Research Projects

↓ Calorimetric Benchmark Energies of Adsorbed Intermediates, Solvent Effects and Solvent/Catalyst Bonding

↓ Supported Metal Nanoparticle Catalysts and Electrocatalysts:  Correlating Structure with Function through Energetics

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Supported Metal Nanoparticle Catalysts and Electrocatalysts:  Correlating Structure with Function through Energetics

Overview

Nanoparticles of late transition metals are used as catalysts and electrocatalysts for industrial chemical reactions that produce fuels, convert them to electricity and clean up pollution associated with the generation and use of fuels. For such applications, they usually are bonded onto the surfaces of oxide or carbon support materials. To provide the energy needed for sustained economic development, we must develop new and improved solid catalysts and electrocatalysts for a variety of reactions that take better advantage of traditional and alternate energy sources (solar, wind, biomass or nuclear) and avoid serious environmental problems. 

Our experimental research program aims to provide the basic understanding needed to develop new and improved catalysts and electrocatalysts for a variety of reactions that involve nanoparticles of late transition metals supported on oxide and carbon materials. Specifically, we study well-defined model catalysts consisting of metal and bimetallic nanoparticles supported on single-crystalline oxide, mixed-oxide and carbon surfaces, structurally characterized using a variety of ultrahigh vacuum surface science techniques. We use calorimetry techniques invented in our lab and available nowhere else in the world to measure the energies of the metal atoms in these particles, the metal/support adhesion energies and the energy of adsorbed intermediates on these particles. 

Our prior results showed that the chemical potential of the metal atoms in the particles, which we measure directly by metal adsorption calorimetry, is an important descriptor for catalytic performance. For particles smaller than 6 nm, it depends strongly on their size and the nature of the oxide or carbon support upon which the particles sit, and correlates with their catalytic performance (resistance to sintering, bond energies to adsorbed catalytic reaction intermediates, catalytic activity and selectivity). Our prior results led us to a quantitative relationship that accurately predicts metal chemical potential versus particle size and the metal/support adhesion energy (E_adh). Thus, knowing how E_adh varies with the metal and support material is crucial to predicting metal chemical potential for different catalyst materials, and thus their catalytic performance. We also discovered how to predict E_adh for different metals on a given oxide support, once E_adh is known for one metal.

We are refining these relationships, extending them to other oxides and carbon supports, thus enabling predictions of adhesion energies for new metal/support combinations without measurement. We measure quantitative relationships between metal chemical potential and catalytic properties for metals in model structures where their chemical potential is tuned by independently varying the particle size and the support strength, and by alloying with other metals. We correlate this tuned chemical potential with (1) calorimetrically-measured adsorption energies of two important and ubiquitous adsorbed catalytic intermediates (-CH₃, -OCH₃) on these nanoparticles, and (2) their sintering rates. We also measure the adsorption energies of metal monomers on these supports, which are crucial parameters in kinetic models for sintering rates. 

Our work provides the basic understanding needed to develop better catalyst materials for clean, sustainable energy technologies. These measured energies are key benchmarks (that cannot be provided by any other laboratory, nor by any theoretical methods currently available) needed for developing more accurate computational tools for heterogeneous catalysis and surface science. A marked improvement is such computational tools is revolutionizing research in many areas.

Introduction

Nanoparticles of late transition metals dispersed across support materials form the basis for a wide variety of catalysts, electrocatalysts and photocatalysts that are either currently used industrially for energy, chemical and environmental technologies, or hold promise for such applications in the future.  It is well known that the rates (per surface metal atom) and selectivities of catalytic, electrocatalytic and photocatalytic reactions often depend strongly upon both the particle size and the nature of the material upon which they are supported, especially when the particles are smaller than ~6 nm in diameter.^1-17 A holy grail of catalysis research is to understand, at a predictive level, how particle size and support affect activity and selectivity for a given catalytic metal. A common problem with such catalysts is that they deactivate with time-on-stream by sintering (also called coarsening), whereby the particles increase in average size and diminish in number. Another major goal of catalysis research is to be able to predict how catalyst structure can be altered to enhance sintering resistance. [P12]

We have recently proven that both catalytic reactivity and the rate of catalyst sintering correlate strongly with the chemical potential of the metal atoms in these supported metal particles.^18-22[P2,P6,P12]  The definition of “chemical potential” underlies the reason for using these particular words to describe this highly important thermodynamic property: It describes the potential of a given species (in this case, metal atoms) to do chemistry.  The higher its chemical potential, the less stable and thus the more reactive it is. In terms of “higher reactivity” for metal atoms, we refer here both to their strength of adsorption of small molecules and their rate of deactivation by sintering.  Metal chemical potential is one very important descriptor for understanding and even predicting catalytic performance.

Let us consider first sintering rates, where the relationship to metal chemical potential is already quantitatively established. The loss of activity over long time due to sintering is a huge problem in catalysis,^22-29 so there has been much work in developing models that predict the rate of sintering. Our goal is to be able to predict particle sizes at the long times needed for industrial applications (~1 year) based on short-term measurements of size versus time.^{21, 30} Generally, the sintering rates of individual particles has been shown to increase with m(R), the chemical potential of metal atoms in that particle, which itself is a function of the particle radius R (or “effective radius” for particles that are not partial spheres, defined such that the particle’s volume equals that for a hemisphere of that radius). We define the reference state of zero chemical potential such that m(R) is the chemical potential relative to that for an infinitely large particle of the same metal (i.e., for bulk metal(solid)). As an example of a sintering rate equation, we developed a model for sintering rates ^{21, 22} that is based on an atomistic mechanism originally developed by Wynblatt and Gjostein.³¹ When Ostwald ripening is the dominant mechanism, the radius of any given particle at any time changes with a rate ^{21, 22}:

where K and E_tot are system-dependent constants, and R* is the equilibrium radius for the concentration of diffusing monomers at that time. This concentration is determined by the entire size distribution of particles.  Particles with R < R* (radius smaller than this critical radius) get smaller with time, and those with R > R* get larger. The rates at which this happens for a given radius is a very strong function of m(R) in a way very similar to it making a negative contribution to the activation energy.^{21, 22} The higher m(R) is for a small particle, the faster it gets smaller. Our more recent measurements of chemical potentials of metals on more strongly interacting supports like CeO₂ (where m(R) is lower for a given R) and their comparisons to sintering rates have further verified the validity of this rate expression.^{19, 32}

This rate expression was derived assuming that monomer detachment from the particle is rate determining.  The values E_tot and K are combinations of prefactors and energies for elementary atom-migration steps.  They depend on fundamental properties of the metal and the support, such as the energy difference between a metal atom when it is an isolated monomer on the support surface versus when present in a metal particle of infinite size. The sintering kinetic model in ^{21, 22} was further improved by Datye’s group³³ by improving the way m(R*) is calculated. 

The factor e^μ(R)/kT in Eq. (1) also appears in a variety of other rate expressions for sintering kinetics derived assuming different elementary steps control the rate instead.²¹ Other kinetic models for sintering mechanisms are required when the rate is dominated by particle diffusion/agglomeration instead of Ostwald ripening, and that mechanism sometimes dominates under certain conditions.[P12]  This factor e^μ(R)/kT also appears directly in the rate expression for sintering by that alternate mechanism, at least in some derivations based on an atomistic mechanism.²¹

A consequence of metal chemical potential that is even more important than its effect on sintering rates is its effect on the reactivity of supported nanoparticles in their interactions with gases, e.g., when binding adsorbed catalytic reaction intermediates. The same metal atoms will bind small adsorbates more strongly when they are in a structure with high chemical potential, and more weakly when in a structure with lower chemical potential.^{18, 19, 34}  When present in the form of tiny (1-2 nm effective diameter) nanoparticles, where the metal chemical potential is very high,^{18, 19, 34} they bind small adsorbates more strongly than large particles or bulk metal.^{18, 19, 34, 35} For example, oxygen adatoms bind to ~1 nm Au nanoparticles on TiO₂(110) so strongly that the activation energy for their desorption as O₂ is ~50 kJ/mol O₂ (~40%) higher than from bulk Au single crystal surfaces.²⁰  The dominant effect here is associated with the fact that the surface metal atoms are more coordinatively-unsaturated on smaller particles, as investigated in detail by DFT on unsupported Au and Pt clusters from 13 to 1415 atoms, or ~0.7−3.6 nm in diameter,^{36, 37} which is the same reason step edges usually bind small adsorbates more strongly than close-packed terraces. Conversely, when metal atoms are present in certain bimetallic surfaces where they are more stable (i.e., have lower chemical potential) than in the pure bulk metal (e.g., when combined with other metals with which they make highly exothermic alloys), they bind adsorbates more weakly than the surface of the pure bulk metal.^{18, 19, 38, 39} For example, for a Pd monolayer on Ta(110), the isosteric heat of adsorption of CO is smaller than on pure Pd(111) by ~63 kJ/mol (~40%), and the TPD peak for adsorbed CO is shifted by more than 200 K to lower temperature.³⁸  Rodriguez and Goodman⁴⁰ showed that for seven such metal-on-metal systems involving Pd and Ni monolayers, the more stable the Ni or Pd monolayer (as estimated by its peak temperature in temperature programmed desorption (TPD) from the underlying metals), the more weakly it adsorbs CO (as estimated by the CO TPD peak temperature). Recent DFT calculations by Abild-Petersen’s group have shown a similar trend for later transition metal atoms in metal surfaces: the higher the energy cost to remove that metal atom from the solid, the more weakly it bonds -OH and -CH₃ groups.⁴¹ It is clear that the thermodynamic stability of metal atoms correlates in many systems with their chemical reactivity: the higher the metal’s chemical potential, the more strongly it bonds small adsorbates. (This is true in chemisorption, but likely to break down when van der Waals interactions dominate the adsorption energy rather than usual chemisorption bonds, since larger metal particles should have higher polarizability.) 

Metal chemical potential is a powerful descriptor for catalytic performance, whether with regards to the catalyst’s chemical reactivity or its long-term resistance to deactivation by sintering. We are motivated to develop predictive ability in estimating metal chemical potential in catalyst nanostructures. This goes hand-in-hand with the goal of developing a fundamental understanding of such oxide- and carbon-supported metal catalysts, in particular the relationships amongst the atomic-level structural properties of these complex nanomaterials and their catalytic performance properties (activity, selectivity and long-term stability under reaction conditions). We are conducting experimental research to provide a basic understanding of these relationships, particularly by developing quantitative relationships between the key structural properties of the catalyst material (i.e., metal nanoparticle size, chemical composition of the support material, particle-particle separation) and the calorimetrically-measured stability (or chemical potential) of the metal atoms in the particles. We are also developing more quantitative correlations between this chemical potential and the catalytic performance properties of these materials.

Our Research

We study model catalysts which are structurally very well defined, consisting of size-controlled metal and bimetallic nanoparticles on clean surfaces of single-crystalline oxide and carbon supports. Our lab utilizes our unique abilities for calorimetric measurements on such model catalysts to examine both the energies of the surface metal atoms that make up the catalyst material itself and the strength with which they bond adsorbed intermediates. 

Our experiments are designed to first clarify the structural factors that control the chemical potential of catalytic metal atoms in supported nanoparticles. Our recent calorimetry measurements under this DOE-funded project have proven that for monometallic particles, this chemical potential increases very strongly with decreasing particle diameter (D) below ~6 nm. An example for Ag on slightly reduced CeO₂(111) is shown in Fig. 1.  The magnitude of the change is huge (~80 kJ/mol) and has dramatic consequences for catalytic performance. We first derived a Gibbs-Thomson-like relation proving that the chemical potential of metal atoms in a large hemispherical particle of diameter D on support material A differs from that in the bulk of the metal by:[P2]

μ(D) = (γm  – Eadh)(2Vm / D),                                  (1)

where γ_m  is the surface energy of the bulk metal, E_adh is the adhesion energy at the bulk metal/oxide interface, and V_m  is the molar volume of the bulk metal. By comparing to our calorimetric measurements of metal atom chemical potential on oxide-supported metal nanoparticles as a function of particle size for five different metal/oxide combinations, we found that our measured chemical potential exceeds that predicted bt Eq. (1) when the particles get smaller than ~5 nm, and especially below 2 nm (see Fig. 1). For late transition metals on all the oxides studied, we showed a very good fit to the measured values for particles down to only a few atoms by a modified version of Eq. (1):[P6]

μ(D) = [(3γm  – Eadh)(1 + Do/D)](2Vm / D),                      (2)

where D_o is a constant for all systems equal to ~1.5 nm. The added factor (1 + D_o/D) is an empirical correction that accounts for the fact that both the metal’s surface energy and E_adh increase rather strongly with decreasing size when D drops below 5 nm. The surface energy increases due to the increasing fraction of coordinatively-unsaturated surface metal atoms (e.g., step, kink and corner sites).²²[P6]  An example of the high quality of fit is shown in Fig. 1.

Since γ_m and V_m are well known for all late transition metals, all one needs to know to gain predictive ability for the chemical potential versus particle size for a given support is E_adh. Learning what factors control metal/support adhesion energies is especially important when we recognize that E_adh also determines the equilibrium shape of the metal particle,¹⁸ and shape also effects catalytic activity and selectivity.^42-44  Our current research continues to systematically study the factors that control E_adh and the metal’s chemical potential versus particle size, using the same calorimetric method as in Fig. 1.

As shown in Fig. 2, on the basis of such calorimetric measurements, we have proven that, for a given oxide surface, E_adh increases linearly from metal to metal with increasing magnitude of the heat of formation of the most stable oxide of the metal from metal gas plus O₂, per mole of metal (i.e., ΔH_sub,M-ΔH_f,MOx, where ΔH_sub,M is the metal’s heat of sublimation and ΔH_f,MOx is the standard heat of formation of the most stable bulk oxide of that metal, per mole of metal). This factor is what we proposed as a convenient descriptor of the oxophilicity of the gaseous metal atom, since it directly reflects the strength of the chemical bonds this metal atom can make to oxygen. It is divided by V_M^2/3 (where and V_M is the volume per mole in the bulk metal solid) to convert this energy from “per mole” to “per unit area”, i.e., the units of E_adh.  We previously published this correlation [P2] and later found that Ni fit closely to the lines extrapolated from our earlier correlation. This provides strong proof that this linear scaling relation actually has predictive ability even well outside the range where we first developed it. 

Our work comprises the most extensive collection of adhesion energies ever reported for clean metal/oxide interfaces by far. The correlations we determine prove that metal-oxygen bonds dominate interfacial bonding. This is consistent with density-functional theory (DFT) calculations of isolated metal adatoms, which show they bind most strongly to oxygen anion sites of these oxide surfaces (when no vacancies are present).^{18, 45, 46} For the two different stoichiometric oxide surfaces studied with multiple different metals in Fig. 2 (MgO(100) and CeO₂(111)), the slopes of their correlations (i.e., metal adhesion energy vs oxophilicity) are nearly the same, but their offsets are very different (CeO₂(111) > MgO(100) by 2 J/m²). This suggests that the slope is nearly independent of oxide. We are working to further confirm this constant slope with measurements on other oxides, which will allow estimations of adhesion energies for many metals on a given oxide based on the measurement of only one metal, as suggested by the dashed line in Fig. 2 for α-Al₂O₃(0001) based on a single measurement on Cu.

The offsets of the lines in Fig. 2 show that, for a given metal, its adhesion energies to different oxides (i.e., the offset between lines in Fig. 2) are quite different. Although we have found a rough correlation, we are continuing research to fully explain these differences.[P6-7] Our data for Ag on rutile-TiO₂(100) in Fig. 2 is well above the point for Au on rutile-TiO₂(110) suggest that the adhesion energy line for the (100) face of rutile is higher than that for its (110) face. This can be qualitatively explained by our previous work [P6-7] that E_adh increases in proportion to the number of coordinatively-unsaturated surface O anions per unit area (which is 40% higher on the (100) face). That work was based on our demonstration that the interfacial bonding is mainly between metal atoms of the metal nanoparticle and surface O atoms of the oxide (to explain the lines in Fig. 2, as noted above).

We are continuing experiments to further clarify how these adhesion energy trend lines in Fig. 2 vary between different oxide surfaces for the same metal.

Recent publications from this DOE-funded and jointly-funded project 

1. Calorimetric Measurement of Adsorption and Adhesion Energies of Cu on Pt(111), E. James, S. L. Hemmingson, J. R.V. Sellers and C. T. Campbell, Surface Science 657, 58–62 (2017) (Selected as Editor’s Choice). 
2. Trends in Adhesion Energies of Metal Nanoparticles on Oxide Surfaces: Understanding Support Effects in Catalysis and Nanotechnology, S. L. Hemmingson and C. T. Campbell, ACS Nano 11, 1196-1203 (2017). (36 citations)
3. Correction to: “Trends in Adhesion Energies of Metal Nanoparticles on Oxide Surfaces: Understanding Support Effects in Catalysis and Nanotechnology”, S. L. Hemmingson and C. T. Campbell, ACS Nano 11, 1196-1203 (2017), ACS Nano 11, 4373 (2017).
4. Energetics of 2D and 3D Gold Nanoparticles on MgO(100): Influence of Particle Size and Defects on Gold Adsorption and Adhesion Energies, S. L. Hemmingson, G. M. Feeley, N. J. Miyake and C. T. Campbell, ACS Catalysis 7, 2151-2163 (2017).
5. The Degree of Rate Control: A Powerful Tool for Catalysis Research, C. T. Campbell, ACS Catalysis (Invited Viewpoint) 7, 2770-2779 (2017).  (63 citations)
6. The Chemical Potential of Metal Atoms in Supported Nanoparticles: Dependence upon Particle Size and Support, Charles T. Campbell and Zhongtian Mao, ACS Catalysis 7, 8460-8466 (2017). (22 citations)
7. Correction to “The Chemical Potential of Metal Atoms in Supported Nanoparticles: Dependence upon Particle Size and Support”, Charles T. Campbell and Zhongtian Mao, ACS Catalysis 2017, 7, 8460-8466. Charles T. Campbell and Zhongtian Mao, ACS Catalysis 8, 8763−8764 (2018).
8. Energetics of Au adsorption and film growth on Pt(111) by single-crystal adsorption calorimetry, Gabriel M. Feeley, Stephanie L. Hemmingson and Charles T. Campbell, Journal of Physical Chemistry C 123, 5557-5561 (2019).
9. Apparent Activation Energies in Complex Reaction Mechanisms: A Simple Relationship via Degrees of Rate Control, Zhongtian Mao and Charles T. Campbell, ACS Catalysis 9, 9465−9473 (2019). 
10. The Degree of Rate Control of Catalyst-Bound Intermediates in Catalytic Reaction Mechanisms: Relationship to Site Coverage, Zhongtian Mao and Charles T. Campbell, Journal of Catalysis 381, 53–62 (2020).
11. Kinetic Isotope Effects: Interpretation and Prediction Using Degrees of Rate Control, Zhongtian Mao and Charles T. Campbell, ACS Catalysis 10, 4181−4192 (2020). (Also selected for inclusion in the ACS Catalysis Virtual Issue: Blurring the Lines Between Catalysis Subdisciplines.) 
12. Energetics and Structure of Ni Atoms and Nanoparticles on MgO(100), Zhongtian Mao, Wei Zhao, Ziareena Al-Mualem and Charles T. Campbell, Journal of Physical Chemistry C 124, 14685−14695 (2020). 
13. Calorimetric Metal Vapor Adsorption Energies for Characterizing Industrial Catalyst Support Materials, Wei Zhang and Charles T. Campbell, Catalysis 392, 209–216 (2020). 
14. Energetics of Ag Adsorption on and Adhesion to Rutile TiO₂(100) Studied by Microcalorimetry, Zhongtian Mao, John R. Rumptz, and Charles T. Campbell, Journal of Physical Chemistry C (submitted)
15. Electrocatalytic Hydrogenation of Phenol over Platinum and Rhodium: Unexpected Temperature Effects Resolved, N. Singh, Y. Song, O. Y. Gutiérrez, D. M. Camaioni, C. T. Campbell, J. A. Lercher, ACS Catalysis 6, 7466–7470 (2016).   (34 citations)
16. The physical chemistry and materials science behind sinter-resistant catalysts, Yunqian Dai, Ping Lu, Charles T. Campbell and Younan Xia, Chemical Society Reviews 47, 4314-4331 (2018). (23 citations) 
17. Impact of pH on Aqueous-Phase Phenol Hydrogenation Catalyzed by Carbon-Supported Pt and Rh, Nirala Singh, Mal-Soon Lee, Sneha A. Akhade, Guanhua Cheng, Donald M. Camaioni, Oliver Y. Gutiérrez, Vassiliki-Alexandra Glezakou, Roger Rousseau, Johannes A. Lercher and Charles T. Campbell, ACS Catalysis 9, 1120-1128 (2019).
18. Heats of Adsorption of N₂, CO, Ar and CH₄ versus Coverage on the Zr-Based MOF NU-1000: Measurements and DFT Calculations, Graeme Vissers, Wei Zhang, Oscar E. Vilches, Wei-Guang Liu, Haoyu S. Yu, Donald G. Truhlar and Charles T. Campbell, Journal of Physical Chemistry C 123, 6586–6591 (2019). 
19. Quantifying adsorption of organic molecules on platinum in aqueous phase by hydrogen site blocking and in situ X-ray absorption spectroscopy, Nirala Singh, Udishnu Sanyal, John L. Fulton, Oliver Y. Gutiérrez, Johannes A. Lercher and Charles T. Campbell, ACS Catalysis 9, 6869–6881 (2019).
20. A Simple Bond-Additivity Model Explains Large Decreases in Heats of Adsorption in Solvents Versus Gas Phase: A Case Study with Phenol on Pt(111) in Water, Nirala Singh and Charles T. Campbell, ACS Catalysis 9, 8116–8127 (2019). 
21. Adhesion Energies of Solvent Films to Pt(111) and Ni(111) Surfaces by Adsorption Calorimetry, John R. Rumptz and Charles T. Campbell, ACS Catalysis 9, 11819−11825 (2019). 
22. Aqueous phase catalytic and electrocatalytic hydrogenation of phenol and benzaldehyde over platinum group metals, Nirala Singh; Udishnu Sanyal; Griffin Ruehl; Kelsey Stoerzinger; Oliver Y Gutiérrez; Donald M Camaioni; John L Fulton; Johannes A Lercher and Charles T Campbell, Journal of Catalysis 382, 372–384 (2020).
23. Ni Nanoparticles on CeO₂(111): Energetics, Electron Transfer and Structure by Ni Adsorption Calorimetry, Spectroscopies and DFT, Zhongtian Mao, Pablo G. Lustemberg, John R. Rumptz, M. Verónica Ganduglia-Pirovano and Charles T. Campbell, ACS Catalysis 10, 5101−5114 (2020).
24. Catalytic Properties of Model Supported Nanoparticles, Charles T. Campbell, Nuria Lopez, and Stefan Vajda, Journal of Chemical Physics 152, 140401 (2020) (3 pages).
25. Silver Adsorption on Calcium Niobate(001) Nanosheets: Calorimetric Energies Explain Sinter-Resistant Support, Wei Zhang, Ritesh Uppuluri, Thomas E. Mallouk and Charles T. Campbell, Am. Chem. Soc. 142, 15751−15763 (2020).

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