(4bd) Dynamic Catalysis over Mixed Metal Oxides for Clean Energy and Sustainability

Catalysis is the powerhouse of chemical industries and modern-day energy harvesting technologies. The need of the hour to tackle the ever-rising global energy demand, and the grave consequences on the environment is a significant shift to greener technologies and energy efficient catalytic reactions. Heterogeneous catalysis has long revolved around steady state kinetics and despite being a non-equilibrium process the field has relied heavily on thermodynamic arguments. Theoretical and experimental approaches probed the most stable catalyst surface states, reaction intermediates and attributed the catalytic throughput to those most dominant thermodynamic states. However, heterogeneous catalysis is inherently a dynamic process and statistical ensemble phenomena over a myriad of surface catalytic sites and intermediates. Advances in operando techniques have recently been able to showcase this dynamic nature of catalysts – be it the reaction dependent evolution of surface composition, transformation of metal clusters to dispersed single metal catalytic sites, or the encapsulation of most surface dominant metals by the support materials. It must be noted here that thermodynamically dominant active sites and intermediates may not be always linked to the kinetically important species. Precise control of these highly active less populated catalytic sites, thus, dictates the key towards enhanced reactivity and addressing the challenge of improved greener technologies for a cleaner environment.

Metal oxides and complex mixed metal oxides are critically important in this regard. Oxides have been in use for industrially relevant catalytic reactions for decades – as supports, redox platforms, oxygen storage materials and more. While the long-standing appeal of oxides is the material composition tuning capability via stable doping techniques, the essence of oxides for next-generation catalytic processes lies in the tunability of (i) their precise shape, size, surface composition, facets, terminations and defects; (ii) acidity and basicity of surface sites; and (iii) metastable surface intermediates through controlled synthesis techniques and under varying reaction environment. While several groups have studied the effect of each of these catalyst properties for some key reactions of interest, the scientific community still lacks the knowledge of the behavior of complex oxide phases, under transient reaction conditions. While simulation results are broadly dependent on assumptions and heuristic approaches, most of the experimental investigations are limited to probing the most dominant species. The intricate correlations of dynamic evolution of defect states, oxide interfaces, relevant atomistic descriptors, and the resultant macroscopic catalytic rates are not well documented either. The recent advances in machine learning (ML) is of prime importance here, towards capturing this relationship of catalytic descriptors – reaction condition – turnover frequency, and henceforth, be able to predict ways of harnessing the most efficient catalytic rates. To address these current challenges, I present my future research focus as the following.

(A) ML approaches towards rational design of oxide catalysts for clean energy harvesting.

• A holistic search for the best reaction-catalyst fit (accounting for ab-initio descriptors and experimentally reported surface characteristics and reaction rates under varying conditions) for enhanced CH₄ and CO₂ conversion performance under dynamic conditions.

(B) Elucidating the dynamic behavior of metal oxides under reaction conditions.

• Mechanistic understanding of catalytic reactions over dynamically evolving surface terminations and transient oxide/precious metal interfaces under varying redox conditions.

(C) Enhanced catalytic activity through strategic reaction parameter modulation.

• Tailoring the reaction modulation parameters designed for metal oxides, with the best reaction-catalyst fit, towards harvesting stable and sustainable catalytic rates exceeding the Sabatier maximum.

In this pursuit, I am planning to follow a combined computational and experimental approach, where my group contributes towards the in-silico design of materials, theoretical insights of the dynamic evolution of active sites and mechanistic pathways with experimental support from collaborators across departments and National Laboratories.

Research Experiences:

(A) PhD in Chemical Engineering – University of South Florida [Aug 2013 – Aug 2018]

(advisors: Prof. John N. Kuhn and Prof. Venkat R. Bhethanabotla)

• Exploring the reducibility properties of various surface terminations and facets of mixed perovskite oxides (La_(1-x)Sr_xFe_(1-y)Co_yO₃) for thermochemical CO₂ conversion application. [1]
• Prediction of perovskite oxides for the purpose of low temperature CO₂ conversion via reverse water gas shift chemical looping process – a combined ab-initio and experimental approach. [2, 3]
• Effect of support interactions on CO₂ conversion capabilities of binary metal oxide supported La₇₅Sr_0.25FeO₃ perovskite oxide. [4, 5]
• Probing the surface acidity of precious metal-Ni-Mg/ceria-zirconia catalysts for low temperature methane reforming reactions. [6, 7]
• Metal oxynitrides for photocatalytic and optoelectronic applications. [8, 9]

(B) Postdoctoral fellow – University of Houston [Dec 2018 – current]

(advisors: Prof. Lars C. Grabow, Prof. Michael P. Harold)

• Stability of single atom alloys through ab-initio calculations and machine-learning approaches. [10]
• Metal migration and dynamic surface catalyst site modification on precious metal-spinel oxide monolith catalyst. [11]
• Methane activation over single atom transition metal promoted Pt(211) and Pd(211) – the role of dopant site specificity, electronic and geometric effects. [12]
• Enhanced reducibility and methane activation performance over dopant induced single atom sites on spinel oxides.
• Unifying methane oxidation descriptor and oxide reducibility descriptor for spinel oxides – prediction of superior methane oxidation catalyst for dynamic operation.

(C) Postdoctoral fellow – Idaho National Lab in collaboration with University of Houston

(advisors: Prof. Lars C. Grabow, Dr. Rebecca Fushimi) – starting Aug 2021

• Ammoxidation of small hydrocarbons under forced dynamic operation.
• Intrinsic spatio-temporal kinetics over bismuth molybdate catalysts using temporal analysis of products (TAP) reactor, coupled with machine-learning techniques.

Teaching Philosophy:

Chemical engineering is such a unique field of science because its principles apply to a vast range of topics and applications. I would love to invoke the interests of students towards these fundamental principles and postulates that govern a plethora of applications in our daily life. Based on my teaching experiences during my graduate studies, a student-centric approach with the use of visual models, student engagement and participation, along with regular feedback and evaluation will be the basis of my teaching philosophy.

Teaching Interests:

Throughout my graduate studies, I have served as teaching assistant for core chemical engineering courses and graduate level elective courses. Frequent lectures in this classes, developing a section of a course for Molecular Thermodynamics, and regular student interactions have been of great help towards developing my teaching philosophy. As much as I would love to teach any core Chemical Engineering courses, I would prefer Chemical Engineering Fundamentals, Materials and Energy Balances, Chemical Reaction Engineering, Thermodynamics, and Transport Processes. In future, I would like to develop an elective course “Fundamentals of Catalysis” revolving around surface science, heterogeneous catalysis, and the computational aspects. This course would provide a fundamental understanding of heterogeneous catalysis, introduce the general practices in catalysis industry and the concepts and theoretical methods to probe catalytic reactions. This course will prepare students for the current research and development challenges, both in academia and in industry.

Selected Publications:

1. Maiti, D.; Daza, Y. A.; Yung, M. M.; Kuhn, J. N.; Bhethanabotla, V. R., Journal of Materials Chemistry A 2016, 4 (14), 5137-5148.
2. Maiti, D.; Hare, B. J.; Daza, Y. A.; Ramos, A. E.; Kuhn, J. N.; Bhethanabotla, V. R., Energy & Environmental Science 2018, 11 (3), 648-659.
3. Hare, B. J.; Maiti, D.; Meier, A. J.; Bhethanabotla, V. R.; Kuhn, J. N., Industrial & Engineering Chemistry Research 2019, 58 (28), 12551-12560.
4. Hare, B. J.; Maiti, D.; Ramani, S.; Ramos, A. E.; Bhethanabotla, V. R.; Kuhn, J. N., Catalysis Today 2019, 323, 225-232.
5. Hare, B. J.; Maiti, D.; Daza, Y. A.; Bhethanabotla, V. R.; Kuhn, J. N., ACS Catalysis 2018, 8 (4), 3021-3029.
6. Elsayed, N. H.; Maiti, D.; Joseph, B.; Kuhn, J. N., Catalysis Letters 2018, 148 (3), 1003-1013.
7. Zhao, X.; Walker, D. M.; Maiti, D.; Petrov, A. D.; Kastelic, M.; Joseph, B.; Kuhn, J. N., Industrial & Engineering Chemistry Research 2018, 57 (3), 845-855.
8. Maiti, D.; Meier, A. J.; Cairns, J.; Ramani, S.; Martinet, K.; Kuhn, J. N.; Bhethanabotla, V. R., Dalton Transactions 2019, 48 (33), 12738-12748.
9. Maiti, D.; Cairns, J.; Kuhn, J. N.; Bhethanabotla, V. R., The Journal of Physical Chemistry C 2018, 122 (39), 22504-22511.
10. Rao, K. K.; Do, Q. K.; Pham, K.; Maiti, D.; Grabow, L. C., Topics in Catalysis 2020, 63 (7-8), 728-741.
11. Chen, P. W., Maiti, D., Liu, R.-F., Grabow, L. C., Harold, M. P., Submitted, Catalysis Science & Technology.
12. Maiti, D., Harold, M. P., Grabow, L. C., In preparation.