Materials Data on N2 by Materials Project
N2 crystallizes in the cubic P2_13 space group. The structure is zero-dimensional and consists of four ammonia molecules. N is bonded in a 1-coordinate geometry to atoms.
SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
N2 crystallizes in the cubic P2_13 space group. The structure is zero-dimensional and consists of four ammonia molecules. N is bonded in a 1-coordinate geometry to atoms.
P is Copper structured and crystallizes in the cubic Fm-3m space group. The structure is zero-dimensional and consists of four phosphane molecules. P is bonded in a 1-coordinate geometry to atoms.
Hg is Copper structured and crystallizes in the trigonal R-3m space group. The structure is zero-dimensional and consists of three mercury molecules. Hg is bonded in a 12-coordinate geometry to atoms.
He is Hg_xSn structured and crystallizes in the hexagonal P6/mmm space group. The structure is zero-dimensional and consists of one helium molecule. He is bonded in a 1-coordinate geometry to atoms.
Xe is Copper structured and crystallizes in the trigonal R-3m space group. The structure is zero-dimensional and consists of three xenon molecules. Xe is bonded in a 1-coordinate geometry to atoms.
Kr is Tungsten structured and crystallizes in the cubic Im-3m space group. The structure is zero-dimensional and consists of two krypton molecules. Kr is bonded in a 1-coordinate geometry to atoms.
Kr is alpha Samarium structured and crystallizes in the trigonal R-3m space group. The structure is zero-dimensional and consists of nine krypton molecules. Kr is bonded in a 1-coordinate geometry to atoms.
Kr is alpha La structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is zero-dimensional and consists of four krypton molecules. Kr is bonded in a 1-coordinate geometry to atoms.
Xe is Tungsten structured and crystallizes in the cubic Im-3m space group. The structure is zero-dimensional and consists of two xenon molecules. Xe is bonded in a 1-coordinate geometry to atoms.
Hg is alpha La structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is zero-dimensional and consists of four mercury molecules. Hg is bonded in a 6-coordinate geometry to atoms.
Na is alpha Po structured and crystallizes in the cubic Pm-3m space group. The structure is zero-dimensional and consists of one sodium molecule. Na is bonded in a 1-coordinate geometry to atoms.
Xe crystallizes in the orthorhombic Cmce space group. The structure is zero-dimensional and consists of eight xenon molecules. Xe is bonded in a 1-coordinate geometry to atoms.
Xe is alpha Samarium structured and crystallizes in the trigonal R-3m space group. The structure is zero-dimensional and consists of nine xenon molecules. Xe is bonded in a 1-coordinate geometry to atoms.
Hg is Magnesium structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is zero-dimensional and consists of two mercury molecules. Hg is bonded in a 12-coordinate geometry to atoms.
Xe is alpha La structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is zero-dimensional and consists of four xenon molecules. Xe is bonded in a 1-coordinate geometry to atoms.
Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3–20× compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies.
Abstract Metal–organic frameworks (MOFs) are demonstrated to be readily activated by treatment with the low surface tension, low boiling point solvent dimethyl ether (DME). The mildness of the method enables access to high surface areas by avoiding structural changes in the framework that often plague thermal activation methods. A distinction from previous methods is that DME activation succeeds for materials with coordinatively unsaturated sites (CUS) and non‐CUS MOFs as well. DME displaces solvent molecules occupying the pores of the MOF as well as those coordinated to metal centers; reducing evacuation temperature by using a coordinating, yet highly volatile guest enables low temperature activation with structural retention as demonstrated surface area measurements that match or exceed existing activation protocols.
Understanding heterogeneous catalysts is a challenging pursuit due to surface site nonuniformity and aperiodicity in traditionally-used materials. One example is sulfated metal oxides, which function as highly-active catalysts and as supports for organometallic complexes. These applications are due to traits such as acidity, ability to act as a weakly coordinating ligand, and aptitude for promoting transformations via radical cation intermediates. Research is ongoing about the structural features of sulfated metal oxides that imbue the aforementioned properties, such as sulfate geometry and coordination. To better understand these materials, metal–organic frameworks (MOFs) have been targeted as structurally-defined analogs. Composed of inorganic nodes and organic linkers, MOFs possess features such as high porosity and crystallinity, which make them attractive for mechanistic studies of heterogeneous catalysts. In this work, a Zr 6 -based MOF NU-1000 is sulfated and characterized using atomically-precise techniques such as single crystal X-ray diffraction (SCXRD) in addition to diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Here, the dynamic nature of the sulfate binding motif is found to transition from monodentate, to bidentate, to tridentate depending on the degree of hydration, as supported by density functional theory (DFT) calculations. Heightened Brønsted acidity compared to the parent MOF was observed upon sulfation, and probed through trimethylphosphine oxide (TMPO) physisorption, ammonia sorption, in-situ ammonia DRIFTS, and DFT studies. With the support structure benchmarked, an organoiridium complex was chemisorbed onto the sulfated MOF node and the efficacy of this supported catalyst was demonstrated for stoichiometric and catalytic activation of benzene-d6 and toluene with structure-activity relationships derived.