The identification of key materials' parameters correlated with the performance can accelerate the development of heterogeneous catalysts and unveil the relevant underlying physical processes. However, the analysis of correlations is often hindered by inconsistent data. Besides, nontrivial, yet unknown relationships may be important, and the intricacy of the various processes may be significant.
View Article and Find Full Text PDFArtificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called "materials genes" of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data meets the quality requirement for data-efficient AI.
View Article and Find Full Text PDFAbstract: The performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions).
View Article and Find Full Text PDFUnderstanding what controls the strength of bonding of adsorbed intermediates to transition-metal surfaces is of central importance in many technologies, especially catalysis and electrocatalysis. Our recently measured bond enthalpies of -OH, -OCH , -O(O)CH and -CH to Pt(111) and Ni(111) surfaces are fit well (standard deviation of 7.2 kJ mol ) by a predictive equation involving only known parameters (gas-phase ligand-hydrogen bond enthalpies, bond enthalpies of adsorbed H atoms to that surface, electronegativities of the elements, and group electronegativities of the ligands).
View Article and Find Full Text PDFThe pure rotational spectra of deuterated propiolic acids (HCCCOOD and DCCCOOH), 1-fluorobenzene (4-d1), and 1,2-difluorobenzene (4-d1) in their ground states have been measured using two Fourier transform microwave (FTMW) spectrometers at the University of Arizona. For 1-fluorobenzene (4-d1), nine hyperfine lines of three different ΔJ = 0 and 1 transitions were measured to check the synthesis method and resolution. For 1,2-difluorobenzene (4-d1), we obtained 44 hyperfine transitions from 1 to 12 GHz, including 14 different ΔJ = 0, 1 transitions.
View Article and Find Full Text PDFThe microwave spectrum of the mono-fluoro-benzoic acids, 2-fluoro-, 3-fluoro-, and 4-fluoro-benzoic acid have been measured in the frequency range of 4-14 GHz using a pulsed beam Fourier transform microwave spectrometer. Measured rotational transition lines were assigned and fit using a rigid rotor Hamiltonian. Assignments were made for 3 conformers of 2-fluorobenzoic acid, 2 conformers of 3-fluorobenzoic acid, and 1 conformer of 4-fluorobenzoic acid.
View Article and Find Full Text PDFThe concerted proton tunneling frequency for the propiolic acid-formic acid dimer was calculated using a relaxed ab initio double-well potential in the imaginary-frequency mode of the saddle point, and new measurements were made for the deuterated propiolic acid-formic acid (ProOD-FAOD) isotopologue. It is important to have consistent calculated tunneling frequency values between normal and deuterated isotopologues since parameters can be readily adjusted to get good agreement with one isotopologue. High-resolution rotational spectra of deuterated (ProOD-FAOD) dimer were measured using a newly constructed Fourier Transform microwave spectrometer.
View Article and Find Full Text PDFNew microwave spectra were measured to obtain rotational constants and centrifugal distortion constants for the DCCCOOH···HOOCH and HCCCOOD···DOOCH isotopologues. Rotational transitions were measured in the frequency range of 4.9-15.
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