Multiphase gas-liquid flow governs mass and energy transport in geothermal reservoirs, wellbores, and surface facilities. Because direct resolution of interfacial fluid dynamics is generally impractical, simplified approaches such as drift-flux formulations are widely used to predict fluid-phase behavior including phase slip and void fraction. However, most commonly used correlations were developed for air-water and oil-gas systems under near-ambient conditions, introducing uncertainty in their applicability to geothermal environments where fluid properties vary strongly with temperature. In this study, the performance of widely used drift-flux correlations was evaluated using 421 measurements obtained from water-N2 and water-NaCl-N2 experiments conducted in the Geothermal-Flowloop (GFL) at the University of Iceland under conditions of 20-180 degrees C and 1-20 barg. Void fraction was determined by gamma densitometry, and the results were compared with predictions from classical drift-flux formulations. At ambient temperatures (20-30 degrees C), the existing models reproduced the measurements well, and comparison with two independent flow-loop datasets confirmed the consistency of the data. However, all major correlations progressively overpredict void fraction with increasing temperature and total superficial velocity, indicating that important temperature-and flow-dependent effects are not adequately represented. To address these deficiencies, a modified drift-flux model was developed from the Zuber-Findlay framework. Analysis of the experimental data showed that phase slip increases systematically with decreasing liquid viscosity and increasing superficial velocity. Accordingly, the classical drift-velocity expression was reformulated using normalized liquid viscosity and total superficial velocity, and a transition function was additionally introduced to extend applicability across different flow regimes. The resulting formulation substantially improves agreement with experimental observations across the full range of temperatures, flow velocities, and void fractions, reducing systematic void-fraction overestimation, lowering RMSE from 0.20 to 0.05 at 180 degrees C, and reproducing the observed thermal and flow-rate trends. The results demonstrate that explicit incorporation of temperature-dependent fluid properties and flow-dependent slip behavior is important for accurate multiphase flow modelling in geothermal systems and provide an extended drift-flux framework applicable to high-temperature gas-liquid flows.
Photoelectrochemical (PEC) catalysis has attracted considerable attention in the context of sustainability, enabling opportunities for solar powered processes such as water splitting to hydrogen, CO2 reduction to hydrocarbons (fuels), or conversion of nitrogen-based species (e.g. nitrates) to ammonia. The PEC catalytic systems considered here entail light absorption, charge generation, bulk charge-carrier transport, recombination, interfacial charge transfer and surface reactions, inherently spanning multiple length and time scales. Such processes have traditionally been developed through chemical intuition and trial-and-error experimentation, but computational methods are increasingly proving useful in guiding innovation in this field. Yet, while electronic-structure methods provide indispensable microscopic descriptors, and continuum models address device-level transport, a persistent gap remains in the handoffs through the mesoscopic regime, where spatial heterogeneity, stochasticity, and non-equilibrium driving forces collectively determine catalytic performance.In this Review, we examine modelling approaches for PEC catalysis across multiple scales. We first discuss recent advances in electronic-structure methods for describing excited states, band alignment, charge localisation, polarons and excitons. We then proceed to explore the atomistic modelling approaches for treating nonadiabatic dynamics, thermally assisted charge transfer, polaron hopping and elementary reaction mechanisms. Finally, we consider mesoscale kinetic and multiscale transport-reaction models, including kinetic Monte Carlo (kMC) and related stochastic approaches, emphasising how different choices of mesoscale state variables enable descriptions of charge transport, reaction kinetics, material evolution and reactor-scale coupling. Building on this multiscale perspective, we discuss emerging challenges specific to PEC catalysis and conclude by outlining future directions towards integrated multiscale modelling frameworks that connect parameter transfer and atomistic reaction energetics with stochastic reaction dynamics and macroscopic PEC performance.
Delivering exogenous compounds into fish eggs is a powerful tool for early stage manipulation. However, efficient delivery is constrained by the chorion barrier, which restricts the passive entry of macromolecules and therefore limits non-invasive manipulation. Here, we demonstrate that bath immersion enables efficient delivery of fluorescently labeled morpholino oligomers (MOs) into Atlantic salmon eggs when conjugated to the guanidinium rich cell-penetrating peptide nona-arginine. The conjugate Lis-Ssdnd-MO-ZP9 was successfully internalized in both unfertilized and fertilized eggs, supporting a scalable, microinjection free delivery approach. Uptake efficiency was increased by raising the morpholino concentration in the medium, providing a second pulse of MOs during incubation, and modulating extracellular conditions, including temperature (16 degrees C) and alkaline pH (8.6). Confocal imaging revealed strong fluorescence accumulation at the chorion and a sharper decline in signal toward the cytoplasm, indicating that the chorion remains the dominant barrier to uptake. Sperm mediated delivery was feasible but inefficient. Together, these findings define key physicochemical parameters for optimizing immersion-based MOs delivery in Atlantic salmon and establish a foundation for scaling the approach to aquaculture applications.
The International Maritime Organisation targets net-zero greenhouse gas emissions from international shipping by 2050, considering green ammonia to be a promising fuel for decarbonisation. However, development and deployment of this fuel is contingent not only on aspects such as techno-economic feasibility and supportive policy frameworks, but also on social acceptance (SA). This study examines the community-level SA of a potential green ammonia hub for maritime uses in Ísafjörður, Iceland, a remote coastal settlement with a strong maritime history. Based on sixteen semi-structured interviews with local stakeholders, the study identifies the key drivers and barriers influencing SA. It reveals that perceived benefits such as job creation and environmental protection function as drivers, while barriers stem from perceived concerns regarding safety, cost, and lack of clear governance. Guided by a community-adapted corporate sustainability classification and a PESTLE lens, identified themes are mapped across political, economic, social, technological, legal and environmental dimensions. The findings indicate that political and governance conditions function as an important enabling condition for SA, particularly through the perceived need for clear legislative frameworks and government incentives. Rather than operating independently, these political conditions were closely intertwined with economic and technological considerations, influencing investment risk, infrastructure development, affordability, safety, and equitable benefit distribution. The study concludes that for a just maritime energy transition to succeed, national policymakers must prioritise the implementation of robust incentive mechanisms and strategic roadmaps that satisfactorily address local socio-economic realities and contexts.
We present a kernel-based methodology for constructing Lyapunov functions for nonlinear dynamical systems using approximate Koopman eigenfunctions. Our approach decomposes principal Koopman eigenfunctions into linear and nonlinear components, where the linear part is obtained from the system's linearization and the nonlinear part is computed by solving a partial differential equation using symmetric kernel collocation in reproducing kernel Hilbert spaces (RKHS). The resulting Lyapunov function is constructed as a quadratic form in the approximate eigenfunctions. We establish error bounds relating the approximation quality to the fill distance of collocation points and provide a certification procedure using continuous piecewise affine (CPA) methods. Numerical experiments on benchmark systems, including a polynomial system and the Duffing oscillator, demonstrate the effectiveness of our approach.
