Thermodynamic Modeling and Mechanical Properties of Mg-Zn-{Y, Ce} Alloys: Review

Magnesium alloys are a strong candidate for various applications in automobile and aerospace industries due to their low density and specific strength. Micro-alloying magnesium with zinc, yttrium, and cerium enhances mechanical properties of magnesium through grain refinement and precipitation hardening. In this work, a critical review of magnesium-based binary systems including Mg-Zn, Mg-Y, Mg-Ce, Zn-Y, and Zn-Ce is presented. Based on the CALPHAD approach and first-principles calculations, thermodynamic modeling of Mg-Zn-Y and Mg-Zn-Ce ternary phase diagrams have been summarized. The influence of micro-alloying (yttrium and cerium) on the mechanical properties of magnesium is discussed. A comparison between mechanical properties of magnesium commercial alloys and magnesium–zinc–{yttrium and cerium} have been summarized in tables.

Developing Practical Models of Complex Salts for Molten Salt Reactors

Molten salt reactors (MSRs) utilize salts as coolant or as the fuel and coolant together with fissile isotopes dissolved in the salt. It is necessary to therefore understand the behavior of the salts to effectively design, operate, and regulate such reactors, and thus there is a need for thermodynamic models for the salt systems. Molten salts, however, are difficult to represent as they exhibit short-range order that is dependent on both composition and temperature. A widely useful approach is the modified quasichemical model in the quadruplet approximation that provides for consideration of first- and second-nearest-neighbor coordination and interactions. Its use in the CALPHAD approach to system modeling requires fitting parameters using standard thermodynamic data such as phase equilibria, heat capacity, and others. A shortcoming of the model is its inability to directly vary coordination numbers with composition or temperature. Another issue is the difficulty in fitting model parameters using regression methods without already having very good initial values. The proposed paper will discuss these issues and note some practical methods for the effective generation of useful models.

Strategies for High-Temperature Corrosion Simulations of Fe-Based Alloys Using the Calphad Approach: Part I

The environmental degradation of materials at high temperatures limits the useful life of different industrial components and hinders the development of more economical and environmentally friendly processes for the energy production. Despite the importance of this phenomena, a model to predict lifetime of materials that degrade due to high-temperature corrosion has up till now been lacking due to limitations of the computational possibilities and the complex nature of oxidation. In the present work we develop some strategies to model high-temperature corrosion in Fe-based alloys using the Calphad (Calculation of Phase Diagrams) approach. It is proposed that kinetic-based simulations for oxidation of Al and Cr can accurately represent the lifetime of the protective layers in FeCrAl and FeCr alloys at different temperatures in air. The oxide systems are in addition investigated by equilibrium calculations. The corrosion mechanisms of FeCr and FeCrAl alloys are discussed based on theoretical and experimental knowledge.

Thermodynamics Controlled Sharp Transformation from InP to GaP Nanowires via Introducing Trace Amount of Gallium

Growth of high-quality III–V nanowires at a low cost for optoelectronic and electronic applications is a long-term pursuit of research. Still, controlled synthesis of III–V nanowires using chemical vapor deposition method is challenge and lack theory guidance. Here, we show the growth of InP and GaP nanowires in a large area with a high density using a vacuum chemical vapor deposition method. It is revealed that high growth temperature is required to avoid oxide formation and increase the crystal purity of InP nanowires. Introduction of a small amount of Ga into the reactor leads to the formation of GaP nanowires instead of ternary InGaP nanowires. Thermodynamic calculation within the calculation of phase diagrams (CALPHAD) approach is applied to explain this novel growth phenomenon. Composition and driving force calculations of the solidification process demonstrate that only 1 at.% of Ga in the catalyst is enough to tune the nanowire formation from InP to GaP, since GaP nucleation shows a much larger driving force. The combined thermodynamic studies together with III–V nanowire growth studies provide an excellent example to guide the nanowire growth.

Thermodynamic modelling of the binary indium-lithium system, a promising li-ion battery material

The binary In - Li system is a promising Li-ion battery anode material as well as a part of the important ternary Ge - In - Li system. The thermodynamic descriptions of metallic systems are widely used to retrieve information necessary for alloy applications. In this work, a thermodynamic model of a binary indium - lithium system prepared by the Calphad approach is proposed. The liquid phase was described by an associate model, and the solid phases determined by the ab-initio calculation were included in thermodynamic modeling. The obtained set of self-consistent thermodynamic parameters well reproduces the available experimental data and enables further calculations of multi-component systems. A good agreement between the calculations and the available experimental data was found. The proposed model can be used for further descriptions of ternary systems.

Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence

In this work, we studied a Ti-Nb-Zr-Sn system for exploring novel composition and temperatures that will be helpful in maximizing the stability of β phase while minimizing the formation of α” and ω-phase. The Ti-Nb-Zr-Sn system is free of toxic elements. This system was studied under the framework of CALculation of PHAse Diagram (CALPHAD) approach for determining the stability of various phases. These data were analyzed through artificial intelligence (AI) algorithms. Deep learning artificial neural network (DLANN) models were developed for various phases as a function of alloy composition and temperature. Software was written in Python programming language and DLANN models were developed utilizing TensorFlow/Keras libraries. DLANN models were used to predict various phases for new compositions and temperatures and provided a more complete dataset. This dataset was further analyzed through the concept of self-organizing maps (SOM) for determining correlations between phase stability of various phases, chemical composition, and temperature. Through this study, we determined candidate alloy compositions and temperatures that will be helpful in avoiding/minimizing formation of α” and ω-phase in a Ti-Zr-Nb-Sn system. This approach can be utilized in other systems such as ω-free shape memory alloys. DLANN models can even be used on a common Android mobile phone.