Three double-decker complexes of cerium(IV) were synthesized, which commonly have a 5,10,15,20-tetrakis(4-docosyloxyphenyl)porphyrin (C22OPP) moiety as one of the two tetrapyrrole rings. The three complexes-Ce(Pc)(C22OPP), Ce(C22OPP)2, and Ce(BPEPP)(C22OPP)-are distinguished by the other rings, which are Pc (=phthalocyanine), C22OPP, and BPEPP (=5,15-bis[4-(phenylethynyl)phenyl]porphyrin), respectively. The rate of inter-ring rotation of Ce(BPEPP)(C22OPP) was estimated to be approximately 3 s(-1) in solution at room temperature. These complexes assemble into ordered arrays at the interface of 1-phenyloctane and the highly oriented pyrolytic graphite surface, owing to the affinity of the long alkyl chains toward the surface, as revealed by means of scanning tunneling microscopy (STM) with molecular resolution. The shape of the upper ring is reflected in the STM image. Thus, Ce(Pc)(C22OPP), Ce(C22OPP)2, and Ce(BPEPP)(C22OPP) were observed as circular, square, and elliptic features, respectively. Possible molecular arrangements in the array of Ce(BPEPP)(C22OPP) are proposed by comparing STM images and molecular models. In the mixed arrays of Ce(BPEPP)(C22OPP) and H2(C22OPP), the double-decker complexes were distinguished by brighter features. Competitive adsorption experiments showed that the adsorption of Ce(BPEPP)(C22OPP) is less favorable than that of H2(C22OPP) by DeltaG(app) = 2.7 kJ mol(-1). Ce(BPEPP)(C22OPP) molecules appeared elliptic when placed within their own row, while they appeared isotropic when flanked by H2(C22OPP) molecules. Implications of the differences in the observed shapes to the inter-ring rotation are discussed.

A reliable set of electron collision cross sections for water vapor, including elastic, rotational, vibrational, and electronic excitation, electron attachment, and ionization cross sections, is estimated by the electron swarm method. In addition, anisotropic electron scattering for elastic and rotational excitation collisions is considered in the cross section set. Electron transport coefficients such as electron drift velocity, longitudinal diffusion coefficient, and effective ionization coefficient are calculated from the cross section set by Monte Carlo simulation in a wide range of E/N values, where E and N are the applied electric field and the number density of H 2 O molecules, respectively. The calculated transport coefficients are in good agreement with those measured. The obtained results confirm that the anisotropic electron scattering is important for the calculation at low E/N values. Furthermore, the cross section set assuming the isotropic electron scattering is proposed for practical use.

The phrase within the first sentence in subsection 3.1, 'and the detailed cross sections of the ionization collision are plotted in figure 4', should be removed. This phrase should be embedded within the first sentence in subsection 3.2 as follows:'Figure 3 shows the set of electron collision cross sections for TEOS vapour proposed in this work as a function of electron energy ε, and the detailed cross sections of the ionization collision are plotted in figure 4.' ORCID iDs

A novel direct numerical method to calculate the electron velocity distribution function (EVDF) in hydrodynamic equilibrium under a uniform DC electric field is presented. In the present method, an artificial feedforward neural network learns the EVDF governed by both the Boltzmann equation and boundary conditions. The present method dost not require the expansion of the EVDF in the Legendre polynomials and the discretization of both the EVDF and the Boltzmann equation. As a benchmark, the EVDF in Reid's ramp model gas and Ar gas was calculated by the present method, and then the validity of the present method was demonstrated by comparing electron energy distributions and electron transport coefficients deduced from the EVDF with those calculated by Monte Carlo simulation.

Low-temperature plasma processing technologies is essential for material synthesis, device fabrication, and surface treatment. The development of plasma-related products and services requires an understanding of the multiscale complex behaviors of plasma and the hierarchical integration of plasma generation, energy and mass transports through sheath region, surface reactions, and other processes. The importance of science-based and data-driven approaches to controlling systems is argued. The state-of-the-art of deep learning, machine learning, and artificial intelligence in low-temperature plasma science and technology is reviewed. In this review, the requirements and challenges for plasma parameter prediction and processing recipe discovery are asserted by researchers in the fields of material science and plasma processing. It also outlined a science-based, data-driven approach for development of virtual metrology in plasma processes.