Related papers: Water Isotope Separation using Deep Learning and a…
Hydrogen isotope separation has been effectively achieved using gaseous H2/D2 filtered through graphene/Nafion composite membranes. Nevertheless, deuteron nearly does not exist in the form of gaseous D2 in nature but in liquid water. Thus,…
Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H$_2$O) and heavy water (D$_2$O) within the isothermal-isobaric ensemble. In particular, the deep neural network is…
Driven by the growing demand in the energy, medical, and industrial sectors, we investigate a hydrogen isotope separation technique that offers both a high separation factor and economic feasibility. Our findings reveal that filtering…
The separation of liquid mixture has been studied for a long time. Separation proceeds based on the difference in physical properties including pore size and electrostatic interaction. Therefore, there are many difficulties in separation of…
Due to the similar physical and chemical properties, isotopes are usually hard to separate. On the other hand, the isotope shifts are very well separated in a high-resolution spectrum, making them possible to be addressed individually by…
The isotope effects in x-ray absorption spectra of liquid water are studied by a many-body approach within electron-hole excitation theory. The molecular structures of both light and heavy water are modeled by path-integral molecular…
A general method of separating isotopes by centrifuging dissolved chemical compounds in a liquid solution is introduced. This technique can be applied to almost all elements and leads to large separation factors. The method has been…
Unraveling the atomistic and the electronic structure of solid-liquid interfaces is the key to the design of new materials for many important applications, from heterogeneous catalysis to battery technology. Density functional theory (DFT)…
A comprehensive microscopic understanding of ambient liquid water is a major challenge for $ab$ $initio$ simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES)…
Wet particle separation is widely used in mineral processing and plastic recycling to separate mixtures of particulate materials into further usable fractions due to density differences. This work presents efforts aiming to numerically…
The photodissociation of the HD+ molecular ion in intense short- pulsed linearly polarized laser fields is studied using a time- dependent wave-packet approach where molecular rotation is fully included. We show that applying a coherent…
Graphene-based laminates, with ultralong and tortuous nanocapillaries formed by simply stacking graphene flakes together, have great promises in filtration and separation. However, the information on liquid water trans-membrane permeation…
In this review, we discuss disruptive decoupled water splitting schemes, in which the concurrent production of hydrogen and oxygen in close proximity to each other in conventional electrolysis is replaced by time- or space-separated…
Nanoporous materials have the potential to be used as \emph{molecular sieves} to separate chemical substances in a mixture via selective adsorption and kinetic sieving. The separation of isotopologues is also possible via the so-called…
Isotope separation is one of the grand challenges of modern society and holds great potential for basic science, medicine, energy, and defense. We consider here a new and general approach to isotope separation. The method is based on an…
Water and silicon are chemically dissimilar substances with common physical properties. Their liquids display a temperature of maximum density, increased diffusivity on compression, they form tetrahedral crystals and tetrahedral amorphous…
The detection and classification of microplastics in water remain a significant challenge due to their diverse properties and the limitations of traditional optical methods. Standard spectroscopic techniques often suffer from the strong…
We demonstrate, on the basis of molecular dynamics simulations, the possibility of an efficient water-ethanol separation using nanoporous carbon membranes, namely carbon nanotube membranes, nanoporous graphene sheets, and multilayer…
A core problem in machine learning is to learn expressive latent variables for model prediction on complex data that involves multiple sub-components in a flexible and interpretable fashion. Here, we develop an approach that improves…
We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a…