Tetrabutylammonium nitrate est un composé chimique enregistré sous le numéro CAS 1941-27-1, de formule brute C₁₆H₃₆N·NO₃, et de masse molaire 304.47 g/mol.
La molécule contient 57 atomes reliés par 55 liaisons chimiques. Le nom systématique IUPAC complet de ce composé est tetrabutylazanium nitrate. Le modèle 3D interactif ci-dessus permet de visualiser l'arrangement spatial des atomes — vous pouvez le faire pivoter à la souris, zoomer avec la molette et basculer entre les modes de visualisation squelette / Ball & Stick / Space Fill.
Du point de vue des propriétés physico-chimiques, surface polaire PSA = 62.9 Ų, 0 donneurs et 3 accepteurs de liaisons hydrogène. Ces paramètres sont essentiels pour évaluer la biodisponibilité et l'interaction avec les biomolécules.
Data from PubMed (NIH NLM), OpenAlex (CC0), Semantic Scholar (AI2). AI Summary generated by Semantic Scholar TLDR. Articles marked with require paid access.
📜 Licence et provenancePREDICTED MODEL · NOT VERIFIEDMolGod_License_1
PREDICTED MODEL — NOT VERIFIED computed by MolGod Scientific · experimental feature (beta) · may contain errors ILLUSTRATIVE FRAGMENT ARRANGEMENT — relative positions are not structural data
No source 3-D conformer was available to MolGod for this substance. MolGod Scientific computed this geometry from the structural formula with RDKit 2026.03.6 (ETKDGv3 conformer generation, MMFF94 force-field optimisation; lowest-energy converged conformer of a fixed-seed set). It is not a measured structure, it does not come from PubChem, and it has not been compared with experimental data. It shows one possible conformation.
This substance consists of 2 separate components (for example ions). Each component was computed separately; they are arranged radially around a layout anchor for visualisation only (at least 3.0 Å apart). Their relative positions are not a crystal structure, coordination geometry, ionic contacts or any experimentally determined arrangement.
Methods and references
RDKit: Open-source cheminformatics, version 2026.03.6. Zenodo. doi:10.5281/zenodo.22140358 (all versions: doi:10.5281/zenodo.591637)
Riniker S, Landrum GA. Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation. J. Chem. Inf. Model. 2015, 55, 2562-2574. doi:10.1021/acs.jcim.5b00654
Wang S, Witek J, Landrum GA, Riniker S. Improving Conformer Generation for Small Rings and Macrocycles Based on Distance Geometry and Experimental Torsional-Angle Preferences. J. Chem. Inf. Model. 2020, 60, 2044-2058. doi:10.1021/acs.jcim.0c00025
Halgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J. Comput. Chem. 1996, 17, 490-519. doi:10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P
Tosco P, Stiefl N, Landrum G. Bringing the MMFF force field to the RDKit: implementation and validation. J. Cheminform. 2014, 6, 37. doi:10.1186/s13321-014-0037-3
Heller SR, McNaught A, Pletnev I, Stein S, Tchekhovskoi D. InChI, the IUPAC International Chemical Identifier. J. Cheminform. 2015, 7, 23. doi:10.1186/s13321-015-0068-4
Bolton EE, Kim S, Bryant SH. PubChem3D: Conformer generation. J. Cheminform. 2011, 3, 4. doi:10.1186/1758-2946-3-4 (cited for contrast: no source 3-D conformer was available to MolGod for this record)
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