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The Porosoff group focuses on developing new catalysts for upgrading C1 and C2 resources for efficient energy storage and low-cost production of plastics, chemicals and fuels. Understanding the relationships between chemical reactivity and catalyst electronic/structure properties are extremely important for developing catalysts that exploit particular reaction pathways. This approach requires controlled synthesis of catalysts combined with in situ techniques and theoretical calculations. In particular, target areas of research are three types of catalytic reactions for improved shale gas utilization and lowering CO2 emissions:
Catalyst development for CO2 hydrogenation
Selective synthesis of light olefins from CO and H2
Accelerating decarbonization by representing catalysts with natural language
Experimental work combines an mix of catalyst synthesis and characterization, reactor studies and in situ spectroscopy.
Catalysts for CO2 conversion to plastics, chemicals and fuels
Catalysts for CO2 conversion to plastics, chemicals and fuels
Through a variety of catalytic reactions, waste CO2 can be converted to valuable oxygenates, specialty chemicals and hydrocarbons for synthetic fuel. There are many examples in literature of conversion of CO2 into CO, methane (CH4) and methanol; however, research of direct CO2 hydrogenation to light olefins (C2-C4 unsaturated hydrocarbons ie. ethylene and propylene) is limited. Converting CO2 to light olefins offers a means to store energy and convert waste CO2 into plastics, chemicals and fuels; however, the pathway is challenging because of the thermodynamic and kinetic limitations of activating the highly stable CO2 molecule.
The current approach for CO2 hydrogenation to hydrocarbons uses two reactors in series with high temperature reverse water-gas shift (RWGS, CO2 + H2 ↔ CO + H2O), followed by CO hydrogenation via Fischer-Tropsch (FT, nCO + 2nH2 → CnH2n + nH2O) synthesis. While there are groups investigating CO2hydrogenation through direct Fischer-Tropsch (CO2-FT) over a single catalyst, the scientific challenges of precisely controlling selectivity to the desired products remain. The Porosoff group is investigating novel, dual-functional catalysts to selectively produce a tight distribution of hydrocarbons from CO2.
Selective synthesis of light olefins from CO and H2
Selective synthesis of light olefins from CO and H2
Light olefins (C2-C4) are important raw materials for manufacturing plastics, chemicals and synthetic fuels. Olefin synthesis mainly proceeds through steam cracking of naphtha, ethane and propane. Another route is coal gasification with O2 and H2O to make synthesis gas (CO + H2), which is converted to methanol over Cu/ZnO/Al2O3 catalysts. Methanol is then reacted over zeolites to synthesize olefins. Recent research efforts focus on direct conversion of synthesis gas to olefins through Fischer-Tropsch (FTO), bypassing the methanol intermediate. However, achieving high selectivity towards light olefins is difficult, because the products generally follow the Anderson-Schulz-Flory (ASF) distribution, pictured left.
Future directions for designing selective catalysts will explore a combination of techniques to synthesize high surface area, mesoporous supports with effective promoters to control the size distribution of olefins. Experiments use a combination of in situ X-ray absorption fine structure (XAFS) and Fourier transform infrared (FTIR) spectroscopy to understand the effect of promoters on catalyst structure and electronic properties.
Accelerating decarbonization by representing catalysts with natural language
Accelerating decarbonization by representing catalysts with natural language
Artificial intelligence (AI)-directed design of experiments is poised to transform chemical catalysis. Instead of using traditional structural and electronic features of catalysts characterized by expensive and difficult experiments, the project leverages recent progress in large language models (LLMs) to represent catalysts by the text of synthesis procedures and reaction conditions. The approach has potential to accelerate discovery of earth-abundant, active, and selective catalysts to bring rise to an emerging carbo-chemical industry that makes low-cost products from carbon dioxide (CO2). The broader impacts of this project will serve two purposes: (1) Educate and excite students about LLMs and AI for materials discovery; and (2) demonstrate that language-based representations are universal and can be applied to any process that is expressed with language. The LLM methodology accelerates catalyst predictions with natural language processing and Bayesian optimization (BO), while leveraging chemical intuition to develop hypotheses that guide the size and composition of the experimental space. The project focuses initially on understanding and developing trimetallic catalysts for the reverse water-gas shift (RWGS) reaction. Trimetallic catalysts are more difficult to characterize than bimetallic catalysts, making them a good fit for an approach that does not need the catalyst structure to be predictive. By representing the catalysts with language, physical-chemical details such as those due to catalyst restructuring during the reaction, are captured instead by the experimental conditions as included in the text-based representation. Beyond the core approach of utilizing language to identify novel catalysts for RWGS reaction, the project will assess the effects of experimental artifacts and irreproducible results on the model’s performance. The language-based workflow will be integrated with existing computation
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