Proposal of an open-source computational toolbox for solving PDEs in the context of chemical reaction engineering using FEniCS and complementary components

Data from steady-state simulation and economic evaluation in the power to methane context for synthetic natural gas production and power generation

The data presented in this article is generated by a steady-state simulation for performing a techno-economic assessment for comparing three electrolysis technologies in the PtM context. The data is focused on two aspects. First, the description of the steady-state simulation of six PtM systems modeled using Aspen Custom Modeler (ACM) and Aspen Plus (AP). Second, an economic assessment is carried out for each of the mentioned PtM systems to compare the feasibility, the profitability and performance of these systems on a larger scale to produce synthetic natural gas, power generation and carbon utilization given in the main research article. Three electrolysis technologies (namely Alkaline Electrolysis - AE, Proton Exchange Membrane Electrolysis - PEME and Solid Oxide Electrolysis - SOE) were modeled having in mind two methane applications: a combined cycle for power generation and the syngas generation. In addition, on each PtM system is carried out an economic evaluation by calculating fixed capital investment (FCI) and manufacturing costs (MC).

Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke

Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA₂DS₂-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction-known as Virchow's triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation. Over the past decade, cardiac imaging and image-based biophysical modelling have emerged as powerful tools for reproducing the mechanisms of thrombogenesis. Clinical imaging modalities such as cardiac computed tomography, magnetic resonance and echocardiographic techniques can measure blood flow velocities and identify LA fibrosis (an indicator of endothelial dysfunction), but imaging remains limited in its ability to assess blood coagulation dynamics. In-silico cardiac modelling tools-such as computational fluid dynamics for blood flow, reaction-diffusion-convection equations to mimic the coagulation cascade, and surrogate flow metrics associated with endothelial damage-have grown in prevalence and advanced mechanistic understanding of thrombogenesis. However, neither technique alone can fully elucidate thrombogenicity in AF. In future, combining cardiac imaging with in-silico modelling and integrating machine learning approaches for rapid results directly from imaging data will require development under a rigorous framework of verification and clinical validation, but may pave the way towards enhanced personalised stroke risk stratification in the growing population of AF patients. This Review will focus on the significant progress in these fields.