Removing methane from the air is possible, but do the costs outweigh the benefits? This note explores the question of whether removing methane from the atmosphere is justifiable. Destruction of methane by oxidation to CO2 eliminates 97% of the warming impact on a 100-yr time scale. Methane can be oxidized by a variety of methods including thermal or ultraviolet photocatalysis and various processes of physical, chemical or biological oxidizers. Each removal method has energy costs (with the risk of causing embedded CO2 emission that cancel the global warming gain), but in specific circumstances, including settings where air with high methane is habitually present, removal may be competitive with direct efforts to cut fugitive methane leaks. In all cases however, great care must be taken to ensure that the destruction has a net positive impact on the total global warming, and that the resources required would not be better used for stopping the methane from being emitted. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.
Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function (R-2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
Both multiple exciton generation (MEG) in semiconductor nanocrystals and singlet fission (SF) in molecular chromophores have the potential to greatly increase the power conversion efficiency of solar cells for the production of solar electricity (photovoltaics) and solar fuels (artificial photosynthesis) when used in solar photoconverters. MEG creates two or more excitons per absorbed photon, and SF produces two triplet states from a single singlet state. In both cases, multiple charge carriers from a single absorbed photon can be extracted from the cell and used to create higher power conversion efficiencies for a photovoltaic cell or a cell that produces solar fuels, like hydrogen from water splitting or reduced carbon fuels from carbon dioxide and water (analogous to biological photosynthesis). The similarities and differences in the mechanisms and photoconversion cell architectures between MEG and SF are discussed.
Advances in functional materials for energy conversion and storage technologies are crucial in addressing the global challenge of green sustainable energy. This article aims to demonstrate the valuable role that modern modelling techniques now play in providing deeper fundamental insight into novel materials for rechargeable lithium batteries and solid oxide fuel cells. Recent work is illustrated by studies on important topical materials encompassing transition-metal phosphates and silicates for lithium battery electrodes, and apatite-type silicates for fuel cell electrolytes.
In this paper, we illustrate how advanced computational modelling and simulation can be used to investigate drug-induced effects on cardiac electrophysiology and on specific biomarkers of pro-arrhythmic risk. To do so, we first perform a thorough literature review of proposed arrhythmic risk biomarkers from the ionic to the electrocardiogram levels. The review highlights the variety of proposed biomarkers, the complexity of the mechanisms of drug-induced pro-arrhythmia and the existence of significant animal species differences in drug-induced effects on cardiac electrophysiology. Predicting drug-induced pro-arrhythmic risk solely using experiments is challenging both preclinically and clinically, as attested by the rise in the cost of releasing new compounds to the market. Computational modelling and simulation has significantly contributed to the understanding of cardiac electrophysiology and arrhythmias over the last 40 years. In the second part of this paper, we illustrate how state-of-the-art open source computational modelling and simulation tools can be used to simulate multi-scale effects of drug-induced ion channel block in ventricular electrophysiology at the cellular, tissue and whole ventricular levels for different animal species. We believe that the use of computational modelling and simulation in combination with experimental techniques could be a powerful tool for the assessment of drug safety pharmacology.
The optical characteristics of third-generation synchrotron sources and their influence on the simulation of X-ray topographs for various experimental settings, both for the Laue and Bragg case, are discussed. A new generation of simulation programs that allow easy modification of the model of deformation is described.
