Open Access Publications
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Open access publications by faculty, staff, postdocs, and graduate students from the Delaware Energy Institute.
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Item OpenMKM: An Open-Source C++ Multiscale Modeling Simulator for Homogeneous and Heterogeneous Catalytic Reactions(Journal of Chemical Information and Modeling, 2023-06-12) Medasani, Bharat; Kasiraju, Sashank; Vlachos, Dionisios G.Microkinetic modeling is invaluable for coupling “microscale” atomistic data with “macroscale” reactor observables. We introduce an Open-source Microkinetic Modeling (OpenMKM) multiscale mean-field microkinetics modeling toolkit targeting mainly heterogeneous catalytic reactions but applies equally to homogeneous reactions. OpenMKM is a modular, object-oriented, C++ software, built on top of the robust open-source Cantera built mainly for homogeneous reactions. Reaction mechanisms can be input from human-readable files or automatic reaction generators, avoiding tedious work and errors. The governing equations are also built automatically, unlike Matlab and Python manual implementations, providing speed and error-free models. OpenMKM has built-in interfaces with numerical software, SUNDIALS, for solving ordinary differential equations and differential-algebraic equations. Users can choose various ideal reactors and energy balance options, such as isothermal, adiabatic, temperature ramp, and an experimentally measured temperature profile. OpenMKM is tightly integrated with pMuTT for thermochemistry input file generation from density functional theory (DFT), streamlining the workflow from DFT to MKM and eliminating tedious work and human errors. It is also seamlessly integrated with the RenView software for visualizing the reaction pathways and performing the reaction path or flux analysis (RPA). OpenMKM includes local sensitivity analysis (LSA) by solving the augmented system of equations or using the one-at-a-time finite difference (first or second order) method. LSA can identify not only kinetically influential reactions but also species. The software provides two techniques for large reaction mechanisms for which LSA is too expensive to run. One is the Fischer Information Matrix, which is approximate but comes at nearly zero cost. The other is a new method that we term RPA-guided LSA, which is a finite difference-based method but uses RPA to select kinetically relevant reactions instead of exploiting the entire reaction network. Users can quickly set up and conduct microkinetic simulations without writing code. The user inputs are conveniently divided into reactor setup files and thermodynamic and kinetic definition files to set up different reactors. The source code and documentation are openly available at https://github.com/VlachosGroup/openmkm.Item Dynamic Electrification of Dry Reforming of Methane with In Situ Catalyst Regeneration(ACS Energy Letters, 2023-02-10) Yu, Kewei; Wang, Cong; Zheng, Weiqing; Vlachos, Dionisios G.We report the design and performance of a rapid pulse Joule heating (RPH) reactor with an in situ Raman spectrometer for highly endothermic, reversible reactions. We demonstrate it for methane dry reforming over a bimetallic PtNi/SiO2 catalyst that shows better performance than its monometallic counterparts. The catalyst temperature ramp rate can reach ∼14000 °C/s, mainly owing to the low thermal mass and resistivity of the heating element. Joule heating elements afford temperatures unachievable by conventional technology to enhance performance and more than double the energy efficiency. Dynamic electrification can increase syngas productivity and rate. Extensive characterizations suggest that pulse heating creates an in situ catalyst regeneration strategy that suppresses coke formation, sintering, and phase segregation, resulting in improved catalyst stability, under many conditions. Potentially driven by renewable electricity, the RPH can provide superb process advantages for high-temperature endothermic reactions and lead to negative carbon emissions.Item Predicting band gaps and band-edge positions of oxide perovskites using density functional theory and machine learning(Physical Review B, 2022-10-28) Li, Wei; Wang, Zigeng; Xiao, Xia; Zhang, Zhiqiang; Janotti, Anderson; Rajasekaran, Sanguthevar; Medasani, BharatDensity functional theory (DFT) within the local or semilocal density approximations, i.e., the local density approximation (LDA) or generalized gradient approximation (GGA), has become a workhorse in the electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. The accurate prediction of band gaps using first-principles methods is time consuming, requiring hybrid functionals, quasiparticle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we use DFT and machine learning techniques to correct band gaps and band-edge positions of a representative subset of ABO3 perovskite oxides. Relying on the results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band-gap correction of ∼1.5 eV for this class of materials, where ∼1eV comes from downward shifting the valence band and ∼0.5eV from uplifting the conduction band. The main chemical and structural factors determining the band-gap correction are determined through a feature selection procedure.Item Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective(Reaction Chemistry and Engineering, 2022-01-25) Batchu, Sai Praneet; Hernandez, Borja; Malhotra, Abhinav; Fang, Hui; Ierapetritou, Marianthi; Vlachos, Dionisios G.We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. We discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.Item Improved slit-shaped microseparator and its integration with a microreactor for modular biomanufacturing(Green Chemistry, 2021-04-30) Bhattacharyya, Souryadeep; Desir, Pierre; Prodinger, Sebastian; Lobo, Raul F.; Vlachos, Dionisios G.Modular and distributed biomanufacturing requires continuous flow microreactors integrated with efficient separation units operating at comparable time scales: biphasic reactive extraction of 5-hydroxymethyl furfural (HMF) by fructose dehydration is an excellent example. The liquid–liquid extraction (LLE) and fast reaction kinetics in biphasic microchannels can immensely benefit from a downstream microseparator enabling separation of an HMF-rich organic extract and an aqueous raffinate. Here we demonstrate the successful implementation of an effective slit-shaped microseparator for eleven organic-water biphasic systems. The microseparator successfully separates six of these over reasonable flow rates. The ratio of capillary and hydraulic pressures qualitatively rationalizes the separation performance, while a transition to non-segmented flow patterns correlates with performance deterioration. Acids and salts, integral parts of the chemistry, significantly expand the flow rates for efficient separation enabling a broader slate of organic solvents. For the MIBK/water biphasic system, we demonstrate perfect separation performance over a 16-fold variation in the organic to aqueous flow ratio. Here we also integrate the microseparator and extractive microreactor into a modular system and achieve an HMF yield of up to 93% – the highest reported fractional HMF productivity of 27.9 min−1 – at an ultrashort residence time of 2 s. This unprecedented performance is maintained over a 50-fold fructose concentration range and is stable with time-on-stream. This microseparator exhibits a ten-fold reduction in separation time and substantial energy savings over conventional decanters. As such, it holds promise for continuous process intensification and modular biomanufacturing.