Particle nucleation and growth of crystalline manganese oxide nanoparticles was examined in a complementary experimental and modelling study. Gas-to-particle conversion occurred in a flame-assisted chemical vapor deposition process whereby a premixed stagnation flame drove the high-temperature synthesis. The structure of the stagnation flame was computed using pseudo one-dimensional and axisymmetric two-dimensional methods to assess the accuracy of using a faster similarity-based calculation for flame-deposition design. The pseudo one-dimensional computation performs reasonably well for the narrow aspect ratio stagnation flow currently studied as evidenced by reasonable agreement between the measured flame position and both computational methods. Manganese oxide nanoparticles having II,more »
This content will become publicly available on December 1, 2022
High-throughput reaction engineering to assess the oxidation stability of MAX phases
Abstract The resistance to oxidizing environments exhibited by some M n+1 AX n (MAX) phases stems from the formation of stable and protective oxide layers at high operating temperatures. The MAX phases are hexagonally arranged layered nitrides or carbides with general formula M n +1 AX n , n = 1, 2, 3, where M is early transition elements, A is A block elements, and X is C/N. Previous attempts to model and assess oxide phase stability in these systems has been limited in scope due to higher computational costs. To address the issue, we developed a machine-learning driven high-throughput framework for the fast assessment of phase stability and oxygen reactivity of 211 chemistry MAX phase M 2 AX. The proposed scheme combines a sure independence screening sparsifying operator-based machine-learning model in combination with grand-canonical linear programming to assess temperature-dependent Gibbs free energies, reaction products, and elemental chemical activity during the oxidation of MAX phases. The thermodynamic stability, and chemical activity of constituent elements of Ti 2 AlC with respect to oxygen were fully assessed to understand the high-temperature oxidation behavior. The predictions are in good agreement with oxidation experiments performed on Ti 2 AlC. We were also able to explain more »
- Award ID(s):
- 1852535
- Publication Date:
- NSF-PAR ID:
- 10216526
- Journal Name:
- npj Computational Materials
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2057-3960
- Sponsoring Org:
- National Science Foundation
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