skip to main content

Explore Research Products in the NSF-PAR

Title: Site-averaged kinetics for catalysts on amorphous supports: an importance learning algorithm
Ab initio calculations have greatly advanced our understanding of homogeneous catalysts and crystalline heterogeneous catalysts. In contrast, amorphous heterogeneous catalysts remain poorly understood. The principal difficulties include (i) the nature of the disorder is quenched and unknown; (ii) each active site has a different local environment and activity; (iii) active sites are rare, often less than ∼20% of potential sites, depending on the catalyst and its preparation method. Few (if any) studies of amorphous heterogeneous catalysts have ever attempted to compute site-averaged kinetics, because the exponential dependence on variable activation energy requires an intractable number of ab initio calculations to converge. We present a new algorithm using machine learning techniques (metric learning kernel regression) and importance sampling to efficiently learn the distribution of activation energies. We demonstrate the algorithm by computing the site-averaged activity for a model amorphous catalyst with quenched disorder.  more » « less
Award ID(s):
1725797
NSF-PAR ID:
10203887
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Reaction Chemistry & Engineering
Volume:
5
Issue:
1
ISSN:
2058-9883
Page Range / eLocation ID:
77 to 86
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , Reaction Chemistry & Engineering)
    null (Ed.)
    Ab initio computational studies have made tremendous progress in describing the behavior of molecular (homogeneous) catalysts and crystalline versions of heterogeneous catalysts, but not for amorphous heterogeneous catalysts. Even widely used industrial amorphous catalysts like atomically dispersed Cr on silica remain poorly understood and largely intractable to computational investigation. The central problems are that (i) the amorphous support presents an unknown quenched disordered structure, (ii) metal atoms attach to various surface grafting sites with different rates, and (iii) the resulting grafted sites have different activation and catalytic reaction kinetics. This study combines kernel regression and importance sampling techniques to efficiently model grafting of metal ions onto a non-uniform ensemble of support environments. Our analysis uses a simple model of the quenched disordered support environment, grafting chemistry, and catalytic activity of the resulting grafted sites. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Materials Chemistry A)
    null (Ed.)
    Using water as a hydrogen source is a promising strategy for alternative hydrogen peroxide (H 2 O 2 ) synthesis. By a series of ab initio molecular dynamics (AIMD) simulations and reactive molecular dynamics (RxMD) calculations, fundamental details have been revealed regarding how liquid water interacts with oxygen on a metal-free carbon nitride catalyst, and the two-step reaction mechanism of H 2 O 2 synthesis. Metal-free porous graphitic carbon nitride (g-C 5 N 2 ) catalysts are also systematically screened by using a thermodynamics approach through the ab initio density functional theory (DFT) method. Key results include: (a) pristine g-C 5 N 2 is most active to catalyze the H 2 O/O 2 reaction and produce H 2 O 2 ; (b) the adsorption and activation of water at unsaturated carbon sites of g-C 5 N 2 are critical to initiate the H 2 O/O 2 reaction, producing HOO* intermediates; (c) interfacial free water and adsorbed water at g-C 5 N 2 form a synergetic proton transfer cluster to promote HOO* intermediates to form H 2 O 2 . To the best of our knowledge, this work presents long-needed theoretical details of direct H 2 O 2 synthesis via the water/oxygen system, which can guide further optimization of carbon-based catalysts for oxygen reduction reactions. 
    more » « less
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Advanced Functional Materials)
    Abstract

    Platinum‐based catalysts are critical to several chemical processes, but their efficiency is not satisfying enough in some cases, because only the surface active‐site atoms participate in the reaction. Henceforth, catalysts with single‐atom dispersions are highly desirable to maximize their mass efficiency, but fabricating these structures using a controllable method is still challenging. Most previous studies have focused on crystalline materials. However, amorphous materials may have enhanced performance due to their distorted and isotropic nature with numerous defects. Here reported is the facile synthesis of an atomically dispersed catalyst that consists of single Pt atoms and amorphous Fe2O3nanosheets. Rational control can regulate the morphology from single atom clusters to sub‐nanoparticles. Density functional theory calculations show the synergistic effect resulted from the strong binding and stabilization of single Pt atoms with the strong metal‐support interaction between the in situ locally anchored Pt atoms and Fe2O3lead to a weak CO adsorption. Moreover, the distorted amorphous Fe2O3with O vacancies is beneficial for the activation of O2, which further facilitates CO oxidation on nearby Pt sites or interface sites between Pt and Fe2O3, resulting in the extremely high performance for CO oxidation of the atomic catalyst.

     
    more » « less
  4. ; ; ; ; ; ; ; ; ; ( , Nature Communications)
    Abstract

    The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond thed-band center metric of binding sites.

     
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al ( , Energy & Environmental Science)
    Atomically dispersed and nitrogen-coordinated single Ni sites ( i.e. , NiN x moieties) embedded in partially graphitized carbon have emerged as effective catalysts for CO 2 electroreduction to CO. However, much mystery remains behind the extrinsic and intrinsic factors that govern the overall catalytic CO 2 electrolysis performance. Here, we designed a high-performance single Ni site catalyst through elucidating the structural evolution of NiN x sites during thermal activation and other critical external factors ( e.g. , carbon particle sizes and Ni content) by using Ni–N–C model catalysts derived from nitrogen-doped carbon carbonized from a zeolitic imidazolate framework (ZIF)-8. The N coordination, metal–N bond length, and thermal wrinkling of carbon planes in Ni–N–C catalysts significantly depend on thermal temperatures. Density functional theory (DFT) calculations reveal that the shortening Ni–N bonds in compressively strained NiN 4 sites could intrinsically enhance the CO 2 RR activity and selectivity of the Ni–N–C catalyst. Notably, the NiN 3 active sites with optimal local structures formed at higher temperatures ( e.g. , 1200 °C) are intrinsically more active and CO selective than NiN 4 , providing a new opportunity to design a highly active catalyst via populating NiN 3 sites with increased density. We also studied how morphological factors such as the carbon host particle size and Ni loading alter the final catalyst structure and performance. The implementation of this catalyst in an industrial flow-cell electrolyzer demonstrated an impressive performance for CO generation, achieving a current density of CO up to 726 mA cm −2 with faradaic efficiency of CO above 90%, representing one of the best catalysts for CO 2 reduction to CO. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email