@article{ruan_akutsu_yang_zayan_dou_liu_bose_brody_lamb_li_2023, title={Hydrogenation of bio-oil-derived oxygenates at ambient conditions via a two-step redox cycle}, volume={4}, ISSN={["2666-3864"]}, url={https://doi.org/10.1016/j.xcrp.2023.101506}, DOI={10.1016/j.xcrp.2023.101506}, abstractNote={A key challenge in upgrading bio-oils to renewable fuels and chemicals resides in developing effective and versatile hydrogenation systems. Herein, a two-step solar thermochemical hydrogenation process that sources hydrogen directly from water and concentrated solar radiation for furfural upgrading is reported. High catalytic performance is achieved at room temperature and atmospheric pressure, with up to two-orders-of-magnitude-higher hydrogen utilization efficiency compared with state-of-the-art catalytic hydrogenation. A metal or reduced metal oxide provides the active sites for furfural adsorption and water dissociation. The in situ-generated reactive hydrogen atoms hydrogenate furfural and biomass-derived oxygenates, eliminating the barriers to hydrogen dissolution and the subsequent dissociation at the catalyst surface. Hydrogenation selectivity can be conveniently mediated by solvents with different polarity and metal/reduced metal oxide catalysts with varying oxophilicity. This work provides an efficient and versatile strategy for bio-oil upgrading and a promising pathway for renewable energy storage.}, number={7}, journal={CELL REPORTS PHYSICAL SCIENCE}, author={Ruan, Chongyan and Akutsu, Ryota and Yang, Kunran and Zayan, Noha M. and Dou, Jian and Liu, Junchen and Bose, Arnab and Brody, Leo and Lamb, H. Henry and Li, Fanxing}, year={2023}, month={Jul} }
 @article{cai_brody_tian_neal_bose_li_2023, title={Numerical modeling of chemical looping oxidative dehydrogenation of ethane in parallel packed beds}, volume={469}, ISSN={["1873-3212"]}, url={https://doi.org/10.1016/j.cej.2023.143930}, DOI={10.1016/j.cej.2023.143930}, abstractNote={Chemical looping oxidative dehydrogenation (CL-ODH) of ethane has the potential to be a highly efficient alternative to steam cracking for ethylene production. Accurate reactor modeling is of critical importance to efficiently scale up and optimize this new technology. This study reports a one-dimensional, heterogeneous packed bed model to simulate the CL-ODH of ethane to ethylene with a Na2MoO4-promoted CaTi0.1Mn0.9O3 redox catalyst. The overall reaction kinetics was well-described by coupling the gas-phase steam cracking of ethane with the reduction kinetics of the redox catalyst by H2 and C2H4. The impact of H2 on the formation rate of CO2 byproduct from C2H4 conversion was also thoroughly investigated to validate the applicability of the kinetic model under operational environments. The temperature variation within the different CL-ODH steps and the temperature distribution along the bed were also carefully considered. The accuracy of the model was validated by experiments conducted in a large lab-scale packed bed reactor (200 g catalyst loading), with an average deviation of 2.8% in terms of ethane conversion and ethylene yield. The model was subsequently used to optimize the operating parameters of the CL-ODH reactor, indicating that up to 63.7% single-pass C2 + olefin yield can be achieved with the current redox catalyst bed whereas further optimization of the redox catalyst to inhibit C2H4 activation can result in 69.4% single-pass C2 + yield while maintaining low CO2 selectivity.}, journal={CHEMICAL ENGINEERING JOURNAL}, author={Cai, Runxia and Brody, Leo and Tian, Yuan and Neal, Luke and Bose, Arnab and Li, Fanxing}, year={2023}, month={Aug} }
 @article{bose_westmoreland_2020, title={Predicting Total Electron-Ionization Cross Sections and GC-MS Calibration Factors Using Machine Learning}, volume={124}, ISSN={["1520-5215"]}, url={https://doi.org/10.1021/acs.jpca.0c06308}, DOI={10.1021/acs.jpca.0c06308}, abstractNote={Concentrations in GC-MS using electron-ionization mass spectrometry can be determined without pure calibration standards through prediction of relative total-ionization cross sections. An atom- and group-based artificial neural network (FF-NN-AG) model is created to generate EI cross sections and calibrations for organic compounds. This model is easy to implement and is more accurate than the widely used atom-additivity-based correlation of Fitch and Sauter (Anal. Chem. 1983). Ninety-two new measurements of experimental EI cross sections (70-75 eV) are joined with different interlaboratory datasets, creating a 396-compound cross-section database, the largest to date. The FF-NN-AG model uses 16 atom-type descriptors, 79 structural-group descriptors, and one hidden layer of 10 nodes, trained 500 times. In each cycle, 96% of the compounds in this database are freshly chosen at random, and then the model is tested with the remaining 4%. The resulting r2 is 0.992 versus 0.904 for the Fitch and Sauter correlation, root mean square deviation is 2.8 versus 9.2, and maximum relative error is 0.30 versus 0.73. As an example of the model's use, a list of cross sections is generated for various sugars and anhydrosugars.}, number={50}, journal={JOURNAL OF PHYSICAL CHEMISTRY A}, publisher={American Chemical Society (ACS)}, author={Bose, Arnab and Westmoreland, Phillip R.}, year={2020}, month={Dec}, pages={10600–10615} }