Search Results (1,244)

Wireless power transfer (WPT) systems for autonomous underwater vehicles (AUVs) are gaining traction in marine exploration due to their operational convenience, safety, and flexibility. Nevertheless, disturbances from ocean currents and marine organisms frequently induce rotational, axial, and air-gap misalignments, significantly degrading the output power stability. To mitigate this issue, this paper proposes a novel reconfigurable WPT system utilizing coaxial antipodal dual DD (CAD-DD) coils, which strategically switches between a detuned S-LCC topology and a detuned S-S topology at a fixed operating frequency. By characterizing the output power versus the coupling coefficient (P-k) profiles under both reconfiguration modes, a parameter design methodology is developed to ensure stable power delivery across wide coupling variations. Experimental validation using a 1.2 kW AUV charging prototype demonstrates remarkable tolerance to misalignment: ±30° rotation, ±120 mm axial displacement, and 20–50 mm air-gap variation. Within this range, the output power fluctuation is confined to within 5%, while the system efficiency exceeds 85% consistently, peaking at 91.56%. Full article

As a typical porous medium, unsaturated loess demonstrates critical hydro-mechanical coupling properties that fundamentally influence geohazard mitigation, groundwater resource evaluation, and foundation stability in geotechnical engineering. This investigation develops a novel theoretical framework to overcome the limitations of existing models in converting electrical resistivity tomography (ERT) profiles into water content distributions for unsaturated loess through quantitative inversion modeling. Systematic laboratory investigations on remolded loess specimens with controlled density and water content conditions revealed distinct resistivity–water interaction mechanisms. A characteristic two-stage decay pattern was identified: resistivity exhibited an exponential decrease from 420 Ω·m (water saturation (S_w = 10%)) to 90 Ω·m (S_w = 40%), followed by asymptotic stabilization at S_w ≥ 40%. The derived quantitative correlation provides a robust mathematical basis for water content profile inversion. Field validation through integrated ERT and borehole data demonstrated exceptional predictive accuracy in shallow strata (<20 m depth), achieving mean absolute errors of <5%. However, inversion reliability decreased with depth (>20 m), primarily attributed to density-dependent charge transport mechanisms. This underscores the necessity of incorporating coupled thermo-hydro-mechanical processes for deep-layer characterization. This study provides a robust framework for engineering applications of ERT in loess terrains, offering significant advancements in geotechnical monitoring and geohazard prevention. Full article

The competitive retention of pollutants in water tables determines their environmental fate and guides routes for their removal. To distinguish the fine differences in competitive binding at zeolite adsorption centers, a group of neonicotinoid pesticides is compared, relying on theoretical (energy of adsorption, orientation, charge distribution) and experimental (spectroscopic and thermogravimetric) analyses for quick, inexpensive, and reliable screening. The MOPAC/QuantumEspresso platform was used for theoretical calculation, indicating close adsorption energy values for acetamiprid and imidacloprid (−2.2 eV), with thiamethoxam having a lower binding energy of −1.7 eV. FTIR analysis confirmed hydrogen bonding, among different dipole-dipole interactions, as the dominant adsorption mechanism. Due to their comparable binding energies, when the mixture of all three pesticides is examined, comparative adsorption capacities are evident at low concentrations, owing to the excellent adsorption performance of the FAU zeotype. At higher concentrations, competition for adsorption centers occurs, with the expected thiamethoxam binding being diminished due to the lower bonding energy. The catalytic impact of zeolite on the thermal degradation of pesticides is evidenced through TG analysis, confirming the adsorption capacities found by UV/VIS and HPLC/UV measurements. Detailed analysis of spectroscopic results in conjunction with theoretical calculation, thermal profiles, and UV detection offers a comprehensive understanding of neonicotinoids’ adsorption and can help with the design of future adsorbents. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

This paper presents a data-driven energy management scheme for fuel cell and battery electric vehicles, formulated as a constrained optimal control problem. The proposed method employs a co-state network trained using real-time measurements to estimate the control law without requiring prior knowledge of the system model or a complete dataset across the full operating domain. In contrast to conventional reinforcement learning approaches, this method avoids the issue of high dimensionality and does not depend on extensive offline training. Robustness is demonstrated by treating uncertain and time-varying elements, including power consumption from air conditioning systems, variations in road slope, and passenger-related demands, as unknown disturbances. The desired state of charge is defined as a reference trajectory, and the control input is computed while ensuring compliance with all operational constraints. Validation results based on a combined driving profile confirm the effectiveness of the proposed controller in maintaining the battery charge, reducing fluctuations in fuel cell power output, and ensuring reliable performance under practical conditions. Comparative evaluations are conducted against two benchmark controllers: one designed to maintain a constant state of charge and another based on a soft actor–critic learning algorithm. Full article

The detection of proteins plays a key role in disease diagnosis and drug development. For this, we numerically investigated a novel microfluidic motor actuated by an induced-charge electro-osmotic (ICEO) whirling flow. An alternating current–flow field effect transistor is engineered to modulate the profiles of ICEO streaming to stimulate and adjust the whirling flow in the circle microfluidic chamber. Based on this, we studied the distribution of an ICEO whirling flow in the detection chamber by tuning the fixed potential on the gate electrodes by the simulations. Then, we established a fluid–structure interaction model to explore the influence of blade structure parameters on the rotation performance of microfluidic motors. In addition, we investigated the rotation dependence of microfluidic motors on the potential drop between two driving electrodes and fixed potential on the gate electrodes. Next, we numerically explored the capability of these microfluidic motors for the detection of low-abundance proteins. Finally, we studied the regulating effect of potential drops between the driving electrodes on the detection performance of microfluidic motors by numerical simulations. Microfluidic motors actuated by an ICEO whirling flow hold good potential in environmental monitoring and disease diagnosis for the outstanding advantages of flexible controllability, a simple structure, and gentle work condition. Full article

Biogenic silver nanoparticles (AgNPs) were synthesized via a green chemistry strategy using wheat extract and subsequently functionalized with a carboxymethyl chitosan–cinnamaldehyde (CMC=CIN) conjugate through covalent imine bonding. The resulting nanohybrid (AgNP–CMC=CIN) was extensively characterized to confirm successful biofunctionalization: UV–Vis spectroscopy revealed characteristic cinnamaldehyde absorption peaks; ATR-FTIR spectra confirmed polymer–terpene bonding; and TEM analysis evidenced uniform nanoparticle morphology. Dynamic light scattering (DLS) measurements indicated an increase in hydrodynamic size upon coating (from 59.46 ± 12.63 nm to 110.17 ± 4.74 nm), while maintaining low polydispersity (PDI: 0.29 to 0.27) and stable surface charge (zeta potential ~ −30 mV), suggesting colloidal stability and homogeneous polymer encapsulation. Antifungal activity was evaluated against Fusarium oxysporum, Penicillium citrinum, Aspergillus niger, and Aspergillus brasiliensis. The minimum inhibitory concentration (MIC) against F. oxysporum was significantly reduced to 83 μg/mL with AgNP–CMC=CIN, compared to 708 μg/mL for uncoated AgNPs, and was comparable to the reference fungicide tebuconazole (52 μg/mL). Seed priming with AgNP–CMC=CIN led to improved germination (85%) and markedly reduced fungal colonization, while maintaining a favorable phytotoxicity profile. These findings highlight the potential of polysaccharide-terpene-functionalized biogenic AgNPs as a sustainable alternative to conventional fungicides, supporting their application in precision agriculture and integrated crop protection strategies. Full article

AISI 347H stainless steel is widely used in high-temperature environments due to its excellent creep strength and oxidation resistance; however, its corrosion performance remains highly sensitive to thermal oxidation, and the effects of thermal history on its passive film stability are not yet fully understood. This study addresses this knowledge gap by systematically investigating the influence of solution treatment on the corrosion and oxidation resistance of AISI 347H stainless steel. The specimens were subjected to solution heat treatment at 1050 °C, followed by air cooling, and then evaluated through electrochemical testing, high-temperature oxidation experiments at 550 °C, and multiscale surface characterization techniques. The solution treatment refined the austenitic microstructure by dissolving coarse Nb-rich precipitates, as confirmed by SEM and EBSD, and improved passive film integrity. The stabilizing effect of Nb also played a critical role in suppressing sensitization, thereby enhancing resistance to intergranular attack. Electrochemical measurements and EIS analysis revealed a lower corrosion current density and higher charge transfer resistance in the treated samples, indicating enhanced passivation behavior. ToF-SIMS depth profiling and oxide thickness analysis confirmed a slower parabolic oxide growth rate and reduced oxidation rate constant in the solution-treated condition. At 550 °C, oxidation was suppressed by the formation of compact, Cr-rich scales with dual-distributed Nb oxides, effectively limiting diffusion pathways and stabilizing the protective layer. These findings demonstrate that solution treatment is an effective strategy to improve the long-term corrosion and oxidation performance of AISI 347H stainless steel in harsh service environments. Full article

The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework’s applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework’s adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Full article

Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging (constant current at 1.2 A) and dynamic charging (stepped current from 400 mA to 800 mA) over 200 charge–discharge cycles. A custom-built, low-cost test platform based on an ESP32 microcontroller was developed to provide real-time monitoring of voltage, current, temperature, and internal resistance, with automated control and cloud-based data logging. The results indicate that static charging provides greater voltage stability and a lower increase in internal resistance (9.3%) compared to dynamic charging (30.17%), suggesting reduced electrochemical stress. Discharge time decreased for both strategies, by 6.25% under static charging and 18.46% under dynamic charging, highlighting capacity fade and aging effects. Internal resistance emerged as a reliable indicator of degradation, closely correlating with reduced runtime. These findings underscore the importance of selecting charging profiles based on specific application needs, as dynamic charging, while offering potential thermal benefits, may accelerate battery aging. Furthermore, the low-cost testing platform proved effective for long-term evaluation and degradation analysis, offering an accessible alternative to commercial battery cyclers. The insights gained contribute to the development of adaptive battery management systems that optimize performance, lifespan, and safety in electric vehicle applications. Full article

This work presents the synthesis, characterization and evaluation of physicochemical and biological properties of two new aminoporphyrazine derivatives bearing magnesium(II) cations in their cores and peripheral pyrrolyl groups. The synthesis was carried out in several stages, using classical methods and the Microwave-Assisted Organic Synthesis (MAOS) approach. The obtained compounds were characterized using spectral techniques: UV-Vis spectrophotometry, mass spectrometry, ¹H and ¹³C NMR spectroscopy. The porphyrazine derivatives were tested for their electrochemical properties (CV and DPV), which revealed four redox processes, of which in compound 7 positive shifts of oxidation potentials were observed, resulting from the presence of free phenolic hydroxyl groups. In spectroelectrochemical measurements, changes in UV-Vis spectra associated with the formation of positive-charged states were noted. Photophysical studies revealed the presence of characteristic absorption Q and Soret bands, low fluorescence quantum yields and small Stokes shifts. The efficiency of singlet oxygen generation (Φ_Δ) was higher for compound 6 (up to 0.06), but compound 7, despite its lower efficiency (0.02), was distinguished by a better biological activity profile. Toxicity tests using the Aliivibrio fischeri bacteria indicated the lower toxicity of 7 compared to 6. The most promising result was the strong photodynamic activity of porphyrazine 7 against the Methicillin-resistant Stapylococcus aureus (MRSA) strain, leading to a more-than-5.6-log decrease in viable counts after the colony forming units (CFU) after light irradiation. Compound 6 did not show any significant antibacterial activity. The obtained data indicate that porphyrazine 7 is a promising candidate for applications in photodynamic therapy of bacterial infections. Full article

We present a buffer-pH-modulated electrokinetic concentration strategy in MEMS microchannels that harnesses simple pH shifts to neutralize and charge proteins, reversibly “pausing” them at a planar electric-gate electrode by tuning to their isoelectric point (pI) and mobilizing them with slight pH offsets under an applied field. This synergistic coupling of dynamic pH control and electrode-gated focusing, which requires only buffer composition changes, enables rapid and tunable protein capture and release across diverse channel geometries for lab-on-chip, preparative, and point-of-care diagnostics. Moreover, it dovetails with established MEMS biomedical platforms ranging from diagnostics to drug delivery and microsurgery to gene and cell-manipulation devices. Future work on tailored electrode coatings and optimized channel profiles will further boost selectivity, speed, and integration in sub-100 µm MEMS devices. Full article

This study investigates the feasibility of producing low-carbon FeCr via metallothermic smelting of Cr concentrate and chromite spinel powder using a complex FeAlSiCa alloy as the reductant in an induction furnace. The proposed approach offers an alternative to conventional carbothermic and oxygen-blown technologies, reducing both the carbon footprint and airborne emissions. Three charge compositions were tested with varying FeAlSiCa additions (12, 14, and 16 kg per 100 kg of Cr source) and partial replacement of Cr concentrate with up to 20% CSP. Thermodynamic and microstructural analyses were conducted, and the effects of the slag basicity, temperature profiles, and holding time were assessed. In optimal conditions, Cr recovery reached up to 80% with minimal Cr₂O₃ losses in slag, and the resulting alloys met ISO 5448-81 requirements for nitrogen-containing low-carbon FeCr. Microstructural examination revealed the formation of Fe-Cr solid solutions and CrN phases, with V incorporation from the FeAlSiCa alloy. The process proved stable and energy-efficient, producing compact, non-disintegrating slag. This study highlights the potential of induction furnace smelting and chromite spinel powder valorization as a sustainable path for FeCr production. Full article

Carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) have attracted significant interest as hybrid reinforcements in epoxy (Ep) composites for enhancing mechanical performance in structural applications, such as aerospace and automotive. These 1D and 2D nanofillers possess exceptionally high aspect ratios and intrinsic mechanical properties, substantially improving composite stiffness and tensile strength. In this study, epoxy nanocomposites were fabricated with 0.1 wt.% and 0.3 wt.% of CNTs and GNPs individually, and with 1:1 CNT:GNP hybrid fillers at equivalent total loadings. Scanning electron microscopy of fracture surfaces confirmed that the CNTGNP hybrids dispersed uniformly, forming an interconnected nanostructured network. Notably, the 0.3 wt.% CNTGNP hybrid system exhibited minimal agglomeration and voids, preventing crack initiation and propagation. Mechanical testing revealed that the 0.3 wt.% CNTGNP/Ep composite achieved the highest tensile strength of approximately 84.5 MPa while maintaining a well-balanced stiffness profile (elastic modulus ≈ 4.62 GPa). The hybrid composite outperformed both due to its synergistic reinforcement mechanisms and superior dispersion despite containing only half the concentration of each nanofiller relative to the individual 0.3 wt.% CNT or GNP systems. In addition to mechanical performance, electrical conductivity analysis revealed that the 0.3 wt.% CNTGNP hybrid composite exhibited the highest conductivity of 0.025 S/m, surpassing the 0.3 wt.% CNT-only system (0.022 S/m), owing to forming a well-connected three-dimensional conductive network. The 0.1 wt.% CNT-only composite also showed enhanced conductivity (0.0004 S/m) due to better dispersion at lower filler loadings. These results highlight the dominant role of CNTs in charge transport and the effectiveness of hybrid networks in minimizing agglomeration. These findings demonstrate that CNTGNP hybrid fillers can deliver optimally balanced mechanical enhancement in epoxy matrices, offering a promising route for designing lightweight, high-performance structural composites. Further optimization of nanofiller dispersion and interfacial chemistry may yield even greater improvements. Full article

In this work, we present a new partially coherent adjustable anomalous vortex laser beam (PCAAVLB) and introduce it into turbulent biological tissue. The equation of such PCAAVLB in turbulent biological tissue is obtained. By numerical analysis, the evolution of the intensity of such PCAAVLB in turbulent biological tissue is analyzed. It is found that the PCAAVLB in biological tissue can lose its ring shape and become a Gaussian beam, and a PCAAVLB with smaller topological charge $M$ or coherence length $\sigma$ will evolve into a Gaussian profile faster. The PCAAVLB in turbulent biological tissue with a smaller small-length-scale factor $l_0$ or larger fractal dimension $D$ will evolve into a Gaussian profile faster and have a larger intensity as z increases. The results may have potential applications in sensing under biological tissue environments and laser imaging in biology. Full article