The molecular aspect of the Raman vibrational selection rules allows for the molecular structural and reactivity determinations of metal oxide catalytic active sites in all types of oxide catalyst systems (supported metal oxides, zeolites, layered hydroxides, polyoxometalates (POMs), bulk pure metal oxides, bulk mixed oxides and mixed oxide solid solutions). The molecular structural and reactivity determinations of metal oxide catalytic active sites are greatly facilitated by the use of isotopically labeled molecules. The ability of Raman spectroscopy to (1) operate in all phases (liquid, solid, gas and their mixtures), (2) operate over a very wide temperature (-273 to >1000 °C) and pressure (UHV to ≫100 atm) range, and (3) provide molecular level information about metal oxides makes Raman spectroscopy the most informative characterization technique for understanding the molecular structure and surface chemistry of the catalytic active sites present in metal oxide heterogeneous catalysts. The recent use of hyphenated Raman spectroscopy instrumentation (e.g., Raman-IR, Raman-UV-vis, Raman-EPR) and the operando Raman spectroscopy methodology (e.g., Raman-MS and Raman-GC) is allowing for the establishment of direct structure-activity/selectivity relationships that will have a significant impact on catalysis science in this decade. Consequently, this critical review will show the growth in the use of Raman spectroscopy in heterogeneous catalysis research, for metal oxides as well as metals, is poised to continue to exponentially grow in the coming years (173 references).

UV-vis diffuse reflectance spectroscopy (DRS) and Raman spectroscopy were used to examine the electronic and molecular structures, respectively, of well-defined Mo(VI) bulk mixed oxide reference compounds ((i) isolated MoO 4 or MoO 6 monomers, (ii) dimeric O 3 Mo-O-MoO 3 , (iii) chains of alternating MoO 4 and MoO 6 units, (iv) MoO 6 -coordinated Mo 7 -Mo 12 clusters, and (v) infinite layered sheets of MoO 5 units), aqueous molybdate anions as a function of solution pH, and supported MoO 3 catalysts (MoO 3 /SiO 2 , MoO 3 /Al 2 O 3 , and MoO 3 /ZrO 2 ). Raman spectroscopy confirmed the identity and phase purity of the different bulk and solution molybdenum oxide structures. UV-vis DRS provided the corresponding electronic edge energy (Eg) of the ligand-to-metal charge transfer (LMCT) transitions of the Mo(VI) cations. A linear inverse correlation was found between Eg and the number of bridging Mo-O-Mo covalent bonds around the central Mo(VI) cation. A relationship between Eg and the domain size (N Mo ) for finite MoO x clusters, however, was not found to exist. Application of the above insights allowed for the determination of the molecular structures of the twodimensional surface MoO x species present in supported MoO 3 catalysts as a function of environmental conditions. The current electronic and molecular structural findings are critical for subsequent studies that wish to establish reliable structure-activity/selectivity relationships for molybdenum oxide catalysts, especially supported MoO 3 catalysts.

Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10−4–10−1 S cm−1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.

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This study revisits the classic volcano curve for HCOOH decomposition by metal catalysts by taking a modern catalysis approach. The metal catalysts (Au, Ag, Cu, Pt, Pd, Ni, Rh, Co and Fe) were prepared by H 2 reduction of the corresponding metal oxides. The number of surface active sites (Ns) was determined by formic acid chemisorption. In situ IR indicated that both monodentate and bidentate/bridged surface HCOO* were present on the metals. Heats of adsorption (ΔH ads) for surface HCOO* values on metals were taken from recently reported DFT calculations. Kinetics for surface HCOO* decomposition (k rds) were determined with TPD spectroscopy. Steady-state specific activity (TOF = activity/Ns) for HCOOH decomposition over the metals was calculated from steady-state activity (µmol/g-s) and Ns (µmol/g). Steady-state TOFs for HCOOH decomposition weakly correlated with surface HCOO* decomposition kinetics (k rds) and ΔH ads of surface HCOO* intermediates. The plot of TOF vs. ΔH ads for HCOOH decomposition on metal catalysts does not reproduce the classic volcano curve, but shows that TOF depends on both ΔH ads and decomposition kinetics (k rds) of surface HCOO* intermediates. This is the first time that the classic catalysis study of HCOOH decomposition on metallic powder catalysts has been repeated since its original publication.