Nitrile IR intensities characterize electric fields and hydrogen bonding in protic, aprotic, and protein environments

Nitriles are widely used as vibrational probes; however, the interpretation of their IR frequencies is complicated by hydrogen bonding (H-bonding) in protic environments. We report a new vibrational Stark effect (VSE) that correlates the electric field projected on the nitrile bond to the transition dipole moment and, by extension, the nitrile peak area or integrated intensity. This linear VSE applies to both H-bonding and non-H-bonding interactions. It can therefore be generally applied to determine electric fields in all environments. Additionally, it allows for semi-empirical extraction of the H-bonding contribution to the blueshift of the nitrile frequency. Nitriles were incorporated at H-bonding and non-H-bonding protein sites using amber suppression, and each nitrile variant was structurally characterized at high resolution. We exploited the combined information now available from variations in frequency and integrated intensity and demonstrate that nitriles are a generally useful probe for electric fields.

Review of a Semi-Empirical Modelling Approach for Cohesive Sediment Transport in River Systems

In this paper, a review of a semi-empirical modelling approach for cohesive sediment transport in river systems is presented. The mathematical modelling of cohesive sediment transport is a challenge because of the number of governing parameters controlling the various transport processes involved in cohesive sediment, and hence a semi-empirical approach is a viable option. A semi-empirical model of cohesive sediment called the RIVFLOC model developed by Krishnappan is reviewed and the model parameters that need to be determined using a rotating circular flume are highlighted. The parameters that were determined using a rotating circular flume during the application of the RIVFLOC model to different river systems include the critical shear stress for erosion of the cohesive sediment, critical shear stress for deposition according to the definition of Partheniades, critical shear stress for deposition according to the definition of Krone, the cohesion parameter governing the flocculation of cohesive sediment and a set of empirical parameters that define the density of the floc in terms of the size of the flocs. An examination of the variability of these parameters shows the need for testing site-specific sediments using a rotating circular flume to achieve a reliable prediction of the RIVFLOC model. Application of the model to various river systems has highlighted the need for including the entrapment process in a cohesive sediment transport model.

Temperature dependent Band gap Correction model using tight-binding approach for UTB device simulations

<div>Ultra-thin body (UTB) devices are being used in many electronic applications operating over a wide range of temperatures. The electrostatics of these devices depends on the band structure of the channel material, which varies with temperature as well as channel thickness. The semi-empirical tight binding (TB) approach is widely used for calculating channel thickness dependent band structure of any material, at a particular temperature, where TB parameters are defined. For elementary semiconductors like Si, Ge and compound semiconductors like GaAs, these TB parameters are generally defined at only 0 K and 300 K. This limits the ability of the TB approach to simulate the electrostatics of these devices at any other intermediate temperatures.</div><div>In this work, we analyze the variation of band structure for Si, Ge and GaAs over different channel thicknesses at 0 K and 300 K (for which TB parameters are available), and show that the band curvature at the band minima has minor variation with temperature, whereas the change of band gap significantly affects the channel electrostatics. Based on this finding, we propose an approach to simulate the electrostatics of UTB devices, at any temperature between 0 K and 300 K, using TB parameters defined at 0 K, along with a suitable channel thickness and temperature dependent band gap correction. </div>

Explanation and Application of the Evolving Contact Traction Fields in Shallow Foundation Systems

The present paper provides a qualitative discussion of the evolution of contact traction fields beneath rigid shallow foundations resting on granular materials. A phenomenological similarity is recognized in the measured contact traction fields of rigid footings and at the bases of sandpiles. This observation leads to the hypothesis that the stress distributions are brought about by the same physical phenomena, namely the development of arching effects through force chains and mobilized intergranular friction. A set of semi-empirical equations are suggested for the normal and tangential components of this contact traction based on past experimental measurements and phenomenological assumptions of frictional behaviors at the foundation system scale. These equations are then applied to the prescribed boundary conditions for the analysis of the settlement, resistance, and stress fields in supporting granular materials beneath the footing. A parametric sensitivity study is performed on the proposed modelling method, highlighting solutions to the boundary-value problems in an isotropic, homogeneous elastic half-space.

Temperature dependent Band gap Correction model using tight-binding approach for UTB device simulations

<div>Ultra-thin body (UTB) devices are being used in many electronic applications operating over a wide range of temperatures. The electrostatics of these devices depends on the band structure of the channel material, which varies with temperature as well as channel thickness. The semi-empirical tight binding (TB) approach is widely used for calculating channel thickness dependent band structure of any material, at a particular temperature, where TB parameters are defined. For elementary semiconductors like Si, Ge and compound semiconductors like GaAs, these TB parameters are generally defined at only 0 K and 300 K. This limits the ability of the TB approach to simulate the electrostatics of these devices at any other intermediate temperatures.</div><div>In this work, we analyze the variation of band structure for Si, Ge and GaAs over different channel thicknesses at 0 K and 300 K (for which TB parameters are available), and show that the band curvature at the band minima has minor variation with temperature, whereas the change of band gap significantly affects the channel electrostatics. Based on this finding, we propose an approach to simulate the electrostatics of UTB devices, at any temperature between 0 K and 300 K, using TB parameters defined at 0 K, along with a suitable channel thickness and temperature dependent band gap correction. </div>

Phase Properties of Different HfO2 Polymorphs: A DFT-Based Study

Background: Hafnium Dioxide (HfO2) represents a hopeful material for gate dielectric thin films in the field of semiconductor integrated circuits. For HfO2, several crystal structures are possible, with different properties which can be difficult to describe in detail from an experimental point of view. In this study, a detailed computational approach has been shown to present a complete analysis of four HfO2 polymorphs, outlining the intrinsic properties of each phase on the basis of atomistic displacements. Methods: Density functional theory (DFT) based methods have been used to accurately describe the chemical physical properties of the polymorphs. Corrective Hubbard (U) semi-empirical terms have been added to exchange correlation energy in order to better reproduce the excited-state properties of HfO2 polymorphs. Results: the monoclinic phase resulted in the lowest cohesive energy, while the orthorhombic showed peculiar properties due to its intrinsic ferroelectric behavior. DFT + U methods showed the different responses of the four polymorphs to an applied field, and the orthorhombic phase was the least likely to undergo point defects as oxygen vacancies. Conclusions: The obtained results give a deeper insight into the differences in excited states phenomena in relation to each specific HfO2 polymorph.

Laboratory Research on Polarized Optical Properties of Saline-Alkaline Soil Based on Semi-Empirical Models and Machine Learning Methods

Currently, soil salinization is a serious problem affecting agricultural production and human settlements. Remote sensing techniques have the advantages of a large monitoring range, rapid acquisition of information, implementation of dynamic monitoring, and low impact on the ground surface. Over the past two decades, many semi-empirical bidirectional polarized distribution function (BPDF) models have been proposed to accurately calculate the polarized reflectance (Rp) on the soil surface. Although there have been some studies on the BPDF model based on traditional machine learning methods, there is a lack of research on the BPDF model based on deep learning, especially using laboratory measurement spectrum data as the processing object, with limited research results. In this paper, we collected saline-alkaline soil in the field as the observation object and measured the Rp at multiple angles in the laboratory environment. We used semi-empirical models (the Nadal–Bréon model, Litvinov model, and Xie–Cheng model) and machine learning methods (support vector regression, random forest, and deep neural networks regression) to simulate and predict the surface Rp of saline-alkaline soils and compare them with experimental results. The measured values of the laboratory are compared and fitted, and the root mean squared error, R-squared, and correlation coefficient are calculated to express the prediction effect. The results show that the predictions of the BPDF model based on machine learning methods are generally better than those of the semi-empirical BPDF model, which is improved by 3.06% at 670 nm and 19.75% at 865 nm. The results of this study also provide new ideas and methods based on deep learning for the prediction of Rp on the surface of saline-alkaline soils.

Integration of Semi-Empirical and Artificial Neural Network (ANN) for Modeling Lithium-Ion Electrolyte Systems Dynamic Viscosity

Dynamic viscosity is a key characteristic of electrolyte performance in lithium-ion battery. This work introduces a one parameter semi-empirical model and artificial neural network (ANN) to predict the viscosity of salt-free solvent mixtures and relative viscosity of Li-ion electrolyte solutions (lithium salt + solvent mixture), respectively. Data used in this study were obtained experimentally, in addition to data extracted from literature. There are seven inputs of the ANN model: salt concentration, electrolyte temperature, salt anion size, solvent melting and boiling temperatures, solvent dielectric constant, and solvent dipole moment. Different configuration of the ANN was tested and the configuration with least error was chosen. The results show the capability of the semi-empirical model to predict the viscosity with an overall mean absolute percentage error (MAPE) of 2.05% and 3.17% for binary and tertiary mixtures, respectively. The ANN model predicted the relative viscosity of electrolyte solutions with MAPE of 4.86%. The application of both models in series, resulted predicted the viscosity with MAPE 2.3%, although the ANN MAPE alone is higher than this value. Thus, this work highlights the promise of using predictive models to complement physical approaches and to provide an effective way to perform initial screening on Li-ion electrolytes.