(4de) Computational Engineering Towards Sustainable High-Pressure Processing and Intelligent Characterization of Porous Materials

High-pressure processing is an essential step in chemical, food, pharmaceutical, materials and many other industries. The conventional solution with mechanical compression of a bulk phase is energy-intensive and concerned with many safety issues. One emerging route is carrying out such process inside nanopores under ambient conditions. Recent discoveries include the occurrence of high-pressure chemical reactions and the formation of high-pressure solid phases inside nanopores under ambient pressure. This ‘green’ route to the high-pressure manufacturing is safe, efficient, energy-saving, and sustainable. The underlying mechanism in the molecular level, however, is still unclear, due in part to the ambiguity of classical thermodynamic concept (e.g., pressure) at nanoscale. Computational and theoretical methods are central in the efforts to understand such process, elucidate the design rules for the optimal pore environment (surface chemistry, pore size etc.), and further advance the field which is still in its infancy. The other associated challenge, in general, is the experimental characterization of nanopores, especially for porous materials with various types of surface roughness and complicated network connections. The future evolution of the characterization techniques requires a holistic understanding of fluids confined in nanopores. My independent research group will address these challenges by focusing on the following aspects: 1) Developing and implementing computational and theoretical methods to understand the physics behind the induced high pressure inside the pore, and to identify appropriate systems for reutilization of CO₂ and wastewater. 2) Developing interpretable machine learning models with rigorous foundations from statistical mechanics for fast prediction of adsorption, with strong focus on establishing a general, reliable, and intelligent platform for characterization of porous materials. 3) Utilizing the computational tools (mainly Monte Carlo simulation and molecular dynamics), statistical mechanical theory, and experimental techniques to understand and measure the microscopic thermodynamic properties, such as microscopic pressure tensor, leveraging the discovered knowledge for the previous two aspects.

My previous research training provides me with a solid foundation for future proposed research. In my Ph.D. research with Professor Keith Gubbins at North Carolina State University, I developed two novel theoretical models to account for the surface heterogeneities of solid substrate. For surfaces with energetic heterogeneities, I developed a conformal sites theory [1] which simplifies the theoretical representations of both van der Waals and electrostatic interactions between non-spherical molecules with the energetic sites on the surface. For surfaces with geometric roughness, I developed a 2D effective solid-fluid potential using the free-energy-averaging method [2]. Both theoretical models can be further implemented with molecular simulation, classical density functional theory, and machine learning for fast prediction of adsorption and for characterization of heterogeneous surface. Apart from the studies associated with heterogeneous surfaces, I also investigated the microscopic pressure tensor in thin adsorbed films. The study is motivated by high-pressure phenomena in confined fluids reported in both experiments and molecular simulations. The lack of uniqueness of the microscopic pressure and experimental methods to measure the in-layer pressure has been a major hinderance to progress in the field. With that in mind, I showed that, for the first time, it is possible to define a “unique” coarse-grained pressure tensor [3]. To measure this unique pressure from experiment, a 2D route was proposed, based on a 2D equation of state, taking temperature, layer density and thickness as experimental inputs [4]. I also solved a serious barrier to the computation of the pressure tensor for fluids of molecules and ions having long-range (e.g., Coulombic) interactions [5]. My postdoctoral research with Prof. Randall Snurr at Northwestern University focused on the development of physically inspired features for machine learning to predict the adsorption in crystalline and amorphous porous materials.

Selected Publications (16 total, 7 first-author)

[1] K. Shi, E. E. Santiso, and K. E. Gubbins, “Conformal Sites Theory for Adsorbed Films on Energetically Heterogeneous Surfaces,” Langmuir, vol. 36, no. 7, pp. 1822–1838, Feb. 2020.

[2] K. Shi, E. E. Santiso, and K. E. Gubbins, “Bottom-Up Approach to the Coarse-Grained Surface Model: Effective Solid–Fluid Potentials for Adsorption on Heterogeneous Surfaces,” Langmuir, vol. 35, no. 17, pp. 5975–5986, Apr. 2019.

[3] K. Shi, E. E. Santiso, and K. E. Gubbins, “Can we define a unique microscopic pressure in inhomogeneous fluids?,” J. Chem. Phys., vol. 154, no. 8, p. 084502, Feb. 2021.

[4] K. Shi, K. Gu, Y. Shen, D. Srivastava, E. E. Santiso, and K. E. Gubbins, “High-density equation of state for a two-dimensional Lennard-Jones solid,” J. Chem. Phys., vol. 148, no. 17, p. 174505, May 2018.

[5] K. Shi, Y. Shen, E. E. Santiso, and K. E. Gubbins, “Microscopic Pressure Tensor in Cylindrical Geometry: Pressure of Water in a Carbon Nanotube,” J. Chem. Theory Comput., vol. 16, no. 9, pp. 5548–5561, Sep. 2020.

Teaching Interests:

My teaching philosophy can be summarized as “Teaching as a student”. That means I believe that teaching can be more effective and engaging if the instructor considers more from learners’ perspectives. This learner-centered teaching requires understanding of the learners’ needs and background, engaging students to be active learners, and improving teaching by collecting students’ feedback.

I arrived at this teaching strategy through more than 5-year experiences as teaching assistant and guest lecturer, and through active interaction with both students and instructors in the class. During my Ph.D., I have presented 18 guest lectures in Graduate Thermodynamics to more than 200 graduate students, most of whom are first-year Ph.D. students. I have also had chances to present 3 guest lectures in Undergraduate Thermodynamics. Based on my teaching, I received Praxair Exceptional Teaching Assistant Award (2016) from Chemical Engineering Department, and Mentored Teaching Fellowships (2016-2018) from College of Engineering at North Carolina State University. In 2020, I was invited to give a guest lecture on molecular modeling at Carnegie Mellon University. These experiences had prepared me to build a diverse, energetic, and effective learning environment in the class.

My teaching expertise includes undergraduate and graduate level thermodynamics, transport, reaction engineering and process modeling (advanced mathematics). Outside the core courses, I would like to bring cutting-edge machine learning and data science to both undergraduate and graduate elective courses. I believe a qualified chemical engineer should be able to leverage diverse tools and expertise to solve practical problems and build a better future for humanity.

Selected Awards

2020 James K. Ferrell Outstanding Ph.D. Graduate Award, NC State University, 2021.

AIChE’s CoMSEF Graduate Student Award, AIChE Annual Meeting, Orlando, FL, USA, 2019.

FOMMS Poster Prize, Foundations of Molecular Modeling and Simulation (FOMMS), Delavan, WI, USA, July 18, 2018.

Outstanding Poster Presentation Prize, 8th International Workshop on Characterization of Porous Materials (CPM8), Delray Beach, FL, USA, May 7, 2018.

Mentored Teaching Fellowships (x3), College of Engineering, NC State University, 2016 – 2018.

Praxair Exceptional Teaching Assistant Award, NC State University, 2016.