Search Results (881)

Micro- and nanoplastic (MNP) contamination is a global health issue, with growing concerns regarding human exposure and potential health impacts. Understanding their biodistribution, cellular uptake, and toxicological effects in biological systems requires sensitive analytical tools capable of detecting and localizing particles across multiple scales and matrix compositions. Techniques such as Fourier-transform infrared spectroscopy, Raman spectroscopy, and mass spectrometry provide valuable chemical information but face limitations in nanoscale detection, sensitivity, tissue penetration, and spatial localization within biological matrices. Fluorescent labeling techniques have emerged as a powerful complementary approach, offering high sensitivity, real-time imaging capability, and broad compatibility with in vitro and in vivo platforms. This review summarizes the principal fluorescent labeling strategies used for MNPs, including adsorption-based staining, swelling-diffusion methods, covalent conjugation, and polymerization-based incorporation of fluorescent probes. We also examine the imaging modalities used to visualize and quantify fluorescent MNPs in biological contexts, including fluorescence microscopy, confocal microscopy, flow cytometry, and whole-body optical imaging. Applications in cellular uptake studies, biodistribution in animal models, transport across biological barriers, and cumulative accumulation measurements are highlighted. Persistent challenges such as dye leaching, biological autofluorescence, photobleaching, and polymer-dependent labeling efficiency are addressed, alongside emerging opportunities in near-infrared fluorescence imaging and multimodal detection strategies. Continued development of fluorescent labeling approaches will enhance our ability to track MNPs across biological systems and inform our understanding of their toxicological consequences. Full article

Brazed-plate heat exchangers (BPHEs) are widely used as recuperators in organic Rankine cycle (ORC) systems, but their corrugated passages make full-scale Computational Fluid Dynamics (CFD) simulations prohibitively expensive, limiting the use of CFD for performance prediction and for deriving design-oriented heat-transfer correlations. This work presents a validated domain-reduced CFD methodology for a BPHE recuperator operating with R134a. The computational domain is reduced to a single exchange unit (one vapor and one liquid corrugated channel separated by one heat-transfer plate), further downscaled by truncating the plate length to the fully developed corrugated region, exploiting longitudinal symmetry, and approximating the corrugation profile with a trapezoidal shape; the resulting mesh (≈4.5 × 10⁶ cells) is approximately 44.5 times smaller than the estimated 2 × 10⁸-cell full-stack mesh. Five steady operating points from a micro-scale ORC test rig (mass-flow rate 0.08–0.22 kg/s, Re ≈ 500–18,500) are used for validation by mapping experimental data to the reduced domain through an ε–NTU formulation. CFD predictions of hot- and cold-side temperature changes agree within 1–10% of the reference values. Building on this validated model, single-phase Nusselt-number correlations are proposed, valid over 500 < Re < 1400 for the liquid phase and 6000 < Re < 18,500 for the vapor phase. The proposed method and results support preliminary, geometry- and model-specific assessment of BPHE recuperators; broader transferability requires additional geometries, operating points and model-form validation. Full article

Background: Traditional bulk tissue analyses obscure the precise spatial compartmentalization of nicotine and its molecular effects within individual anatomical regions. This study aimed to develop and apply a high-resolution spatial multi-omics framework to characterize the localized disposition and functional responses induced by an acute nicotine challenge. Methods: We established a spatial multi-omics framework integrating matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-TOF MSI), air-flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI), laser microdissection (LMD)-based microscale data-independent acquisition (microDIA) proteomics, and targeted LC-MS/MS. This platform was used to analyze the kidney and five brain regions in rats subjected to an acute nicotine challenge following an adaptation regimen. Results: Spatial mapping revealed distinct peripheral and central distribution patterns: nicotine, cotinine, and nornicotine accumulated predominantly in the renal cortex and medulla, whereas their distribution in the brain is region-dependent, with a prominent 3-hydroxycotinine signal in the olfactory bulb. Avoiding tissue homogenization enabled these spatial distributions to be linked to localized functional responses. The striatal dopamine/DOPAC axis showed the strongest acute neurochemical response, consistent with increased dopamine turnover. Spatial metabolomics further demonstrated robust, region-specific metabolic reprogramming, with the hippocampus showing the greatest metabolic variance. LMD-resolved proteomics identified protein-level changes, particularly in the olfactory bulb and thalamus. Cross-omics revealed coordinated alterations in purine, pyrimidine, glycerophospholipid, and alanine/aspartate/glutamate metabolism, with the thalamus showing the greatest extensive metabolite–protein concordance. Conclusions: These findings characterize acute nicotine exposure as a spatially compartmentalized process involving renal handling, region-specific brain distribution, and localized molecular response programs. Full article

Surfactants are widely used to promote hydrate formation and improve flow assurance, yet their effects on hydrate surface morphology at the microscale remain poorly understood. Here, in situ Atomic Force Microscopy (AFM) was employed to quantitatively characterize the surface microstructure of tetrahydrofuran (THF) hydrate formed in the presence of five distinct surfactants, with particular emphasis on grain area, protrusion distribution, grain-boundary geometry, and surface roughness. Results show that pure THF hydrate exhibits a relatively smooth and regular grain morphology, with narrow and shallow grain boundaries and only limited protrusions. Surfactant addition induces pronounced surface reconstruction, yet the morphological evolution pathways differed markedly among systems. Surfactants I and II were associated with grain refinement, reducing the average grain area to 71.4 and 89.9 μm², respectively, while protrusions were concentrated mainly near grain boundaries and grain-boundary grooves became deeper, corresponding to a grain-boundary-dominated roughening mode. In contrast, surfactants III–V were associated with grain coarsening, increasing the average grain area to 121.1, 128.2, and 156.0 μm², respectively, together with more pronounced intragranular protrusions and widened grain boundaries, corresponding to an intragranular-dominated roughening mode. All surfactants increase surface roughness of THF hydrate to different extents. These findings reveal that surfactants regulate hydrate surface architecture through site-selective adsorption, offering a mechanistic framework for the molecular design of surfactant additives in hydrate-based applications. Full article

Efficient liquid-fuel combustion in micro-combustors strongly depends on fuel atomization, evaporation, and fuel-air mixture preparation. However, conventional straight vaporizer tubes often provide limited droplet breakup and insufficient gas liquid heat and mass transfer. In this study, a honeycomb-corrugated vaporizer tube was proposed to enhance fuel atomization and evaporation before combustion. A computational fluid dynamics model combined with an orthogonal design was employed to investigate the effects of corrugation height, corrugation radius, honeycomb-hole diameter, and number of honeycomb plates on gas–liquid two-phase flow, the Sauter mean diameter (SMD), and fuel evaporation rate. The results showed that corrugation height and the number of honeycomb plates were the dominant factors affecting atomization and evaporation performance. The optimized tube, with a corrugation height of 1.0 mm, a corrugation radius of 4.0 mm, three honeycomb plates, and a honeycomb-hole diameter of 0.6 mm, reduced the outlet SMD from 79.0 μm to 42.6 μm and increased the fuel evaporation rate from 1.5% to 42.0%. These findings provide a feasible structural approach for improving fuel atomization, evaporation, and mixture preparation in small-scale liquid-fuel combustors. Full article

Accurate and efficient prediction of shale-gas apparent permeability is essential for pore-scale flow analysis and reservoir evaluation. This study develops a simulation-driven machine learning framework combining controllable pore reconstruction, pore-scale lattice Boltzmann method (LBM) simulations, and neural network prediction. A total of 10,000 two-dimensional porous-media samples with controlled porosity and structural anisotropy are generated using the quartet structure generation set (QSGS) method, and their apparent permeabilities are calculated by LBM simulations incorporating microscale gas-transport effects. Two surrogate models are evaluated using identical data partitions: an artificial neural network (ANN) based on geometric descriptors and a convolutional neural network (CNN) using pore-structure images directly. On the representative test set, the CNN achieves a PCC of 0.9256 and an ${\mathrm{R}}^2$ of 0.8506, outperforming the ANN (PCC = 0.8053, ${\mathrm{R}}^2$ = 0.6445) and the Song, Wang, Yao, Li, Sun, Yang, and Zhang semi-analytical baseline (PCC = 0.3583, ${\mathrm{R}}^2$ = 0.1284). Relative to the baseline, the CNN reduces RMSE, MAE, and MAPE by 73.6%, 73.8%, and 75.0%, respectively. Five independent random data partitions further confirm the stability of the overall performance ranking. The proposed framework enables rapid and reliable apparent-permeability prediction for structurally diverse shale porous media. Full article

CO₂ plume geothermal systems (CPGS) hold significant potential for geothermal energy extraction due to their dual benefits of carbon sequestration and efficient heat transfer. Existing studies primarily focus on macroscopic models of geothermal systems, with limited evaluation of real thermal reservoir cores in terms of CPGS heat extraction performance. This study investigates the seepage and heat transfer characteristics of CO₂ under various injection conditions through a combination of experiments and numerical simulations. Experimental results indicate that under the tested conditions, CO₂ exhibits 20–50% higher heat transfer capacity compared to distilled water. The heat transfer capacity increases with injection flow rate, while injection temperature shows a relatively limited influence under high-velocity seepage conditions. Specifically, at low injection flow rates, lower injection temperatures result in higher heat transfer capacity due to the increased thermal driving force between CO₂ and the reservoir rock. Microscale seepage-heat transfer simulations reveal that an increase in injection flow rate enhances CO₂ flow velocity within the pore network, expands the swept volume, and consequently strengthens convective heat transfer and increases the effective heat exchange area. Additionally, higher thermal reservoir temperatures establish a greater temperature gradient between the working fluid and rock, thereby enhancing heat transfer between CO₂ and the reservoir rock. The findings of this study provide valuable insights for optimizing CPGS design, particularly in understanding the impact of injection conditions on heat transfer capacity, with both practical engineering and theoretical implications. Full article

Impedance flow cytometry (IFC) is a label-free technique for high-throughput electrical characterization of individual cells and other micron-scale particles based on transient impedance changes during passage through microfluidic sensing electrodes. Although lock-in demodulation and low-pass filtering are commonly used for signal conditioning, post-demodulated IFC signals can still contain residual noise, baseline drift, periodic interference, and event-like artifacts that reduce event detectability and distort quantitative features. Here, we present an event-preserving neural network-based denoising framework for post-demodulation IFC signal enhancement. The novelty of this work lies in an IFC-specific event-preserving denoising strategy that combines event-focused sampling and an event-weighted loss to suppress noise while preserving sparse bipolar particle events. A lightweight multilayer perceptron (MLP) was trained and evaluated using paired synthetic noisy and clean IFC signals generated with representative noise and artifact components, and further tested on experimental IFC measurements under challenging noise conditions. Neural network denoising improved event detection, reduced amplitude-estimation error under added white noise, and suppressed experimental baseline and background fluctuations while preserving bipolar event morphology. These results suggest that lightweight neural network denoising can serve as a practical optional enhancement step for noisy post-demodulated IFC signals, potentially supporting more reliable electrical detection and analysis of micro- and submicron-scale particles in impedance-based biosensing applications. Full article

The present study experimentally investigates the influence of n-octanol addition to Jet-A fuel on the combustion performance and emission behavior of a micro-turboprop-based hybrid UAV (“Unmanned Aerial Vehicle”) power system. The experiments were conducted on a dedicated hybrid propulsion test bench equipped with a KingTech micro-turboprop engine mechanically coupled to a T-Motor electric generator and supplying a regulated 48 V DC bus. The system is capable of delivering approximately 3 kW of continuous electrical power, with peak values reaching 3.5 kW. Jet-A and three n-octanol/Jet-A blends containing 10%, 20%, and 30% n-octanol by volume, denoted O10, O20, and O30, respectively, were tested under four operating regimes ranging from idle to 2500 W electrical load. Exhaust gas temperature, carbon monoxide, sulfur dioxide, nitrogen oxides, electrical output, and near-field pollutant dispersion were evaluated. The results show that n-octanol addition affects engine behavior in a strongly load-dependent manner. At idle, the O10 blend reduced CO concentration from approximately 2520 ppm for Jet-A to approximately 2270 ppm, corresponding to a reduction of about 9.9%. At the same operating condition, O10 reduced exhaust gas temperature from approximately 498.3 °C to 463.2 °C, while O20 and O30 produced stronger cooling effects. At intermediate regimes, the oxygenated molecular structure of n-octanol contributed to lower CO formation in selected cases, indicating improved combustion-completeness behavior. At high load, however, exhaust gas temperatures converged toward or exceeded those of Jet-A, particularly for O30, showing that higher octanol fractions may introduce additional thermal constraints. Among the tested fuels, O10, corresponding to 10% n-octanol by volume, provided the most balanced behavior across the investigated operating range, from idle to 2500 W electrical load. The dispersion measurements performed at 30 m from the source further showed that ambient pollutant concentrations are strongly influenced by wind speed, wind direction, and plume transport. These findings support moderate n-octanol blending as a promising transitional strategy for small-scale hybrid UAV propulsion systems, while highlighting the need for future repeated testing, direct fuel-flow measurement, and numerical dispersion modeling. Full article

Point-of-care (POC) diagnostic testing based on microfluidic technology plays an important role in coagulation management. Its precision is, however, limited by the inefficient mixing between blood and solid activators in laminar microfluidic flow. In this study, a novel micromixer incorporating periodic oscillatory flow was developed to enhance blood-activator mixing. A computational fluid dynamic (CFD) model was established to investigate the microscale hydrodynamic characteristics and solid dispersion behavior in the oscillatory multiphase flow system. The numerical predictions were validated against tracer mixing experiments. Sample calculations for the air-liquid-solid flow system demonstrated that the initial loading position of activator particles significantly affected the dispersion efficiency due to the spatial variation in the radial velocity field. Compared with the center-initialized case, the solid dispersion level in the corner-initialized case decreased by approximately 42% after two oscillation cycles. Furthermore, the influence mechanism of oscillation period on solid dispersion was clarified through multi-physics coupling analysis. This study provided new insights into solid–liquid mixing in oscillatory microfluidic systems and established an effective CFD-based framework for optimizing microdevice design and operating conditions. Full article

Digital rock technology is essential for characterizing the petrophysical properties of tight reservoirs. However, conventional construction methods often yield models with insufficient pore connectivity due to low porosity and complex nanopore structures. To address this limitation, we propose a novel connectivity algorithm for isolated pore systems. First, a digital rock model is constructed using a random particle packing algorithm that integrates high-resolution SEM parameters, including kaolinite particle morphologies and randomly distributed microfractures. Subsequently, the connectivity algorithm sequentially links isolated pore clusters to the largest continuous pore system, forming an interconnected channel. Pore network extraction reveals that the algorithm produces significantly denser and more continuous structures, with pore–throat size distributions aligning well with experimental observations. Single-phase flow simulations demonstrate that the enhanced model yields porosity and permeability values consistent with laboratory measurements, whereas unenhanced models deviate substantially. To further advance microscale flow characterization, we derive explicit fitting formulas for the dimensionless conductivity of canonical pore cross-sections (equilateral triangle, square, and circle) considering water film boundary layer (WFBL) effects. These formulations are based on a comprehensive parametric study using the ab initio finite element method, followed by regression analysis to yield closed-form expressions. Two-phase flow simulations reveal that the WFBL increases residual saturations, reduces relative permeabilities, and decreases waterflooding displacement efficiency, with effects being more pronounced during secondary imbibition. This integrated approach provides a robust framework for constructing representative digital rock models of tight reservoirs and offers essential theoretical support for accurately modeling nanoscale flow behaviors in complex subsurface systems. Full article

Mapping debris flow susceptibility is essential for disaster risk reduction in mountainous regions. This study proposes a spatially enhanced modeling framework to evaluate these mass-wasting hazards. The framework integrates conventional topographic parameters with localized micro-geomorphic indicators to assess susceptibility in Bomi County, Tibet. Extracted geomorphological variables include the Topographic Position Index (TPI), surface roughness, local relief, and flow accumulation. We applied multi-scale moving window operations to explicitly quantify spatial heterogeneity and sediment connectivity. This approach systematically evaluates the influence of localized landscape variations on debris flow kinematics. The Random Forest (RF) algorithm was utilized to construct the spatial susceptibility model, and the area under the receiver operating characteristic curve (AUC) quantified its predictive capability. The proposed framework achieves a high predictive accuracy with an AUC of 0.9434. Feature importance analysis demonstrates that micro-geomorphic variables contribute significantly to the predictions; specifically, TPI and local relief primarily drive the overall classification performance. Furthermore, the multi-scale spatial enrichment enables the model to effectively capture complex physical interactions across the terrain. Ultimately, this methodology provides an objective, spatially explicit tool for mass-wasting susceptibility mapping, offering reliable data to support disaster prevention and risk management in the Tibetan Plateau and similar highly incised alpine ecosystems. Full article

Triboelectric nanogenerators (TENGs) offer promising opportunities for distributed wind energy harvesting owing to their low-speed responsiveness, structural flexibility, and adaptability to non-stationary airflow. This review examines wind-driven TENGs from the perspective of realistic wind-field constraints, focusing on three representative scenarios: urban micro-winds, offshore wind–wave environments, and low-altitude complex flows. Scenario-specific advances in device architectures, materials and interfaces, environmental protection, power management, and system integration are systematically reviewed. Representative devices are further quantitatively compared in terms of wind-speed range, activation threshold, electrical output, power density, durability, and system-level energy delivery. Particular attention is given to inconsistent definitions of cut-in wind speed, output normalization, electrical loading, and validation conditions that limit cross-study comparison. Field-validation evidence is assessed from controlled laboratory tests to long-term field operation. Key challenges involving usable regulated energy, environmental reliability, lifetime prediction, array scaling, sustainability, and deployment economics are critically discussed. Finally, five grand challenges with actionable milestones are proposed to facilitate the transition of wind-driven TENGs from laboratory prototypes toward deployable distributed micro-energy systems. Full article

The article presents a review of the current state of knowledge on the effect of surface characteristics on contact conditions and leakage in sealed joints with metal gaskets. The sealing mechanism is discussed, in which joint tightness is governed primarily by the deformation of microscale surface irregularities, the increase in the real contact area, and the disruption of the continuity of microgaps forming flow channels. Particular attention is given to the influence of contact geometry and surface topography on the leakage rate. The paper summarizes selected experimental studies on metal gaskets and discusses approaches to modeling the contact of rough surfaces and leakage at their interface. Taken together, the reviewed evidence indicates that reliable prediction of tightness requires simultaneous consideration of contact mechanics, material properties, loading conditions, and the surface geometric structure. The review identifies promising directions for the further development of contact and leakage models. The importance of modeling and the deliberate design of sealing and mating surfaces for improving joint tightness and reducing fluid losses and fugitive emissions is also highlighted. Full article

Background: In-center hemodialysis is the dominant kidney replacement therapy modality in Greece and generates substantial recurring expenditure for the public payer. Home hemodialysis is not currently implemented at national scale, but it may reduce long-term public expenditure if early investment in training capacity, equipment and home support is recovered over time. Objective: To evaluate, from the Greek public-payer perspective, the discounted budget impact and net present value (NPV) of implementing a national home hemodialysis program for 300 patients. Methods: We developed a deterministic investment-appraisal model comparing gradual implementation of home hemodialysis with continued in-center hemodialysis for the same projected cohort over 10 years. The in-center comparator was informed by a 2022 Greek patient-level micro-costing study. Home hemodialysis expenditure was constructed from explicit patient-flow equations, resource quantities, unit costs, capital purchases and hospital-tariff offsets. Annual incremental savings were discounted at 3% in the base case. Alternative discount rates, deterministic one-way sensitivity analyses and program-scale scenarios were examined. Fiscal benefit–cost ratio (BCR) and return on investment (ROI) were also calculated. Results: Undiscounted 10-year public expenditure was EUR 69,681,804 for home hemodialysis and EUR 86,700,267 for continued in-center hemodialysis, yielding savings of EUR 17,018,463. During the first 5 years, the program required EUR 1,724,012 in additional expenditure. At a 3% discount rate, NPV was EUR 12,696,564, the fiscal BCR was 2.89, fiscal ROI was 189.3% and discounted payback occurred during year 6. NPV remained positive at 5% (EUR 10,391,382) and across all tested one-way scenarios (range EUR 706,179 to EUR 24,686,948). Conclusions: The modeled national home hemodialysis program generated a positive 10-year public-payer NPV under the base-case and tested sensitivity assumptions. A positive NPV is not, however, a formal Greek health-system decision rule and does not capture health outcomes, patient and family costs, or equity. The findings support staged pilot implementation and prospective collection of Greek real-world data before wider rollout. Full article