Related papers: Kinetic modelling of the CO2 capture and utilisati…
Kinetic models of solid thermal decomposition are traditionally used for individual fit of isothermal decomposition experimental data. However, this methodology can provide unacceptable errors in some cases. To solve this problem, a neural…
Conventional approaches to the capture of CO_2 by metal-organic frameworks focus on equilibrium conditions, and frameworks that contain little CO_2 in equilibrium are often rejected as carbon-capture materials. Here we use a statistical…
A new steady-state kinetic model of ammonia decomposition is presented and analyzed regarding the electronic properties of metal catalysts. The model is based on the classical Temkin-Ertl mechanism and modified in accordance with…
In this work, we develop a combined convolutional neural networks (CNNs) and finite element method (FEM) to examine the effective thermal properties of composite phase change materials (CPCMs) consisting of paraffin and copper foam. In this…
The global rate of anthropogenic CO2 emission is rising, which urges the development of efficient carbon capture and storage (CCS) technologies. Among the various CO2 capture methods, adsorption by the linkers of the Metal-Organic…
Reactive molecular dynamics (MD) simulation is performed using a reactive force field (ReaxFF). To this end, we developed a new method to optimize the ReaxFF parameters based on a machine learning approach. This approach combines the…
Addressing climate change calls for action to control CO$_2$ pollution. Direct air and ocean capture offer a solution to this challenge. Making carbon capture competitive with alternatives, such as forestation and mineralisation, requires…
Metal-organic frameworks (MOFs) have emerged as promising materials for carbon capture applications due to their high CO2 capacities and tunable properties. Amongst the many possible MOFs, metal-substituted compounds based on M-DOBDC and…
The physicochemical mechanisms underlying the pyrolysis of n-hexane in a high temperature Ar-H2 environment were investigated for plasma pyrolysis process. An optimal chemical kinetics model was developed using the Reaction Mechanism…
Conventional methods for modeling thermocatalytic systems are typically based on the Kohn-Sham density functional theory (KS-DFT), neglecting the inhomogeneous distributions of gas molecules in the reactive environment. However, industrial…
We propose a theoretical approach for predicting thermodynamics and kinetics of guest molecules in nanoporous materials. This statistical mechanical-based method requires a minimal set of physical parameters that may originate from…
Due the alarming rate of climate change, the implementation of efficient CO$_2$ capture has become crucial. This project aims to create an algorithm that predicts the uptake of CO$_2$ adsorbing Metal-Organic Frameworks (MOFs) by using…
Neutral-atom quantum hardware has emerged as a promising platform for programmable many-body physics. In this work, we develop and validate a practical framework for extracting thermodynamic properties of materials using such hardware. As a…
Direct fired supercritical CO2 (sCO2) power cycles allow for the combustion of gaseous fuels under oxyfuel conditions with inherent carbon capture. As the CO2 is captured intrinsically, the efficiency penalty of capture on the overall plant…
Warm dense matter (WMD) describes an intermediate phase, between condensed matter and classical plasmas, found in natural and man-made systems. In a laboratory setting, WDM needs to be created dynamically. It is typically laser or…
A central challenge in materials science is characterizing chemical processes that are elusive to direct measurement, particularly in functional materials operating under realistic conditions. Here, we demonstrate that mechanical strain…
Catalyzing capping layers on top of metal hydrides are often employed to enhance the hydrogenation kinetics of metal-hydride based systems such as hydrogen sensors. Here, we experimentally study the hydrogenation kinetics of capping layers…
With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse…
Various biological system models have been proposed in systems biology, which are based on the complex biological reactions kinetic of various components. These models are not practical because we lack of kinetic information. In this paper,…
In this study, deep learning is used to estimate kinetic parameters for modeling itaconic acid production based on real batch experiments conducted at different agitation speeds and reactor scales. Two deep learning strategies, namely…