Graduate Seminar Series: Three Easy PiecesGraduate Seminar Series

por Prof. J. Richard Elliott (The University of Akron, EUA)

quinta-feira 5 de mar. de 2026 15:00 → 17:30 America/Sao_Paulo

Auditório Martin Aznar

Richard Elliott is a 1980 BS graduate from Newport College in Virginia with a double major in math and chemistry.  He obtained an MS in Chemical Engineering from Va Tech in 1982 and a PhD in Chemical Engineering from Penn State in 1985 working on the API data book project.  He started at the University of Akron in 1986 and rose to professor in 2000.  He served as a Fulbright Lecturer at Bogazici University in Istanbul from 1994-1995.  He is a co-author of Introductory Chemical Engineering Thermodynamics, the Thermodynamics section of Perry's Handbook, and an author of roughly 100 refereed publications on molecular simulations and thermodynamics. He transitioned to "emeritus" status in 2018 so he could work full- time on The Properties of Gases and Liquids, 6ed, published in 2023. He served as a visiting professor at KFUPM in Saudia Arabia from 2024-2025. He is serving as a 2026 Fulbright Scholar at University of Campinas, Brazil. His research focuses on transforming statistical mechanics and molecular simulation into practical engineering tools with applications to supercritical fluids and process simulation. Hydrogen bonding, critical phenomena, and chain molecules are particular interests. Experimental research has focused on demonstrating the benefits of advanced models in phase behavior of supercritical fluids and hydrogen bonding mixtures. He has been awarded with the Dow/ASEE outstanding young faculty award and is the winner of the 2004 AIChE International Fluid Property Simulation Challenge in the category of vapor pressure prediction.

A Brief Overview of Findings from PGL6ed
The Properties of Gases and Liquids has been an essential reference for thermodynamics modeling since its introduction in 1958. During the 20-year interim from 5ed to 6ed, models took on many more molecular aspects in practical ways. For example, predictions of ideal gas properties like heat capacities and heats of formation can now implement quantum mechanical methods. Predictions of vapor pressure and thermal properties now implement molecular simulation models.
Predictions of mixture properties like phase equilibria can use advanced methods like Wertheim’s theory and SAFT. When newer methods are compared to traditional methods using the same standard databases, the newer methods show deviations that are roughly 3x smaller in most cases. For vapor-liquid equilibria, however, the traditional method of using a customized cubic equation with a 2- parameter Gibbs-Excess mixing rule is still quite competitive.

Education in the Age of AI: Highlights from a Presentation by Barbara Oakley

You may recognize Barbara Oakley’s name from the forward she wrote to Felder and Brent’s book on “Teaching Stem.” She is a well-known neuroscientist who has given lecturers all over the world. In August 2025, she presented a seminar at KFUPM that was quite edifying. She brought together her work on neuroscience and brain function with its interface to machine learning and artificial intelligence (AI). She made several points that should be at the forefront of every engineering educator’s mind as we transition to the age of AI.

Evaluating Square Gradient Theory for Correlation and Prediction of Surface Tension
Square gradient theory (SGT) is a specific form of classical density functional theory (CDFT) for the treatment of interfacial properties like surface tension and interfacial tension. 1  Recently, Mejia et al. 2  published an open-source Python library (SGTPy) implementing SGT. In this work, we incorporate the ESD 3–5  equation of state (EOS) into the open-source SGTPy library. The ESD EOS has been reviewed and discussed in the latest edition of The Properties of Gases and Liquids (PGL6ed). 6  Results were shown to be of satisfactory accuracy for correlating and predicting vapor-liquid, liquid-liquid, and solid-liquid equilibria. Also, in PGL6ed, a database of measured surface tensions for roughly 1000 compounds was applied to the evaluation of six implementations of the parachor and corresponding states methods, but no CDFT or EOS methods were considered. We apply a similar database in the present work and extend the comparisons to include SGT with the ESD EOS. Finally, we develop methods for predicting the influence parameters from molecular structure as an alternative means of predicting surface tension.

Deviations of the SGT-ESD model are about 20% smaller than the best available models in each molecular class.

References:
(1)      Rowlinson, J. S.; Widom, B. Molecular Theory of Capillarity; Courier Corporation, 2013.
(2)      Mejia, A.; Müller, E. A.; Chaparro-Maldonado, G. SGTPy: A Python Code for Calculating the Interfacial Properties of Fluids Based on the Square Gradient Theory Using the SAFT-VR Mie Equation of State. J. Chem. Inf. Model. 2021, 61 (3), 1244–1250. https://doi.org/10.1021/acs.jcim.0c01324.
(3)      Elliott, J. R.; Suresh, S. J.; Donohue, M. D. A Simple Equation of State for Nonspherical and Associating Molecules. Ind. Eng. Chem. Res. 1990, 29 (7), 1476–1485. https://doi.org/10.1021/ie00103a057.
(4)      Suresh, S. J.; Elliott, J. R. Multiphase Equilibrium Analysis via a Generalized Equation of State for Associating Mixtures. Ind. Eng. Chem. Res. 1992, 31, 2783–2794. https://doi.org/10.1021/ie00012a025.
(5)      Elliott, J. R. Efficient Implementation of Wertheim’s Theory for Multicomponent Mixtures of Polysegmented Species. Industrial and Engineering Chemistry Research 1996, 35 (5), 1624–1629. https://doi.org/10.1021/ie950566+.
(6)      Elliott, J. R.; Diky, V.; Knotts, T. A.; Wilding, W. Vincent. The Properties of Gases and Liquids, 6th ed.; McGraw-Hill Education, 2023.