Physics of Fluids

The Next Generation Impactor (NGI) is one of the regulatory gold standards for characterizing aerodynamic particle size distributions (APSDs) of orally inhaled drug products (OIDPs); however, its reliance on complex, resource-intensive in vitro testing under tightly controlled environmental conditions limits experimental flexibility and introduces variability. In alignment with the growing regulatory emphasis on New Approach Methodologies (NAMs) for drug development, this study presents a rigorously validated computational fluid particle dynamics (CFPD) based virtual NGI (vNGI) as an in silico method complementary to conventional testing. The vNGI replicates a significant portion of the NGI geometry and airflow physics, enabling high-resolution spatiotemporal analysis of aerosol transport and deposition mechanisms that are otherwise inaccessible experimentally. A comprehensive verification and validation framework was implemented, including mesh and particle independence studies, turbulence model assessment, and comparison of stagewise deposition efficiencies with available in vitro data at 30 L/min. The model's capabilities were further extended to low and high flow rates, and two bio-relevant mouth-throat models and polydisperse particle laden aerosol were added. The model demonstrates strong predictive capability for a few stages and provides mechanistic insight into discrepancies in other stages, depending on the type of analysis. Importantly, this work establishes the vNGI as a fit-for-purpose according to NAM by (i) defining a clear context of use for APSD prediction and inhaler performance evaluation, (ii) capturing physically and biologically relevant air-particle interactions, and (iii) demonstrating technical robustness and reproducibility through systematic validation. The platform can potentially further enable simulation of environmental and physiological conditions, such as humidity effects, that are difficult to control experimentally, thereby improving human relevance and reducing reliance on costly and time-consuming in vitro testing. This study positions the vNGI as a scalable, regulatory aligned NAM capable of supporting early stage drug device combination product development, device optimization, and an alternative bioequivalence assessment, contributing to ongoing efforts to enhance predictive performance, reduce experimental burden, and transition toward human centric, inhalation product evaluation.

Control of fluids is a hallmark of microfluidic systems and fundamental for the successful application of microfluidic devices. Trigger valves use geometric features to autonomously control the release of fluids in microfluidic devices. Our previous work has adapted geometries used in closed trigger valve systems to enable use in open systems, allowing for open microfluidic devices with up to three trigger valves. Here, we focus on the parallel co-flows produced by sequential release of trigger valves and present a model that predicts their layer widths as a function of the geometric characteristics of the different side channels of each trigger valve. We show layered co-flows with widths as low as 50 microns. Additionally, we expand the use of trigger valves in open microfluidic devices by incorporating 1) varied step heights, 2) devices with up to seven trigger valves, and 3) use of varied fluids and plastics. To validate the implementation and use of these trigger valves in open systems, we have developed a theoretical framework to compare predicted outcomes (i.e., fluid travel distance, velocity, and layering width) with our experimental values. This theoretical work offers applications in various fields, including hydrogel patterning for 3D cell culture, organ-on-a-chip models, at-home sample preparation, and autonomous microfluidic systems for biosensing.

Venous thrombosis commonly develops in the vicinity of venous valve pockets, where disturbed haemodynamics, xreduced washout, and endothelial dysfunction promote thrombus initiation. While previous studies have focused on conventional flow descriptors such as velocity, shear stress, recirculation, and residence time, the influence of valve mechanics on flow organisation remains poorly understood. Here, we combine a biomimetic vein-on-a-chip platform with computational fluid dynamics, fluid-structure interaction simulations, Ghost Particle Velocimetry (GPV), whole-blood flow measurements, and particle transport experiments to investigate the interplay between valve biomechanics and venous haemodynamics. Movable venous valve leaflets fabricated by in situ photopolymerisation of poly(ethylene glycol) diacrylate (PEGDA) enabled independent control of leaflet stiffness under physiologically relevant steady and pulsatile flow conditions. Previous experiments demonstrated that leaflet flexibility governs thrombus localisation, with symmetric leaflet stiffness promoting clot formation at the valve tips, while asymmetric stiffness shifts thrombus formation towards the valve sinus. In addition, we identify a previously undescribed Reynolds-number-dependent symmetry-breaking transition in post-valve flow. Above a critical flow condition, an initially symmetric jet spontaneously develops into a stable asymmetric flow pattern. This behaviour was consistently reproduced experimentally using GPV and confirmed by Fluid-structure simulations. Valve compliance delayed the onset of the transition by increasing the effective leaflet opening, whereas valves with a small geometric offset promoted earlier asymmetry through higher local flow velocities within the valve gap. The resulting asymmetric flow generated persistent lateral bias in the transport of red blood cell-sized particles, suggesting enhanced platelet accumulation and prolonged residence within one valve sinus. These findings demonstrate that venous valve mechanics regulate not only local flow fields but also particle transport relevant to thrombosis. The discovery of a stable symmetry-breaking flow state provides a new haemodynamic mechanism linking valve stiffness, asymmetric particle transport, and the preferential localisation of thrombus formation, offering new insight into the mechanobiology of deep vein thrombosis.

Pulsatile milk transport through the lactating mammary ductal tree involves complex interactions between pressure gradients, wall compliance, and non-Newtonian rheology across spatial scales that span nearly two orders of magnitude in lumen radius. Direct experimental characterisation of flow in distal ductal generations remains infeasible due to their sub-millimetre calibre, leaving the haemodynamic environment of the secretory ductules largely unknown. We present a two-stage physicsinformed operator-learning framework that extends validated flow predictions from three instrumented duct generations to twenty generations of a bifurcated mammary network. A Physics-Informed Neural Network (PINN) trained against particle image velocimetry measurements across seven ducts achieved R2 = 0.924-0.997. A Deep Operator Network (DeepONet) distilled from the PINN and refined through physics-constrained training on the governing one-dimensional fluid-structure interaction equations achieved R2(u) = 0.857-0.985 across all validated ducts, with predictions for Generations 4-20 obtained by supplying Murrays Law geometry and mass-conservation-scaled boundary conditions to the frozen operator. Three biophysically significant findings emerge: a mean velocity plateau of 0.14-0.18 m/s across Generations 4-13 produced by Cross shear-thinning compensation offsetting Murray-branching deceleration; a non-monotonic pulsatility index that declines from 0.048 at Generation 1 to a minimum of 0.039 at Generation 5 before rising monotonically to 1.37 at Generation 20 as progressive wall stiffening drives the most distal ductules into a microcirculation-like haemodynamic regime; and a brief elastic-recoil transition zone at Generations 4-5 where mean axial pressure drop reverses sign. To the authors knowledge, these results provide the first quantitative characterisation of pulsatile milk flow across the full hierarchy of a bifurcated mammary ductal tree using a physics-informed operator-learning framework with implications for ductal mechanobiology, milk ejection mechanics, and mastitis pathogenesis.

Chronic, mechanically resilient thrombi remain difficult to remove rapidly and safely using existing therapies, which are limited by slow treatment speeds, reduced efficacy against aged clots and risks associated with embolic debris. Here we introduce Constrained Laser-Induced Cavitation (CLIC), a novel approach that confines laser-induced cavitation bubble generation and collapse within a miniaturized waveguide to enhance thrombolysis. Optimized CLIC removed retracted clots at a mass-loss rate of 393.5 mg/min, [~]40-fold higher than reported state-of-the-art sonothrombolysis under similar conditions. Systematic variation of channel length and laser parameters showed that CLIC efficacy depends strongly on treatment geometry and cavitation dynamics. Post-treatment analysis revealed cylindrical channels consistent with clot removal dominated by fluid jetting and suction-driven evacuation, with cavitation shockwaves likely contributing a secondary role. Debris fragment measurements remained predominantly below a 1 mm embolic-risk threshold, consistent with a promising embolic safety profile. These findings establish CLIC as a viable strategy for rapid thrombolysis of chronic, mechanically resistant thrombi.

Background: Automatic tube compensation (ATC) was designed to compensate for the additional resistive load imposed by the endotracheal tube during spontaneous breathing. In ATC mode, the ventilator adds or subtracts the flow-dependent pressure drop across the tube during both inspiration and expiration so that tracheal pressure remains close to PEEP. Early prototype ventilators achieved true tracheal-pressure control and showed physiological and clinical benefits, but clinical studies with commercial systems have failed to confirm these earlier findings. A 2003 bench study found that commercial ventilators provided, at best, only partial tube compensation, unlikely to result in meaningful clinical benefit. We therefore tested whether this limitation has been remedied in contemporary ICU ventilators. Methods: We performed a bench comparison of five commercial ICU ventilators and an ATC prototype ventilator designed to accurately compensate for the flow-dependent resistance over a wide range of flow rates. An active lung simulator generated spontaneous breathing patterns with weak, moderate, and strong inspiratory efforts at different PEEP levels. We tested each breathing pattern through endotracheal tubes with inner diameters of 7 and 8 mm, and measured airway pressure, tracheal pressure, and flow during CPAP with and without ATC. Breathing through the tube against open atmosphere served as a zero-PEEP/T-piece reference. Results: In CPAP mode, the commercial ventilators showed flow-dependent airway-pressure deviations, amounting to a substantial added resistance of 1.5 - 6.5 mbar/(L/s), whereas the ATC prototype ventilator imposed an added resistance of only 0.6 mbar/(L/s). In ATC mode, the commercial ventilators reduced the resistive load by no more than by 25%, and large tracheal-pressure deviations remained, especially at higher inspiratory effort and during expiration. In some cases, the residual load during ATC was even greater than the load during unsupported breathing through the tube. By contrast, the ATC prototype ventilator maintained tracheal pressure close to PEEP throughout the breathing cycle and eliminated on average 79% of the tube-related resistive load. Conclusions: In the commercial ventilators evaluated in this study, the defining physiological objective of ATC was only partially achieved. Therefore, clinical benefits previously reported for tracheal-pressure control support should be interpreted with caution when applied to commercial ATC implementations, unless effective tube compensation has been demonstrated under relevant conditions. These findings suggest that more advanced control approaches, such as those implemented in the ATC prototype ventilator, may be required to achieve consistent and physiologically accurate tube compensation.

Snake venom composition and its contribution to toxic effects has been heavily researched, but there is a comparative lack of information on venoms fluidic properties and their relationship with fang morphology during the envenomation process. Understanding how venom flows through a fang can shed light on bite site dynamics and potentially explain bite symptoms. In this article we first conduct a broad comparative test of the rheological properties of venom from thirteen snake species, including multiple viperid and elapid snake species, revealing a shear-thinning non-Newtonian flow behavior in all studied species. However, we have not observed strong phylogenetic signal in venom fluidic properties, suggesting that flow properties may vary independently of evolutionary relationships between snake species. Second, we demonstrate that snake venoms fluidic properties can be modeled by other inexpensive, safe, and abundant shear-thinning surrogate fluids. We found that aqueous solutions of bovine serum albumin protein and xanthan gum are useful venom mimics, matching the rheological behavior of venoms from the studied snake species across a range of relevant shear rates. We further evaluated the performance of these snake venom mimics in a simulated venom delivery system, showing good and robust mimetic control of the flow properties as a function of applied pressure. By elucidating the fluidic properties of snake venom and providing a non-toxic, scalable surrogate fluid model to be used in further studies, we provide the biomedical, toxicology, evolutionary biology communities with a tool to study envenomation physics in an inexpensive and safe fashion. We suggest it is possible to design species-specific venom mimics that facilitate research on the biomechanics and fluid dynamics of venom delivery via snake bites, and inform the design of bioinspired puncture and injection devices.

Introduction Anatomical endoscopic enucleation of the prostate (AEEP) techniques, including bipolar enucleation (B-TUEP), holmium laser enucleation (HoLEP), thulium laser enucleation (ThuLEP), and thulium fibre laser enucleation (ThuFLEP), demonstrate comparable clinical outcomes for benign prostatic hyperplasia. As clinical equivalence is increasingly established, cost becomes a key determinant of modality selection. We performed a cost minimisation analysis comparing index procedural costs across AEEP modalities from an institutional perspective. Methods A cost minimisation model was developed from the institutional perspective, incorporating amortised capital costs, maintenance, and consumables. In addition to the base-case scenario of 180 cases per year, we modelled two additional case volume scenarios: low (50 cases/year) and high (500 cases/year) volume. Thu:YAG laser fibres were modelled on two scenarios: disposable single-use, and reusable fibres (up to 10 cases per fibre). Breakeven analysis determined the threshold volume at which each laser modality achieves cost parity with B-TUEP, and one-way sensitivity analysis was performed on key cost parameters. Analysis was limited to index procedural costs calculated in Singapore dollars. Results At the base case of 180 cases per year, B-TUEP had the lowest index procedure cost (SGD 1,018), followed by ThuFLEP (SGD 1,584), ThuLEP (1,599), and HoLEP (SGD 1,655). Breakeven analysis demonstrated that HoLEP, ThuLEP, and ThuFLEP can never achieve cost parity with B-TUEP when laser fibres are single-use, as laser modalities carry higher costs on both capital and per-case dimensions. ThuLEP with reusable fibres (10 uses per fibre) was the only modality to cross below B-TUEP, at a breakeven volume of 198 cases per year. At 500 cases per year with reusable fibres, ThuLEP achieved the lowest cost (SGD 847), representing a 15.4% saving over B-TUEP. Sensitivity analysis identified annual case volume and B-TUEP loop cost as the most influential parameters. Conclusion Index procedural costs in AEEP are strongly influenced by case volume and consumable strategy. While B-TUEP remains cost-efficient at low volume, high-volume practice combined with reusable Thu:YAG fibre technology enables cost parity and potential cost advantage for laser enucleation. These findings highlight the importance of economies of scale and device utilisation in technology adoption.

The suspension culture of pluripotent stem (PS) cells in stirred bioreactors poses a delicate balance between maintaining homogeneous cell dispersion and avoiding excessive shear stress that can compromise cell viability and pluripotency. In this study, we used computational fluid dynamics (CFD) coupled with a discrete particle method (DPM) to simulate iPS cell behavior in a 5 mL delta-impeller stirred tank. Our analysis revealed that upward flow at the tank bottom and downward flow at the top are critical for maintaining a stable suspension. To optimize the stirring protocol, we applied Bayesian optimization to identify a time-dependent stirring schedule that begins with a high-speed phase for resuspension, followed by a low-speed phase for sustained suspension with minimal hydrodynamic stress. The optimized schedule demonstrated improved suspension ratio and reduced slip velocity, indicating lower mechanical stress on cells. These findings provide engineering insights into scalable bioreactor operation, contributing to the design of robust iPS cell manufacturing systems.

Recent models describe drug release from polymeric nanoparticles through an analogy with the quantum tunneling effect, treating the delivery system as a static rectangular potential barrier. In this work, we argue that this analogy is structurally identical to the standard solution of the Schrodinger equation for a rectangular barrier, and that the introduction of a multifractal formalism to describe time evolution -- obtained via a formal Wick rotation (x [->] t) -- lacks direct physical justification. We propose, instead, to treat the barrier height as an effective function of time, Ueff(t) = U0 f(t), with f(t) varying slowly within the barrier region, reflecting the progressive degradation/swelling of the polymeric matrix, under two hypotheses for f(t)-- exponential decay and rational decay (Hill-type). Rather than the thick-barrier WKB approximation, the exact transmission formula is used throughout, which is real-analytic in f(t) and continues smoothly into the resonance (over-barrier) regime once the barrier collapses, avoiding the artificial step-like transitions produced by the WKB approximation used in earlier drafts of this work. Both hypotheses for f(t) predict a finite barrier collapse time, t*, whose dependence on the energy ratio m = U0/E differs qualitatively between them (t* {propto}ln m vs. t* {propto}(m-1)1/n), offering a distinguishable criterion from experimental release data. The model was tested against ex-vivo chicken-skin permeation kinetics of 5-FU digitized from Rata et al. [1] (three systems: NCA-1-5-FU, G-NCA-1-5-FU, G-5-FU). The exact formula substantially improved fit quality relative to the WKB approximation for all three systems. Fits were obtained by global optimization (differential evolution, polished with scipy.optimize.curve_fit for covariance estimates) rather than a single local search, which proved necessary: for G-5-FU and NCA-1-5-FU, the exponential family is well-identified (all parameter uncertainties below 11% and 6% of the estimates, respectively; R2 > 0.999), while the rational (Hill) family remained poorly identified for all three systems despite the improved formula - favoring, by parsimony, the simpler exponential model throughout. Only G-NCA-1-5-FU remained non-identified with m free. A sensitivity check fixing m at the G-5-FU-derived value (m = 1.377) resolves this non-identifiability for G-NCA-1-5-FU at negligible cost in fit quality, consistent with a shared energy ratio for that system; the same constraint applied to NCA-1-5-FU, however, degrades its (already well-identified) fit by a factor of [~]4 in maximum residual; its own energy ratio (m = 1.188 {+/-} 0.007) differs from the shared value by [~]4{sigma}, a formally significant difference, so a single universal m is rejected for the complete set of systems studied. Reference values from the original multifractal study [2-4] and candidate extensions to further aptamer-functionalized nanocarrier systems [5-7] are also discussed. We discuss the implications of this treatment and its limits of validity, and point out paths for further empirical validation.

Computational analysis of physiological and biomedical systems necessitate efficient geometry representations for high fidelity model predictions, including patient or device specificity. Particle-based Lagrangian computational approaches comprise a valuable approach to gain insights from quantitative velocity and pressure data from computational models. Examples include particle dynamics and transport in human vasculature for diseases such as stroke, thrombosis, and embolisms; and modern targeted drug delivery systems in the vascular network and respiratory airways. However, current particle simulation approaches can bear significant computational expense that scales with both number of particles and background fluid mesh resolution. A significant determinant of this computational expense is the contact resolution between particles and anatomically realistic vessel wall. Here, we develop an efficient particle dynamics model that leverages an implicit representation of real anatomical features using a signed distance field to efficiently resolve particle-wall contact. We outline the underlying algorithmic details, followed by a systematic illustration of performance and accuracy using simplified and analytically defined geometries and flow fields. Subsequently, we present a representative simulation of embolic particles along a human vascular segment where we compare our distance field-based approach against classical wall-contact checks based on assessing particle boundary intersection with triangulated surface mesh. Our approach transforms the underlying Lagrangian contact detection operation into an equivalent Eulerian operation, significantly speeding up bulk particle dynamics computations without significantly impacting accuracy or geometric fidelity.

Computational models coupling one-dimensional vascular networks with three-dimensional tissue domains are widely used for predicting blood flow distribution in tumor perfusion, drug delivery, and therapeutic planning. Two prominent coupling paradigms have emerged: the Lateral Average Model (LAM) which implements distributed transmural exchange via a vessel wall conductivity parameter{gamma} (m Pa-1 s-1), and the Sphere of Influence (SOI) model, which employs localized terminal coupling via a source sphere radius{varepsilon} (m). Despite their broad application, systematic quantitative comparisons of their parametric behavior and predictive equivalence remain lacking. We compare LAM and SOI in 3D-1D simulations on a benchmark vascular network and a porcine liver study with a hepatic arterial network reconstructed from CT arteriography. Across a benchmark vascular network under three sink configurations, the LAM net flow rate rose smoothly with{gamma} and saturated at a plateau, while the SOI net flow rate increased with{varepsilon} without saturating; as a result, global-flow equivalence between the two formulations exists only for particular boundary geometries, and not at all within the tested parameter range for one of the three configurations examined. Despite this partial agreement in total flow, the two models diverged substantially in regional perfusion: in a porcine hepatic arterial network reconstructed from CT arteriography, SOI predicted stable perfusion fractions to two regions of interest across its full tested parameter range, whereas LAM predictions for the same regions varied several-fold with vessel wall permeability and, at low permeability, could invert which region received more flow. These results indicate that the choice of coupling model has limited consequence for predicted total organ flow but substantial consequence for predicted local drug delivery, and we provide guidance for selecting between the two formulations depending on the clinical or research question being asked. Author SummaryWhen doctors plan treatments for liver cancer, they often rely on computer simulations to predict how blood flows through the liver and how well a drug will reach the tumor. These simulations depend on mathematical models that describe how blood moves from vessels into surrounding tissue. Two commonly used approaches exist for building these models, but researchers have generally chosen between them based on habit or convenience rather than on a principled understanding of how their predictions differ. In this work, we directly compared these two approaches, one that spreads blood exchange continuously along the vessel wall, and one that delivers blood from the vessel tips into a surrounding spherical zone, using both a simple test network and a realistic pig liver reconstructed from medical imaging. We found that the two approaches can agree on the total amount of blood reaching the liver, but disagree substantially on where that blood goes within the tissue. This distinction matters enormously for treatment planning: a model that predicts the right total blood flow but delivers it to the wrong region of the liver could lead to an inaccurate forecast of drug concentration at the tumor site. Our results provide practical guidance for researchers on which approach to use depending on what information is available and what question is being asked.

Cell therapies hold transformative potential for treating cancer, neurologic disorders, organ failure, diabetes, and other conditions, but their widespread clinical deployment is constrained by the lack of scalable cryopreservation methods that maintain high post-thaw viability. Current standard practice using slow freezing can lead to cell death and impaired cell function. Vitrification offers an alternative by cooling samples rapidly enough to bypass ice formation entirely, better preserving cell structure and function. The high cooling and warming rates required for vitrification have previously been achieved by quenching microliter-scale samples directly into convective cooling and warming baths. Here, we present a cryopreservation platform that overcomes the throughput limitations of existing systems by combining a vibrating orifice aerosol generator with an impinging conical nozzle to generate and confine micrometer-scale droplets mid-flight in liquid nitrogen. This approach mitigates cooling losses due to the inverse Leidenfrost effect, increasing cooling and warming rates by nearly an order of magnitude compared to conventional droplet vitrification, while improving throughput by two orders of magnitude. To test the efficacy of this system, we cryoaerosolized and rewarmed human induced pluripotent stem cells, porcine red blood cells, and human dermal fibroblasts using only 190-25 wt% (2.5-3.7 M) permeating cryoprotective agent, achieving >90% post-thaw viability for HDFs and hiPSCs and 94% recovery for RBCs, with retained colony-forming capacity additionally demonstrated in hiPSCs. This work demonstrates the first scalable vitrification-based cryopreservation method capable of achieving both high cooling ({approx} 200, 000 K min-1) and warming rates ({approx} 1, 000, 000 K min-1) while maintaining the high-throughput processing required ([≥]100 mL h-1) for next-generation cell therapies. Significance StatementCell therapies require robust long-term storage methods to enable widespread clinical deployment. Current approaches utilizing refrigeration or small-scale vitrification cannot meet the scalability and viability requirements for next-generation therapeutics. In this work we demonstrate a cryoaerosolization process that achieves both ultra-rapid cooling rates (>200,000 {degrees}C min-1) and high throughput (>100 mL h-1) by generating micrometer-scale droplets and spraying in a liquid nitrogen impingement stream. Using only as little as 19 wt% cryoprotectant, we achieved >90% cell viability and maintained function, comparable to low-throughput methods, but at two orders of magnitude higher processing rates. We also introduce a just-in-time CPA loading approach that reduces toxicity exposure. This technique enables scalable vitrification-based cryopreservation of large-volume cell products.

The term nanobubble (NB) includes a wide range of gas core, submicron particles. A subgroup of NBs consists of phospholipid-shelled (or coated) nanoparticles stabilizing a perfluorocarbon gas core which have gained recent interest as ultrasound (US) contrast agents. Several methods are available to produce coated NBs. Among these, amalgamation driven self-assembly has been the most utilized. Amalgamation (also referred to as mechanical agitation) is a simple technique currently used for production of commercial and clinically relevant microbubble suspensions. When combined with size-isolation steps, it can also generate submicron NB suspensions with a narrow size distribution. While this technique has been used extensively, no prior work has systematically examined the critical manufacturing parameters needed to produce the optimal coated NB formulation. In this work, we investigate how the precursor lipid dispersion, perfluorocarbon gas to lipid ratio, and pressurized size isolation affect the formation and size isolation of stable, uniform NBs. Results show that the precursor lipid dispersions exhibiting a monomodal size distribution produced the most stable NBs. Additionally, perfluorocarbon volume in excess of lipid dispersion volume is required to form high concentration, stable NBs. Finally, pressurized size isolation resulted in high concentration, US stable NBs. These findings establish the understanding of the key process parameters which affect uniform size and stable NB production via mechanical amalgamation.

Conventional extrusion-based 3D bioprinting encounters limitations in fabricating intricate tissue architectures due to fixed nozzle diameters and fixed deposition orientations. These constraints restrict conformal printing on curved or non-planar surfaces and often necessitate support-intensive fabrication strategies. This work introduces a mechanically simplified extrusion platform inspired by the swivel jet nozzle, featuring a free-degree-of-orientation extrusion head termed the universal extrusion head (Univ-Ex head), coupled with a modular nozzle architecture. The Univ-Ex head employs a swivel-like mechanical design that enables orientation freedom without external actuation in its current implementation, thereby minimizing mechanical complexity while supporting deposition on physiologically relevant, non-planar geometries. Multiple nozzle concepts were developed through comparative CAD iterations, with two representative geometries--a flat nozzle and a conical nozzle--selected for experimental validation. The platform is evaluated through parametric CAD design, stereolithography-printed prototypes, proof-of-concept extrusion experiments, and fluid dynamics simulations performed using FLOW-3D software. Numerical and experimental results demonstrate stable filament formation and clear diameter-dependent extrusion behavior, while simulations further confirm the feasibility of angled and non-planar deposition. A variable-diameter nozzle concept is proposed as a forward design direction to enable real-time adjustment of bioink flow rate and deposition resolution in principle; however, the present study intentionally validates the system using fixed-diameter nozzle variants to maintain stable numerical and experimental boundary conditions. A gear-integrated Univ-Ex head is also presented as a forward upgrade and demonstrated as a single-piece prototype. Collectively, this work establishes a scalable, hardware-focused pathway toward conformal bio-additive manufacturing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/734010v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@8833caorg.highwire.dtl.DTLVardef@33dforg.highwire.dtl.DTLVardef@14d8d11org.highwire.dtl.DTLVardef@685ef0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Microbial fuel cells (MFCs) have been widely investigated as decentralized bioelectrochemical systems capable of converting organic substrates into electricity. However, their long-term autonomous operation is constrained by substrate depletion in the anode compartment, leading to metabolic starvation of electroactive biofilms and a decline in power output. Conventional MFC design treats substrate crossover through the membrane separator as a parasitic loss that reduces coulombic efficiency. In this work, we propose a conceptual inversion of this paradigm by considering controlled cathodic-to-anodic substrate crossover as a passive mechanism to sustain basal microbial metabolism during periods of substrate scarcity. A transport-reaction framework is developed to quantify the balance between membrane-mediated substrate flux and microbial maintenance demand within the anode biofilm. Based on this balance, a dimensionless maintenance crossover Damkohler number (Dam) is introduced to define three operational regimes: starvation-dominated (Dam >> 1), balanced autonomous (Dam {approx} 1), and crossover-dominated (Dam << 1). The framework integrates membrane transport theory with biofilm kinetics to evaluate the effects of separator properties, substrate gradients, and current-dependent electro-osmotic transport on system stability. Order-of-magnitude analysis indicates that achievable crossover fluxes span several orders of magnitude depending on separator characteristics, suggesting that membrane properties critically influence system behavior. This perspective reframes substrate crossover from a loss mechanism to a potential design variable, offering a conceptual tool for enhancing resilience and guiding separator selection in MFCs intended for long-duration, and low-maintenance operation. HighlightsO_LIControlled crossover can sustain microbial metabolism in MFCs C_LIO_LIIntroduces maintenance crossover Damkohler number (Dam) C_LIO_LIIdentifies regimes for autonomous and starvation operation C_LIO_LILinks membrane properties to long-term system stability C_LIO_LIReframes crossover as a design variable, not only a loss C_LI

An e-GLUE is a polymer network containing interpenetrating polycations, which can bond the anionic network of mucosa through interfacial polycation bridging under an electric field. Such an electroadhesion involves electrophoresis of polycations, ionic complexation between polycations and the anionic network, and polycation-network entanglement, yet their quantitative understanding is lacking. Here, we formulate a theoretical model to describe electroadhesion of polymer networks by polycation interfacial bridging. We use a diffusion-drift model coupled with a Bell-like field-dependent chain friction to describe the sticky electrophoresis of polycations in an anionic sea. The formation of ionic bonds is determined by local availability of cations and anions over the penetration depth. To debond, a force must either pull polycations out from the e-GLUE network or first dissociate them from ionic complexes and then pull out from the anionic network. We model chain pullout from the bulk networks to the interface as a viscous drag against water. The adhesion strength is calculated by summing the debonding force for each polycation per unit area across all chains. Our model quantitatively links electric field strength, applied duration, polycation chain length, and cation concentration to polycation electrophoresis kinetics, ionic bond formation, and adhesion strength. We further conduct electroadhesion tests, and our model predicts well with the experimental data. Lastly, we discuss the use of the model to guide the e-GLUE design. TOC graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/730541v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@16524c6org.highwire.dtl.DTLVardef@15163aeorg.highwire.dtl.DTLVardef@673949org.highwire.dtl.DTLVardef@e207a0_HPS_FORMAT_FIGEXP M_FIG C_FIG For Table of Contents use only

The US NEXRAD radar network has monitored the aerosphere over the US and its territories continuously since the 1990s and archived nearly 300 million radar volume scans. These data contain a wealth of information about the movements of birds, bats, and insects. Historically, this biological information was difficult to access due to the amount of data and challenges in analyzing it. In the last 15 years, fueled by computational and methodological advances, large-scale aeroecology research has blossomed. However, comprehensive analyses of the NEXRAD archive remain very costly. We collected measurements of biological activity from every volume scan in the NEXRAD archive--nearly 300 million data files total--to assemble a dataset of aerial biomass over the US from 1995 to 2025. The core data are vertical profiles, which summarize biological activity at different heights above the radar station for each volume scan. We also provide time series data products that aggregate vertical profiles to point measurements at radar stations across time. These data products can support a range of aeroecology analyses at significantly reduced effort.

This brief communication presents two solutions for calculating absolute measures of error from time-difference-of-arrival (TDOA) positioning in underwater acoustic telemetry arrays. First, a Monte Carlo estimation of TDOA positioning error is derived. Next, a computationally inexpensive, approximate solution to the Monte Carlo method is presented. This approximate solution is achieved by solving the Jacobian of a closed-form TDOA positioning model. The positioning error covariance matrix returned from either method can then be used to report the accuracy of TDOA positions or utilized in state-space positioning models. Finally, calculations of the expected radial error are shown which serves as a simple summary statistic for reporting positioning error in real units.

SignificanceComputational modeling and the use of simulation software tools are essential for biomedical optics research. Designing effective simulations often requires in-depth understanding of the underlying physical problems and proper configuration of the software settings, which often constitute key barriers for novice users including students. The rapid emergence of large language models (LLMs) offers new opportunities for natural-language-based interaction, but integrating them with technical software remains challenging because of their limited output reproducibility. Overcoming these limitations would allow more intuitive, efficient, and reproducible interaction between scientists and scientific software. AimWe investigate the use of LLMs in quantitative biophotonics simulation tools, with a goal of enabling novice users to build complex photon simulations using intuitive natural-language-based problem descriptions. ApproachWe have explored prompt engineering strategies that enable LLMs to bridge the gap between natural language descriptions and advanced simulation software by constraining LLM outputs using a data schema (i.e., format) and a modular component architecture, followed by deterministic validation to ensure correctness and reproducibility of the outputs. ResultsUsing Monte Carlo eXtreme (MCX) - a widely used photon transport simulator - as an example, we showcase the capability of the proposed framework to convert user descriptions to structured simulation inputs. Benchmarked using 33 diverse natural language simulation descriptions, our LLM interface, MCX-LLM, achieves 98% accuracy and 99% repeatability, with an average processing time of 8.96 seconds per prompt. The framework also successfully handles various linguistic styles and diverse simulation settings, achieving a 100% success rate on 20 unconstrained real-world prompts. With only minor adjustments, our LLM interface also produces valid inputs for a finite-element-based diffusion solver to demonstrate generality towards other optical simulators. ConclusionsBy combining LLMs capability for textual data comprehension with structured constraints, this work provides a pathway to making complex scientific tools accessible while ensuring the reliability and technical correctness required for rigorous scientific research. MCX-LLM has been integrated with MCX Cloud accessible at https://mcx.space/cloud.