Abstract: Numerous applications have required the study of CO2 plasmas since the 1960s, from CO2 lasers to spacecraft heat shields. However, in recent years, intense research activities on the subject have restarted because of environmental problems associated with CO2 emissions. The present review provides a synthesis of the current state of knowledge on the physical chemistry of cold CO2 plasmas. In particular, the different modeling approaches implemented to address specific aspects of CO2 plasmas are presented. Throughout the paper, the importance of conducting joint experimental, theoretical and modeling studies to elucidate the complex couplings at play in CO2 plasmas is emphasized. Therefore, the experimental data that are likely to bring relevant constraints to the different modeling approaches are first reviewed. Second, the calculation of some key elementary processes obtained with semi-empirical, classical and quantum methods is presented. In order to describe the electron kinetics, the latest coherent sets of cross section satisfying the constraints of “electron swarm” analyses are introduced, and the need for self-consistent calculations for determining accurate electron energy distribution function (EEDF) is evidenced. The main findings of the latest zero-dimensional (0D) global models about the complex chemistry of CO2 and its dissociation products in different plasma discharges are then given, and full state-to-state (STS) models of only the vibrational-dissociation kinetics developed for studies of spacecraft shields are described. Finally, two important points for all applications using CO2 containing plasma are discussed: the role of surfaces in contact with the plasma, and the need for 2D/3D models to capture the main features of complex reactor geometries including effects induced by fluid dynamics on the plasma properties. In addition to bringing together the latest advances in the description of CO2 non-equilibrium plasmas, the results presented here also highlight the fundamental data that are still missing and the possible routes that still need to be investigated.

Abstract: Fluorophores functionalized with heavy elements show enhanced intersystem crossing due to increased spin–orbit coupling, which in turn shortens the fluorescence decay lifetime (<italic>τ</italic>^PL). This phenomenon is known as the heavy-atom effect (HAE). Here, we report the observation of increased<italic>τ</italic>^PLupon functionalisation of near-infrared photoluminescent gold nanoclusters with iodine. The heavy atom-mediated increase in<italic>τ</italic>^PLis in striking contrast with the HAE and referred to as inverse HAE. Femtosecond and nanosecond transient absorption spectroscopy revealed overcompensation of a slight decrease in lifetime of the transition associated with the Au core (ps) by a large increase in the long-lived triplet state lifetime associated with the Au shell, which contributed to the observed inverse HAE. This unique observation of inverse HAE in gold nanoclusters provides the means to enhance the triplet excited state lifetime.

Abstract: Direct experimental measurement of thermal expansion coefficient without substrate effects is a challenging task for two-dimensional (2D) materials, and its accurate estimation with large-scale ab initio molecular dynamics is computationally very expensive. Machine learning-based interatomic potentials trained with ab initio data have been successfully used in molecular dynamics simulations to decrease the computational cost without compromising the accuracy. In this study, we investigated using Gaussian approximation potentials to reproduce the density functional theory-level accuracy for graphene within both lattice dynamical and molecular dynamical methods, and to extend their applicability to larger length and time scales. Two such potentials are considered, GAP17 and GAP20. GAP17, which was trained with pristine graphene structures, is found to give closer results to density functional theory calculations at different scales. Further vibrational and structural analyses verify that the same conclusions can be deduced with density functional theory level in terms of the reasoning of the thermal expansion behavior, and the negative thermal expansion behavior is associated with long-range out-of-plane phonon vibrations. Thus, it is argued that the enabled larger system sizes by machine learning potentials may even enhance the accuracy compared to small-size-limited ab initio molecular dynamics.

Abstract: Two-dimensional materials (2DMs) continue to attract a lot of attention, particularly for their extreme flexibility and superior thermal properties. Molecular dynamics simulations are among the most powerful methods for computing these properties, but their reliability depends on the accuracy of interatomic interactions. While first principles approaches provide the most accurate description of interatomic forces, they are computationally expensive. In contrast, classical force fields are computationally efficient, but have limited accuracy in interatomic force description. Machine learning interatomic potentials, such as Gaussian Approximation Potentials, trained on density functional theory (DFT) calculations offer a compromise by providing both accurate estimation and computational efficiency. In this work, we present a systematic procedure to develop Gaussian approximation potentials for selected 2DMs, graphene, buckled silicene, and h-XN (X = B, Al, and Ga, as binary compounds) structures. We validate our approach through calculations that require various levels of accuracy in interatomic interactions. The calculated phonon dispersion curves and lattice thermal conductivity, obtained through harmonic and anharmonic force constants (including fourth order) are in excellent agreement with DFT results. HIPHIVE calculations, in which the generated GAP potentials were used to compute higher-order force constants instead of DFT, demonstrated the first-principles level accuracy of the potentials for interatomic force description. Molecular dynamics simulations based on phonon density of states calculations, which agree closely with DFT-based calculations, also show the success of the generated potentials in high-temperature simulations.

Abstract: The 2D electron gas (2DEG) at oxide interfaces exhibits extraordinary properties, such as 2D superconductivity and ferromagnetism, coupled to strongly correlated electrons in narrow d-bands. In particular, 2DEGs in KTaO3 (KTO) with 5d t2g orbitals exhibit larger atomic spin-orbit coupling and crystal-facet-dependent superconductivity absent for 3d 2DEGs in SrTiO3 (STO). Herein, by tracing the interfacial chemistry, weak anti-localization magneto-transport behavior, and electronic structures of (001), (110), and (111) KTO 2DEGs, unambiguously cation exchange across KTO interfaces is discovered. Therefore, the origin of the 2DEGs at KTO-based interfaces is dramatically different from the electronic reconstruction observed at STO interfaces. More importantly, as the interface polarization grows with the higher order planes in the KTO case, the Rashba spin splitting becomes maximal for the superconducting (111) interfaces approximately twice that of the (001) interface. The larger Rashba spin splitting couples strongly to the asymmetric chiral texture of the orbital angular moment, and results mainly from the enhanced inter-orbital hopping of the t2g bands and more localized wave functions. This finding has profound implications for the search for topological superconductors, as well as the realization of efficient spin-charge interconversion for low-power spin-orbitronics based on (110) and (111) KTO interfaces. An unambiguous cation exchange is discovered across the interfaces of (001), (110), and (111) KTaO3 2D electron gases fabricated at room temperature. Remarkably, the (111) interfaces with the highest superconducting transition temperature also turn out to show the strongest electron-phonon interaction and the largest Rashba spin splitting. image