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Bottom-Up Approaches for CMOS Scaling in the Nanoscale Era
Published in James E. Morris, Krzysztof Iniewski, Nanoelectronic Device Applications Handbook, 2017
Mrunal A. Khaderbad, V. Ramgopal Rao
Here, TTPOH molecules with different central metal ions have been used to form SAMs on SiO2, HfO2, and Al2O3 gate dielectrics. Moreover, dipolar properties of porphyrin macrocycles can be tuned by incorporating various metal species in them or by using various subgroups. This allows work-function tuning for different technological applications. The Kelvin probe force microscopy (KPFM) characterizations of SAMs show local changes in the surface potential induced by the alignment of molecular dipole moments. Figure 4.7a depicts KPFM imaging of patterned Zn(II) TTPOH SAM on Si, showing higher potential with respect to Si substrate. Figure 4.7b shows the surface potential of Mg, Fe, Co, and Zn TTPOH SAMs on Si. Figure 4.7c shows the estimated dipole moments of metallo-TTPOH molecules using DFT (density functional theory) calculations (performed using the DMol3 module in the Accelrys Materials Studio [91]). It can be clearly seen that TTPOH with Co has less dipole moment as compared to that with Zn central metal.
Computational Techniques on Optical Properties of Metal-Oxide Semiconductors
Published in Inamuddin, Mohd Imran Ahamed, Rajender Boddula, Tariq Altalhi, Optical Properties and Applications of Semiconductors, 2023
Jhilmil Swapnalin, Prasun Banerjee, Chetana Sabbanahalli, Dinesh Rangappa, Kiran Kumar Kondamareddy
The computational study of the material aims mainly to verify whether the electronic and optical properties coincide with the experimental findings or not [69–79]. Theoretical calculations showed how minimal change in the doping amount of tin changes the charge carrier concentration, affecting the optical and electronic properties. It has helped determine the optical bandgap, effective free electron mass, charge carrier concentration, conduction band orbital nature, and plasma frequency in the metal oxide. DFT calculations with quantum chemical software DMol3 and the Perdew and Wang GGA functionals were done to calculate band structure at Fermi option (0K) and the rest other calculations at thermal option (∼6300K), as shown in Figure 10.5 [80].
Solubility Thermodynamics of Organic Energetic Materials
Published in Mark J. Mezger, Kay J. Tindle, Michelle Pantoya, Lori J. Groven, Dilhan M. Kalyon, Energetic Materials, 2017
Sanjoy K. Bhattacharia, Nazir Hossain, Brandon L. Weeks, Chau-Chyun Chen
There are open-source databases of sigma profiles, VT–2005 and VT–2006, for solvents and small molecules (Mullins et al. 2006, 2008). These databases are constructed based on the density functional theory (Delley 1990, 1991; Politzer and Seminario 1995) using the DMol3 module of Accelrys Materials Studio software (DMol3, Materials Studio 7.0 2014). However, recently Islam and Chen (2015) reported that the sigma profiles can be generated from a conceptual segment concept that does not require the quantum chemistry calculations.
Graphene to graphite; a layer by layer experimental measurements and density function theory calculations of electric conductivity
Published in Philosophical Magazine, 2020
Maher S. Amer, Mohammed K. Mohammed, Ali M. Al Mafrage
DFT calculations were conducted using the DMol3® module of the Materials-Studio® simulation software. The simulation ensemble had periodic in-plane (ab-plane) boundary conditions with 1–25 graphene layers along the c-axis. A 10 Å vacuum gap was added at the top of the last layer. In addition, a graphite crystal was simulated. Figure 5 depicts the graphene unit cell used in our simulations along with its 2D hexagonal Brillouin zone. The band structure was calculated along the usual in-plane path Γ-M-K-Γ. We used the generalised gradient approximation for the exchange–correlation potential in the Perdew–Burke–Ernzerhof parametrization [32,33]. The convergence criteria of the geometry optimisation were set to 10−5 Ha, 0.002 Ha/Å, and 0.005 Å for the energy, maximum force, and maximum displacement, respectively. Geometry optimisation was reached by a number of iterations below 500 with a maximum displacement step size of 0.3 Å.