Metal nitrides are intensely investigated because they can offer high melting points, excellent corrosion resistance, high hardness, electronic and magnetic properties superior to the corresponding metals/metal oxides. Thus, they are used in diverse applications including refractory materials, semiconductors, elec- tronic devices, and energy storage/conversion systems. Here, we present a sim- ple, novel, scalable and general route to metal nitride precursors by reactions of metal chlorides with hexamethyldisilazane [HMDS, (Me3 Si)2 NH] in tetrahydro- furan or acetonitrile at low temperatures (ambient to 60◦C/N2). Such reactions have received scant attention in the literature.
The work reported here focuses primarily on the Al-HMDS precursor pro- duced from the reaction of AlCl3 with HMDS (mole ratio = 1:3) characterized by matrix-assisted laser desorption/ionization-time of flight, Fourier-transform infrared spectroscopy, thermogravimetric analysis-differential thermal analysis, and multinuclear nuclear magnetic resonance spectroscopy (NMRs) for chemi- cal and structural analyses. The Al-HMDS precursor heated to 1600◦C/4 h/N2 produces aluminum nitride, characterized by X-ray powder diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy/energy-dispersive X- ray spectroscopy, and magic-angle spinning NMR. On heating to 800–1200◦C/4 h/N2, the precursor transforms to an amorphous, oxygen-sensitive powder with very high surface areas (>200 m2/g) indicating nanosized particles, which can be used as additives to polymer matrices to modify their thermal stabilities. Al2O3 is also presented in the final product after heating, due to its high susceptibility to oxidation.
This approach was extended via proof-of-concept studies to other metal chloride systems, including Zn-HMDS, Cu-HMDS, Fe-HMDS, and Bi-HMDS. The formed precursors are volatile, offering the potential utility as gas-phase deposition pre- cursors for their corresponding metal nitrides.
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Disorder by design: A data-driven approach to amorphous semiconductors without total-energy functionals
Abstract X-ray diffraction, Amorphous silicon, Multi-objective optimization, Monte Carlo methods. This paper addresses a difficult inverse problem that involves the reconstruction of a three-dimensional model of tetrahedral amorphous semiconductors via inversion of diffraction data. By posing the material-structure determination as a multiobjective optimization program, it has been shown that the problem can be solved accurately using a few structural constraints, but no total-energy functionals/forces, which describe the local chemistry of amorphous networks. The approach yields highly realistic models of amorphous silicon, with no or only a few coordination defects (≤1%), a narrow bond-angle distribution of width 9–11.5°, and an electronic gap of 0.8–1.4 eV. These data-driven information-based models have been found to produce electronic and vibrational properties of a -Si that match accurately with experimental data and rival that of the Wooten-Winer-Weaire models. The study confirms the effectiveness of a multiobjective optimization approach to the structural determination of complex materials, and resolves a long-standing dispute concerning the uniqueness of a model of tetrahedral amorphous semiconductors obtained via inversion of diffraction data.
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- Award ID(s):
- 1833035
- NSF-PAR ID:
- 10328599
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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We investigate 29Si nuclear magnetic resonance (NMR) chemical shifts, δiso, of silicon nitride. Our goal is to relate the local structure to the NMR signal and, thus, provide the means to extract more information from the experimental 29Si NMR spectra in this family of compounds. We apply structural modeling and the gauge-included projector augmented wave (GIPAW) method within density functional theory (DFT) calculations. Our models comprise known and hypothetical crystalline Si3N4, as well as amorphous Si3N4 structures. We find good agreement with available experimental 29Si NMR data for tetrahedral Si[4] and octahedral Si[6] in crystalline Si3N4, predict the chemical shift of a trigonal-bipyramidal Si[5] to be about −120 ppm, and quantify the impact of Si-N bond lengths on 29Si δiso. We show through computations that experimental 29Si NMR data indicates that silicon dicarbodiimide, Si(NCN)2 exhibits bent Si-N-C units with angles of about 143° in its structure. A detailed investigation of amorphous silicon nitride shows that an observed peak asymmetry relates to the proximity of a fifth N neighbor in non-bonding distance between 2.5 and 2.8 Å to Si. We reveal the impact of both Si-N(H)-Si bond angle and Si-N bond length on 29Si δiso in hydrogenated silicon nitride structure, silicon diimide Si(NH)2.more » « less
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Lay Description Electron Backscatter Diffraction (EBSD) is a widely used approach for characterising the microstructure of various materials. However, it is difficult to accurately distinguish similar (BCC and BCT) phases in steels using standard EBSD software due to the small difference in crystal structure. One method to tackle the problem of phase distinction is to measure the tetragonality, or apparent ‘strain’ in the crystal lattice, of the phases. This can be done by comparing experimental EBSD patterns with simulated patterns via cross‐correlation techniques, to detect distortion away from a perfectly cubic crystal lattice. However, small errors in the determination of microscope geometry (the so‐called pattern or projection centre) can cause significant errors in tetragonality measurement and lead to erroneous results. This paper utilises a new approach for accurate pattern centre determination via a strain minimisation routine across a large number of grains in dual phase steels. Tetragonality maps are then produced and used to identify phase and estimate local carbon content. The technique is implemented using both simple kinetically simulated and more complex dynamically simulated patterns to determine their relative accuracy. Tetragonality maps, and subsequent phase maps, based on dynamically simulated patterns in a point‐by‐point and grain average comparison are found to consistently produce more precise and accurate results, with close to 90% accuracy for grain phase identification, when compared with an image‐quality identification method. The error in tetragonality measurements appears to be of the order of 1%, thus producing a commensurate error in carbon content estimation. Such an error makes an estimate of total carbon content particularly unsuitable for low carbon steels; although maps of local carbon content may still be revealing.
Application of the method developed in this paper will lead to better understanding of the complex microstructures of steels, and the potential to design microstructures that deliver higher strength and ductility for common applications, such as vehicle components.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-020-64327-3
