Raymond J. Bemish

The dynamics for the NO(X2Π) + N(4S) ↔ N2(X1Σg+) + O(3P) reaction was followed in the 3A′ electronic state using state-to-state (STS) and Arrhenius-based rates from two different high-level potential energy surfaces represented as a reproducing kernel Hilbert space (RKHS, PESB for “Basel PES”) and permutationally invariant polynomials (PIPs, PESM for “Minnesota PES”). Despite the different number of bound states supported by PESB and PESM, the ignition points from STS and Arrhenius rates are at ∼10−6 s whether or not reverse rates are from assuming microreversibility or explicitly given. Conversion from NO to N2 is incomplete if Arrhenius rates are used, but complete turnover is observed if STS information is used. This is due to non-equilibrium energy flow and state dynamics, which requires a state-based description. Including full dissociation asymptotically leads to the correct 2:1 [N]:[O] concentration with little differences for the species’ dynamics depending on the PES used for the STS information. In conclusion, concentration profiles from coarse-grained simulations are consistent over 14 orders of magnitude in time using STS information based on two different high-level PESs.

The prediction of product translational, vibrational, and rotational energy distributions for arbitrary initial conditions for reactive atom + diatom collisions is of considerable practical interest in atmospheric re-entry. Because of the large number of accessible states, determination of the necessary information from explicit (quasi-classical or quantum) dynamics studies is impractical. Here, a machine-learned (ML) model based on translational energy and product vibrational states assigned from a spectroscopic, ro-vibrational coupled energy expression based on the Dunham expansion is developed and tested quantitatively. All models considered in this work reproduce final state distributions determined from quasi-classical trajectory (QCT) simulations with R² ∼ 0.98. As a further validation, thermal rates determined from the machine-learned models agree with those from explicit QCT simulations and demonstrate that the atomistic details are retained by the machine learning which makes them suitable for applications in more coarse-grained simulations. More generally, it is found that ML is suitable for designing robust and accurate models from mixed computational/experimental data which may also be of interest in other areas of the physical sciences.

Orthogonal time-of-flight mass spectrometry has been used to characterize the kinetic energy and charged species distributions from an in vacuo electrospray ion source for four different ionic liquids at volumetric flow rates between 0.3 and 3.3 nanolitres per second. In all cases, the mass spectra revealed charged species consisting of singly charged and multiply charged ions as well as two broad, unresolved droplet distributions occurring in the 10⁴ to 10⁶ atomic mass unit per charge range. The mean jet velocity and mean jet breakup potential were established from analysis of the energy profile of the high mass-to-charge droplets. At the jet breakup point, we find the energy loss and the jet diameter flow rate dependence of the electrospray beam to be similar to that determined by Gamero-Castaño (Phys. Fluids, vol. 20, 2008, 032103; Phys. Rev. Fluids, vol. 8, 2021, 013701) for 1-ethyl-3-methylimidazolium bis(trifluromethylsulfonyl)imide at similar volumetric flow rates. Similar trends are observed for all four liquids over the flow regime. A jet instability analysis revealed that jet electrification and viscous effects drive the jet breakup from the case of an uncharged, inviscid jet; jet breakup occurs at droplet and jet radius ratios that deviate from 1.89. Using the analytically determined ratio and the beam profile, different species are modelled to reconstruct the mass spectra; primary droplets constitute only a fraction of the total species present. The populations of the species are discussed.

Reaction and vibrational relaxation rate computed for C( ³ P) + O ₂ ( ³ Σ _g ⁻ ) ↔ CO( ¹ Σ ⁺ ) + O( ¹ D)/O( ³ P) for a wide range of temperatures using quasiclassical trajectory calculations on five new potential energy surfaces for different electronic states.

Reproducing kernel-based potential energy surface based on MRCI+Q/aug-cc-pVTZ energies for the triplet states of N₂O and quasiclassical dynamical study for the reaction, dissociation and vibrational relaxation.

The determination of thermal and vibrational relaxation rates of triatomic systems suitable for application in hypersonic model calculations is discussed. For this, potential energy surfaces for ground and electronically excited state species need to be computed and represented with high accuracy, and quasiclassical or quantum nuclear dynamics simulations provide the basis for determining the relevant rates. These include thermal reaction rates, state-to-state cross sections, and vibrational relaxation rates. For exemplary systems (i.e., [NNO], [NOO], and [CNO]), all individual steps are described, and a literature overview for them is provided. Finally, as some of these quantities involve considerable computational expense, for the example of state-to-state cross sections, the construction of an efficient model based on neural networks is discussed. All such data is required and being used in more coarse-grained computational fluid dynamics simulations.