02043nas a2200265   4500008004100000022001400041245007100055210006900126260000800195300001100203490000800214520127000222653000801492653000901500653002901509653001201538100001801550700002001568700002001588700002501608700001701633700001301650700001801663856009601681       2020                        eng d  a0169-433200aMixed metal oxide: a new class of catalyst for methanol activation0 aMixed metal oxide a new class of catalyst for methanol activatio  cDEC  a1474490 v5343 a<p>In this work, we propose a mixed metal oxide as a catalyst and demonstrate it's ability to not only activate the MeOH molecule upon adsorption but also dissociate O-H and one of it's C-H bonds. MeOH activation is compared on two prominent facets of ZnAl(2)O(4 )viz. (2 2 0) and (31 1). While spontaneous O-H bond dissociation is observed on both facets, C-H bond dissociates only on the (3 1 1) surface. Multiple factors like atomic arrangement and steps on the surface, coordination of surface atoms, and their effective charges have a combined effect on MeOH activation. The (3 1 1) surface offers higher catalytic activity in comparison with (2 2 0) surface. Having a stepped surface, availability of multiple sites, and variation in the charge distribution are some of the reasons for better catalytic performance of (3 1 1) facet. Effect of orientation of MeOH with respect to the surface adds both, information and complexity to the problem. Observations pertinent to understanding this effect are also reported. A detailed analysis of atomic arrangement on the two surfaces provides a rationale as to why MeOH gets dissociated spontaneously on the mixed metal oxide. The promising results reported here opens up a new class of catalyst for research.</p>
10aDFT10aMeOH10aSpontaneous dissociation10aZnAl2O41 aMehta, Shweta1 aAgarwal, Sheena1 aKenge, Nivedita1 aMekala, Siva, Prasad1 aPatil, Vipul1 aRaja, T.1 aJoshi, Kavita  uhttps://library.ncl.res.in/content/mixed-metal-oxide-new-class-catalyst-methanol-activation02048nas a2200217   4500008004100000022001400041245010000055210006900155260000800224300001500232490000600247520131900253653002201572653002601594653001401620653000801634653002101642100002001663700001801683856012901701       2022                        eng d  a2365-654900aLooking beyond adsorption energies to understand interactions at surface using machine learning0 aLooking beyond adsorption energies to understand interactions at  cOCT  ae2022024140 v73 a<p>
	Identifying factors that influence interactions at the surface is still an active area of research. In this work, the importance of analyzing bond length activations (BLact) along with adsorption energies (E-a) while interpreting Density Functional Theory (DFT) results is emphasized. Investigating adsorption of different small molecules, such as O-2, N-2, CO, and CO2, on commonly studied facets ((100), (110), and (111)) of seven fcc transition metal surfaces (M=Ag, Au, Cu, Ir, Rh, Pt, and Pd) demonstrates the missing linear correlation between E-a and BLact. Further, tree based Machine Learning (ML) models reinforce the missing linear correlation between the two parameters and also highlight the importance of analyzing both to develop a better understanding of adsorption at surfaces. The best performing Random Forest models have a mean absolute error (MAE) of 0.19 eV for E-a prediction, and even lower MAE of 0.012 angstrom for BLact prediction. While often d-band center is correlated with E-a, our observations show that infact the d-band center has a better correlation with BLact. These observations emphasizes the role of BLact in gaining a fuller picture for catalysis. The fact that the factors responsible for BLact is a lesser-explored subject adds to the novelty of the findings.</p>
10aAdsorption energy10aBondlength activation10acatalysis10aDFT10amachine learning1 aAgarwal, Sheena1 aJoshi, Kavita  uhttps://library.ncl.res.in/content/looking-beyond-adsorption-energies-understand-interactions-surface-using-machine-learning