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Batchu, Sai Praneet

Publications and source records attributed to Batchu, Sai Praneet.

Selective dehydra-decyclization of cyclic ethers to conjugated dienes over zirconia

ZrO 2 provides high selectivity (>90%) to conjugated pentadienes through the dehydra-decyclization of C-5 cyclic ethers, even at high conversions. 1,3-Pentadiene was the major product in both reaction of 2-methyltetrahydrofuran and tetrahydropyran over ZrO 2 . The reaction of 3-methyltetrahydrofuran produced nearly stoichiometric amounts of isoprene. Other catalysts, including TiO 2 , γ-Al 2 O 3 , and H-ZSM-5, were generally much less selective and produced a mixture of diene isomers. A combination of TPD and steady-state measurements revealed that both piperylenes are exclusively produced through primary catalytic pathways from 2-methyltetrahydrofuran, avoiding any isomerization once formed. First-principle calculations on ZrO 2 imply the presence of an energetically favored, surface isomerization of ring-opened intermediates to conjugated alkenolates that selectively dehydrate to conjugated dienes, providing high selectivity to the desired products. Finally, the stabilization of the conjugated alkenolate is key for understanding the ability of ZrO 2 to selectively produce conjugated dienes from cyclic ethers, without the need for the thermo-limited diene isomerization.

1,3 -pentadiene↗

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

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. Furthermore, 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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗