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5 results for “Interstellar molecules”
Cartesian coordinates and rate constants obtained in "Radical Addition and H Abstraction Reactions in Ethane, Ethylene and Acetylene: A Gateway for Ethyl and Vinyl Bearing Molecules in the Interstellar Medium"
<p>Set of cartesian coordinates for reactants, pre-reactant complexes, transition states and products for the reactions presented in the manuscript "Radical Addition and H Abstraction Reactions in Ethane, Ethylene and Acetylene: A Gateway for Ethyl and Vinyl Bearing Molecules in the Interstellar Medium". </p> <p>The energy level employed in the optimisation was UMN15-D3BJ/def2-TZVP using a grid=ultrafine and the dispersion correction parameters recommended in Goerigk et al 2017 (https://pubs.rsc.org/en/content/articlelanding/2017/cp/c7cp04913g), all geometries and energies were obtained using Gaussian16 interfaced to ChemShell. </p> <p>Edit: The comment line of every structure includes its electronic energy (in Eh) and the Zero Point Vibrational Energy (in Eh).</p> <p>Edit: Additionally we included the reaction rate constants obtained with the instanton method at the level of theory mentioned above. These values serve as data behind the figure for the article. The first column of the rate constant file correspond to 1000/T in 1/K whereas the second column present the rate constant in s<sup>-1</sup></p> <p> </p>
3D printed interstellar grain model (core+mantle) plus simple molecules
<p>The video shows the 3D printed interstellar grain with the internal silicate core and the water ice mantle derived from the ACO-FROST model of ice.</p>
Floating in Space: How to Treat the Weak Interaction between CO Molecules in Interstellar Ices
<p>All amorphous CO clusters used for calculating binding energies within our manuscript.</p>
Characterization of Interstellar Organic Molecules
Understanding the origins of life has been one of the greatest dreams throughout history. It is now known that star-forming regions contain complex organic molecules, known as Polycyclic Aromatic Hydrocarbons (PAHs), each of which has particular infrared spectral characteristics. By understanding which PAH species are found in specific star-forming regions, we can better understand the biochemistry that takes place in interstellar clouds. Identifying and classifying PAHs is not an easy task: we can only observe a single superposition of PAH spectra at any given astrophysical site, with the PAH species perhaps numbering in the hundreds or even thousands. This is a challenging source separation problem since we have only one observation composed of numerous mixed sources. However, it is made easier with the help of a library of hundreds of PAH spectra. In order to separate PAH molecules from their mixture, we need to identify the specific species and their unique concentrations that would provide the given mixture. We develop a Bayesian approach for this problem where sources are separated from their mixture by Metropolis Hastings algorithm. Separated PAH concentrations are provided with their error bars, illustrating the uncertainties involved in the estimation process. The approach is demonstrated on synthetic spectral mixtures using spectral resolutions from the Infrared Space Observatory (ISO). Performance of the method is tested for different noise levels.
Supporting Information for the article "Enhanced formation of interstellar complex organic molecules on carbon monoxide ice"
<p>Chemical models ("Models.pkl") and structures (*.xyz files, figures 2 and 3) supporting the calculations presented in the article : "Enhanced formation of interstellar complex organic molecules on carbon monoxide ice"</p> <p>The chemical models are stored as a dictionary of dictionaries with the following name per model:<br> <br> e_XXXX_T_YYYY_CR_ZZZZ</p> <p>where XXXX are the values of the epsilon parameter (see original article) sampled for \epsilon=0.5 and \epsilon=1.0 (additionally we release models with \epsilon=0.40, 0.45, 0.55, 0.60, 0.65, not discussed in the original article), YYYY is the dust temperature sampled within np.linspace(8,18, 26) and ZZZZ is the cosmic ray ionization rate, sampled within np.logspace(-18, -15, 22).<br> <br> To open the chemical models, prepare a python script of the type:</p> <p>++++<br> import pickle<br> <br> def ice(dictionary, name, species): #Similar functions for gas molecules or surface molecules can be defined<br> gas = dictionary[name][f"{species}"]<br> surface = dictionary[name]["J"+f"{species}"+"(1)"]<br> mantle = dictionary[name]["K"+f"{species}"+"(1,1)"]<br> return surface + mantle</p> <p><br> Data = read_pkl_file('Models.pkl')<br> name = "e_1.00_T_10.0_CR_1.00e-17" #as an example<br> time = ice(Data, name, "Time")<br> CO= ice(Data, name, "CO") </p> <p>print(CO)<br> ++++</p> <p><br> Legend for large COMs: Y=CH2OH, X=CH3O, Q=NH2, M=CH2. Please check the reaction network in https://iopscience.iop.org/article/10.1088/0004-637X/765/1/60 for further details.</p> <p><br> Version 1.0: Revision release, including chemical models and cartesian coordinates supporting Figures 2 and 3 of the manuscript.<br> <br> If you request further data, please do not hesitate to contact us.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.