supplementary information - media.nature.com · thermostat as implemented in lammps. 3. ......

1. Methods 2

1.1. Experimental Details 2

1.2. Computational Details 5

2. Validation of the reactive force field 11

3. Ab-initio MD simulations 19

4. Electronic origin of the differences in catalytic activity between different metal

surfaces 21

5. Evolution of various bonds during reactive molecular dynamics on various metal

surfaces 24

6. Repeatability of the results of MoNX-Cu nanocomposite coating in PAO 10 28

7. Results of another optimized nanocomposite coating: VN-Cu 29

SUPPLEMENTARY REFERENCES 30

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1. Methods

1.1 Experimental Details

MoNx-Cu coated and uncoated AISI 52100 steel samples were used as test

coupons. After Ar ion etching to remove the surface contaminants, the MoNx-Cu

nanocomposite was deposited using individual Mo (99.95%) and Cu (99.99%) targets in

a dual magnetron sputtering system. To achieve ≈6 at. % Cu in the composite coating,

4,000 W (9 W/cm2) and 200 W (0.45 W/cm2) were applied on Mo and Cu targets,

respectively. The substrate temperature was kept constant at 270oC. The total working

pressure was fixed at 0.4 Pa in a mixture of Ar/N2 (130 sccm/55 sccm, respectively).

After the deposition, the crystallographic phase analysis was done by X-ray

diffraction using monochromatized Cu Kα radiation (Bruker D2 Phaser); the chemical

composition was obtained by X-ray photoelectron spectroscopy (Physical Electronics

PHI 5400) using monochromatic Mg Kα X-ray source. The hardness and elastic

modulus of the coating were studied by nano-indentation (Hysitron Triboindenter TI-

950) with a Berkovich diamond probe and loads in the range of 0.5 mN to 12 mN. A

high-resolution transmission electron microscope (JEOL 2100F) operated at 200 kV

was also used to analyze the chemical and structural nature of the composite films and

the boundary films formed on rubbing surfaces.

The friction and wear experiments were carried out with a ball-on-disk test rig

(Tribometer Nanovea) in which a stationary steel ball (9.5 mm in diameter) was pressed

against a rotating disk (50-mm diameter and 7-mm thick) under 20 N load, which

created 1.3 GPa peak Hertzian contact pressure. The ball-on-disk experiments were

performed with coated and uncoated test pairs in (neat) basestock PAO 10 oil (whose


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kinematic viscosity at 40 and 100oC was 66 and 10 mm2/s, respectively) and in fully

formulated 5W30 oil (ILSAC GF-5 multigrade engine oil whose kinematic viscosity at 40

and 100°C was 61 and 11 mm2/s). The sliding contact surfaces of the ball and disk

specimens had a nominal surface roughness of 0.02 µm RMS. Prior to tribological tests,

all samples were cleaned twice by acetone and isopropanol solvent in an ultrasonic

bath for 5 minutes and then dried in open air. The lubricated sliding tests were

performed at room temperature and in ambient air with ≈40% relative humidity. The fully

formulated 5W30 and the PAO 10 oils were applied to the sliding surface with a syringe

in the amount of 0.05 ml, which was sufficient to cover the whole rubbing surface. The

total sliding distance accumulated during the tests was 3,600 m. The friction force

generated between the sliding ball and flat surfaces was continuously monitored and

later converted to the friction coefficients for the entire test cycle. The wear volumes on

the ball and disk samples were assessed with the help of optical microscopy (Olympus

STM6) and optical 3D non-contact surface profilometry (Bruker Contour GT).

Rubbing surfaces were analyzed by confocal Raman microscopy (inVia Reflex,

Renishaw, Inc.) using appropriate light sources with a range of wavelength (325 nm and

633 nm) to determine the nature of the tribo-chemical films that formed on the rubbing

surfaces during sliding. The Raman instrument was calibrated using an internal silicon

reference, and the spectra were recorded in the range of 100-3,200 cm-1. Fe2O3 micro

powder (≥99%, Sigma Aldrich) and highly oriented pyrolytic graphite (Ted Pella, lacey

carbon) were used as references.

The tribological layer was scrapped off the MoNx-Cu tested ball by using a

tungsten tip in a focused ion beam instrument. The use of the electron beam and ion



beam was kept minimal to prevent damage on the sample. The thin film was

subsequently transferred to an Ominiprobe TEM grid to perform the TEM and EELS

analyses. The sp2 fraction was calculated using the EELS spectra by a very well-known

procedure.1,2 The EELS spectra were calibrated using highly oriented pyrolytic graphite

HOPG (Ted Pella, lacey carbon). The sp2 fraction (x) in the tribological layer was

determined by comparing the ratio of the π* and σ* peaks intensity with that of HOPG

using the equation (S1):

(S1)

The repeatability of the friction and wear tests were confirmed by running multiple

tests on MoNx-Cu coated pairs under the same conditions (Fig. S8 shows the results

from one of these tests). We provide Fig. S9 to show that carbon-rich tribofilm can also

form on Cu-containing VN-based nanocomposite coating.

TOF-SIMS studies of the tribofilm were carried out using an imaging ToF-SIMS

(PHI TRIFT III) instrument which was proven to be very versatile in revealing the

chemical nature of very thin (a few Å to 1–2 nm) surface layers with high mass and

spatial resolutions, providing both chemical and distributional (laterally and in depth)

information for a wide variety of masses down to hydrogen. The raw data streams of the

sputter depth profiles of examined area contain a full mass spectrum of every pixel in

three dimensional space; hence, the TOF-SIMS images of these films can be re-

constructed from selected masses and/or depths to provide chemical information.

Prior to TOF-SIMS analyses, we solvent cleaned the tested MoNx-Cu-coated ball using

heptane in order to remove the remnants of PAO oil. For the generation of SIMS



spectra and images, we used liquid Au cluster ions as the primary ion sources (primary

ions were produced using a 22 keV-pulsed Au+ analytical gun, and the beam current

was between 500 pA and 3 nA). Cesium ions were used for sputtering of the

contaminant layers from top surfaces and for depth profiling purposes. The sputtering

area was kept 3 times larger than the analyzed area to prevent any artificial edge

effects during data collection. The sputtering gun was operated at 2 kV and 1,500 pA

and the sputtering time was 60 s for each cycle. The mass range was from 1 to 800

amu, and the mass resolution m/Δm was better than 2,300 at mass 25.

1.2 Computational Details

Reactive molecular dynamics

We employed reactive molecular dynamics (RMD) to investigate the atomic scale

processes governing the metal catalyzed formation of carbon-based boundary films

from linear olefins under tribological conditions. All RMD simulations are performed

using the classical MD simulation package LAMMPS3 in a canonical ensemble (NVT)

with a time step of 0.25 fs. Our typical computational supercell consists of two metal

slabs held at a vertical distance of ~2 nm apart, which act as rubbing surfaces (Figure

S1); each of these blocks containing 2,560 atoms (~3.5 nm × 4.0 nm × 2.0 nm). Periodic

boundary conditions are employed in the plane of metal slab (i.e., along x- and y-

directions). The lubricating oil is modeled as a mixture of linear terminal alkene chains

(alpha olefins) of various chain lengths (each containing 3 – 20 carbon atoms) oriented

along arbitrary directions. These linear chains are placed at random locations in the

empty space between the two metal slabs such that the overall mixture of alpha olefins



contains ~1100 C atoms. We ensured that the atomic scale pathways identified by our

RMD simulations are indeed robust and are independent of the values of chain lengths,

as well as their distribution employed to model the olefins; the kinetics of the processes,

however, becomes slower as the average chain length increases. To simulate the

friction between two metal slabs, we impart a constant sliding velocity of 0.1 m/s

(following our experiments) along the x-direction while keeping the top metal block fixed

in space. Using this initial configuration, the system is equilibrated at 1000 K; the

constant temperature conditions are maintained by employing the Nosé-Hoover

thermostat as implemented in LAMMPS.3

To accurately capture the tribochemical reactions that occur at the sliding metal-

olefin interfaces, we describe the atomic interactions using a general bond-order based

reactive force field (ReaxFF), which is known to describe formation and dissociation of

chemical bonds well.4-6 In the framework of ReaxFF, the total energy is composed of

several contributions arising from covalent interactions (e.g., bond stretching, angle

bending, dihedral torsion, and over-/under-coordination), as well as non-bonded

interactions (e.g., van der Waals, Coulomb). The short-range bonded interaction terms

are computed from instantaneous bond-orders for each atom pair. This bond order, in

turn, is strongly influenced by the local environment around the particular pair of atoms,

thus, accounting for multi-body effects.4-6 The non-bonded interactions, on the other

hand, are computed for each pair of atoms regardless of its connectivity. In addition, the

atomic charges are evaluated at each step using an electronegativity equalization

scheme.7 Such a general formalism enables ReaxFF to accurately capture covalent,

ionic, and metallic bonding, as well as transition states along a reaction pathway.4-6



Consequently, ReaxFF has been used successfully for a wide range of material

systems, including metals, metal-oxides, ceramics, metal-organics, and hydrocarbons.

Figure S1. Schematic representation of a typical computational supercell

containing two metal slabs (brown) separated by a vertical distance of ~2 nm. The

space between the two slabs is filled with linear alkenes of varying chain lengths

ranging from 3--20 carbon atoms. To simulate tribological conditions, the bottom metal

slab is imparted a constant sliding velocity of 0.1 m/s, while keeping the atoms in top

slab fixed. The C atoms belonging to the alkenes are shown in gray and the H atoms

are depicted in blue (see magnified image).

In this study, we explored five different metals, namely, Cu, Ni, V, and Mo. The

ReaxFF parameters for metal-hydrocarbon interactions are taken from (a) Cu/C/H:

Ref.8, (b) Ni/C/H: Ref.9, and (c) V/C/H: Ref.10. Since ReaxFF parameters are not

available in the literature for Mo/C/H, we employed Modified Embedded Atom Method

Metal

Metal

Olefins

v = 0.1 m/s

~2 nm

~2 nm

~2 nm

x

y



(MEAM) to describe Mo-Mo interactions (parameters from Ref.11), Adaptive

Intermolecular Reactive Empirical Bond Order (AIREBO) potential for C-C, C-H

interactions,12 while the Mo-C, and Mo-H interactions are modeled by Lennard-Jones

(LJ) interactions with parameters obtained from Lorentz-Berthelot mixing rules (Table

S1). The LJ contribution for a pair of atoms, held at a distance r apart is given by:

E = 4es

r

æ

èç

ö

ø÷

12

-s

r

æ

èç

ö

ø÷

6é

ëêê

ù

ûúú, (1)

where σ and ε are independent parameters that define the length and energy scales

respectively. For each of the five metals, we perform NVT-MD simulations at 1,000 K for

1 ns. Using these simulations, we identified that all these transition metals catalyze

dehydrogenation of the alkenes, and breakdown of the longer hydrocarbon chains into

smaller fragments. Note that the reaction pathways predicted by the RMD simulations

are independent of supercell size. Even large system sizes (~ 1 million atoms; ~100,000

C atoms) showed catalytic formation of graphitic carbon tribofilms via reaction pathways

identical to those revealed by smaller supercells. Subsequently, V shows extensive

carbide formation, Mo shows reduced propensity to form carbides particularly at 1,000

K, while Cu and Ni do not form any carbide.

To validate the findings of ReaxFF with another interatomic potential model, as

well as to facilitate long MD simulation times, we repeated the MD simulations for

Cu/olefins system using EAM to describe Cu-Cu interactions,13 AIREBO for C-C, C-H

hydrocarbon interactions12 and LJ (Eq. 1) to describe the cross interactions (i.e, Cu-C,

and Cu-H); LJ parameters are listed in Table S1. The AIREBO interatomic potential

model has been successfully employed to study interactions between graphene and



sliding interfaces under tribological conditions in a recent work.14 The parameters for the

LJ interactions are listed in Table S1. We performed NVT-MD simulations for the Cu-

olefins system at 1,000 K for 2 ns. In a typical tribological experiment, the free hydrogen

formed via dehydrogenation of olefins, and breakdown of longer alkenes into shorter

ones can escape the system through a free surface or can get adsorbed into the bulk

metal slab. To emulate this loss of atomic/molecular hydrogen from the system following

dehydrogenation/chain scission, an efficient hydrogen removal scheme is necessary.

Experimentally, the carbon-hydrogen (C-H) and hydrogen-hydrogen (H-H) bond lengths

are reported to be 1.09 Å and 0.74 Å, respectively; these bond lengths are accurately

reproduced by the AIREBO potential.15 In our simulations, we identified the hydrogen

atoms to be removed (either as free atomic hydrogen or as H2 dimer) from the

simulation cell using distance criteria. Specifically, we monitored the distance between

carbon and hydrogen atoms every 1 ps (Note, we verified that an increased frequency

of monitoring distances does not impact the results significantly). If the separation

between a previously bonded C-H pair is ≥ 1.5 Å, then the C-H bond was assumed to

be broken and the respective H atom was removed from the simulation cell. This is

certainly reasonable since the bond cut-off distance in AIREBO potential is 1.5 Å.

Similarly, if two H atoms are ≤ 0.75 Å apart, we considered them to have formed a H2

dimer; we removed the dimer to simulate loss of hydrogen gas.



Table S1. Lennard Jones parameters ε and σ (Eq. 1) for various atomic pair types

derived by Lorentz-Berthelot mixing rules. For mixing, the LJ parameters for the

individual species are taken from the references listed for each pair.

Atom-pair ε (eV) σ (Å) References

Cu-C 3.433 × 10-4 2.838 12,16

Cu-H 2.495× 10-4 2.463 12,17

Mo-C 0.0536 3.070 12,18

Mo-H 0.0389 2.695 12,18

Ab-initio molecular dynamics simulations

To validate the atomic scale pathways predicted by our ReaxFF based MD

simulations, we employed ab-initio MD simulations (AIMD) within the generalized

gradient approximation (GGA) using the projector-augmented wave formalism as

implemented in the Vienna Ab-initio Simulation Package (VASP).19,20 The exchange

correlation is described by the Perdew-Burke-Ernzerhof (PBE) functional,21 and the

plane wave energy cut-off is set at 400 eV. The Brillouin zone is sampled at the -point

only. Using the AIMD simulations in the canonical ensemble (NVT), we monitor the

temporal evolution of 1-pentene (a representative olefin) molecule on three different

metal surfaces: Cu (111), Mo (001) and V (001) as well as MoN (100) and VN (100) at

various temperatures in range of 1,000 K-1,500 K for 10 ps using a timestep of 0.5 fs.

The surface slab supercells consisted of 5 atomic layers (100-200 atoms) with



dimensions ~1.5 nm x 1.5 nm in the plane of each layer. Periodic boundary conditions

are employed along all directions; a vacuum of 2 nm was employed in the direction

normal to the surface to avoid spurious interactions across the periodic boundaries. The

pentene molecule is initially placed at a random location on the metal /nitride surface at

a vertical height of 0.2 nm. Constant temperature conditions are maintained using a

Nose thermostat as implemented in VASP.19,20

2. Validation of the reactive force field

To assess the suitability of ReaxFF for investigating tribochemical processes in

olefins, we first compared the ReaxFF predicted energies of an elaborate set of

configurations (~20,000) of various representative hydrocarbons, namely benzene,

butane, butadiene, cyclohexane, ethylene, icosene, octane, pentane, pyrene, and

propene; 2,000 distinct configurations for each of these hydrocarbons were sampled via

high temperature (1500 K) ReaxFF MD simulations. Indeed, such a data intensive

validation of the ReaxFF force field for the olefin system has not been reported before.

These configurations were used as input structures and single point energies of these

olefin configurations were determined at M06-2X/6-311++G(d,p) level of theory using

Gaussian 09. The minimum energy of these clusters was taken as ground states and

the relative QM single point energies were computed.

Figure S2 shows the comparison between the relative configurational energies

derived from ReaxFF with that obtained from quantum calculations. Figure S2 (a)

compares the ReaxFF predicted energies (relative to the most stable geometry for a

given hydrocarbon) for these confirmations with those obtained from density functional



theory (DFT) calculations. To place these comparisons in perspective, we have also

shown the performance of AIREBO, an accomplished interatomic potential for

hydrocarbons in Figure S2 (b). Note the extensively large of configurations sampled (~

2,000 for each of the different olefin systems) from the MD trajectories. By employing

such a large cross-validation test set, we are ensuring that the test set amply samples

the energy landscape; this test set provides good representation of the diverse

configurations, which are expected to be encountered during elongation of C-C, and C-

H bonds, bending of C-C-C angles, and other hydrocarbon activation processes that

can occur under tribological conditions. We find that there is an excellent correlation

between the predictions of the force fields and the quantum calculations. The average

differences in the energy values predicted by force fields as compared to those

quantum calculations (mean absolute error; MAE) are ~0.04-0.05 kcal/mol for all the

cases, which suggests an excellent agreement. The MAE in the energy values

predicted by ReaxFF when compared to DFT values is 0.04 eV/atom with a standard

deviation of 0.03 eV/atom; AIREBO provides similar accuracy with error of 0.05 ± 0.04

eV/atom. It is important to note that since even DFT calculations are not expected to

reach chemical accuracy (0.04 eV/atom), both ReaxFF and AIREBO potentials are

performing essentially at the limit of DFT accuracies. Also, a comparison of the

energetic ordering of the solvated structures between the force field and the quantum

calculations shows excellent agreement. Given the remarkable accuracy of the force

field employed in this work, it is quite reasonable to expect that the lowest energy

configuration clusters chosen from a diverse set of MD configurations adequately

represent the most thermodynamically stable cluster structures upon which further



quantum calculations can be performed. Furthermore, we have also performed ab initio

MD simulations of the hydrocarbon dissociation process (see Main text) and observe

that the elementary steps involved in the catalytic process such as dehydrogenation of

the hydrocarbon chains (C-H bond scission), and scission of the C-C backbone of the

chains are in remarkably good agreement with the predictions of the reactive force

fields. Collectively, our rigorous and extensive cross-validation of the ReaxFF by AIMD

simulations and M06-DFT calculations shows remarkably good agreement and provides

sufficient degree of confidence in the predictive power of the force fields for systems

involving olefins interactions with the different metals investigated in this study.

Figure S2. Comparison of cohesive energies of a wide range of structural

configurations (sampled via reactive MD simulations at 1500 K) for numerous

hydrocarbons, as predicted by (a) ReaxFF, and (b) AIREBO empirical potentials, with

those obtained from DFT calculations. For each hydrocarbon, the energies plotted are

relative to their most stable configuration.

Charge transfer events between metal surfaces and olefins play a crucial role in

activating the hydrocarbons, which underpins the response of olefins to tribiological

environment. Indeed, the reactive force field (ReaxFF) employed in this work [Ref. 8] to



describe the interactions between Cu, C and H accounts for transfer of electronic

charge between atoms using electronegativity equalization method (EEM) [Ref. 7].

Using this method, the equilibrium charges on each atom are determined self-

consistently at each step during a molecular dynamics simulation; these charges are

strongly dependent on the local atomic arrangement. Owing to the environmental

dependent charges, the ReaxFF is able to accurately predict the bond formation and

bon breakage phenomena and thus is able to handle the reactive processes within the

framework of classical molecular dynamics. The EEM technique has been found to

accurately describe charge-transfer (i.e., redistribution of charges) in a wide range of

systems including metals, oxides, semiconductors, polymers and hydrocarbons.5,6,14,22-

24 An accurate description of the charge transfer is important for describing bond

energetics for any given system. More relevant to the current study, the ReaxFF

parameters employed in the current study accurately predict bond-dissociation, angle

distortions, and chemical reaction energies associated with Cu/hydrocarbon

complexes.8 For instance, ReaxFF predicts dissociation energies within < 0.5 eV/atom

of those obtained from quantum mechanical (QM) calculations, as shown in Table S2

[Ref.8]. These QM calculations were performed using the hybrid density functional

theory functional B3LYP, which is known to give accurate description of reaction profiles

for transition metal complexes with organic molecules.8,25

Table S2. Comparison of bond dissociation energies predicted by ReaxFF with

those obtained from QM calculations [Ref. 8]



Reactant Products EQM

(eV/atom)

EReaxFF

(eV/atom)

CuCH3 Cu + CH3 2.35 2.18

Cu(CH3)2 CH3Cu+ CH3 1.14 1.31

Cu(CH3)3 (CH3)2Cu + CH3 1.22 0.71

CH3-Cu=CH2 CH3-Cu + CH2 1.94 1.56

Cu-C6H5 Cu + C6H5 0.11 0.11

More importantly, we note that the electron transfer (re-distribution of charges)

obtained from the electronegativity equalization method (EEM) influences the bonding

potential. This unique feature of ReaxFF makes it an appropriate interatomic potential

model to represent the dissociation and formation of bonds via electron transfer. In fact,

the distinguishing feature of ReaxFF compared to other “variable charge models”, which

also employ EEM, lies in its ability to capture chemical bond formation/breakage within

the framework of molecular dynamics simulations. For instance, upon combining EEM

with simplistic (non-bond order) functional forms such as Morse or Buckingham (termed

variable charge models, e.g., Ref. 26), the charge-redistribution affects only the

Coulombic terms; the absence of bond-order terms make them inherently incapable of

capturing breakage of an already existing bond or the formation of a new chemical bond

that occur via electron transfer. Such variable charge potential models can describe

structure, mechanics, energetics, and atomic charge variations (values between 0 and

atomic valency) under dynamical conditions. However, it cannot model reactive

processes such as oxidation. In essence, a variable charge model, which employs non-

bonded interactions combined with EEM, cannot describe any kind of chemical reaction.



ReaxFF, on the other hand, is a powerful method precisely because of its ability

to identify bond order between every atom pair based on its local atomic environment,

i.e., all atoms within a sphere of a certain cut-off radius (typically 10 Å) around this pair.

This in conjunction with charge redistribution via EEM method allows it to treat complex

chemical reactions within the MD framework.5 For the sake of simplicity, the bond order

can be considered to be equivalent to the number of chemical bonds in the system. For

example, in ethene (H2C=CH2), the bond between two carbon atoms has a bond order

of 2 (1, 1 π bond), while C-H bonds have bond order of 1 (1 bond).

Figure S3 Dependence of the total bond order and its components as a function

of interatomic distance between two carbon atoms [Ref. 27].

In molecular orbital theory, the bond order BO is defined as half the difference

between the number of bonding electrons Nb, and anti-bonding electrons Nab, written as:



BO =Nb - Nab

2.

Linus Pauling (Ref. 28) demonstrated that the partial bond-order contributions

BOij

a

(a value between 0 and 1) arising from a bond type (i.e, , π, or ππ bonds) for a

given atomic pair i-j can be described as a continuous function of the instantaneous

distance between atoms i and j (rij) Such a function results in a partial contribution of 1

for rij values within a specific distance (i.e, equilibrium i-j bond length) As rij increases,

BOij

a

decreases smoothly before eventually vanishing at large distances. In the

framework of ReaxFF, the total bond order for an atomic pair i-j is defined as the sum of

partial contributions from , π, and ππ bonds, written as:

BOij = exp aij

a rij

r0

a

æ

èç

ö

ø÷

bijaé

ë

êê

ù

û

úú

a

å Dij

a, a Î {s ,p,pp},

whereDij

a

is a correction term that accounts for over-coordination and under-

coordination effects, while aij

a

and bij

a

are specific (empirical) bond parameters. The total

bond order BOij can take values between 0 (no bond) and 3 (1, 1 π, and 1 ππ bond).

As shown by Fig. R2, this total bond-order becomes 3 below equilibrium bond length,

while vanishing at large inter-atomic distances.. The different ranges of , π, and ππ

partial contributions guarantee a large flexibility in the set-up of reactive force fields.

Note that the bond-order can be uniquely determined for all pairs of atoms from the

inter-atomic distance rij. In addition, multi-body effects are accounted via a rigorous

bond-order correction scheme, such that the bond order between a given pair i-j is also



influenced by all the atomic pairs within a prescribed cut-off (typically sphere of 10 Å

radius) around it.

Briefly, in ReaxFF, all connectivity dependent (bonded) interactions, including the

bond, valence, and torsion angles are strong functions of bond order; these bond orders

are updated at every MD timestep. Although this makes ReaxFF computationally more

intensive compared to other empirical potentials, the bond-order dependence of all

bonded interactions makes it capable of capturing bond dissociation/formation during a

typical MD run. At each step MD step, the redistribution of atomic charges is derived via

EEM scheme; the electron transfer described by EEM impacts the atomic coordination,

and interatomic distances, and thereby, affects the bond orders and eventually bonding

interactions. This unified bond order formalism coupled with the geometry dependent

charge calculation scheme (EEM) accounts for polarization effects, and ensures that

catalytic reactions, wherein, electron transfer influences the electron occupation of the

bonding, and anti-bonding orbitals are adequately captured. In the literature, ReaxFF

has been successfully used to model several reactive processes, including transition

metal catalyzed growth of carbon nanotubes,29,30 catalytic oxidation of hydrocarbons,6,31

thermal decomposition of polymers,24 reduction of graphene oxide,32 superlubricity

enabled by graphene nanoscrolls,14 and aqueous protonation across graphene.33



3. Ab-initio MD simulations

In addition to ensuring that the energetic predictions of ReaxFF are on par with

DFT calculations, we also validated the ReaxFF predicted atomic scale pathways via ab

initio molecular dynamics (AIMD) simulations [Fig. S3]. To accomplish this, we

monitored the temporal evolution of a 1-pentene (a representative olefin) molecule on

three different metal surfaces: Cu (111), Mo (001) and V (001) at various temperatures

(1,000-1,500 K) over 10 ps. As shown in Fig. S3 (a-c), on the Cu (111) surface, the

pentene molecule undergoes dehydrogenation (C-H dissociation), and scission of C-C

bonds identical to ReaxFF predictions; we note that these events are independent of

temperature within the simulated range. For Mo (001), significant de-hydrogenation of

pentene is observed. However, despite the existence of stable Mo-carbides, no carbide

formation is observed; in fact, the C-C bonds remain intact [Fig. S3 (d-f)]. This is

possibly due to lack of necessary thermal energy required to surmount reaction barriers

at the temperatures considered here. More importantly, this is in excellent agreement

with our reactive MD simulations, which show C-H bond dissociation without any C-C

scissions resulting in largely linear chains of longer hydrocarbons rather than a graphitic

structure. In the case of V (001), identical to our ReaxFF MD predictions, AIMD shows

violent de-hydrogenation, C-C bond breaking, followed by formation of V-C bonds [Fig.

S3 (g-i)].



Figure S3. Snapshots at selected times during AIMD simulations of 1-pentene on

(a-c) Cu (111), (d-f) Mo (001), and (g-i) V (001) surfaces at 1,000 K. In all the panels,

top views of the metal surfaces are shown; the atoms are represented as spheres,

colored by their type: C (black), H (blue), Cu (brown), Mo (red), and V (cyan). Videos of

the various ab-initio trajectories are provided in the supporting information.

The excellent agreement between the atomic scale dynamic events predicted our

reactive MD simulations, with those obtained from AIMD further ascertains the validity of

our employed empirical potential in describing reaction pathways over ns timescales

and nanometer length scales under tribological conditions. We note that such length

0 ps 5 ps 10 ps

d e f

a b c

0 ps 1 ps 10 ps

0 ps 0.5 ps 10 ps

g h i

Figure 5. Snapshots at selected times during AIMD simulations of

1-pentene on (a-c) Cu (111), (d-f) Mo (001), and (g-i) V (001)

surfaces at 1000 K.



and timescales, necessary to study the tribo-chemical formation of graphitic structures

from olefins in operando, are not tractable via AIMD simulations.

4. Electronic origin of the differences in catalytic activity between different metal

surfaces

To investigate the effect of electronic structure on the different catalytic activity of these

metal surfaces, we turn to DFT calculations. For the sake of simplicity, we choose

ethene as a representative olefin for this task. First, we identified the most probable

adsorption geometry of ethene on Cu (111), Mo(001), and V(001) surfaces respectively,

via AIMD simulations. Using these initial configurations, we allowed the atoms to relax

to minimum energy state via conjugate-gradient minimization within the framework of

DFT and generalized gradient approximation (GGA), and Perdew-Burke-Ernzerhof

exchange correlation functional.19-21 We found that ethene merely physisorbs onto

Cu(111) surface as indicated by a low adsorption energy Ea of -0.34 eV, while it strongly

binds to Mo(001) [Ea = -2 eV] and V(001) [Ea = -1.5 eV].



Figure S4. Projected electronic density of states for the sp2 and pz orbitals of the

two C atoms in ethene for (a) isolated molecule, and when adsorbed on (b) Cu, (c)

Mo, and (d) V, In panels (b-d) the partial density of states belonging to the d-orbitals of

the transition metals in the top surface layer are also shown.

To understand the changes in the electronic structure of ethene molecule when



adsorbed onto the various metal surfaces, we plot the density of states projected onto

the C atoms of ethene, as well as the metal atoms in the top layer of the surface closest

to the adsorbed ethene molecule [Fig. S4]. In an isolated ethene molecule, as expected,

the projected density of electronic states (PDOS) of sp2 (i.e, s, px and py) orbitals as well

as pz are filled, as shown in Fig. S4(a). Upon adsorption on Cu (111) surface, the

projected density of states on the C atoms, and surface Cu atoms follow the well-known

Dewar–Chatt–Duncanson bonding mechanism for adsorption of organic molecules on

metals.34 Essentially, the occupied pz (π) orbitals of ethene donate electrons to Cu d-

orbitals, which manifests itself as a strong mixing between the bonding pz states (below

Fermi level) with the Cu d orbitals in the PDOS plot [Fig. S4(b)]. These d-orbitals, in

turn, move the donated electrons into the unoccupied π* states of ethene, as indicated

by emergence of a broad peak in pz states beyond Fermi level [Fig. S4(b)]. In addition,

the bonding sp2 states are far below the Fermi level, and do not mix with the Cu d

states. This is also consistent with the charge transfer analysis [Fig. S5(a)], which

shows a weak charge density transferred from Cu surface to ethene. On the other hand,

the d-orbitals of both Mo and V exhibit strong hybridization with the sp2 and pz states of

ethene, at energy states (5-6 eV) below the Fermi level [Fig. S4(b,c)]. Furthermore, only

slight mixing of C pz and d-states of Mo and V is observed beyond the Fermi level

indicating weak anti-bonding states. This results in strong binding between the surface

Mo/V atoms with the carbon atoms of ethene, which is further evidenced by extensive

charge transfer between ethene and metal surface [Fig. S5(b,c)]. Such a strong binding

results in formation of carbide phases, which precludes the formation of the solid

lubricating films.



Figure S5. Cross-sectional view of the electronic charge transfer between the

adsorbed ethene molecule and four different substrates , namely (a) Cu, (b) Mo,

and (c) V as obtained from DFT calculations. The plane shown in panels (a-d) is normal

to the surface of the substrate, and contains the two carbon atoms of the ethene

molecule. The transferred charge densities are shown in e-/bohr3.

5. Evolution of various bonds during reactive molecular dynamics on various

metal surfaces

We plotted the temporal evolution of the number of C-H, C-C, metal-C, metal-H, and

H-H bonds for Cu, Mo and V surfaces as shown in Fig S6. On Cu (111), the olefins lose

~85% of their H atoms in ~ 0.7 ns which diffuse to the bulk of the Cu slab as atomic H or

leave the system as H2 molecule. No Cu-C bonds form, while there is ~40% increase in

the number of C-C bonds [Fig. S6(a-d)]. The dehydrogenation occurs over faster

timescales on Mo and V surfaces, as compared to Cu [Fig. S6(e-l)]. However, Mo (001)

results in lower C-H bond dissociations as compared to Cu (only ~50% H atoms are

removed from the olefins) within ~0.2 ns, beyond which the system essentially reaches

equilibrium [Fig. S6(e-h)]. Similar to our AIMD simulations, our reactive MD simulations

also do not predict any Mo carbide formation [Fig. S6(g)]. V (001) shows violent

Cu (111) a b

-0.01

0.01

0

c V (001) Mo (001)



dehydrogenation – the olefins lose nearly all their H atoms within ~ 5 ps. In addition,

contrary to Cu and Mo, on V (001), the number of C-C bonds drops drastically to ~50%

of its initial value within ~5 ps [Fig. S6(i-l)]. Fig. S6(k) clearly shows that nearly all the C-

atoms get bonded to V, indicating extensive carbide formation.

Figure S6. Evolution of chemical bonds during a typical reactive molecular

dynamics simulation under tribological conditions for olefins on (a-d) Cu (111), (e-

h) Mo (001), and (i-l) V(001) surfaces. The number of C-C (a,e,i; red), C-H (b,f,j; blue),

and metal (M)-C (d,h,l) bonds are normalized by the number of C atoms, while the M-H

and H-H bonds are normalized by the number of H atoms initially present in the system



We have investigated the effect of different crystallographic orientations of Cu,

Mo, and V on the atomic scale dynamics that occur under tribological conditions. We

found that the atomic scale events, i.e., dehydrogenation, C-C bond scission, and

carbide formation (on V) occur irrespective of the crystallographic orientation of the

surface. Also, we found that the formation (or lack of it) of DLC-like structures on a

metal surface is primarily controlled by the propensity of the metal substrate to form

carbide, and does not strongly depend on the crystallographic orientation of the surface.

On the other hand, the kinetics associated with the different atomic scale events (i.e,

dehydrogenation, C-C scission, and carbide formation) are, indeed, related to the

surface orientation.

Fig. S7(a-d) shows the evolution of the number of C-C, C-H, M-C, M-H and H-H

bonds during typical reactive MD simulations of olefins on three high symmetry Cu

surfaces (i.e., 111, 100 and 110) under tribological conditions. We find that there is a

direct correlation between the stability of a surface, and the rate (and extent) of

dehydrogenation. Owing to its face-centered cubic symmetry, the stability of surfaces

decreases as 111 > 100 > 110; as shown by Fig. S7 (b,d) the extent as well as the rate

of dehydrogenation is highest for 110 (the least stable surface). However, as shown by

Fig. S7(a), the formation of C-C bonds follow similar rate irrespective of the orientation.

Moreover, there is no carbide formation for Cu surface of any orientation. Figs. S7(e-g)

illustrate the configuration of the olefins after 1 ns of tribo-chemical activity via reactive

MD simulation; all the surface orientations result in the formation of DLC-like structure.

Nevertheless, a systematic study of the effect of surface orientation on reaction kinetics

is a subject worth pursuing further.



Figure S7. Effect of crystallographic orientation of the surface on the tribo-

chemical formation DLC-like carbon from olefins during reactive MD simulations.

The evolution of (a) C-C, (b) C-H, (c) Cu-C, and (d) Cu-H + H-H bonds during a typical

MD run are shown on Cu (111) [red], Cu (100) [blue] and Cu (110)[green] surfaces.

The number of C-C, C-H and Cu-C bonds are normalized by the number of C atoms,

while the Cu-H and H-H bonds are normalized by the number of H atoms initially

present in the system. We have also provided MD snapshots at 1 ns for each of the

high symmetry surfaces (e) Cu (111), (f) Cu (100), and (g) Cu (110).



6. Repeatability of the results of MoNx-Cu nanocomposite coating tested in PAO

10

Fig. S8. Experiment showing very close repeatability of the friction results from

MoNx-Cu coating in PAO 10. MoNx-Cu nanocomposite coating was tested twice to see

the reproducibility of the results in terms of friction, wear and carbon-rich tribofilm

formation; the results shows that the tribological behavior of the coating is fairly

reproducible.



7. Comparison of MoNx-Cu and VN-Cu coatings

Figure S9. Comparison of MoNx-Cu with another catalytically active

nanocomposite coating (VN-Cu) developed at Argonne National Laboratory. VN-

Cu nanocomposite coating was deposited in a similar manner to that of MoNx-Cu, the

results show similar tribological behavior in terms of wear and friction (except for the

initially unsteady frictional behavior of VN-Cu). The generation of the amorphous carbon

protective tribofilm can be observed in the Raman spectra (633 nm laser) of both the

VN-Cu and MoN-Cu coated balls; the presence of G and D bands shows that the nature

of the tribofilms is the same, i.e., primarily based on amorphous carbon.



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