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MINI REVIEW

Cite this: Catal. Sci. Technol., 2019,
9, 5173

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Reaction mechanisms at the homogeneous–
heterogeneous frontier: insights from firstprinciples studies on ligand-decorated metal
nanoparticles
Manuel A. Ortuño

* and Núria López

*

The frontiers between homogeneous and heterogeneous catalysis are progressively disappearing. The decoration of transition metal nanoparticles (NPs) with ligands, also known as surface modifiers or capping
agents, primarily allows NP size control but dramatically impacts activity and selectivity in catalysis. Computational tools have shown their capability of providing insight at atomic level in both homogeneous and
heterogeneous areas but, due to the complexity of these interfaces, the underlying reaction mechanisms
are often not described and certainly not well understood. In this mini-review, we describe the main chalReceived 8th July 2019,
Accepted 9th August 2019

lenges in modelling and survey the most recent computational studies that emphasise the role of ligands in
tuning catalytic performance. We focus on density functional theory (DFT) simulations of the interfaces between transition metals (ruthenium, palladium, platinum, gold) and organic ligands (NHC, amine, phos-

DOI: 10.1039/c9cy01351b

phine, thiol), surfactants, and ionic liquids. Revealing the reaction pathways that operate at this hidden
interface between homogeneous and heterogeneous worlds will provide guiding rules to design new sys-

rsc.li/catalysis

tems that circumvent linear scaling relationships and foster a unified theory of catalysis.

1. Introduction
Much before the advent of nanoscience, catalysis was one of the
areas where small metal structures, either alone or modified by
organic moieties, were most widely employed. In particular,
metal nanoparticles (NPs) outstand in catalysis1,2 due to their
surface/volume ratio and nanometric size, which grant them
unique catalytic properties at the frontier between bulk materials and molecules. Nowadays, although controlling the size
and shape of metal NPs has been achieved with a high degree of
control,3–7 the surface functionalization8 by ligands can further
enhance and fine-tune the catalytic performance by merging the
versatilities of both heterogeneous and homogeneous worlds.
But what is the exact role of ligands? They initially serve
as capping agents to limit the growth and maintain welldispersed small-size metal NPs and can modify shapes
themselves,9,10 which in turn impacts their catalytic
performance.11–13 More importantly, such a ligand–metal
interface is actually quite dynamic and can potentially (i)
open or block catalytic sites on the surface, (ii) alter the
exposed metal surface, (iii) change the electronic structure of
the metal, (iv) impose stereo-electronic effects on reactants
Institute of Chemical Research of Catalonia (ICIQ), Barcelona Institute of Science
and Technology (BIST), Av. Països Catalans 16, 43007 Tarragona, Spain.
E-mail: mortuno@iciq.es, nlopez@iciq.es

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and products, and (v) tune properties on the metal/ligand/solvent boundaries.14 By analogy with the sergeants-and-soldiers
principle,15,16 its role may be described as a cooperative action of small individuals that contributes to amplify a certain
chemical property, such as activity, selectivity, or stability.
The concept of surface functionalization is not new;
rather, it was commonly referred to as molecular modifiers
doing poisoning or passivation of unselective sites. A textbook example is the Lindlar catalyst for the selective hydrogenation of alkynes to alkenes,17,18 where Pd NPs supported on
CaCO3 are modified with lead and quinoline to modulate hydrogen coverage, alkyne adsorption, alkene desorption, and
the size of active Pd ensembles.19
The purpose and fate of ligands in nanocatalysts have
been reviewed in recent years, mostly trying to unravel their
impact at structural level.8,20–33 However, detailed information on the operating mechanisms of these complex systems
is still scarce34 and this hampers a steady innovation in the
field. Here is where density functional theory (DFT) simulations come into play to provide insight into catalytic processes at the atomic scale,35–37 particularly since the methods
to study molecular and periodic systems are now robust
enough to maintain a common framework for both
homogeneous- and heterogeneous-like contributions. In this
mini-review, we will analyse recent first-principles studies of
reaction mechanisms promoted by decorated metal NPs, with

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special emphasis on the role of ligands in controlling activity
and selectivity. As discussed below, it would soon become
clear that the main computational challenges concern the
huge system size and the dynamic features intrinsically associated with it. In this line, using open-access platforms to
store and share computational results, such as ioChem-BD38
or Catalysis Hub,39 is crucial for reproducibility and data
mining40 of metal–ligand interfaces.

Challenges of metal–ligand interface simulations
The first hurdle when employing DFT to describe metal NPs
relates to its size. For diameters larger than 2 nm, the
so-called scalable regime, NPs are represented by infinite surfaces.41 This approach is not always possible and particularly
in the very small regime, ca. 1 nm diameter or less, the optimization of the nanostructured system constitutes a better
approach as extensively reported for Au nanoclusters.42,43 In
addition, ligands at the interface may be relatively large44 for
which several configurations need to be thoroughly sampled
as they cover the surface with very dense concentrations.
Another challenge when modelling interfaces is that the
metal NP is better represented as a solid with continuous
bands, while typical organic ligands are finite in size and
characterised by molecular orbitals and the interactions have
a large contribution from van der Waals forces.45,46 In those
cases where the ligands have formal charges, such as surfactants or ionic liquids, the description of different types of
bonds (covalent vs. ionic vs. dispersion) needs to be carefully
balanced. For solvent average contributions, they may be
taken into account either explicitly or implicitly, but again a
detailed balance of all bonding contributions is required.47,48
Finally, for detailed modelling of solvents and ionic
liquids, the dynamic behaviour of the layer constitutes a
major issue. Thus, the study of complex reactivity in these
highly computationally demanding interfaces is at the frontier of theoretical simulations.
The mini-review is structured according to the nature of
the species at the interface with the metal catalyst: molecular
organic ligands, surfactants, and ionic liquids (Fig. 1). We
then follow up with a brief section on deactivation pathways.
Overall, understanding the concepts behind each system at

Fig. 1 Ligand–metal interfaces reviewed herein.

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the atomic scale might lead to the generation of unified theories for future catalyst design.

2. Organic ligands
This section collects computational mechanistic contributions on metal NPs and surfaces coated with organic ligands
regardless of their origin: as-synthesised, post-synthetic exchange, added during catalysis, etc.
C-based ligands
N-heterocyclic carbenes (NHCs)49 are versatile ligands not
only for transition metal molecular catalysts but also for surfaces and materials.50,51 Recent combined experiment–theory
studies are devoted to structural features and binding modes
of NHCs on NPs52,53 and surfaces54–56 but detailed investigations on reaction mechanisms are less common.
Muratsugu, Doltsinis, Glorius, and co-workers reported
the synthesis of NHC-decorated Pd NPs for the hydrogenolysis of bromobenzene.57 The C–Br bond breaking process was computed at DFT level using clean and modified
Pd13 clusters. Smaller energy barriers were estimated for
NHC-decorated clusters bearing aromatic groups (<0.10 eV);
cf. those involving alkyl moieties (0.13 eV) and the clean catalyst (0.17 eV). Such a trend did correlate with computed
ionization potentials, which highlighted the electronic effects of these strong σ-donating ligands. In other words,
considerations and knowledge from traditional descriptors
in organic chemistry can be successfully transferred to decorated metal NPs.
Wen, Copéret, Chang, and co-workers supported a chelating NHC on Pd electrodes for electrochemical CO2 reduction
(CO2RR) to formate and CO.58 Compared to clean surfaces,
the NHC-decorated Pd system generated a significant amount
of formate. To rationalise these results, they performed DFT
calculations on clean and NHC-decorated Pd(111) surfaces
with adsorbed H atoms to estimate activation energies
(Scheme 1). The modified surface systematically provided
lower Gibbs energy barriers for the first and second hydrogen
transfers to yield formic acid. The same trend was observed

Scheme 1 Electrochemical CO2 reduction catalysed by NHCdecorated Pd(111) surfaces.

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for the C–O bond cleavage to form CO, which further
explained the selectivity experimentally observed.
The reactants themselves can also modify the mechanism.
A clear example is the semi-hydrogenation of propyne on Ag
surfaces reported by Vilé et al. (Scheme 2).59 DFT simulations
predict an endothermic H–H bond breaking process on
Ag(211) with a large energy barrier of 1.33 eV. However, the
coordination of propyne on a stepped Ag(211) induces a
metastable intermediate that promotes an exothermic and
heterolytic H2 activation process with a lower barrier of 1.03
eV. This implies that surface decoration can take place even
from reactants.
N-based ligands
The use of chiral ligands, particularly amines and alkaloids,
has been widely exploited to promote enantioselective transformations in surface catalysis.60 Pioneering studies by
Baiker and co-workers extensively studied metal-catalysed
enantioselective hydrogenations induced by cinchona alkaloids as surface modifiers (Scheme 3).61 They employed computational approaches to identify the active centre pockets responsible for chirality transfer from the metal–alkaloid
interface to the products.62,63 Nevertheless, the adsorption
mode of some aromatic amines is still under debate.64,65
Kunz and co-workers explored the asymmetric hydrogenation of keto esters catalysed by Pt NPs decorated with amino
acids.66 Computational studies described the adsorption of
alanine on Pt(111) via the N atom and proposed different
binding modes between the reactant methyl acetoacetate and
the ligand moiety to account for the chirality transfer.67
N-containing compounds can also play an important role
in hydrogenation reactions via the so-called frustrated Lewis
pair (FLP) activity.68 FLP-type mechanisms in heterogeneous
catalysis69 typically involve the organic ligand as a Lewis base
and the metal surface as a Lewis acid. Such approach is commonly employed in Au-based catalysts70 to overcome their inertness towards H2 activation.71
Guo and co-workers firstly reported a computational study
on the heterolytic splitting of dihydrogen on Au(111) using
small N-based compounds as probe Lewis bases.72 While the
clean Au surface breaks the H–H bond in a homolytic way demanding 1.20 eV (Scheme 4a), the lone pair of ammonia promotes a heterolytic cleavage via 0.65 eV (Scheme 4b). When
using the more sterically demanding 2-propanimine ligand,
the energy barrier further decreases to 0.43 eV. However, the

Scheme 2 Homolytic and alkyne-promoted heterolytic H–H bond
activations on Ag(211) surfaces.

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Scheme 3 Enantioselective hydrogenation of α-keto esters catalysed
by alkaloid-decorated Pt NPs.

lack of bond between the N and the surface would impose severe entropic penalties to these transition states.
Later, Rossi and co-workers reported the Au-catalysed
semi-hydrogenation of alkynes using amines as additives73
or grafted ligands.74 DFT simulations confirmed the role of
different amines in promoting the heterolytic H2 dissociation. In contrast to the high activation barriers and endothermicity associated with clean Au surfaces, the piperazinecapped system cleaves the H–H bond with an activation barrier of 0.32 eV, forming a thermoneutral product. The reaction continues with subsequent hydride and proton transfers
to yield the corresponding alkene. From these results, theory
predicted a linear correlation between H–H bond activation
energies for bidentate ligands and experimental reaction
rates (Fig. 2a), thus identifying for the first time a descriptor
for the ligand directly from the computational results.73 Similar trends were found for processes assisted by 1,10phenanthroline (Fig. 2b).74
Shi and co-workers reported the synthesis of DMF from dimethylamine and CO2/H2 using Cu catalysts.75 One major issue of this process is further hydrogenation of the product
DMF to trimethylamine. However, they managed to control
the selectivity by adding 1,10-phenanthroline. According to
DFT studies, they claimed that C–H bond forming and C–O
bond breaking reactions via decorated Cu(111) required
higher energy barriers than those via clean metal, which explains the higher selectivity experimentally observed.
Poteau and co-workers computed the interaction of Ru55
NPs76 with aminidates for hydrogenation processes77 and
4-phenylpyridine for the hydrogen evolution reaction (HER).78
Atomistic information about the role of the ligands in the operating mechanisms is not available yet.

Scheme 4 Homolytic (a) and N-promoted heterolytic (b) H–H bond
activations on Au(111) surfaces.

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Phosphine compounds are common ligands in homogeneous
catalysis due to their fine-tuning properties.79 In the heterogeneous world, phosphines are usually employed as NP
stabilisers, but their tunability for catalytic performance is
attracting great interest.80
Lambert and co-workers first reported the use of ligandstabilised Au NPs, [Au55IJPh3P)12Cl6], as selective catalysts for
styrene oxidation by dioxygen.81 Benzaldehyde was the major

product of the reaction, followed by styrene epoxide and
acetophenone. Based on these experimental findings, Zeng
and co-workers performed a DFT study on these liganddecorated Au55 NPs (Scheme 5).82 They thoroughly explored
the configurational space of Au nanoclusters and identified
Au6 faces, so-called triangle sockets, as the reactive sites. The
initial O2 dissociation83 at clean Au55 clusters was unlikely,
presenting an energy barrier of 1.95 eV and a reaction energy
of 0.97 eV. In contrast, the PPh3-decorated Au6 face allowed a
two-step process via a superoxo-like intermediate with an
overall energy barrier of 1.22 eV and a reaction energy of 0.45
eV. Further calculations demonstrated that the ligands not
only stabilise the cluster but also impact the electronic structure of the gold core, promoting O2 activation.84 After O2
dissociation, they computed the reaction mechanisms of styrene oxidation to account for all observed products.82 While
the formation of styrene oxide and acetophenone required
1.08 and 1.16 eV, respectively, the confinement of ligands
around the active site favoured the pathway towards benzaldehyde with a lower energy barrier of 0.86 eV.
van Leeuwen and co-workers employed secondary phosphine oxide (SPO) ligands85 to prepare gold catalysts for the
selective hydrogenation of α,β-unsaturated aldehydes.86 In
particular, SPO-stabilised Au NPs catalysed the hydrogenation
of acrolein to allyl alcohol (Scheme 6), even though the hydrogenation of CC bonds is thermodynamically favoured.
To rationalise the experimental product distribution, López
and co-workers modelled a Au55 NP decorated with 27 Ph2PO
ligands.87 The H2 dissociation on clean Au55 presented an energy barrier of 0.78 eV and an endothermic reaction energy of
0.10 eV. The decorated NP, however, exhibited a lower energy
barrier of 0.54 eV with an exothermic reaction energy of 0.64
eV. Such a sharp difference comes from the organic ligand
which actively participates in the reaction by promoting a
concerted transfer hydrogenation mechanism. Similar to the
previously discussed FLPs, the lone pair of the oxygen

Scheme 5 Oxidation of styrene catalysed by phosphine-decorated
Au55 NPs (Au = yellow, Cl = green, P = orange, C = grey, H = white).

Scheme 6 Hydrogenation of acrolein catalysed by SPO-decorated
Au55 NPs (Au = yellow, P = orange, O = red, C = grey, H = white).

Fig. 2 Linear correlation between computed activation energies and
experimental reaction rates (a) and transition state structure for the
heterolytic H–H bond activation via 1,10-phenanthroline on Au(111)
surfaces (b).

P-based ligands

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abstracts one H atom as a proton while the other H atom is
adsorbed on the metal surface as a hydride. Such a chargeseparation feature favoured the selective hydrogenation of
the more polar CO moiety, compared to the CC bond, by
0.08 eV. Finally, the authors built a multidimensional linear
scaling relationship using a two-variable descriptor based on
the electronic (the energy difference between the HOMO of
the ligand and the HOMO of the substrate) and geometric effects (the empty sites on the NP surface obtained from experimental NP sizes and metal : ligand ratios).
For metal catalysts where H2 activation is not rate-determining, such as Pd and Pt, steric effects become more relevant. Honkala and co-workers found that the selectivity of
acrolein hydrogenation on Pd(111) and Pt(111) is controlled
by the concentration of the reactant at the surface, where
high coverage favours CC bond hydrogenation.88
Chatterjee and Jensen reported the synthesis of Pd NPs
capped with phosphine ligands to catalyse the deoxygenation
of fatty acids to alkenes via anhydride intermediates
(Scheme 7).89,90 While the PPh3-modified Pd catalyst
performed poorly, the use of bidentate Xantphos-like ligands,91 such as DPEphos, resulted in good yields and excellent selectivity towards terminal alkenes.89 Inspired by these
results, we computationally addressed the deoxygenation of
pentanoic anhydride as a model substrate on phosphinedecorated Pd(111) surfaces to unravel the underlying reaction
pathway.92 We observed that monodentate PPh3 ligands fully
cover the Pd surface, forming a self-assembled monolayer,
which poisons the NP surface and prevents the approach of
the substrate (Fig. 3a). On the other hand, the chelating effect of bidentate phosphines created a transient cavity on the
surface (Fig. 3b) which was able to capture the reactant (adsorption energy of −0.62 eV) and promote the deoxygenation
reaction (activation energy of 1.19 eV). Moreover, the steric
hindrance imposed by the ligands at the catalytic site
prompted the release of terminal alkene products, thus precluding their further isomerization into internal derivatives.
The displacement of adsorbed alkenes by ligands has also
been suggested to prevent over-hydrogenation of alkynes on
Cu NPs.93 In this case, thermodynamic selectivity, i.e. the
ranking of adsorption energies between all the species involved, turns out to be an easy yet practical descriptor.94
S-based ligands

Mini review

Fig. 3 Steric environment imposed by monodentate (a) and bidentate
(b) phosphines on Pd(111) surfaces. The cavity is highlighted with a
white square (Pd = light blue, P = orange, O = red, C = grey, H =
white).

is vast102 and computational studies on structural and
electronic features can be found elsewhere.42,43,95,103–109 Instead, we will focus on recent theoretical contributions that
directly address reaction mechanisms.
Jin and co-workers reported a selective CO2RR to CO using
thiolate-capped Au25 clusters.110 Following that study,
Kauffman and co-workers computed the electrochemical
pathway using Au25IJSCH3)18 models.111,112 They found an onset potential of −2.04 V for the formation of adsorbed
COOH,112 which dramatically differed from the experimental
value of −0.19 V (vs. reversible hydrogen electrode).110 The removal of one ligand created an accessible catalytic site to stabilise the key COOH intermediate (Scheme 8), which led to
an onset potential of −0.34 V, much in line with experiments.
Mpourmpakis and co-workers computationally proved the
relevance of ligand removal to understand CO2RR trends for
different Au25 morphologies.113 They performed a systematic
comparison between CO2RR and HER on Au25IJSCH3)18 and
Au25IJSCH3)17 for several charge states.114 They found that H2
formation is favoured for all systems but Au25IJSCH3)17−,
which would be selective towards CO2 reduction by 0.28 eV.
In the same line, Kauffman and co-workers reported that
Cu–S interfaces at mixed Cu/Au NPs stabilised the binding of
CO with respect to that of H, thus enhancing selectivity.115
However, the release of ligands would hamper the reaction
via irreversible CO adsorption.
Continuing with electrochemical processes, Lee, Jiang,
and co-workers reported mono-atom-doped Au clusters for efficient HER.116,117 First-principles studies indicated that the
H atom behaves as a metal in [H-M1-Au24IJSR)18] species, in

Due to the particular properties of the Au–S interface,95,96
thiol ligands have been extensively used to stabilise atomically precise Au nanoclusters.97–101 The literature on this field

Scheme 7 Decarbonylation of anhydrides catalysed by phosphinedecorated Pd NPs.

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Scheme 8 Electrochemical CO2 reduction to CO catalysed by
thiolate-decorated Au25 nanoclusters.

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contrast to their hydride or electron-withdrawing role found
in phosphine-capped derivatives.118
Jin and co-workers reported the selective hydrogenation
of α,β-unsaturated ketones and aldehydes to alcohols
catalysed by Au25IJSR)18 nanoclusters.119 Jiang and co-workers
explored several reaction pathways to explain these experimental results.120 Since dissociation of H2 on Au25IJSCH3)18
involved an energy barrier of >2 eV, they proposed H2 splitting assisted by the substrate. For all computed mechanisms, the H–H bond activation process occurred via hydride transfer to the Au nanocluster and proton transfer to
the CO and CC groups of the substrate (Scheme 9). The
lower activation energy found for CO (0.99 eV) cf. CC
(1.12 eV) was attributed to the different polarity of the
bonds. The explicit inclusion of ethanol solvent molecules as
proton shuttles did not significantly impact the CO hydrogenation barrier (0.94 eV).
Jin and co-workers performed the Ullmann C–C coupling
reaction between two different iodobenzenes using Au25IJSR)18
and found that selectivity towards the heterocoupling product
was substantially increased with aryl thiolates compared to
alkyl derivatives.121 After removal of one ligand to create a vacant site, DFT predicts isoenergetic transition states for
hetero- and homocoupling products for S-methyl, while the
transition state leading to the hetero-coupling product is energetically favoured by 0.12 eV for S-naphthyl.
Zheng, Häkkinen, and co-workers reported the hydrogenation of ketones to alcohols under mild conditions catalysed
by thiolate-protected Cu25H10 nanoclusters (Scheme 10).122
DFT indicates that neither H2 nor formaldehyde can initially
bind to the cluster. Instead, one hydride from the catalyst is
transferred to the C atom of CO while the O atom binds to
Cu. After that, the reaction continues via two competitive
pathways: (i) heterolytic H2 splitting at the Cu–O interface or
(ii) second transfer followed by H2 dissociation on the metal
cluster.
Wen, Liu, and co-workers capped Au NPs with S-based
tetradentate porphyrin ligands for electrocatalytic CO2RR.123
DFT calculations resulted in a large stabilisation energy of
0.55 eV for the COOH intermediate when comparing ligandmodified vs. clean Au(111) surfaces.
Flake, Xu, and co-workers computed HER and CO2RR intermediates on Au(211) facets capped with 2-phenylethanethiol (PET) and 2-mercaptopropanoic acid (MPA), where the
former showed a better performance for CO2RR.124 On the

Scheme 9 Hydrogenation of α,β-unsaturated ketones catalysed by
thiolate-decorated Au25 nanoclusters.

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Scheme 10 Hydrogenation of carbonyl groups catalysed by thiolatedecorated Cu25H10 nanoclusters.

one hand, PET ligands created local negative dipole moments
that stabilised adsorbed COOH species. On the other hand,
MPA ligands extended over the surface and clashed with
adsorbed H and COOH intermediates.
Moving away from Au, thiols were also employed to coat
the surface of other metal NPs.125,126 Janik, Medlin, and coworkers reported the effect of alkanethiol-modified Pd catalysts for the selective hydrogenation of furfuryl alcohol to
methylfuran (Scheme 11a).127 DFT studies suggested that
conversion of furfural alcohol to either methylfuran via
hydrodeoxygenation (HDO) or furan via decarbonylation (DC)
are comparable for clean Pd(111) and Pd(221) surfaces. On
the contrary, methanethiol-coated Pd(221) surfaces created a
confined environment that induces the vertical adsorption of
the substrate and precluded the co-adsorption of DC intermediates, thus increasing the selectivity towards methylfuran via
HDO (Scheme 11b). Similar flat and tilted configurations
were reported by Vlachos and co-workers as a function of furfural coverage.128
Other ligands
Neurock, Tysoe, and co-workers studied the formation of vinyl acetate on Pd(111) at low and high coverage situations.129
Following a Samanos-type mechanism, an adsorbed acetate
reacted with ethylene forming an acetoxyethyl intermediate
which then underwent β-H elimination (Scheme 12). DFT
studies predicted a significantly lower energy barrier for the
C–O coupling at high acetate coverage. Such a trend was
explained as a competition between neighbouring acetate
groups, which decreased their nucleophilicity and thus enhanced their reactivity towards ethylene. Similarly, a realistic
simulation of formate coverage provided reaction rates in line

Scheme 11 Hydrogenation of furfuryl alcohol catalysed by thiolatedecorated Pd NPs (a) and reaction mechanisms imposed by coverage (b).

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Scheme 12 Synthesis of vinyl acetate catalysed by acetate-decorated
Pd(111) surfaces.

with experiments for Cu-catalysed CO2 hydrogenation to
methanol.130 Poteau, Philippot, and co-workers predicted a
promising HER catalyst based on acetate-capped Ru NPs.131
Wang and co-workers modified Cu electrodes with amino
acids to enhance the selectivity of electrochemical CO2RR towards hydrocarbons, where glycine performed best in terms
of faradaic efficiency.132 They computed the free energy
change of CO2 and CO protonation for clean and zwitterionicglycine-decorated Cu(110) surfaces. DFT studies showed that
the NH3+ group from glycine stabilised adsorbed COOH and
CHO intermediates via H-bond-like interactions by ca. 0.5 and
0.2 eV, respectively, with respect to the clean surface.
Saeys and co-workers computed the reaction mechanism
of catalytic transfer hydrogenation of ketones on Cu(111).133
Using a coverage-dependent microkinetic model, they demonstrated a direct proton transfer between the sacrificial alcohol and the ketone, which avoids surface-bound hydride
intermediates.
Knecht, Naik, Heinz, and co-workers reported Stille crosscoupling reactions catalysed by peptide-capped Pd NPs,134–136
where the catalytic activity was estimated by computing the
energy associated with the rate-determining leaching of one
Pd atom from the bulk NP.137 In analogy to peptides, organic
polymers can also coat metal surfaces and have an impact in
catalysis. However, these systems are beyond the scope of the
current work.

3. Surfactants
Surfactants may behave differently from typical organic ligands as they are constituted by close contact ion pairs, involving ionic and van der Waals interactions with the metal.
One of the most successful systems involving surfactants is
the so-called NanoSelect™ patented by BASF.138 The watersoluble
hexadecylIJ2-hydroxyethyl)dimethylammonium
dihydrogenphosphate (HHDMA, Fig. 4a) is employed to prepare
Pd catalysts for the hydrogenation of terminal alkynes to

Fig. 4 Structure of HHDMA (a) and its coordination modes on Pd(111)
surfaces (b).

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Mini review

alkenes, surpassing Pb-free Lindlar systems.139,140 To fully
understand the performance of NanoSelect™, the structure
of such hybrid catalysts should be first characterised. In that
sense, atomistic simulations were carried out at the interface
between HHDMA and Pd(111).141,142 DFT results showed that
the anion binds to Pd, which might involve O–H dissociation,
while the cation stays close to the anion by electrostatic and
H-bond interactions. At low concentration of HHDMA, the aliphatic chain lies flat on the metal surface. With increasing
ligand loadings, it moves upwards and forms a monolayer
that coats the NP (Fig. 4b). Classical molecular dynamics
(MD) simulations indicated that such long chains are highly
mobile and can adapt the approach of substrate molecules to
the surface under catalytic conditions.
Pérez-Ramírez and co-workers employed NanoSelect™ for
the selective direct synthesis of hydrogen peroxide
(Scheme 13).143 The reaction mechanisms for clean and
HHDMA-decorated Pd(111) surfaces were computed at DFT
level. The mechanism starts with O2 adsorption on the catalyst surface followed by hydrogenation via hydroperoxy OOH
intermediates. Although the activation energy to form H2O2
is lower for the clean surface (0.74 eV) than that for the modified catalyst (1.18 eV), side reactions are faster for the former.
Indeed, the environment imposed by the surfactant–metal
interface induces a vertical adsorption of O2 and OOH intermediates (Scheme 13), i.e. a conformational change that precludes alternative pathways involving O–O bond cleavages
and, therefore, increases selectivity.
Pt-based NanoSelect™ catalysts have also been used for
the hydrogenation of nitroarenes to anilines.144–147 Habertype mechanisms were computed for Pb-modified Pt(111) as
well as clean Pt(111),146 where nitrobenzene was adsorbed in
a vertical way to mimic the environment imposed by the surfactant. Further DFT simulations using a fully atomistic
model of decorated Pt NPs (Fig. 5a)147 showed that the ion
pairs can induce a local electrostatic field148,149 at the surfactant–metal interface, which promoted an electrochemical-like
effect with heterolytic H2 splitting in the presence of water
(Fig. 5b), as well as the formation of high-energy electrons
confined in the metal. These electrons can then be effectively
transferred to the nitrobenzene, increasing the adsorption
and thus the reactivity.

Scheme 13 Direct synthesis of hydrogen peroxide catalysed by clean
and HHDMA-decorated Pd NPs.

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Fig. 5 Surfactant-decorated Pt260 nanoparticle (a) and closer look at
the electrochemical interface (b) (Pt = dark blue, P = orange, O = red,
N = blue, C = grey, H = white).

In the same line, HHDMA-decorated Ru NPs have been
employed for the hydrogenation of levulinic acid to
γ-valerolactone (Scheme 14).150 The complete reaction network was explored for a clean Ru(0001) surface, while only
the most relevant steps were considered for the HHDMAdecorated Ru(0001) model. DFT calculations predicted a
barrierless H2 dissociation on the surface, followed by C–OH
cleavage, ring closing, and hydrogenation steps. The key role
of the surfactant relies on its ability to locally make the
metal–ligand–water interface more acidic, thus opening faster
reaction pathways compared to those for the clean surface.

4. Ionic liquids
Ionic liquids (ILs) are widely employed as stabilising agents
of metal NPs during their synthesis151 and catalytic applications.152 Contrary to the previously discussed ligands, where
different coverages may be considered, ILs should be described as a condensed phase to properly account for their
electrostatic and dynamic properties.153 Otherwise, cluster
models154 would not account for the effect of the bulk liquid
and continuum methods155 would fall short to describe key
solute–solvent interactions. In such a situation, the computational modelling of metal–IL interfaces is mainly dominated
by force-field-based MD simulations; see for instance the IL
solvation of metal surfaces156,157 and metal NPs.158–160 The
role of ILs on non-metal surfaces has also been studied, such
as shifts in TiO2 band levels161 and interactions at the interface with sapphire162 and graphite.163 Despite the valuable
structural information that can be obtained from classical

Scheme 14 Hydrogenation of levulinic acid catalysed by HHDMAdecorated Ru(0001) surfaces.

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MD, ab initio simulations are required to properly describe
bond-breaking and bond-forming processes during chemical
transformations.
Lawson and co-workers studied the decomposition of ILs
at the interface with Li(100) by means of ab initio MD.164 For
[C4C1pyrr]ijTFSI] (Chart 1), they observed that only anions decompose, and the resulting F and O atoms penetrate the Li
layers. On the other hand, [EMIM]ijBF4] (Chart 1) was stable.
Fenter and co-workers reported the reconstruction of Bi
electrodes in alkyl-imidazolium-based ILs under CO2RR
conditions. Interestingly, such behaviour was not observed
when using electrolytes containing tetrabutylammonium
(TBA).165 A combined experiment–theory effort later explained the origin of such a phenomenon.166 The authors
performed reactive MD simulations with a three-component
model consisting of graphene, Bi(001), and cationic species
[BMIM] (Chart 1) and TBA. In line with experiments, they observed that [BMIM] cations bind more strongly and induce a
higher degree of distortion to the charged Bi(001) slab than
TBA. Additional DFT calculations in smaller systems confirmed the previous adsorption energy trends and predicted
the partial dissolution of Bi at negative potentials.
MacFarlane and co-workers reported high selectivity for
electrochemical nitrogen reduction reaction (NRR) over HER
using metal NPs and fluorinated ILs.167,168 Later, Ortuño
et al. modelled a metal–IL interface using [C4C1pyrr]ijFAP]
(Chart 1) to understand the origin of such selectivity.169 First,
they built a bulk IL phase on a Ru(0001) slab. After equilibration with classical and ab initio MD, they computed NRR and
HER intermediates for clean and IL-modified metal surfaces
at DFT level. They found that stabilising interactions between
the key intermediate Ru–N2H and the F atoms of the anions
steered the reaction towards ammonia production
(Scheme 15).

5. Deactivation pathways
Although sometimes overlooked, considering catalyst stability
is crucial for the computational prediction of realistic and
feasible systems. Metal stability in multi-metallic materials is
typically employed as a filter in high-throughput computational screening of (electro)catalysts.170 Indeed, such studies
also apply to heterometallic metal–ligand interfaces, as recently shown by Taylor and Mpourmpakis,171 but for such
complex systems a few extra criteria need to be taken into

Chart 1 Chemical structures of IL pairs.

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Scheme 15 N2 reduction catalysed by IL-decorated Ru(0001) surfaces
via the N2H intermediate. Only one IL pair is shown for clarity (Ru =
dark green, P = orange, F = pink, N = blue, C = grey, H = white).

account, namely: (i) ligand desorption, (ii) ligand degradation, and (iii) ligand-promoted metal leaching.
While strong σ-donating carbenes and phosphines
strongly bind to transition metal surfaces, other ligands such
as amines may detach under catalytic conditions. A clear example is the amine-assisted FLP H2 dissociations on Au
discussed previously (Scheme 4 and Fig. 2). In those situations, the ligand had to be added in large excess as it was
consumed during the catalytic cycle.73 This problem was later
solved by anchoring a N-doped graphene-like layer on top of
the catalyst surface.74
Regarding ligand stability, phosphine ligands are known
to decompose on metal surfaces under thermal treatment. Indeed, such ligand degradation is often exploited as a synthetic protocol to prepare metal phosphides.172 In this line,
recent DFT calculations predicted relatively low energy barriers for the P–C bond breaking process of alkyl phosphines
on Ni surfaces.173 Although these processes usually involve
first-row transition metals, ligand decomposition on heavier
metals cannot be fully ruled out.174
Finally, metal leaching is fairly common in heterogeneous
catalysis, but the accurate computation of this process would
require the explicit representation of ligands or solvent molecules in the surroundings of the leached species. Heinz and
co-workers systematically evaluated abstraction energies of
Pd atoms from Pd NPs containing up to 1000 atoms.137
Zvereva and co-workers found that ILs stabilised Pd clusters
through strong polarization interactions,175 and they further
demonstrated that the leaching of Pd atoms can occur via
ionic [PdX] species rather than via “naked” atoms.176 Catalytic ensembles involving molecular and surface active species are indeed dynamic,177 and both homogeneous and
heterogeneous cycles can co-exist via oxidative leaching and
deposition processes.178

6. Summary and perspectives
Throughout this mini-review we highlighted computational
contributions that helped in revealing mechanistic features
on ligand-decorated metal NPs in heterogeneous catalysis.
There are intrinsic challenges to these interfaces that include:

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(i) the size of the systems, (ii) the variety of bonds, (iii) the
lack of detailed experimental structural knowledge at the
interface, and (iv) the dynamic nature of most of the ligands.
The ultimate consequence of these limitations is that firstprinciples reaction mechanisms are still scarce, but the potentiality of bifunctional mechanisms makes them a very attractive research field. Nevertheless, from these pioneering
studies we can already see that the combination of a few experimental data and ligand-based descriptors is the faster
path forward in the prediction of the properties of complex
interfaces and the establishment of robust structure–performance relationships.
As computational catalyst design179 establishes in
heterogeneous180–182 and homogeneous183–185 fields, new opportunities arise for systems at the interface between these
two worlds. Together with metal–support186–188 and photo-189
and electrochemical190,191 interfaces, the modelling of
multicomponent192 ligand-decorated nanocatalysts has the
potential to circumvent typical scaling relationships and thus
enhance catalytic performance.
The avenue of new machine learning193 techniques is rapidly approaching catalysis194–196 and materials science.197,198
Chemical descriptors, which typically describe catalytic activity and selectivity, are usually employed as input for algorithms. On the homogeneous side, machine learning protocols have been recently employed to uncover metal–oxo
intermediates199 as well as to screen transition metal catalysts for cross-coupling reactions.200 On the heterogeneous
side, García-Muelas and López demonstrated how principal
component analyses and regressions provide adsorption energies on metal surfaces from covalent (d-band center) and
ionic (reduction potential) descriptors.201 Ulissi et al. relied
on surrogate models to fully explore the syngas reaction network (CO + H2) on Rh(111).202 Later, Tran and Ulissi further
employed a machine-learning-based workflow to compute
1499 intermetallic materials for CO2RR and HER.203 Despite
the heavy dependence of these techniques on the descriptors'
performance, the combination of electronic structure
methods with database repositories and machine learning algorithms seems particularly suited to address the complexity
of the catalytic properties of ligand-decorated nanoparticles.
We finally point out the importance of interpretability to provide solid scientific meaning to the results obtained from statistical learning models.204

Conflicts of interest
There are no conflicts to declare.

Acknowledgements
M. A. O. acknowledges the support of the Beatriu de Pinós
postdoctoral programme of the Government of Catalonia's
Secretariat for Universities and Research of the Ministry of
Economy and Knowledge (2017-BP-00039). M. A. O. and N. L.
also acknowledge the Spanish Supercomputing Network

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(RES) and the Barcelona Supercomputing Center (BSC)
(QCM-2019-1-0030).

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