Search Results (796)

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

Direct grid-connected wireless charging based on direct AC–AC conversion is attractive for electric vehicles (EVs) because it can reduce power conversion stages and improve charger compactness. In matrix-converter-based wireless power transfer (WPT) systems, the grid-frequency AC voltage can be directly converted into high-frequency AC voltage without using bulky DC-link electrolytic capacitors. However, the removal of the intermediate energy-storage stage also makes the EV wireless charger more sensitive to grid-voltage fluctuation. For an LCC-S compensated WPT system, the voltage-source output characteristic makes the charging-side voltage sensitive to grid-voltage disturbance, resulting in severe MC output-current and battery charging-current overshoot. This transient overcurrent may threaten both the power converter and the EV battery charging process. In this paper, a dual-frequency state-space model is developed for the matrix-converter-based electrolytic-capacitor-less LCC-S WPT system to analyze the disturbance propagation from the grid side to the high-frequency resonant stage and the EV battery side. Based on the model, the current-overshoot suppression capability and bandwidth limitation of the conventional dual closed-loop control strategy are investigated. To further enhance transient current protection, a grid-voltage feedforward strategy is proposed to compensate for the disturbance before severe current overshoot is formed. Finally, experimental results verify that the proposed method effectively suppresses the MC output-current and battery charging-current overshoot under grid-voltage fluctuation, thereby improving the grid-disturbance resilience and dynamic safety of direct grid-connected EV wireless charging systems. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

Background: Type 2 diabetes mellitus (T2DM) is a global health challenge characterized by chronic hyperglycemia and oxidative stress. Pithecellobium dulce root has long been recognized for its antidiabetic potential; however, its specific bioactive constituents and mechanisms of action remain poorly defined. This study aimed to evaluate the antidiabetic and antioxidant properties of extracts and isolated molecules from P. dulce root bark. Methods: The DCM/MeOH crude extract of P. dulce root bark was fractionated with n-hexane (PDEH) and ethyl acetate (PDAE), followed by chromatographic purification and spectroscopic characterization, yielding seventeen compounds (1–17). The antioxidant activity (DPPH, ABTS, FRAP) and antidiabetic potential of PDEH, PDAE, and 1–17 were assessed in vitro using yeast-derived enzymes and in silico (targeting human α-glucosidase [PDB: 2QLY] and human α-amylase [PDB: 4GQR]). The in vitro α-glucosidase experiments used saccharomyces cerevisiae enzyme, which varies from the human target. Therefore, these results should be taken as preliminary screening data that needs confirmation with human enzymes. Results: Compound 1 was identified as new, while 2 was isolated for the first time from a natural source. The cell-free chemical tests DPPH, ABTS, and FRAP measured antioxidant capability. These tests quantify radical-scavenging and electron-transfer capabilities in vitro and are preliminary chemical screening methods. They do not directly represent biological antioxidant activity in cells or organisms. PDEH demonstrated strong radical scavenging against DPPH (IC₅₀ = 15.30 μg/mL) and ABTS (IC₅₀ = 12.80 μg/mL), while pristriol (16) showed ferric reducing power (EC₅₀ = 4200 μM FeSO₄/g). Enzyme inhibition assays demonstrated activity against α-amylase (IC₅₀ 53.88–112.24 µg/mL; acarbose IC₅₀ = 91.20 µg/mL) and α-glucosidase (IC₅₀ 18.38–136.88 µg/mL; acarbose IC₅₀ = 11.31 µg/mL). Compounds 15, 1, and 2 showed superior activity compared to acarbose for α-amylase, with effect sizes (Cohen’s d) of 2.15, 0.94, and 0.82, respectively, and IC₅₀ values of 53.88, 88.15, and 92.62 µg/mL; for α-glucosidase, IC₅₀ values were 18.38, 39.25, and 36.40 µg/mL, respectively. Docking studies supported these findings, revealing binding energies of −9.08, −8.34, and −7.22 kcal/mol for compounds 1, 2, and 15 with α-amylase, and −10.35 and −9.79 kcal/mol for compounds 1 and 2 with α-glucosidase. ADME profiling further identified 1 and 2 as promising lead candidates for dual-enzyme inhibition. Conclusions: P. dulce root bark represents a potent source of bioactive molecules with both antioxidant and dual-enzyme-inhibitory properties. These findings validate its traditional use and highlight its potential in the development of multitarget therapies for T2DM management. Full article

This paper presents a bidirectional power flow control strategy for a grid-connected solar photovoltaic (PV)-based water pumping system employing a brushless DC (BLDC) motor drive. The proposed system enables continuous water pumping operation under varying solar irradiance conditions without the use of phase-current sensors while maintaining the motor at its rated operating speed. A single-phase voltage source converter (VSC) employs a unit vector template (UVT)-based control scheme that regulates bidirectional power flow between the utility grid and the dc-link, thereby supporting both grid-to-load and PV-to-grid power transfer. Excess photovoltaic energy can be exported to the utility grid during periods of reduced pumping demand, improving overall utilization of the available solar power. The voltage source inverter (VSI) driving the BLDC motor employs a PWM_ON_PWM switching scheme to reduce torque ripple while operating at fundamental frequency to minimize switching losses. The proposed system also incorporates maximum power point tracking (MPPT), power factor correction, and harmonic mitigation to improve power quality and ensure compliance with IEEE-519 requirements. The effectiveness of the proposed control strategy is evaluated through detailed MATLAB/Simulink R2023a simulations under various operating conditions. The simulation results demonstrate stable dc-link voltage regulation, bidirectional power flow capability, continuous pumping operation, and reduced torque ripple, highlighting the suitability of the proposed system for grid-interactive solar water pumping applications. Full article

Modern power systems are becoming increasingly complex due to the rapid integration of renewable energy sources, the widespread use of nonlinear power-electronic devices, and the deployment of microgrids operating in parallel with conventional power grids. These evolving conditions intensify the occurrence of diverse and highly complex power quality disturbances (PQDs), demanding accurate and computationally efficient monitoring strategies. This paper presents a novel multi-stage hierarchical framework for PQD detection and classification, comprising an initial training stage with a dedicated 1D Convolutional Neural Network (1D-CNN), a transfer learning stage, and a subsequent fine-tuning stage. The proposed approach operates directly on raw voltage waveforms, eliminating the need for any signal preprocessing, as the CNN performs internal feature extraction. The framework is evaluated using a comprehensive dataset that includes synthetic signals, Matlab/Simulink (version R2022a) time-domain simulations, and real voltage sag events. Additionally, up to 29 types of disturbances, including complex multi-event combinations defined by the IEEE-1159 Standard, are generated using the PQ-SyDa toolbox. The proposed model achieves an F1-score of 97.8% using a three-cycle analysis window and further improves to 98.86% when five cycles are used. These results highlight the robustness and generalization capability of the proposed approach for the real-time PQD monitoring task in modern electrical networks. Full article

This paper presents a multibeam hybrid beamforming (MHBF) architecture for microwave power transfer (MPT), enabling wireless power delivery to multiple receivers with a reduced number of RF chains. The proposed architecture decouples beam control into the horizontal and vertical dimensions, where horizontal multibeams are generated in the baseband through digital precoding, while the vertical beam direction is controlled by a Butler-matrix-based analog beamformer. In particular, multibeam transmission is achieved using multi-tone signals with distinct phase weights assigned to each tone, enabling beams to be steered toward different directions, while the Butler-matrix-based analog beamformer provides vertical beam-steering capability. Compared with fully digital beamforming (DBF), MHBF enables simultaneous multibeam formation in the horizontal domain with fewer RF chains, thereby reducing hardware overhead and system complexity. To validate the proposed architecture, a 5.8 GHz prototype was designed and fabricated. The experimental results demonstrate three-beam and four-beam operation under a transmit power of 30.57 dBm, while the average received RF power in the single-beam case was 12.11 dBm at a distance of 1 m. In the three-beam and four-beam cases, average received RF power levels of 7.3 dBm and 6.1 dBm per beam were achieved, respectively. RF-to-DC conversion measurements under 430 $\Omega$ and 680 $\Omega$ load conditions further showed average PCE values of up to 38.77% and 35.05% for the three-beam and four-beam cases, respectively. These results confirm the feasibility of simultaneous multibeam wireless power delivery and its potential as an effective solution for multi-receiver operation with reduced RF-chain requirements. Full article

Inductive pulsed power remains attractive for demanding electromagnetic acceleration systems because of its high-current capability and rapid discharge capability. Within this class, XRAM is especially appealing because it combines series charging with parallel discharging of storage inductors. Under high-energy conditions, however, the conventional all-parallel XRAM topology suffers from concentrated blocking-voltage stress on the total output switch and limited effectiveness in transferring stored current to the representative railgun load considered in this work. To address these issues, this paper proposes a three-module grouped XRAM topology and examines its output behavior, parameter dependence, and commutation mechanism. Baseline comparison results show that the grouped arrangement establishes the load-driving path earlier and redistributes device stress more favorably. Its advantage is retained when both topologies are individually optimized with respect to the triggering threshold, indicating that the grouped topology offers a more effective route for high-current electromagnetic acceleration drive through earlier commutation establishment and more effective current transfer. Full article

Constant current/voltage (CC/CV) output of wireless power transfer (WPT) systems deviates due to increased load resistance during charging and mutual inductance variations caused by misalignment. Dynamically regulating the DC input voltage can maintain a stable output at the preset value, and predicting the mutual inductance and load resistance can help monitor charging status. However, joint prediction of characteristics and regulation degree can be nonlinear and complicated. This work proposes a data-driven method for characteristic prediction and output optimization for WPT systems based on the current waveform from only the transmitter side. A Multi-Scale Parallel Convolutional (MSPC) neural network is applied to simultaneously predict the load resistance, mutual inductance, output deviation factor and regulation coefficient. By leveraging its multi-scale feature extraction capabilities, it can accurately estimate the aforementioned parameters based on only the AC current waveform at the transmitter side. To improve the model’s generalizability under practical conditions, transfer learning (TL) is utilized to minimize the discrepancy between simulated and physical data. Finally, a 140 W prototype of the series-series (SS)-compensated WPT system is built to validate the effectiveness of the proposed method. Full article

To address heat accumulation, localized hot spots, and non-uniform temperature distribution in large-capacity lithium iron phosphate energy storage battery modules under high ambient temperature and high-rate charge/discharge conditions, this study proposes a fin-enhanced phase change material (PCM)-air hybrid thermal management structure for a 100 Ah prismatic lithium iron phosphate battery and a 2P18S energy storage battery module. First, the battery thermal model is validated using single-cell experimental data reported in the literature. Subsequently, a three-dimensional transient fluid–solid coupled heat transfer model is established by considering transient battery heat generation, PCM solid–liquid phase change, air-side flow and heat transfer, and temperature-dependent thermophysical properties. User-defined functions are employed to implement the transient heat source and temperature-dependent material properties. Under identical boundary conditions, the thermal management performances of three configurations, namely Fin-Air, PCM-Air, and Fin-PCM-Air, are compared. The effects of ambient temperature (20 °C, 25 °C, and 30 °C) and inlet air velocity (1 m/s, 2 m/s, and 3 m/s) on the maximum module temperature, temperature uniformity, PCM liquid fraction evolution, and flow field distribution are quantitatively analyzed. The results show that, compared with the Fin–Air system without PCM and the PCM-Air system without fins, the Fin-PCM-Air configuration reduces the maximum module temperature by 1.57% and 0.25%, respectively, at an ambient temperature of 30 °C and an inlet air velocity of 3 m/s. After four charge–discharge cycles, the peak maximum temperature of the module is approximately 38.56 °C, and the peak maximum temperature difference remains below 3.6 K, indicating good temperature uniformity and latent heat buffering capability. In addition, the air velocity trade-off analysis indicates that increasing the inlet air velocity can improve cooling performance but also increases the air-channel pressure drop and fan power consumption. Therefore, the Fin-PCM-Air structure is more suitable for high-thermal-load conditions, and its practical application should comprehensively consider cooling benefits, additional mass, manufacturing cost, and long-term reliability. This study provides a reference for the design and engineering application of hybrid thermal management structures for large-capacity energy storage battery modules. Full article

Traditional cryogenic air separation units are unsuitable for distributed, small-scale liquid oxygen production. Cryocooler-based liquefaction technology offers an alternative solution, featuring a large cooling capacity, high efficiency, a compact structure, and rapid start–stop capability. In this paper, an oxygen liquefaction system based on a high-capacity Stirling cryocooler was developed. Because the heat transfer performance of cryocoolers varies significantly across different temperature ranges, heat exchanger designs must be tailored to specific operating conditions. However, research on cold-end heat exchangers for large-capacity cryocoolers used in liquefaction systems remains limited. In the liquid oxygen temperature range, factors such as liquid film formation and incomplete condensation severely affect heat transfer performance and must be considered. In this paper, numerical simulations were performed to analyze the condensation behavior of oxygen, with particular attention paid to the matching between the heat exchange structure and the cooling capacity. Subsequently, a small-scale experimental system was constructed and tested. The successful operation of the experimental system validated the feasibility of the proposed heat exchanger design. Under the conditions of 300 K and an oxygen inlet gauge pressure of 0.45 MPa, the system achieved a liquefaction capacity of 7.4 L/h, corresponding to a cooling capacity of 787 W. The specific power consumption was 0.89 kW·h/kg, with a coefficient of performance (COP) of 0.116. This performance is competitive among small-scale cryocooler-based oxygen liquefaction systems. This study provides both theoretical and experimental support for further performance optimization and engineering application of such cryocoolers in liquid oxygen production. Full article

Multi-terminal voltage source converter-based high voltage direct current (VSC-MTDC) technology has become an efficient solution for grid integration of large-scale and long-distance offshore wind power. When onshore grid power fluctuations elevate the DC voltage of VSC-MTDC system, the surplus power causing the DC overvoltage issue can be effectively transferred through the power shifting method. To enhance the power shifting capability of receiving-end converters (RECs) and mitigate DC overvoltage, this paper proposes a coordinated power shifting strategy for VSC-MTDC based on the extended operation region. Firstly, the topology and control model of the VSC-MTDC system integrating offshore wind farms is established. Secondly, considering constraints containing apparent power, AC bus voltage, AC current, and voltage modulation ratio, the extended operation region model with regard to the overload capacity of REC is constructed. Furthermore, the coordinated active power shifting strategy for multiple converters is proposed to cope with onshore grid power fluctuations. Finally, simulation models of three-terminal and six-terminal VSC-MTDC systems are built using PSCAD V5 software. Simulation results show that the proposed strategy can exploit the system’s operational flexibility and reduce the risk of DC overvoltage, thus enhancing the disturbance immunity of VSC-MTDC system against onshore grid fluctuations. Full article

Microbial fuel cells (MFCs) are promising bioelectrochemical systems for simultaneous wastewater treatment and energy recovery. However, their practical application is still limited by insufficient power output and weak transient energy-supply capability under fluctuating operational conditions. Herein, a bifunctional CF/MXene/FeS composite anode was fabricated through a one-step hydrothermal strategy to simultaneously enhance electricity generation and capacitive charge storage in MFCs. Unlike conventional bioanode modifications that primarily target conductivity enhancement alone, the constructed hierarchical composite integrates conductive MXene nanosheets and electroactive FeS phases to synergistically improve extracellular electron transfer and interfacial charge-storage behavior. The modified electrode exhibited enhanced surface roughness, abundant electroactive sites, and improved biofilm-supporting interfaces. Benefiting from the integrated conductive and electroactive composite framework, the CF/MXene/FeS anode achieved a maximum power density of 1.69 W/m², which was 70.7% higher than that of pristine CF, together with an increased open-circuit voltage of 0.711 V. In addition, the composite electrode delivered a high total charge density of 13,192.09 C/m² under the C900/D900 condition. Microbial community analysis further revealed substantial enrichment of electroactive bacteria, with the relative abundance of Geobacter increasing from 0.0058% to 22.84%. This work provides a promising strategy for integrating electricity generation and transient energy storage in bioelectrochemical systems, offering potential applications for energy-buffered MFCs under fluctuating power-demand conditions. Full article

The increasing adoption of electric vehicles (EVs) demands highly efficient bidirectional DC–DC converters capable of seamless energy transfer between the grid and vehicle batteries. This paper introduces a Fixed-Frequency Dual-Active-Bridge (DAB) resonant converter featuring four degrees of freedom, achieved through a combination of triple phase-shift (TPS) modulation and a current-controlled variable inductor (VI). The proposed control strategy aims to minimize conduction and switching losses by simultaneously managing reactive power, RMS current, and soft-switching conditions across wide variations in voltage and power. Unlike conventional phase-shift or variable-frequency modulations, the fixed-frequency operation maintains full zero-voltage switching (ZVS) for the two bridges, and zero-current switching (ZCS) in the bridge that is receiving energy, enhancing overall system reliability and control simplicity. The proposed converter is validated through simulations and experimental results from a SiC MOSFET-based 14 kW prototype operating at 122 kHz, demonstrating peak efficiencies above 97% under both charging and discharging modes. The experimental results confirm that the proposed DAB topology and modulation scheme significantly improve efficiency and controllability, making it a promising solution for next-generation on-board chargers and vehicle-to-grid (V2G) applications. Full article

Though the charge transfer efficiency of a liquid–liquid (L-L) nanogenerator is much higher than that of the solid–liquid and solid–solid counterparts, there is still much room for improving the level of interfacial charge transfer. In this study, a power generation system was designed using an ionic liquid (IL)-based immiscible L-L interface via the contact/separation mode. The maximum output electric current reaches as high as about 8.12 μA by contacting a saturated NaCl droplet with an immiscible IL droplet. The magnitude of the generated electric current signals increases with an increase in the ionic concentration of the NaCl solution, the droplet contact area, and the liquid volume of IL. Moreover, the magnitude of the signals varies slightly when the pH value is under 12 and increases sharply in strong alkaline conditions. A maximum instantaneous power output of about 82 nW was obtained with a 100 kΩ resistor in series. The IL-based immiscible L-L interface configuration has been proven to be capable of sensing metal ions at an ultra-low concentration via the contact/separation mode. Full article