis, design, models, and evaluation. A comprehensive methodological approach was adopted, including dynamic system modeling and BESS sizing based on regulatory parameters. The method was tested with real data from a thermal plant under the conditions of the Colombian electricity market. The simulation results highlight the effectiveness of the proposed BESS, with a response time of approximately 0.6 s and regulation maintenance for over 30 s, reducing mechanical stress and preventing frequency overshoot. The control strategy was designed to maintain the energy neutrality of the BESS, thereby stabilizing its state of charge over the operational horizon. The results show that the BESS targets high-frequency transients and the generator focuses on low-frequency adjustments, managed by an Energy Management System (EMS) with a unified control approach. Full article

The N,N′-Bis(salicylidene)-1,3-propanediamine Schiff base (Salpn) was synthesized, characterized, and assessed as a corrosion inhibitor for low-carbon steel (LCS) in a 0.5 mol L⁻¹ HCl solution. The study included chemical, electrochemical, and quantum mechanical methods to provide a comprehensive assessment. Experimental results revealed that the inhibition efficiency (IE) of Salpn increased with concentration, reaching a maximum of 69.1% at 300 ppm and 298 K, while a slight decrease to 64.3% was observed as the temperature increased. Tafel plot identified Salpn as a mixed-type inhibitor, while electrochemical impedance spectroscopy (EIS) revealed that the double layer capacitance decreased while the charge-transfer resistance increased as the concentration of Salpn increased. The thermodynamic study revealed that the adsorption of Salpn on the LCS surface follows the Langmuir isotherm model. The calculated standard free energy of adsorption (ΔG^°_ads) values ranged from −27.53 to −30.17 kJ mol⁻¹, confirming that the inhibition process occurs via a mixed mechanism involving both physisorption and chemisorption. The presence of a protective film on the LCS surface was suggested by SEM observations, while EDX analysis showed an increase in C, O, and N signals, providing further indication of the inhibitor’s integration into the surface layer. Density functional tight-binding (DFTB+) calculations supported the high inhibitory performance by showing a low hardness value (0.091 eV). The compound’s high global softness (σ = 10.989 eV⁻¹) suggested that it is an effective corrosion inhibitor. The Monte Carlo (MC) simulations demonstrated a strong interaction with a highly negative adsorption energy of −654.145 kJ mol⁻¹. These findings collectively validate Salpn as an effective and strongly adsorbing corrosion inhibitor. Full article

This study presents an in situ synthesis of a novel manganese ferrite/carbon (MF/C) composite material via a citrate sol–gel route followed by calcination in an inert argon (Ar) atmosphere. The structural and morphological and porosity properties were characterized using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDX), and N₂ gas physisorption analysis. Electrochemical evaluation of the MF/C in a 3 M KOH electrolyte in a three-electrode configuration showed a high specific capacity of 39.26 mAh g⁻¹ at 1 Ag⁻¹ and a rate capability of 69% at 5 Ag⁻¹ and an equivalent series resistance (ESR) of 0.798 Ω. Subsequently, an asymmetric hybrid supercapacitor device (MF/C//AC) was fabricated using MF/C as the positive electrode and human-derived activated carbon (AC) as the negative electrode. The assembled device exhibited remarkable performance, with a wide operating voltage window of 1.4 V, a high sweeping potential of 1 V s⁻¹, a specific capacity, energy, power and maximum power of 42.4 mAhg⁻¹, 16.35 Wh kg⁻¹, 1944 W kg⁻¹ and 236 kW kg⁻¹, respectively, and excellent capacitance retention of 92% after 15,000 charge–discharge cycles. The in situ preparation approach significantly reduced synthesis time and cost compared to conventional multi-step methods, as less equipment was required, while still achieving comparable or superior electrochemical performance to other supercapacitors in the literature. Full article

As a promising alternative for cleaner vehicles, the growth of Battery Electric Vehicle (BEV) adoption should be supported by a reliable charging infrastructure. Therefore, projecting the charging load is required to ensure that the electricity supply is adequate as BEV adoption increases. This study proposes a multistage approach for projecting BEV charging load demand, linking a One-dimensional Convolutional Neural Network (1D-CNN) forecasting model with BEV users’ travel behavior analysis to perform spatiotemporal agent-based trip and charging simulations, which model various types of BEVs traveling across multiple regions. The 1D-CNN model achieves high performance with an RMSE of 0.073 and an R² of 0.881, providing a 10-year BEV adoption outlook. The empirical study in nine regions of Greater Jakarta, Indonesia, shows the one-week temporal charging load demand for three milestone periods—2025, 2030, and 2035—exploring weekday and weekend demand, as well as home and public charging demand at points of interest (POIs). This study identifies a difference between aggregate charging load demand and per-vehicle load intensity: the aggregate demand concentration occurs in South Jakarta (21% for public charging and 22% for home charging), while the highest per-vehicle spatial concentration ratio occurs in Depok (36% for public charging and 16% for home charging) due to long-distance travel patterns. The distribution of charging demand at the subdistrict level provides a basis for charging infrastructure placement, transformer sizing, and charging tariff design. Full article

Amorphous gallium oxide thin-film transistor photodetectors are promising for ultraviolet detection because of their wide bandgap and low dark current. Magnetron sputtering is compatible with low-temperature processing, but device performance is sensitive to sputtering conditions. Poor parameter choices can introduce oxygen vacancies and interface charges, degrading optoelectronic performance. Here, a three-factor, three-level orthogonal design is used to vary sputtering power, Ar/O₂ flow ratio, and film thickness. Nine device sets are fabricated and compared based on transfer characteristics and transient photocurrent–time (I-t) responses measured at a wavelength of 254 nm, with clear differences observed among process combinations. To identify the origin of these differences, representative samples with significant responsivity variations were modeled using TCAD. By fitting the simulated I-t curves to measured transients, the interface fixed charge density and defect-state densities were extracted, and the photon absorption distribution of different samples was analyzed. This analysis, from both defect and UV absorption perspectives, revealed the reasons for the differences in responsivity. The absorption coefficients at 254 nm measured by ellipsometry for the two samples were also compared, and the absorption trends observed in both the simulation and ellipsometry were consistent, confirming the accuracy of the simulation results. This work presents an integrated experimental and TCAD approach for process optimization and mechanistic analysis of a-GaO_x TFT-PDs. Full article

Recent advances in energy storage systems and in increasingly efficient, safe, and energy-dense cell chemistries have driven the need for commercial Battery Management System (BMS) architectures with greater control, data acquisition, and communication capabilities, primarily oriented towards customization. This demand introduces a significant change in how electrical systems are modeled and simulated when they integrate active electrochemical elements such as lithium-ion cells. This work presents the development and modeling of a BMS for critical and high-efficiency applications, based on active balancing techniques and incorporating an additional safety stage to respond to failures when charging ${\mathrm{LiFePO}}_4$ cells. The electrochemical model was built using an equivalent RLC circuit and RC pairs to represent the Thevenin response of the cell. For the simulation of active balancers, LTspice was employed, while charging and discharging processes and their effects on state of charge (SOC) and state of health (SOH) were complemented through analysis in MATLAB R2024a.The proposed approach offers an efficient tool for evaluating cell dynamics and validating battery management strategies in demanding scenarios. While the current approach prioritizes the individual modeling of electrical conversion systems, our framework presents an innovative multisystem macromodel, where not only is the electrical behavior simulated but also the control, efficiency, and safety of the system are determined, prioritizing reproducibility through SPICE tools. Full article

In grid-deficient environments, residential energy systems face severe carbon emission penalties due to mandatory reliance on diesel standby generators during supply interruptions. In Iraq, summer peak loads routinely exceed grid capacity, triggering prolonged generator operation and dramatically increasing household carbon footprints. This study presents a deep Q-network (DQN) reinforcement learning framework for intelligent battery energy storage system (BESS) scheduling, targeting carbon emissions reduction through strategic peak shaving. The DQN agent learns optimal battery dispatch strategies by internalizing diurnal patterns in load and solar generation through temporal state features, enabling anticipatory control without requiring explicit external forecasting models. The system is trained on one-year operational data from a representative Iraqi residential installation and evaluated over the critical summer period (122 days, 35.5% grid unavailability). The results demonstrate a 54.8% ${\mathrm{CO}}_2$ reduction (306.5 kg versus 677.4 kg baseline), a 25.5% reduction in generator runtime, and a 23.7% reduction in operating costs for the studied configuration. The learned policy approaches 89.6% of perfect-foresight MILP performance while executing 35,000 times faster. A reward function sensitivity analysis across five weighting schemes confirms that the 20:1 carbon-to-cost priority ratio optimally balances environmental and economic objectives. Ablation studies quantify the mechanism contributions: anticipatory pre-charging accounts for 58% of the total improvement, discharge optimization for 44%, and real-time PV coordination for 22%. These findings establish DQN-based BESS optimization as a practically deployable decarbonization approach for residential systems in grid-constrained developing regions. Full article

Hierarchical In₂MnS₄ microflowers were synthesized via a hydrothermal approach and evaluated as multifunctional photo-/electrocatalysts for crystal violet (CV) dye degradation and methanol oxidation. The synthesis strategy produced three-dimensional flower-like architectures composed of nanoscale subunits with high crystallinity and uniform elemental distribution. Optical characterization revealed strong visible-light absorption with a bandgap of approximately 1.74 eV, indicating suitability for solar-driven photocatalysis. In₂MnS₄ microflowers achieved 96.6% degradation of CV dye within 100 min, whereas negligible activity was observed without the catalyst. Kinetic analysis followed a pseudo-first-order model with an apparent rate constant of 0.029 min⁻¹. The catalyst maintained stable performance over four consecutive cycles, confirming good recyclability. Photoelectrochemical measurements showed a stable photocurrent response and reduced charge-transfer resistance, indicating efficient separation and transport of photogenerated charge carriers. Furthermore, electrochemical measurements revealed increased anodic responses and sustained current behavior in the presence of methanol, suggesting an electrochemical response upon methanol addition. These results highlight In₂MnS₄ microflowers as promising visible-light-responsive materials for environmental remediation and energy-related catalytic applications. Full article

As a critical barrier for power network safety, insulating materials are susceptible to internal microcracks, delamination, and other hidden defects that can trigger dielectric strength degradation and space charge accumulation, ultimately leading to insulation breakdown. Ultrasonic shear wave non-destructive testing enables defect identification without damaging the material. Therefore, this paper focuses on the identification and imaging of internal defects in insulating components using ultrasonic shear waves. First, a physical model for ultrasonic shear wave NDT is established. Based on the refraction and reflection characteristics of ultrasonic waves in materials with different acoustic impedances, a defect localization formula is derived. Through simulation verification, for the three defects set at different positions in the defect model, the positioning error is less than 0.5 mm. Subsequently, defects such as circular holes, triangular shapes, cracks, and bottom grooves were simulated. Analysis of the echo data revealed a correlation between the distance from the sensor to the defect and the echo amplitude. For groove defect imaging, the differential SAFT algorithm was employed, achieving a width error of 1 mm for imaging a 2 mm wide by 5 mm high groove, clearly presenting the defect morphology. Finally, an imaging software program for defect structure reconstruction was developed based on the simulation model presented in this article. We collected side and back view data through the constructed ultrasonic transverse wave non-destructive testing experimental platform, and visualized defects in insulation materials with grooves using this ultrasonic imaging program. This study achieved defect localization and imaging through simulation of various defect types combined with synthetic aperture focused imaging algorithms, providing a reference for visualization and industrial application of ultrasonic shear wave non-destructive testing technology. Full article

In this work, a novel nanostructured Mn₃O₄-based electrochemical sensor was developed for the determination of heavy metals in aqueous media. The Mn₃O₄ nanostructure was solvothermally synthesized in the sole presence of propylene glycol (PG). Under the specific synthetic conditions, PG provided surface coating and stabilization by decomposition products and/or residual PG molecules that have been adsorbed on Mn₃O₄ NPs surfaces, creating a thin organic layer. This imparts a negative surface charge (zeta potential), enhancing colloidal stability in dispersions and electrochemical performance. The physicochemical properties of the resulting NPs were characterized via X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Thermogravimetric Analysis (TGA), and Dynamic light scattering (DLS) and ζ-potential measurements, as well as SEM imaging of the modified electrode surface, confirming its successful formation and favorable structural properties. The LODs of Cd²⁺, Pb²⁺, Zn²⁺, and Cu²⁺ for their simultaneous determination are 2.9 μg·L⁻¹, 5.2 μg·L⁻¹, 7.1 μg·L⁻¹, and 2.5 μg·L⁻¹, respectively, with relative standard deviations of about 5.24%, 4.43%, 7.74%, and 4.53%, respectively. As a result of this study, a simple, sensitive, and reproducible electrochemical sensor based on a carbon paste electrode (CPE) modified with novel synthesized manganese nanoparticles and employing voltammetric techniques was applied in water and wastewater. Full article

Persistent organic pollutants, such as Rhodamine B (RhB), pose significant environmental and health risks, necessitating the development of advanced oxidation technologies for effective removal. While heterogeneous photo-Fenton catalysts are known for their high degradation efficiency, their practical application is often limited by complex synthesis processes, catalyst detachment, and difficult recovery. This study proposes an innovative laser-induced, one-step synthesis strategy to fabricate metal/carbon nanocomposite catalytic layers directly onto flexible carbon paper. The as-prepared composites exhibit strong interfacial interaction between metal nanoparticles and the carbon matrix, as indicated by XPS analysis, and demonstrate enhanced catalytic activity in the UV/H₂O₂ system. Notably, the integrated composites exhibit exceptional catalytic activity in the UV/H₂O₂ system, achieving complete degradation of a 20 mg/L RhB solution within just 1.5 h. The enhanced performance is attributed to the facilitated Fe³⁺/Fe²⁺ cycling and efficient generation of hydroxyl radicals (·OH), although the underlying charge separation mechanism requires further investigation with techniques such as photoluminescence spectroscopy and transient photocurrent measurements. This work not only demonstrates the high activity and stability of the photo-Fenton catalyst but also provides a green, rapid fabrication approach for the development of efficient and integrable catalytic devices for wastewater treatment. Full article

Configuring a battery energy storage system (BESS) is an effective approach to alleviating the peak shaving and valley filling burden on conventional thermal power units. However, excessive capacity increases investment cost, whereas insufficient capacity limits operational effectiveness. To address this trade-off, a multi-objective optimization framework is proposed to simultaneously maximize annual economic revenue and minimize load variance. The model comprehensively incorporates investment, operation and maintenance, decommissioning, environmental benefits, and deferred grid investment revenue, together with practical operational constraints on power limits, state of charge (SOC), charge/discharge states, and daily energy balance. A multi-objective particle swarm optimization (MOPSO) algorithm is employed to obtain the Pareto frontier, and the technique for order preference by similarity to ideal solution (TOPSIS) is applied to select the final optimal configuration. Simulation results based on a typical 24 h load profile indicate that the optimal BESS configuration is 27.7 MW/78.3 MWh, which reduces load variance by 32.15% and peak demand by 13.5%, while achieving an average annual revenue of 5.73 million CNY. Comparative analysis shows that the proposed method outperforms the traditional weighted-sum approach in both economic and technical indicators. Furthermore, the framework is extended to a WSCC nine-bus system with photovoltaic (PV) integration by introducing node voltage fluctuation as an additional objective. The results verify that the optimized BESS configuration can effectively mitigate voltage fluctuations under high PV penetration, demonstrating the scalability and applicability of the proposed method in renewable-energy integrated power systems. Full article