Search Results (478)

Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, these chips faced challenges owing to optical interference caused by light scattering from the porous membrane, which hinders cell observation. Trans-epithelial electrical resistance (TEER) measurement offers a non-invasive method for assessing barrier integrity in these chips. However, existing electrode-integrated MPS chips for TEER measurement have non-uniform current densities, leading to compromised measurement accuracy. Additionally, chips made from polydimethylsiloxane have been associated with drug absorption issues. This study developed an electrode-integrated MPS chip for TEER measurement with a uniform current distribution and minimal drug absorption. Through a finite element method simulation, electrode patterns were optimized and incorporated into a polyethylene terephthalate (PET)-based chip. The device was fabricated by laminating PET films, porous membranes, and patterned gold electrodes. The chip’s performance was evaluated using a perfused Caco-2 intestinal model. TEER levels increased and peaked on day 5 when cells formed a monolayer, and then they decreased with the development of villi-like structures. Concurrently, capacitance increased, indicating microvilli formation. Exposure to staurosporine resulted in a dose-dependent reduction in TEER, which was validated by immunostaining, indicating a disruption of the tight junction. This study presents a TEER measurement MPS platform with a uniform current density and reduced drug absorption, thereby enhancing TEER measurement reliability. This system effectively monitors barrier integrity and drug responses, demonstrating its potential for non-animal drug-testing applications. Full article

Electric fields (EFs) have emerged as effective, non-chemical tools for modulating microbial populations in complex matrices such as wastewater. This review consolidates current advances on EF-induced alterations in microbial structures and functions, focusing on both vegetative cells and spores. Key parameters affected include membrane thickness, transmembrane potential, electrical conductivity, and dielectric permittivity, with downstream impacts on ion homeostasis, metabolic activity, and viability. Such bioelectrical modifications underpin EF-based detection methods—particularly impedance spectroscopy and dielectrophoresis—which enable rapid, label-free, in situ microbial monitoring. Beyond detection, EFs can induce sublethal or lethal effects, enabling selective inactivation without chemical input. This review addresses the influence of field type (DC, AC, pulsed), intensity, and exposure duration, alongside limitations such as species-specific variability, heterogeneous environmental conditions, and challenges in achieving uniform field distribution. Emerging research highlights the integration of EF-based platforms with biosensors, machine learning, and real-time analytics for enhanced environmental surveillance. By linking microbiological mechanisms with engineering solutions, EF technologies present significant potential for sustainable water quality management. Their multidisciplinary applicability positions them as promising components of next-generation wastewater monitoring and treatment systems, supporting global efforts toward efficient, adaptive, and environmentally benign microbial control strategies. Full article

Candida infections pose a significant health threat, and conventional antifungal drugs like nystatin are limited due to poor solubility, skin permeability, and frequent dosage requirements. Nystatin effectively targets Candida species by disrupting cell membranes, but formulation issues hinder clinical use. Lipid-based vesicular carriers, or novasomes, provide controlled, prolonged drug release and enhanced skin penetration. This study focuses on developing nystatin-loaded novasomal gels as an advanced drug delivery system to enhance therapeutic efficacy, bioavailability, and patient compliance. The formulation was prepared using a modified ethanol injection technique, combining stearic acid, oleic acid, Span 60, cholesterol, and Carbopol to produce a stable transdermal gel. Comprehensive in vitro characterization using FTIR, SEM, XRD, and thermal analysis confirmed the chemical compatibility, morphological uniformity, and physical stability of the nystatin-loaded novasomal gel. Entrapment efficiency differed significantly among the formulations (p < 0.05), with F7 achieving the highest value (80%). All formulations maintained pH levels within the skin-friendly range of 5.5 to 7.0. Viscosity measurements, ranging from 3900 ± 110 to 4510 ± 105 cP, confirmed their appropriate consistency for dermal use. Rheological analysis showed a dominant elastic response, as indicated by storage modulus values consistently higher than the loss modulus. Particle size ranged from 4143 to 9570 nm, while PDI values remained below 0.3, reflecting uniform particle distribution. Zeta potential values were strongly negative, supporting physical stability. XRD studies indicated reduced crystallinity of nystatin within the formulations, while FTIR confirmed drug-excipient compatibility. SEM images showed spherical particles within the micrometer range. In vitro release studies demonstrated sustained drug release over 12 h, with F6 releasing the highest amount. The novasomal gel formulations-maintained stability for 30 days, with no notable alterations in pH, viscosity, or entrapment efficiency. Antifungal evaluation showed a larger inhibition zone (23 ± 2 mm) compared with the plain drug solution (15 ± 1.6 mm), while the MIC value was reduced (4.57 µg/mL), indicating greater potency. Skin irritation assessment in rats revealed only minor, temporary erythema, and the calculated Primary Irritation Index (0.22) confirmed a non-irritant profile. These findings suggest that the developed novasomal gel offers a promising approach for enhancing the treatment of fungal infections by enabling prolonged drug release, minimizing dosing frequency, and improving patient compliance. Full article

Partial shading in photovoltaic (PV) systems causes multiple local maximum power points (LMPPs), complicating tracking and reducing energy efficiency. Conventional maximum power point tracking (MPPT) methods, such as Perturb and Observe (P&O), often fail because of oscillations and entrapment at local maxima. To address these shortcomings, this study proposes a hybrid MPPT strategy combining artificial neural networks (ANNs) and the P&O algorithm to enhance tracking accuracy under partial shading while maintaining implementation simplicity. The research employs a detailed PV cell model in MATLAB/Simulink (2019b) that incorporates dynamic shading to simulate non-uniform irradiance. Within this framework, an ANN trained with the Levenberg–Marquardt algorithm predicts global maximum power points (GMPPs) from voltage and irradiance data, guiding and accelerating subsequent P&O operation. In the hybrid system, the ANN predicts the maximum power points (MPPs) to provide initial estimates, after which the P&O fine-tunes the duty cycle optimization in a DC-DC converter. The proposed hybrid ANN–P&O MPPT method achieved relative improvements of 15.6–49% in tracking efficiency, 16–20% in stability, and 14–54% in convergence speed compared with standalone P&O, depending on the irradiance scenario. This research highlights the potential of ANN-enhanced MPPT systems to maximize energy harvest in PV systems facing shading variability. Full article

Objective: To develop and validate a radiomics-based MRI model for prediction of diffuse-type tenosynovial giant cell tumor (D-TGCT), which has higher postoperative recurrence and more aggressive behavior than localized-type (L-TGCT). The study was conducted under the hypothesis that MRI-based radiomics models can predict D-TGCT with diagnostic performance significantly greater than chance level, as measured by the area under the receiver operating characteristic (ROC) curve (AUC) (null hypothesis: AUC ≤ 0.5; alternative hypothesis: AUC > 0.5). Materials and Methods: This retrospective study included 84 patients with histologically confirmed TGCT (54 L-TGCT, 30 D-TGCT) who underwent preoperative MRI between January 2005 and December 2024. Tumor segmentation was manually performed on T2-weighted (T2WI) and contrast-enhanced T1-weighted images. After standardized preprocessing, 1691 radiomic features were extracted, and feature selection was performed using minimum redundancy maximum relevance. Multivariate logistic regression (MLR) and random forest (RF) classifiers were developed using a training cohort (n = 52) and tested in an independent test cohort (n = 32). Model performance was assessed AUC, sensitivity, specificity, and accuracy. Results: In the training set, D-TGCT prevalence was 32.6%; in the test set, it was 40.6%. The MLR model used three T2WI features: wavelet-LHL_glszm_GrayLevelNonUniformity, wavelet-HLL_gldm_LowGrayLevelEmphasis, and square_firstorder_Median. Training performance was high (AUC 0.94; sensitivity 75.0%; specificity 90.9%; accuracy 85.7%) but dropped in testing (AUC 0.60; sensitivity 62.5%; specificity 60.6%; accuracy 61.2%). The RF classifier demonstrated more stable performance [(training) AUC 0.85; sensitivity 43.8%; specificity 87.9%; accuracy 73.5% and (test) AUC 0.73; sensitivity 56.2%; specificity 72.7%; accuracy 67.3%]. Conclusions: Radiomics-based MRI models may help predict D-TGCT. While the MLR model overfitted, the RF classifier demonstrated relatively greater robustness and generalizability, suggesting that it may support clinical decision-making for D-TGCT in the future. Full article

Novel blend membranes containing S-PVA and PEBAX 1657 at a blend ratio of 8:2 were doped with varying amounts of titanium dioxide phosphate (TiO₂PO₄) as a nanoparticle filler at concentrations of 0, 3, 5, and 7 wt%. The membranes were fabricated using the solution-casting technique. The effect of the TiO₂PO₄ nanofiller on the polymer matrix was thoroughly investigated. Our aim was to investigate how the incorporation of TiO₂PO₄ nanofillers into non-fluorinated SPP-based membranes affects their structural, physicochemical, and electrochemical properties for application in fuel cells. Crystallinity of the samples was checked by means of X-ray diffraction (XRD), while FTIR was used to investigate the contact between the nanofiller and the polymers. The good compatibility resulted in strong interactions between the constituents and led to increased crystallinity of the membrane as well. Furthermore, SEM images confirmed the uniform distribution of the nanofiller. These structural features led to good thermal stability, as evidenced by thermogravimetric analysis (TGA), and good mechanical strength, as proved by tensile tests. Among the samples investigated, the highest water uptake of 51.70% was achieved on the composite membrane containing 3 wt% TiO₂PO₄, which also showed the highest ion exchange capacity at room temperature, reaching 1.13 meq/g. In line with these properties, among the synthesized membranes, the membrane labeled SPP 3% TiO₂PO₄ has the highest current density and power density, with values of 175.5 mA/cm² and 61.52 mW/cm², respectively. Full article

Bifacial photovoltaic (PV) modules have the advantage of using light reflected off of the ground to contribute to power production. Predicting the energy gain is challenging and requires complex models to do so accurately. Often, module degradation over time is neglected in models for the sake of simplicity or is underestimated. Comparing outdoor and indoor current–voltage (I–V) performance for bifacial modules is more challenging than for monofacial modules, as there are additional variables to consider such as rear albedo non-uniformity, cell mismatch, and their effects on temperature. This challenge is compounded when heterogeneous degradation modes occur, such as polarization-type potential-induced degradation (PID-p). To examine the effects of PID-p on I–V predictions using an empirical data-driven approach, 16 bifacial PERC modules are installed outdoors on racks with different albedo conditions. A subset is exposed to high-voltage biases of −1500 V or +1500 V. Outdoor data are traced at irradiance ranges of 150–250 W/m², 500–600 W/m², and 900–1000 W/m². These curves are corrected using control module temperature, wire resistivity, and module resistance measured indoors. We examine several methods to transform indoor I–V curves to accurately, and more simply than existing methods, approximate outdoor performance for bifacial modules without and with varying levels of PID-p degradation. This way, bifacial performance modeling can be more accessible and informed by fielded, degraded modules. Distributions of percent errors between indoor and outdoor performance parameters and Mean Absolute Percent Errors (MAPEs) are used to assess method quality. Results including low-irradiance data (150–250 W/m²) are discussed but are filtered for quantifying method quality as these data introduce substantial errors. The method with the most optimal tradeoff between low MAPE and analysis simplicity involves measuring the front side of a module indoors at an irradiance equal to plane-of-array irradiance plus the product of module bifaciality and albedo irradiance. This method gives MAPE values of 1–6.5% for non-degraded and 1.6–5.9% for PID-p degraded module performance. Full article

Compared with traditional piezoelectric transducers, Capacitive Micromachined Ultrasonic Transducers (CMUTs) have advantages such as better impedance matching with air, smaller size, lighter weight, higher sensitivity, and ease of array formation. Acoustic temperature measurement is a technology that utilizes the relationship between sound velocity and temperature to achieve non-contact temperature detection, with advantages such as fast response and non-invasiveness. CMUT-based acoustic temperature field measurement can achieve temperature detection in situations with narrow spaces, portability, and high measurement accuracy. This paper investigates an air-coupled CMUT device for acoustic temperature measurement, featuring a resonant frequency of 220 kHz, and composed of 16 × 8 cells. The design and fabrication of the CMUT array were completed, and the device characteristics were tested and characterized. A temperature field measurement method using mechanical scanning was proposed. A temperature measurement experimental system based on CMUT devices was constructed, achieving preliminary measurement of acoustic transmission time in both uniform and non-uniform temperature fields. Using a temperature field reconstruction algorithm, the measurement and imaging of the temperature field above an electric heating wire were accomplished and compared with the thermocouple-based temperature measurement experiment. The experimental results verified the feasibility of CMUT devices for non-contact temperature field measurement. Full article

Aqueous aluminum-ion batteries (AAIBs) face significant challenges due to interfacial instability and parasitic side reactions during the reversible deposition of aluminum. Here, we introduce a hybrid electrolyte incorporating triethyl phosphate (TEP), a non-flammable co-solvent that reconstructs the Al³⁺ solvation environment by suppressing water activity. This design extends the electrochemical stability window and enables uniform Al–Zn alloy formation at the anode interface. As a result, symmetric Al–Zn cells achieve over 4000 h of stable cycling. In full-cell configurations with V₂O₅/C cathodes, the system demonstrates high capacity retention (~96% over 450 cycles at 2 A g⁻¹) and coulombic efficiency. This work underscores the potential of solvation structure engineering via functional, flame-retarding co-solvent to advance the development of safe and durable aqueous electrolytes. Full article

Rotating stall is an abnormal flow phenomenon in pumps and pump-turbines, which can cause severe vibration, noise, and even cause head hump. A pump-turbine model under pump mode is researched in this study to reveal the formation mechanism of rotating stall. The causes, development laws, and influencing factors of rotating stall is revealed, which can help professionals achieve a deeper understanding of the rotating stall mechanism and suppress it through optimized design. The flow simulation method is mainly adopted in the study, and it is verified through experiment. The research results show that stall in the guide vanes is often caused, maintained and aggravated by rotor–stator interaction (RSI). A stall cell is often difficult to cause the adjacent flow channel to stall. However, under the action of RSI, stall can be induced in the adjacent flow channel, and then rotating stall is gradually formed. Rotating stall can be suppressed by various methods of reducing RSI. To a certain extent, the research makes up for the problem that conventional theory does not fully consider non-uniform and unsteady complex incoming flow when analyzing rotating stall. A connection between rotating stall and RSI is established, which can provide an important basis for further research on how to eliminate rotating stall. Full article

While Forgetting Factor Recursive Least Square (FFRLS) algorithms with evaluation mechanisms have been developed to address SOC-dependent parameter mapping shifts and their efficacy has been proven in Li-ion batteries, their applicability to lithium–sulfur (Li-S) batteries remains uncertain due to different electrochemical characteristics. This study critically evaluates the applicability of a Fisher information matrix-constrained FFRLS framework for online parameter identification in Li-S battery equivalent circuit network (ECN) models. Experimental validation using distinct drive cycles showed that the identification results of polarization-related parameters are significantly biased between different current excitations, and root mean square error (RMSE) variations diverge by 100%, with terminal voltage estimation errors more than 0.05 V. The parametric uncertainty under variable excitation profiles and voltage plateau estimation deficiencies confirms the inadequacy of such approaches, constraining model-based online identification viability for Li-S automotive applications. Future research should therefore prioritize hybrid estimation architectures integrating electrochemical knowledge with data-driven observers, alongside excitation capturing specifically optimized for Li-S online parameter observability requirements and cell nonuniformity and aging condition consideration. Full article

Objectives: This study aimed to assess the precision of dose delivery to the target in adaptive carbon ion radiotherapy (CIRT) for locally advanced non-small cell lung cancer (LA-NSCLC) in cumulative dosimetry. Methods: Forty-six patients who received CIRT were included (64 Gy[relative biological effectiveness, RBE] in 16 fractions) with treatment plan computed tomography (CT) and weekly CT scans. Offline adaptive radiotherapy (ART) was administered if the dose distribution significantly worsened. Daily doses were calculated from weekly CTs and integrated into plan CT scans using deformable image registration. The dosimetry parameters were compared between the as-scheduled plan and adaptive replan in patients receiving ART. Survival outcomes and toxicity were compared between the ART and non-ART groups. Results: ART was implemented for 27 patients in whom adaptive replans significantly increased the median V_98% of the clinical tumor volume from 96.5% to 98.1% and D_98% from 60.5 to 62.7 Gy(RBE) compared with the as-scheduled plans (p < 0.001). The conformity and uniformity of the dose distribution improved (p < 0.001), with no significant differences in the doses to normal tissues (lungs, heart, esophagus, and spinal cord) from the as-scheduled plans (p > 0.05). The ART and non-ART groups demonstrated comparable local control, progression-free survival, and overall survival (p > 0.05). No grade 3 or higher radiation-related toxicities were observed. Conclusions: ART enhanced target dose coverage while maintaining acceptable normal tissue exposure, supporting weekly CT monitoring integration during CIRT for the timely intervention for anatomical variations, ensuring precise dose delivery in LA-NSCLC. Full article

Battery thermal management is a critical factor in ensuring the performance, safety, and longevity of electric vehicle (EV) battery packs. This study investigates the effectiveness of a forced air convection cooling system, optimized cell spacing and suitable configuration in maintaining optimal battery cell temperatures. A 3D computational model was developed to analyze the temperature distribution of a battery pack under varying airflow velocities, cell spacings and configurations. The numerical simulations were validated through experimental testing, demonstrating a strong correlation between simulated and measured results. The findings reveal that with a 2 m/s velocity of the fan, the battery’s maximum temperature is reduced by 7% compared to the case of natural convection, while the fan consumed only 4% of the battery pack available capacity. An AI algorithm was trained on the experimental data obtained to perform data-driven predictions of failures. The results provide valuable insights for optimizing air cooling systems in EV applications. Future work will explore the effect of non-uniform air flow distribution in reducing the risk of thermal runaway and avoiding hot spots in the battery pack for optimal performance. Full article

Background/Objectives: Guided bone regeneration (GBR) requires barrier membrane materials that balance biodegradation with mechanical stability. Magnesium (Mg)-based metals have good prospects for use as biodegradable barrier materials due to their elastic modulus, good biocompatibility, and osteogenic properties. In this study, gallium (Ga) was introduced into Mg to enhance the mechanical strength and optimize the degradation behavior of the alloy, addressing the limitations of conventional magnesium alloys in corrosion control and strength retention. Methods: Mg-xGa alloys (x = 1.0–3.0%, wt.%) were evaluated for biocompatibility, degradation, and osteogenic potential. Corrosion rates were calculated via weight loss, Mg²⁺ release, and pH changes. Osteogenic effects were assessed using rat bone marrow mesenchymal stem cells (rBMSCs) for alkaline phosphatase (ALP) activity, extracellular matrix (ECM) mineralization, and osteogenic-related gene expression. Optimal alloy was fabricated into barrier membranes with different pore sizes (0.85–1.70 mm) for the rabbit mandibular defect to evaluate the porosity effect on new bone formation. Results: Cytocompatibility tests established a biosafety threshold for Ga content below 3 wt.%. Mg-1Ga demonstrated uniform corrosion with a rate of 1.02 mm/year over 28 days. In vitro, Mg-1Ga enhanced ALP activity, ECM mineralization, and osteogenic gene expression. The 1.70 mm pore size group exhibited superior new bone formation and bone mineral density at 4 and 8 weeks. Conclusions: These results highlight Mg-1Ga’s biocompatibility, controlled degradation, and osteogenic properties. Its optimized pore design bridges the gap between collagen membranes’ poor strength and titanium meshes’ non-degradability, offering a promising solution for GBR applications. Full article

Atomic magnetometers (AMs), recognized for their ultra-high magnetic sensitivity, demand highly uniform pump light fields to maximize measurement accuracy. In this paper, a phase modulation-based method using convolutional neural networks (CNN) and the Gerchberg–Saxton (GS) algorithm is proposed to generate the pumping light field, and the model was trained using a supervised learning approach with a custom dataset. The specific training settings are as follows: the backpropagation algorithm was adopted as the training algorithm, and the Adam optimization method was used for network training, with a learning rate of 0.001 and a total of 100 training epochs, utilizing a liquid crystal spatial light modulator (LCSLM) to regulate the light field phase distribution dynamically. By transforming Gaussian beams into flat-top beams, the method significantly enhances polarization uniformity within vapor cells, leading to improved magnetometric sensitivity. The proposed hybrid algorithm reduces the mean square error from 35% to 19% and peak non-uniformity from 21% to 7.6%. A reflective LCSLM-based optical setup is implemented to produce circular and square flat-top beams with a measured non-uniformity of 5.1%, resulting in an enhancement of magnetic sensitivity from 14.54 fT/Hz^1/2 to 7.80 fT/Hz^1/2. Full article