Rivers are Earth's most renewable and accessible freshwater resource1, yet global estimates of the magnitude and variability in river water storage have remained few and inconsistent1-9. Previous estimates of variability have relied either on sparse and asynchronous remote-sensing observations10 or on hydrological models constrained by incomplete understanding of surface-water balance and poorly known river channel characteristics2,3. The insufficient knowledge of temporal variations in river water storage across space hinders effective management of this critical freshwater resource11,12. Here we present near-global-scale observations of active river channel geometry and associated monthly changes in water storage at the reach scale derived from the first water year (October 2023 to September 2024) of the Surface Water and Ocean Topography (SWOT) mission at 126,674 reaches worldwide. Clear patterns of riverbed shape and storage variability expectedly emerge across major basins. SWOT reveals a range of 313.1 ± 129.5 km³ in global annual river storage variability, approximately 28% lower than the lowest previously modelled estimates for the same wide reaches. Although the Amazon's 2024 record drought, the observational challenges in the Arctic and the revisit frequency of SWOT almost certainly contribute to the discrepancy, the observations point to distinct knowledge limitations in surface-water science. These findings highlight key opportunities to improve the fundamental representation of surface-water dynamics in global models and to better inform water resource management and disaster mitigation at scale.

This study explores the passive separation of solid particles from liquid suspensions in spiral separators fabricated using fused filament fabrication (FFF) and stereolithography (SLA). Building on prior work, we investigate the effect of microchannel geometry, circular vs. square cross-sections of equal area, and printing method on separation performance. Devices were tested across a wider range of flow rates (150 mL min−1–350 mL min−1), extending into transitional regimes, to examine geometry-induced inertial effects. Separation performance was quantified using the normalized outlet mass difference (Δ) for talc, precipitated calcium carbonate, and quartz. Maximum separation was obtained for quartz sand in the SLA separator at 250 mL min−1 (Δ = 0.2175 g per 100 mL), while talc showed the highest mass difference in the square FFF separator at 300 mL min−1 (Δ = 0.1196 g per 100 mL). For calcium carbonate, the highest separation occurred in the SLA device at 250 mL min−1 (Δ = 0.1721 g per 100 mL), though performance was limited by agglomeration and clogging in FFF devices. Overall, separation was predominantly mass-based rather than strictly size-selective, with channel geometry, flow regime, and fabrication method jointly governing performance.

The morphological evolution of river channels is a continuous process driven by hydrodynamic fluctuations and human interventions. This study evaluates the spatio-temporal changes in the channel geometry of the lower Subarnarekha River basin in Balasore, Odisha, over several decades. Utilizing multi-temporal Landsat imagery (MSS/TM 4-5 and OLI 8)integrated with ArcGIS and Google Earth Pro, the research quantifies shifts in river course and estuary dynamics.Statistical validations were performed using Origin-Pro 2022 and MS EXCEL. Findings reveal significant morphologicalinstability, particularly between 2015 and 2020, characterized by an increase in the Sinuosity Index within specific grids(A5) and river points (5-6). A notable reduction in water volume has been observed since 2014, while sand dunes andsand banks have shown a declining trend since 2004, likely due to intensive sand mining and monsoonal dischargevariations. Interestingly, the Braided Index showed a gradual decrease from 1985 until a rapid resurgence in 2005.Furthermore, localized analysis of the Subarnarekha estuary (Grid A7) indicates a steady increase in sand bar formationsince 2005. These results underscore the highly dynamic nature of the Subarnarekha riverbed, providing a criticalbaseline for flood management and sustainable river-resource regulation.

ABSTRACT Nanofluidic channels provide an ideal platform for mimicking the remarkable ion selectivity and permeability of biological ion channels, but current architectures are limited by mismatched pore dimensions, low surface charge, and insufficient functionalization, resulting in subpar ion selectivity and flux. Here, we present rationally designed covalent organic framework (COF) nanofluidics with dual‐confinement channels. By precisely modulating the building block length (from 0.58 to 1.22 nm) and spatially programmable functional group density (from −0.13 to −0.79 µC cm −2 ), we synergistically integrate geometric confinement with affinity interactions to tailor the channel microenvironments. The optimized COF nanofluidics achieved exceptional cation selectivity, enhanced from 0.80 to 0.95, while maintaining high ion throughput that increased from 1.93 × 10 14 to 5.12 × 10 14 ions s −1 . As a result, the COF nanofluidics‐based generator delivers an impressive output power density of 11.3 W m −2 by using natural seawater, capable of continuously powering various electronics. Molecular dynamics simulations reveal the dual‐confinement COF nanofluidics with nanoscale channel geometry and strong affinity interactions facilitate an ion‐hopping process that boosts ion selectivity by 4‐fold and ion throughput by 2.5‐fold compared to unoptimized counterparts. This work provides design principles for developing dual‐confinement nanofluidics, showing potential in desalination and ion separation.

Next-generation warfare assets, including Active Electronically Scanned Array (AESA) radars, Electro-Optical Targeting Systems (EOTS), and Land Combat System avionics, face unprecedented thermal loads. Power densities for these systems now frequently exceed hundreds of W/cm² while operating under strict SWaP-C (Size, Weight, Power, and Cost) constraints. Microchannel cooling systems are critical for effective thermal management of high-power radar transmit/receive (T/R) modules. This analysis presents a combined analytical, numerical, and design optimization study of a water-cooled microchannel plate. Pressure drop and outlet temperature are evaluated using classical fluid mechanics and energy balance principles and validated through CFD simulation. Furthermore, channel geometry optimization and an AI-enabled condition-based are proposed to enhance thermal performance, reliability, and lifecycle efficiency.

Abstract The escape of Lyman-α ( Lyα) radiation encodes valuable information on the neutral interstellar medium and is often used as a proxy ionizing photon escape. Yet, the theory of Lyα transfer through anisotropic gas distributions remains underdeveloped. We present Monte Carlo radiative transfer simulations of Lyα propagation through porous, inhomogeneous neutral gas, systematically exploring the effects of channel geometry, outflows, dust, and lognormally distributed column densities. We find that Lyα photons do not preferentially escape through the lowest-column-density pathways, but instead traverse gas of substantial optical depth, leading to suppressed central flux and the absence of strongly beamed escape. Subdividing channels has little impact, indicating geometry and covering fraction are more important than porosity. Channels containing moderate amounts of neutral hydrogen alter escape in characteristic ways, including the appearance of quadruple-peaked spectra, which can be captured by a simple flux–channel relation. Outflows reshape the spectra by facilitating escape through dense media, redshifting photons, and blending central features, while dust modulates the visibility of small channels by suppressing flux at line center; in both cases, we develop an analytical model that predicts the resulting central fluxes. Extending to lognormal column density fields, we show Lyα photons probe a broad range of optical depths, producing skewed spectra that can be approximated by weighted sums of homogeneous models. Our results have direct implications for using Lyα as a tracer of gas properties and ionizing photon escape; for instance, spectra suggestive of high column densities may nonetheless allow LyC leakage through narrow channels.

This study presents a comprehensive numerical simulation of reservoir dam failure based on the two-dimensional hydrodynamic model MIKE 21. To reproduce the real accident process, a detailed digital elevation model derived from LiDAR survey data was constructed, incorporating valley microtopography, river channel geometry, and hydraulic structure elements. The modeling was performed in a stepwise manner and included the simulation of breach formation using a time-varying digital elevation model, the drawdown of the reservoir, and the propagation of the dam-break flood wave in the downstream reach, as well as an assessment of the hydrodynamic impact of the flow on small dams located further downstream. The simulations produced spatiotemporal distributions of flow depths and velocities, quantified the temporal evolution of reservoir water volume, and determined overflow parameters at the small dams. Based on the analysis of bed shear stress distribution, zones of increased hydrodynamic loading were identified and compared with observed damage areas. The results confirm the applicability of the adopted modeling framework for detailed reconstruction of dam-break events. The proposed approach can be applied both to the analysis of past dam failures and for predictive purposes when assessing the potential consequences of possible accidents at other reservoirs. The methodology enables preliminary evaluation of inundation zones, erosion intensity, and impacts on downstream hydraulic structures, making it a valuable tool for safety assessment and the planning of protective measures in areas with complex terrain conditions.

Abstract Rivers self‐organize to convey water and sediment, giving rise to robust downstream scaling between channel geometry and drainage area, underpinning landscape evolution models. However, these relations rely on limited observations per watershed. We quantify downstream changes in channel slope and bankfull width for six gravel rivers. We develop a novel method to automatically extract bankfull width and determine high‐resolution (10‐m), catchment‐specific width‐area scaling, revealing new insights on the covariation between slope and width hidden in large data compilations. We identify a threshold slope, below which average width is slope‐independent. Notably, slope and width deviations display contrasting patterns depending on the channel's elevation profile. Deviations are anticorrelated when knickpoints are present and correlated when they are absent. High‐resolution, catchment‐specific scaling laws capture systematic, interpretable deviations reflecting underlying controls on channel adjustment and fluvial erosive power. With growing availability of high‐resolution topography, our approach provides new insights into river process and form.

A challenge in biophysics is to devise mathematically tractable descriptions of complex processes in molecular and cellular biology. Twenty-five years ago, an elegant formalism was posited to describe channel-facilitated transport across biological membranes. This seminal work derived an effective escape rate that depends on the channel geometry and the diffusivity inside and outside of the channel. This prior escape rate formula has been used in many subsequent investigations to study a variety of biophysical systems. In this paper, we derive a new escape rate formula and rigorously show its validity in various parameter regimes. Our analysis resolves some counterintuitive predictions of the prior formula. We conclude that the prior formula is appropriate in limited biophysical circumstances.

The escalating demand for efficient particle separation in microfluidic systems necessitates innovative design solutions. This study presents a simulation-based topology optimization method to passively separate particles within a 2D microfluidic channel, eliminating the need for external forces. Leveraging a coupled Navier-Stokes solver and particle advection simulation, the framework iteratively refines the channel's geometry by minimizing an objective function quantifying particle mis-sorting. Our approach computationally generated optimal, manufacturable topologies, demonstrating a peak sorting efficiency of 0.6667 (66.67%) achieved by the second iteration, which then stabilized in subsequent iterations to 0.6111 (61.11%), significantly surpassing the adaptability and robustness of traditional, manually designed microfluidic channels. This work provides a robust, physics-based framework for exploring complex design spaces, representing a significant advancement in the development of high-performance, next-generation microfluidic devices.

Challenges of open-channel flow are discussed, with emphasis on energy dissipation and the difficulties brought by channel design for hydraulic pressure and velocity measure. The influence of the rheology of the slurry on channel design is also complicated. The objective of this work is to study the flow behavior in open channels of different shapes and with variable wall stability (fixed and movable). ANSYS Fluent (Release 2, 2021) Computational Fluit Dynamic (CFD) simulations were performed for the velocity distribution and pressure profiles in four configurations of channels: parallel, zigzag, wavy and curved. The study examines the effects of channel height variations and various inlet velocity (6, 3, and 0.3 m/s) on flow behavior. Findings indicate that increasing channel height reduces internal pressure, while lowering the height increases it, with pressure also varying by channel geometry. The curved channel shows the maximum pressure at a height of 0.5 m, and the channel with wavy shape exhibits the maximum pressure at 2831.92 MPa, with the curved channel reaching 3384.85 MPa under fixed-wall conditions

ABSTRACT Additive manufacturing (AM) enables the production of complex components with different materials, making it a promising approach for compact heat exchangers. This study investigates the feasibility of AM for fabricating polymer-based heat exchangers using the LCD photopolymerization technique with ABS resin. Three heat exchangers were manufactured: one with straight channels and two with complex 3D geometries (V-shape and Honeycomb). Experimental tests were performed to evaluate thermal performance under different mass flow rates and temperatures, measuring both heat transfer rates and pressure drops. An analytical model, based on the ε-NTU method, was developed to predict the thermal behavior of the straight-channel exchanger. The model demonstrated good agreement with experimental data, with an average difference of about 10%. In contrast, the heat exchangers with complex channels exhibited lower thermal efficiency than the straight-channel exchanger. The reduced performance of the chaotic geometries was attributed to the large wall thickness and low thermal conductivity of the polymer material. Although the chaotic channels increased surface area and induced turbulence, the low thermal conductivity limited the overall heat transfer. This study demonstrates that optimizing channel geometry alone is insufficient to ensure high performance; wall thickness and material selection are critical factors for achieving efficient heat exchangers. The results suggest that AM is a viable technology for fabricating compact polymer-based heat exchangers, with potential for further improvement through enhanced print quality, reduced wall thickness, and the use of materials with higher thermal conductivity.

Accurate characterization of river channel geometry is essential for hydrological and hydraulic analyses, yet the increasing use of unmanned aerial vehicle (UAV) photogrammetry introduces challenges related to uneven point density, shadow-induced data gaps, and spurious outliers. This study proposed a novel approach for reconstructing 3D river channels from UAV-derived point clouds, emphasizing K-nearest neighbor local regression (KLR), and compared it with the LOWESS model. Method performance was examined through controlled simulations of trapezoidal, triangular, and U-shaped synthetic channels, where KLR consistently preserved morphological fidelity and produced lower RMSE than LOWESS, particularly at channel bends and bed undulations, while a neighborhood selection heuristic approach demonstrated robust results across varying data densities. Synthetic channel experiments show that the proposed K-nearest-neighbor local linear regression (KLR) method achieves RMSE values below 0.06 all tested geometries. In contrast, LOWESS produces substantially larger errors, with RMSE values exceeding 0.9 across all channel shapes. Subsequent application to two South Korean field sites reinforced these findings. In the data-scarce Migok-cheon stream, KLR effectively interpolated missing surfaces while maintaining geomorphic realism, whereas LOWESS generated over-smoothed representations. Within the dense Ogsan Bridge dataset, KLR retained small-scale bed features critical for hydraulic simulations and cross-sectional delineation, while LOWESS obscured local variability. Conclusively, the results demonstrate that KLR provides a more reliable and computationally efficient framework for UAV-based 3D river channel reconstruction, with clear implications for hydraulic modeling, flood risk management, and the advancement of digital-twin systems in operational hydrology.

Functional precision oncology complements genomic approaches by directly testing treatment options on patient-derived models. However, existing platformssuch as patient-derived xenografts (PDXs) and patient-derived organoids (PDOs), face major barriers in clinical use due to technical challenges, including limited standardization, high costs, long assay times, scalability constraints, and incomplete recapitulation of the patient tumor microenvironment (TME). Here, we present a scalable, low-cost Organ Chip (OC) platform fabricated entirely from thermoplastics via injection molding. Leveraging a patented channel geometry and surface treatment, the device achieves barrier-free hydrogel confinement through capillary pinning without porous membranes, micropillars, or other barrier structures. This automation-compatible platform supports tissue-specific extracellular matrices and co-culture through versatile perfusion modes, with robust imaging compatibility. We demonstrate its feasibility for drug sensitivity testing using multiple cell lines and patient-derived primary cells, with imaging-based phenotypic profiling for accurate quantification of drug responses, closely aligning with clinical outcomes. Additionally, we integrated a deep learning-based image translation model that predicts fluorescence staining from bright-field images. This approach enables longitudinal, label-free phenotypic analysis with higher sensitivity than conventional endpoint staining. Together, this integrated cancer OC system overcomes key technical challenges and offers a promising framework for functional precision oncology through high-throughput, patient-relevant drug testing.

This experimental investigation examines the relative roller length (Lr/h1) of hydraulic jumps taking place in composite rectangular channel featuring positive inclinations and roughened minor beds, aiming to address gaps in understanding the effects of channel geometry, slope, and bed roughness. Experiments were conducted in a laboratory flume with a main channel width of 25 cm and a minor bed width of 14.4 cm. The experimental variables included channel slope (tan (α) = 0 to 0.015), bed roughness (ε = 0 to 12 mm), sill height (hs = 2.5 to 21 cm), and inlet flow depths (h1 = 2.5–4 cm), producing Froude number ranging from 1.5 to 9 and Reynolds number between approximately 24500 and 1025000. Results demonstrate that increasing bed roughness reduces the relative roller length by 21–29%, thereby enhancing turbulence and energy dissipation, while positive slopes stabilize hydraulic jumps by promoting smoother flow transitions. A novel dimensionless empirical model relating Lr/h1 to F1, tan (α), and relative roughness (ε/b) was developed and validated, exhibiting high accuracy (R² = 0.99) with predictions within ±5% of experimental values. This model provides a robust predictive tool for hydraulic structure design, enabling reductions in stilling basin dimensions by 23–28% while maintaining equivalent energy dissipation, yielding significant cost (17–22%) and space savings. These findings address critical gaps in understanding hydraulic jump dynamics in complex channel geometries and offer practical insights for designing energy dissipators, spillways, and flood control infrastructure to enhance flow stability, efficiency, and sustainable water management.

Multi-criteria evaluation of symmetric tesla-like valve flow channel geometry on proton exchange membrane fuel cell performance using analytic hierarchy process- entropy weight method

Enhancing river channel geometry estimation for bankfull conditions in Iowa using machine learning

An in-depth study of the impact of diverse vacuum channel geometries on enhancing photoelectron emission performance in photocathodes

Figures of merit are used to translate science to technology. For organic mixed ionic-electronic conductors (OMIECs), the product of mobility and volumetric capacitance, i.e., the µC* product, is the figure of merit used to guide the development of technologies ranging from bioelectronics to neuromorphics. While organic electrochemical transistors (OECTs) are used extensively to measure the µC* product, if there is a kink in the transistor current, then the µC* product is overestimated. Here, we show that the origin of the kink is a change in the rate with which the ions diffuse into the channel. We also discover that the OECT channel geometry has an unprecedented role on how the ions are distributed within the OMIEC. Finally, we observe that electronic charge carriers are first be injected and drift in the OMIEC, before ions can drift from the electrolyte into the OMIEC. Overall, the diverse charge transport phenomena identified here are essential for understanding the complexities of integrating novel OMIECs into traditional device structures, providing the key information needed to realize their promising application potential.

Cell therapy products are rapidly transforming clinical practice, but their short shelf-lives and inability to undergo terminal sterilization create major challenges for sterility testing. Conventional rapid microbiological methods (RMMs) are hindered by the dense cellular background of therapeutic samples, which masks rare microbial contaminants and necessitates pre-analytical processing. Efficient separation of microorganisms from high-density cell suspensions is therefore a critical prerequisite for enabling real-time, in-process sterility assurance. Here, we systematically elucidate the Dean flow–dominated migration mechanism and determine its effective range for continuous, label-free separation of non-typical contaminants ≤ 5 μm in microchannels exceeding 40 μm in height. We demonstrate that particles with ap/h < 0.05 undergo exclusive Dean-induced lateral migration, while those near the inertial focusing threshold (ap/h ≈ 0.07) exhibit a Reynolds number–dependent transition between unfocused and centerline-focused streams. Leveraging these principles, we designed optimized channel geometries that achieved > 95% separation efficiency and > 96% purity of T cells versus three morphologically distinct bacteria at 10⁵ bacteria/mL. At ultra-low loads (< 10 CFU/mL), culture-based assays confirmed 100% detection for inocula > 1 CFU/mL. Our findings validate Dean migration as a governing mechanism for submicron particle separation and provide a path toward integrating microfluidic modules into closed CAR-T manufacturing platforms, advancing real-time microbial quality control in cell therapy production.