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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Abstract Computation of binding constants from spectrophotometric titration data is a very popular application of chemometric hard modeling. However, the calculated values are misleading if the correct binding model is not used. Given that many supramolecular systems of interest feature unknown speciation, a priori determination of binding stoichiometry constitutes an important unsolved problem in chemometrics. We present a new and reliable algorithm for accomplishing this task, implemented using a hybrid particle swarm optimization technique. Simultaneous optimization of stoichiometry ratios and binding constants allows the optimal binding model to be calculated in just a few minutes for systems with up to four reactions. Simulated data studies demonstrate that the algorithm finds the correct stoichiometry with up to nine reactions in the absence of noise, including accurately determining species with unusual stoichiometry, such as H2G5. Application to four experimental datasets shows the algorithm is robust to experimental errors for a variety of chemical systems and binding models. This algorithm will facilitate the discovery of complex binding models, increase efficiency in titration analysis, and avert incorrect stoichiometry models, thereby improving the reliability of binding constant information in spectrophotometric titrations.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-020-64327-3
