![]() Predicting the initiation and subsequent evolution of the size distribution of ice particles in clouds from a distribution of aerosol particles is one of the most important problems in atmospheric science. Dust is believed to be a critical aerosol particle in the Earth system mainly because the dust particles themselves, and particles that are chemically and possibly biologically modified as they are transported from their source, are believed to be the most important ice nuclei in a global sense and because dust particles are transported to many parts of the globe. The goal of this research was to determine how desert dust affects the nucleation of ice particles in convective and layer clouds and the subsequent development of precipitation and glaciation of the clouds. These were led by Professor Alan Blyth (University of Leeds) and Professor Thomas William Choularton (The University of Manchester) The functions are available to all academic researchers, non-academic users require a CSD-Particle licence- contact us here to request a quote, demo, or trial.The UK ICE-D project was funded by the Natural Environement Research Council (NERC) with the grant references: NE/M00340X/1 and NE/M001954/1. The analytical tools in CSD-Particle can be accessed through our desktop program Mercury, or via the CSD Python API.Quickly compare facets, particles, or structures by quantifying the density of H-bond donors, acceptors, aromatic bonds, unsatisfied H-bond donors, RMSD, surface area, rugosity, kurtosis, and skewness. Examine surface chemistry, topology, and interactions through interactive 3D visualizations. Visual and numerical results are possible.What form are CSD-Particle results given in?.Explore how the formulation and solid form impacts these properties computationally, to reduce costs and understand your product earlier. By understanding the mechanical and chemical properties of your product, potential issues in wettability, flow or sticking, tabletability, and electrostatic interactions can be identified at an early stage.How can CSD-Particle predict manufacturing bottlenecks for pharmaceuticals and fine chemicals?.See surface roughness in clear, 3D graphics.Ĭompare facets, particles, or structures easily by quantifying the density of H-bond donors, acceptors, aromatic bonds, unsatisfied H-bond donors, RMSD, surface area, rugosity, kurtosis, and skewness.Īccess all these features in the CSD Python API for fully customizable analyses. Understand interactions within the crystal lattice.Įxplore system shape and stability, and see how internal bonding impacts surface termination and exposed groups on a given facet.Īssess hydrogen bonding dimensionality, and how discreet, 3D, or sheet hydrogen bonding impacts the mechanical properties of your product. For example, assess hydrophilicity with the water oxygen probe, or hydrophobicity with methyl carbon. Change probes to explore interaction types. #Docs2 particles full#Gain insights into wettability, tabletability, flow, and sticking by understanding the interactions at the particle surface.Ĭalculate and visualize full interaction maps (FIMs) on the surface, showing the positions a given probe is most likely to interact. See the potential causes of wettability, stickiness, flow, and electrostatic issues easily.ĭetermine mechanical properties and guide formulation choices to support better tabletting, flow, and milling. Predict the particle shape by predicting which facets are available.Ĭommunicate the distribution of H-bond donors and acceptors on the surface with clear, colour-coded graphics. Get results fast, with minimal compute power, through a simple desktop interface. Communicate findings across your team with visual displays of surface chemistry, charge and topology.ĭriven by big-data insights from the world’s database of experimentally determined small-molecule organic crystal structures, and powerful algorithms for molecular analysis.Perform particle shape and surface analyses to anticipate manufacturing issues such as sticking, wettability, and tabletability.Rapidly analyse the mechanical and chemical properties of crystalline particles with a suite of visual and statistical tools. Anticipate manufacturing bottlenecks and guide formulation decisions with a deep understanding of particle behaviour. ![]()
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