Conductive Graphitic Networks: From Atoms to Fuel Cells

(1)

Conductive Graphitic Networks:

from Atoms to Fuel Cells

Emanuela NEGRO

(2)

(3)

Conductive Graphitic Networks:

from Atoms to Fuel Cells

Proefschrift

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus Prof. ir. K. C. A. M. Luyben, voorzitter van het College voor Promoties,

in het openbaar te verdedigen op

vrijdag 14 November 2014 om 12:30 uur door

Emanuela NEGRO

Master of Science in Chemical Engineering at Politecnico di Torino (IT) and KTH Stockholm (SE)

(4)

Dit proefschrift is goedgekeurd door de promotoren:

Prof. dr. J. H. van Esch Prof. dr. S. J. Picken

Copromotor: Dr. ing. G.J.M. Koper

Samenstelling promotiecommissie:

Rector Magnificus Technische Universiteit Delft, voorzitter Prof. dr. J. H. Van Esch Technische Universiteit Delft, promotor Prof. dr. S. J. Picken Technische Universiteit Delft, promotor Dr. ing. G. J. M. Koper Technische Universiteit Delft, copromotor Prof. dr. F. Kapteijn

Prof. dr. W. J. Briels Prof. dr. P. Tsiakaras Prof. dr. A. López Quintela

Technische Universiteit Delft Universiteit Twente

University of Thessaly

University of Santiago de Compostela

The work described in this thesis was carried out in the Advanced Soft Matter group at the Delft University of Technology and was founded by the Dutch programme “A green Deal in Energy Materials” ADEM Innovation Lab.

ISBN: 978-‐94-‐6259-‐374-‐9

Cover design by Alessandro Squatrito, Product Designer Printed by Ipskamp Drukkers, Enschede

All rights reserved. The author encourages the communication of scientific contents and explicitly allows reproduction for scientific purposes, provided the proper citation of the source. Parts of the thesis have been published in scientific journals and copyright is subject to different terms and conditions.

An electronic version of this thesis is freely available at http://repository.tudelft.nl

(5)

Alla mia famiglia, rendervi orgogliosi di me è la mia gioia più grande

“It is imperfection -‐ not perfection -‐ that is the end result of the program written into that formidably complex engine that is the human brain, and of the influences exerted upon us by the environment and whoever takes care of us during the long years of our physical, psychological and intellectual development.”

Rita Levi-‐Montalcini from “In Praise of Imperfection'', 1988 “Non exiguum temporis habemus, sed multum perdidimus” Lucio Anneo Seneca, “De brevitate vitae”, before 49 A.D.

(6)

(7)

2.1 Introduction ... 36 2.2 Experimental ... 37 2.2.1 Chemicals ... 37 2.2.2 Microemulsion Preparation ... 37 2.2.3 Microemulsion Characterization ... 38 2.2.4 NP synthesis ... 38 2.2.5 Reaction kinetics ... 38 2.2.6 NP Characterization ... 38 2.3 Results ... 39 2.3.1 Reactants ... 39 2.3.2 Microemulsion composition ... 43 2.3.3 Stability ... 45 2.4 Discussion ... 46 2.5 Conclusion ... 49

3 Synthesis of Graphitic Networks from Dense Microemulsions ... 51

3.1 Introduction ... 52

3.2 Experimental ... 52

3.2.1 Materials ... 52

3.2.2 Catalyst Preparation and Support Transfer ... 52

3.2.3 Chemical Vapor Deposition ... 53

3.2.4 Instrumentation ... 54

3.3 Results and Discussion ... 54

(8)

PART II: Conductive Carbon Networks for PEM Fuel Cells Electrodes ... 63

4 PEM Fuel Cells: History, Basics and Challenges ... 65

4.1 Fuel cells ... 65

4.2 PEM Fuel Cells: Basics and thermodynamics ... 68

4.3 PEMFC Electrodes ... 70

4.3.1 Electrode Design ... 70

4.3.2 Electrode Degradation ... 71

4.3.3 Electrode Activity and Cost ... 72

5 CNNs as Durable Platinum Support for PEM Electrodes ... 75

5.1 Introduction ... 76

5.2 Experimental ... 77

5.2.1 Materials ... 77

5.2.2 Catalyst Deposition ... 77

5.2.3 Characterization ... 78

5.3.1 Carbon Support Characterization ... 79

5.3.2 Platinum Deposition ... 81

5.3.3 Electrochemical Characterization ... 82

5.4 Conclusion ... 87

6 Non noble Fe-‐N/CNNs as ORR catalysts for low temperature fuel cells ... 89

6.1 Introduction ... 90

6.2 Experimental ... 91

6.2.1 Chemicals ... 91

6.2.2 Carbon Support and Electrocatalyst Synthesis ... 91

6.2.3 Support and Electrocatalyst Characterization ... 92

6.2.4 Electrochemical characterization ... 92

6.3.1 Physical-‐chemical characterization of Supports and Electrocatalysts ... 93

6.4 Conclusion ... 102

7 Pt Electrodeposition on CNNs Grown directly over Carbon Paper ... 103

7.1 Introduction ... 104

7.2 Experimental ... 106

7.2.1 Materials ... 106

7.2.2 CNNs growth over CP ... 106

7.2.3 Oxidation of carbon support and Pt Electro-‐deposition ... 107

7.2.5 Instrumentation ... 108

7.3.1 Influence of synthesis parameters on ESA and Corrosion Resistance ... 108

7.3.2 CNN Electrochemical Functionalization ... 114

7.3.3 Pt Electro-‐deposition ... 117

7.3.4 Durability Tests ... 119

(9)

Summary ... 123 Samenvatting ... 129 Bibliography ... 135 Acknowledgements ... 149 Curriculum Vitae ... 153 Publications ... 154

(10)

(11)

List of Abbreviations

AA Atomistic (all atoms) AC After Corrosion

ADT Accelerated Durability Test AFC Alkaline Fuel Cell

AOT Dioctyl sodium sulfosuccinate

BC Before Corrosion

BET Brunauer–Emmett–Teller theory of physical adsorption

BME Bicontinuous Microemulsions

CG Coarse-‐Grained

CNF Carbon Nano Fibers CNN Carbon Nano Network CNTs Carbon Nano tube CP Carbon Paper CV Cyclic Voltammetry

CV Coefficient of Variation (in Chapter 3) CVD Chemical vapor Deposition

DLC Double Layer Capacitance DLS Dynamic Light Scattering DMFC Direct methanol Fuel Cell

ECSA Electrochemically Active Surface Area ED Electro-‐deposition

EDS Energy Dispersive Spectroscopy ESA Electrochemical Surface Area FC Fuel Cell

FF Force Field

GDL Gas Diffusion Layer

IEA International Energy Agency LSV Linear Sweep Voltammetry MCFC Molten Carbonate Fuel Cell

MD Molecular Dynamics

ME Microemulsions

MeOH Methanol

MOF Metal Organic Framework MPL Microporous Layer

(12)

MSD Mean Squared Displacement

NPs Nanoparticles

OCV Open Circuit Voltage ORR Oxygen reduction Reaction PAFC Phosphoric Acid Fuel Cell PEM Polymer Electrolyte Membrane PFSA Perfluorosulphonic acid

RDE Rotating Disc Electrode

RHE Reversible Hydrogen Electrode RM Reverse Micelles

SAXS Small Angle X-‐ray scattering SEM Scanning Electron Microscopy SOFC Solid Oxide Fuel Cell

TEM Transmission Electron Microscopy TGA Thermo Gravimetric Analysis XPS X-‐ray Photoelectron Spectroscopy XRD X-‐ray Diffraction

(13)

Introduction

Sustainability and Energy

The last decades have been characterized by an increasing concern about energy production and management,1_{as
direct
consequence
of
the
consolidated
awareness
of}

both scarcity of widely used earth resources and worry about how pollution created by our lifestyle affects the planet.2, 3_{The
concept
of
sustainable
development,
first
defined}

in 1987 in the report “Our common Future” of the United Nations World Commission on Environment and Development as “Development that meets the needs of the present without compromising the ability of future generations to meet their own needs”,4, 5 has become more and more relevant, being the main driving force to technological innovations.1 Words such as “sustainable”, “renewable”, “clean” or “green” are appearing in every field of technology and are recurring in the political agenda of every country.2, 5

Fossil fuels are the main energy source exploited nowadays, in 2012 they supplied more than 80% of the world primary energy consumed and in 2011 almost 70% of world electricity was produced from them as recorded by the International Energy Agency.6 Fossil fuels are not sustainable: the rate at which they are consumed is by far quicker than the rate at which they are naturally produced. Fossil fuels are not environmentally friendly, when burned they release CO2, being the greenhouse gas mostly contributing

to global warming, as well as other environmental poisonous species such as nitrogen oxides, sulfur dioxide and other volatile organic compounds.7_{Fossil
fuels
are
normally}

used in energy systems with very low efficiency, e.g. the efficiency of combustion engines lies between 20% and 30%7_{.
Finally,
the
majority
of
the
global
oil
suppliers
are}

politically unstable countries, dependency on which is rather not to count on to build a stable economy. The second most exploited source of non renewable energy is nuclear fission, being the 5.1% of the world primary energy consumed in 2011 and the source of the 11.7% of the electricity produced in 2012.6_{Nuclear
power
is
greenhouse
gas}

emission-‐free. However, no sustainable solution to process the radioactive waste has been developed yet 8, 9_{and
uranium
is
expected
to
be
exhausted
in
less
then
100
years.}

Nuclear fusion is in principle safer and more attractive because of the easier radioactive waste management, but it has not reached yet a sufficient technological level to be profitable.7, 9

(14)

Introduction

According to forecast for world population growth and economical development, without determined action, previsions are dramatic: by 2050 energy demand will quadruple, resulting in an 80% increase in CO2 emissions.2 Beside changing our lifestyle

in order to reduce energy consumption and waste, transition to a carbon-‐free economy must be encouraged and advertised by public policies, not only to preserve the environment but also to put the basis for future prosperity and peace.1, 3 International organizations have set up environmental treats such as the Kyoto protocol to the United Nations Framework Convention on Climate Change to limit the increase of global temperature to 2 degrees Celsius; in the last 100 years the average increase was 0.74 +/-‐ 0.18°.7 Developed countries need to meet 75-‐100% of power demand with carbon-‐free sources, compared to 30% globally today, to hit the emission targets.3_{The
rest
could
be}

produced by coal with carbon dioxide capture and storage and by natural gas.3

In order to answer the question “How to power the world in a sustainable way?” one of the first things to consider is obviously to reduce to a minimum or exclude fossil fuels consumption from our daily life.

One solution is to use renewable energy, that comes from sources naturally replenished independently on their consumption, such as solar light, wind, tides, and geothermal energy naturally stored in the Earth.2 Great development in this sense has been achieved in the last years, and from 1973 to 2011 the world total primary energy and electricity produced from these sources has increased from 0.1 to 1% and from 0.6 to 4.5%, respectively,6 mainly thanks to government subsidies both for research and installations.1 The technological innovation is directly proportional to public investment, as the correlations to patent filing demonstrates.3 Beside the fact that the source is renewable, energy production by these techniques does not affect the environment with polluting emissions.2 Additionally, wind turbines and solar panels could be installed also in remote areas where connection to the grid is impossible, allowing increase in quality of life also in poorer countries.10 Unfortunately, renewable energy efficiency is not very high, e.g.it is around 15% for solar energy, and technology is still expensive, even though the price has been constantly falling during the last 30 years, e.g. photovoltaic about 10% per year and wind turbine roughly 5% per year.3 Moreover, the supply of energy from these sources is not continuous but ruled by natural fluctuations. Renewable energies must be then combined either with other sources of energy, working as backup, or excess of energy in peak production time has to be stored, minimizing the energy losses, so that it can be used during the production breaks.3 Energy can be stored in different ways, for example mechanically as potential energy in pumped hydroelectric storage or as kinetic energy in flywheel storage, as electricity, in capacitors and supercapacitors, or as chemical energy, in batteries or with electrolyzers by producing hydrogen from water electrolysis.8, 11 Led by China, Europe, USA and Japan the alternative energy sector is booming worldwide.3 China, Germany and Europe plan to have respectively 15%, 35% and 20% of electricity produced by renewable by 2020.2, 3

Another path to reduce economic dependency on fossil fuels, is to increase energy production efficiency of currently available technologies.5_{In
this
sense,
fuel
cells
are
a}

very promising future technology.12, 13 Fuel cells are simple devices in which a fuel (hydrogen, methanol, ethanol, methane etc.) and oxygen electrochemically react producing electricity and heat. Their biggest advantages over internal combustion

(15)

Sustainability and Energy

engines are none or low CO2 emissions, depending on the fuel, electrical efficiency up to

60% and combined heat and power efficiency up to 90%, silent operation, no moving parts and thus long mechanic life time and no need of lubricants.12, 14, 15_These

advantages make fuel cells promising alternatives to combustion engines both for portable and stationary combined heat and power applications.16_{Especially
for
the}

transport sector, fuel cells have major advantages over batteries, a more mature power alternative for automotive applications. They have a much faster refueling time and a longer ride range because of higher energy density.12, 13, 17 Replacing combustion engines with fuel cells in the transport sector would have an incredible impact on CO2 emissions,

being the sector accounting for 22% of European CO2 emissions and 25% primary energy

consumption (data from 2009).7_{The
main
issues
preventing
the
mass-‐scale}

commercialization of such promising devices are their cost and their durability that are not meeting the worldwide target requirements, as well as the necessity to find cheap, clean and efficient solution to produce and store hydrogen.13

It is clear that in this situation, research aimed to improve existing technology in terms of material costs, higher efficiencies and safer management is crucial. Especially electrochemical devices, such as batteries, capacitors, supercapacitors, electrolyzers and fuel cells play a fundamental role in the future of energy conversion and storage.11, 18

Large part of the research in these sectors is aimed at the improvement of carbon-‐based materials, widely employed in these devices because of their high electronic conductivity, light weight, low cost, easy and versatile preparation, abundance of raw materials. Improvements are intended in terms of surface area and durability in order to allow faster and easier mass transports, better dispersion of functionalizing materials and increase device lifetime.19-‐21_{Graphitic
materials,
such
as
carbon
nano
tubes,
carbon}

nano fibers and graphene, have attracted a great interest in many fields of technology because of their excellent electrical, mechanical and chemical properties.18-‐21

This thesis modestly contributes to this global research by investigating new carbon nanomaterials and their use in Fuel Cell electrodes. These new nanomaterials are based on an interconnected carbon nanostructure, called Carbon Nano-‐Networks (CNNs), synthesis of which has recently been patented by TU Delft spin-‐off company Carbon X.22

Structure of the thesis

The thesis is a collection of six publications, thus every chapter provides all the introductory and experimental information to understand it independently on the others. The work is divided into two parts, the first one deals with the synthesis of CNNs and the second part with their use in Fuel Cells electrodes.

CNNs are produced by Chemical Vapor Deposition (CVD) of ethene over metal catalyst nanoparticles synthesized in bicontinuous microemulsions (BMEs). Chapter 1 deals with the characterization of dense microemulsions, both experimentally and computationally, using a coarse-‐grained molecular dynamics simulation tool. Bicontinuity of microemulsions is visualized. Chapter 2 deals with the synthesis of Platinum nanoparticles (NPs) in BMEs. We analyze the effect of the precursors and of the microemulsion composition on the size, polidispersity and stability of the NPs

(16)

Introduction

deals with the synthesis of CNNs via CVD of ethene over metallic particles synthesized in BMEs. The effect of synthesis parameters on the final structure is studied. Properties of CNNs, such as porosity and conductivity are investigated.

In the second part, Chapter 4 gives a brief overview of PEM Fuel Cells basics, materials and challenges while Chapters 5-‐7 deal with the use of CNNs as electrode material. In Chapter 5, activity and durability of Pt deposited over CNNs is compared to Pt over CNTs and to commercial catalyst. In Chapter 6, CNNs are used as support for non-‐noble metal catalyst. Performances are evaluated in-‐situ and ex-‐situ. Chapter 7 deals with an innovative manufacturing technique for an electrode: CNNs are grown directly over carbon paper. Resistance to corrosion as a function of synthesis parameters is evaluated. Pt is electrodeposited over the synthesized electrode support, and its activity and durability is evaluated and compared to commercial catalyst.

(17)

PART I

Synthesis of Conductive Graphitic Networks

(18)

(19)

1 Characterization of Dense

Microemulsion Systems

Microemulsions are exciting systems that are promising as tunable self-‐assembling templating reaction vessels at the nanoscale. Determination of the nano-‐structure of microemulsions is, however, not trivial, and there are fundamental questions regarding their design. We were able to reproduce experimental data for an important microemulsion system, sodium-‐AOT/n-‐heptane/water, using coarse-‐grained simulations involving relatively limited computational costs. The simulation allows visualization and deeper investigation of controversial phenomena such as bicontinuity and ion mobility.

Simulations were performed using the Martini coarse-‐grained force field. AOT bonded parameters were fine-‐tuned by matching the geometry obtained from atomistic simulations. We investigated several compositions with a constant ratio of surfactant to oil while the water content was varied from 10 to 60% in weight. From mean square displacement calculation of all species, it was possible to quantify caging effects and ion mobility. Average diffusion coefficient of charged species qualitatively matched the variation in conductivity as a function of water content. The scattering function was calculated for the hydrophilic species and up to 40% water content quantitatively matched the experimental data obtained from Small Angle X-‐ray Scattering measurements. In particular, bicontinuity of water and oil was computationally visualized by plotting the coordinates of hydrophilic beads. Equilibrated coarse-‐grained simulations were reversed to atomistic models in order both to compare ion mobility and to catch finer simulation details. It was possible to capture the intimate ion pair interaction between the sodium ion and the surfactant head group.

This chapter is published as:

(20)

Chapter 1

1.1 Introduction

Bicontinuous Microemulsions (BMEs) are a special class of thermodynamically stable, single phase emulsions, type IV in the Winsor classification,23_{where
surfactant
is
mixed}

with almost equal amounts of oil and water in such a way that both water and oil form continuous channels. Such a structure is stabilized by a large amount of surfactant (~50% in weight with respect to the final mixture). The concept of BMEs was introduced in 197624_{and
since
then,
these
colloidal
structures
have
been
extensively
studied
using}

various techniques. However, their potential for application has been largely overlooked in comparison to that of reverse micelles (RMs).

BMEs recently resulted in an optimal template for synthesis of metallic nanoparticles

25-‐28_{and
Metal
Organic
Framework
(MOF)
nanocrystals,}29_{for
enzymatic
catalysis,}30_and

for assembly of mesoporous nanocomposites.31 Additionally, they recently attract again great interest for surfactant “Enhanced Oil Recovery” applications having oil scarcity as the main driving force.32 BMEs provide a sponge-‐like mesoporous nanostructure with a large interfacial area between polar and apolar domains. They allow the confinement of species in the water phase as do RMs but they have the advantage that the template dynamics is itself constrained. For example, these structures are believed to be crucial for the synthesis of stable nanoparticles since they allow the metal precursor ions to diffuse freely but they do not allow the diffusion of nanoparticles.27 In that way, the reaction timescale is much faster than the growth timescale, resulting in the formation of monodisperse nanoparticles. Additionally, higher yields can be achieved because of the higher water content (up to 40% in weight) compared to conventional RM synthesis.26, 27 Understanding how ME structure depends on composition is crucial for providing a molecular basis for interpreting experimental results and investigating the effects on for example nanoparticles synthesized in them.

Molecular Dynamics (MD) simulations have been widely used to investigate microemulsion systems, especially concerning RM size, shape and shape transitions,33-‐35

water behaviour as a function of the distance from the interface 36-‐41 and head-‐group-‐ solute interactions.40, 42, 43_{However,
simulations
of
ternary
systems
were
limited
to
low}

surfactant concentrations and deal mainly with RM systems. One of the reasons is that most of the simulations were atomistic, or all atoms (AA), whose computational costs are in general too high to capture timescales and length scales necessary to characterize microemulsion systems containing large amounts of surfactant, in which diffusion is slowed down by the higher viscosity of the system and where characteristic dimensions are on the order of tens of nanometers.44

Coarse-‐grained (CG) simulations allow significant computational cost reduction compared to AA simulations and are suitable to simulate systems requiring microseconds and micrometres. Hybrid systems, using AA for interfacial regions and CG for the rest, have also been adopted to reduce computational costs.44, 45 The Martini CG force-‐field (FF), a fast, easy and efficient simulation tool, was developed in 2003 by Marrink at al. 46 for biomolecular applications, becoming in less than a decade one of the most widely used CG force field for a broad range of applications. In particular it appears

(21)

Characterization of dense Microemulsions Systems

well suited to study formation of micelles, allowing simulation times long enough to equilibrate this kind of systems.35, 47-‐49

In this work, we aim to investigate dense microemulsion systems formed with dioctyl

sodium sulfosuccinate or docusate sodium, known as Na-‐AOT, because of its widespread

use in many applications and long standing investigation in our group as template for metal nanoparticles synthesis. This molecule has been widely investigated by AA MD simulations 34, 38, 40, 41, 50 and more recently by a few CG models.32, 51

The goal of this work is to provide a Martini CG model for the system water/Na-‐ AOT/n-‐heptane, to map out an important part of the ternary phase diagram and to compare the resulting structures/morphologies to geometrical models available in the literature52 and to experimental characterizations such as conductivity and small angle X-‐ray scattering (SAXS) data. First, Na-‐AOT and n-‐heptane bonded interaction parameters are fine tuned in Martini according to AA MD simulations and part of the phase diagram is mapped out and compared to a theoretical model. Bicontinuity is computationally visualized. Secondly, diffusion coefficients of charged species are compared to experimental conductivity data. Mean square displacements (MSDs) of charged species are used to investigate caging effects. Thirdly, scattering functions calculated from radial distribution functions of hydrophilic species are compared to SAXS data. Finally, after back mapping equilibrium structures to the AA model of three CG simulations, further AA MD simulations are performed to evaluate effects due to CG loss of detail compared to AA.

1.2 Methods

1.2.1 Phase diagram calculation – The geometrical model

A phase diagram of the ternary system employed in this study was calculated using a simplistic geometrical model developed by Andre et al.,52 that predicts structural transitions and supra-‐aggregation processes which are imposed by geometrical constraints. The model is solely based on a geometry of surfactant and related curvature of the oil-‐water interface and therefore requires calculations of the: (i) composition of the system, here expressed as the water weight fraction,𝑤; (ii) surfactant parameter, s, which is given by the ratio v/(𝑙!a0) where v is the molecular volume of the surfactant, 𝑙!

is the effective chain length of the surfactant tail, and a0 is the optimal head group

surface area;53-‐55 and (iii) packing parameter, which is the ratio between the volume actually occupied by the cylinders/spheres and that of the unit cells.52 During all calculations it is assumed that all the surfactant is located in the oil-‐water interface.52 Based on the geometrical model, radii of spherical, 𝑅!_!, and cylindrical, 𝑅!_!,

aggregates can be deduced according to equations (1.1) and (1.2), respectively:

𝑅_{!_!} = 3𝑤𝑠𝑙_! (1.1) 𝑅_{!_!} = 2𝑤𝑠𝑙_! (1.2)

(22)

Chapter 1

1.2.2 Materials and Experimental Methods

The surfactant, sodium bis(2-‐ethylhexyl) sulfosuccinate, also known as Na-‐AOT (C20H37NaO7S, 99%), and the oil, n-‐heptane (99.9%), were purchased from Sigma-‐Aldrich

BV and used as received. Water produced by Milli-‐Q Ultra-‐Pure-‐Water purification system of Millipore BV was used in all sample formulations. All preparations and analysis were carried out at room temperature and atmospheric pressure. Surfactant and oil were mixed in the ratio 2:1 in weight and sonicated for one hour to speed up dissolution of the surfactant. Subsequently, water was added to the mixture of oil and surfactant in different ratio in weight. After circa 1 hour, the microemulsions were found to be clear and were considered homogeneous and ready for further experiment or analysis. Conductivity measurements were carried out using a conductivity meter model 712 from Metrohm AG. Small angle X-‐ray scattering (SAXS) was conducted using an AXS D8 Discover instrument from Bruker AG. Microemulsion samples where filled in a 1 mm thick quartz capillary set at a distance of 30 cm from the detector. The X-‐ray source was a tube operated at 40 kV and 40 mA and produced predominantly copper Kα radiation of wavelength 0.154 nm. The scattering data from the experiments were radially integrated obtaining the intensity as a function of d-‐spacing obtained by applying Bragg’s law. Dynamic light scattering (DLS) measurements were performed on the Zetasizer Nano ZS from Malvern Instruments Limited using the 173° angle non-‐invasive back-‐scatter mode and the M3-‐phase analysis light scattering mode, respectively. The instrument had a red 4.0 mW 633 nm He–Ne laser. The multiple peak high-‐resolution fitting procedure was used to obtain the particle size distribution from the auto-‐ correlation function.

1.2.3 Computational Methods

All simulations were performed with the GROMACS simulation package version 4 56 using periodic boundary conditions in all directions.

Coarse-‐grained Simulations

The CG Martini FF version 2.0 developed by Marrink at al.46 was employed to model the present system. This FF uses a basic 4-‐to-‐1 mapping scheme of chemical functional groups to single beads. Beads are classified according to their polarity as Q (charged), P (polar), N (neutral), and C (apolar). Na-‐AOT was parameterized using 8 beads, a Qd bead with charge +1 for the hydrated Na+-‐ion, a Qa bead with charge -‐1 for the surfactant sulphonate anion head, 2 Na beads for the ester groups and 4 C1 beads for the aliphatic chains. The d and a additions to bead types denote hydrogen bond donor and acceptor capabilities, respectively. N-‐heptane was parameterized using 2 C1 beads. Groups of 4 water molecules are represented by one polar P4 bead. The only charged beads in this model are the ones representing the hydrated Na+_{-‐ion
and
the
sulphonate
anion.
Figure}

1-‐1 shows the scheme for Na-‐AOT and n-‐heptane mapping. The bead types determine the strength of the non-‐bonded interactions according to the interaction matrix published by Marrink et al.46 Non-‐bonded interactions were modelled by shift functions, which cause the distance-‐dependent potentials and forces to smoothly go to zero at the cut-‐off distance instead of showing a discontinuity there. A cut-‐off of 1.2 nm was employed for all non-‐bonded interactions. The Lennard-‐Jones (LJ) potential was

(23)

smoothly shifted to zero between 0.9 and 1.2 nm. A similar approach was employed for the Coulomb potential with a relative permittivity of 15 and a shift function from 0 to 1.2 nm.

CG systems were prepared by first randomly placing 100 AOT molecules in a cubic simulation box of dimensions x·∙y·∙z nm; values are specified for each simulation in Table 1-‐2, Table 1-‐3, Table 1-‐4 and Table 1-‐5. Next, 222 n-‐heptane molecules were added randomly to the same volume, and finally, the volume was filled with randomly placed ion and water beads. Each system was energy minimized using the steepest descent method for 500 steps. These systems were then replicated once in each dimension, creating a simulation volume 8 times larger than the initial set-‐up. After a short MD relaxation run, simulations of 1 µs were performed, allowing the system to self-‐ assemble and equilibrate. Analysis of morphology, diffusion, and structural characterization was performed on the final 500 ns of the simulation. All simulation times reported here are unscaled (see also discussion of diffusion results). The initial velocities were randomly assigned from a Maxwell distribution at the reference temperature. The equations of motion were integrated numerically using a 20 fs time step. Water, surfactant molecules, ions and oil were separately coupled to a Berendsen thermostat at 298 K with a common coupling time of 1 ps.57_{For
CG
simulations}

containing 10, 15 and 60 % water, the pressure was isotropically controlled at 1 bar using a Berendsen barostat with a coupling time of 3 ps with an isothermal compressibility of 3·∙10-‐5 bar-‐1. For simulations containing 10, 25, 30, 35, 40, 45, 50 and 55% water, the pressure was anisotropically controlled at 1 bar using a Berendsen barostat with a coupling time of 3 ps and with an isothermal compressibility of 3·∙10-‐5 bar-‐1_{in
x,
y,
z
directions
and
off-‐diagonal
compressibility
set
to
0
in
order
to
keep
the}

box rectangular. Figure 1-‐1 The coarse-‐grained model for Na-‐AOT surfactant and n-‐heptane. Circles include the atoms that are grouped into beads and are coloured to indicate their nature: red-‐Q, blue-‐N,

green-‐C. The numbers reflect the order in which the model is represented in the topology. Titel van de presentatie 6

Qd Qa Na Na C1 C1 C1 C1 C1 C1

(24)

Chapter 1

Bonded Parameter Fine-‐tuning Based on AA Simulations

AA simulations (see below) were carried out to gain more insight into the atomistic details of the structure and to assess how the morphology obtained at CG level behaves at AA level, and initially also in order to refine angle and bond parameters for the bonded interactions between the beads, a procedure that is regularly used in building Martini models, see e.g.58, 59_{We
chose
as
the
reference
composition
the
one
containing}

20% water, that according to previous experimental studies gives rise to a bicontinuous phase.25_{Angle
and
bond
length
distributions
were
calculated
for
a
representative}

sample of AOT and n-‐heptane molecules after mapping the AA structures from an equilibrated 100 ns simulation to the CG structure. Mapping was done according to the scheme shown in Figure 1-‐1, and based on calculation of the centre of mass of the (united) atoms constituting a CG bead. The same distributions were calculated for a CG simulation using default Martini FF parameter values, here referred to as Standard CG. A number of bond lengths and angles were found to be significantly different between AA and Standard CG simulations, and these were adjusted in a new topology, here referred to as either Refined CG or CG model.

Atomistic Simulations

AA simulations were performed using a united-‐atom FF, parameter files available upon request, based on GROMOS53A6.60_{The
SPC
water
model}61_{was
used.
All
bonds}

were constrained using the Lincs 62 algorithm, while water was treated as a rigid molecule using the SETTLE 63_{algorithm.
A
non-‐bonded
cut-‐off
of
1.4
nm
was
used
for
LJ}

and Coulomb interactions. Whereas the LJ potential employs a straight cut-‐off, Coulomb interactions were smoothly scaled to zero at the cut-‐off distance by using the reaction field method due to Tironi et al.,64 with a relative dielectric constant of 62. Non-‐bonded interactions within 0.9 nm were calculated each step, and interactions between 0.9 and 1.4 nm were calculated every 10 steps together with an update of the neighbour list and assumed constant in between neighbour list updates. A time-‐step of 2 fs was used to integrate the equations of motion, which were coupled to a Berendsen barostat57 with a compressibility of 4.6·∙10-‐5_bar-‐1_{and
a
coupling
time
of
0.5
ps,
as
well
as
to
a
Berendsen}

thermostat at the reference temperature of 298 K and a coupling time of 0.1 ps. Periodic boundary conditions were used, either in a cubic or rectangular box, similar to the CG set-‐ups (see above). Water and ions formed one temperature group separate from a temperature group containing the AOT and n-‐heptane.

Back mapping Methods

Resolution changes between AA and CG representations were achieved using the methods due to Rzepiela et al.,65_{which
requires
a
special
version
of
GROMACS,
for
the}

fine-‐tuning of CG parameters and the more recent one due to Wassenaar et al.,66 which is computationally more efficient and more user friendly, for the diffusion coefficients and morphology investigations. AA structures were mapped to CG structures by calculating the centre of mass of the (united) atoms assigned to their respective beads, according to the scheme shown in Figure 1-‐1. Thus, CG positions are uniquely defined in terms of AA positions. The AA positions are not uniquely defined by the CG positions. Back mapping procedures assign initial positions to AA particles based on the CG positions, and then try to relax the initial structure to a relevant and reasonable AA

(25)

structure compliant with the AA FF. The method due to Rzepiela et al. assigns the AA positions associated with a particular CG bead randomly within a sphere around the CG bead position, and then anneals the structure, starting at high temperature and with a modified AA FF in which large forces due to unfavourable contacts are capped to a maximum value. The method due to Wassenaar et al. allows for a more controlled reconstruction of AA positions based on CG bead positions in which the connectivity between CG beads may be used to place AA particles already in correct orientations. In our experience, this second method usually is more stable and requires less computational effort in obtaining a reasonable starting structure for subsequent MD simulations. In back mapping, we replaced a water bead by four water molecules and a Na+_{bead
by
a
Na}+_{ion
and
three
water
molecules.}

1.2.4 Analysis

Morphology and Continuity Investigation

The overall morphology was determined by visual inspection using the program VMD.67 Colouring the different types of components differently, and rendering a surface around the water beads establishes the morphology. The morphology was classified as one of the following. If the structure contains reverse micelles or interconnected reverse micelles (water spheres surrounded by surfactant) the morphologies are denoted by RM and IRM, respectively. Worm-‐like and interconnected worm like structures are denoted W and IW, respectively, and bicontinuous structures (water and oil are both continuous throughout the system) are classified as BME. In case of cylinders, cylinders hexagonally packed, cylinders of 2 different sizes (water channels are continuous in one dimension, but otherwise not connected), the morphologies are denoted C, HC and BC, respectively. Lamellar structures (water and oil are both continuous in two dimensions, but stacked in a third) are denoted by L, interconnected lamellar structures (water and oil are both continuous in two dimensions and at least one is interconnected in the third) IL. Finally, spherical structures (large water spheres, RM, separated from each other by a thin surfactant and oil layer) can be stacked in body-‐centered cubic, BCC, or face centred cubic, FCC, pattern, and oil in water micelles (oil spheres surrounded by surfactant) are denoted M. Combinations of these morphologies are possible. While visual inspection using VMD provides some insight into the morphology of the structure, the continuity of certain components may be seen more clearly by simple plots showing the beads in a number of slices through the simulation box. Such plots can also be used to estimate the size of compartments and/or channels. Here, we represented the selected beads by a circle with a diameter of 0.5 nm. The box was divided in slices of 1 nm thickness, and beads in different slices were given a different colour. The combined plots showing the slices in the xy, yz, and xz planes provide a rapid insight into the connectedness of the component of interest. Continuity in x, y, z direction can also investigated by plotting the probability profile to find hydrophilic beads (surfactant head group, Na+ and water) or hydrophobic beads (surfactant tails and n-‐heptane) across a cell dimension. Such plots give a rapid idea of the continuity of the two phases and the dimensions of the domains.

(26)

Chapter 1

Mean squared displacement and Diffusion Coefficient

The mean squared displacement (MSD) was calculated by the built-‐in GROMACS tool g_msd in order to estimate the spatial extent of random motion for water beads, Na+

beads, AOT group of beads and n-‐heptane group of beads. The software calculates the average directional MSDx, MSDy and MSDz, according to equation (1.3)

𝑀𝑆𝐷_!(𝑡) = 𝑘_!!!− 𝑘_! !

!,! (1.3)

where k= x, y, z, time t is from 0 up to 250 ns, 𝜏 is the reference time, and sample size N is 100 in this work. The MSDs of individual particles are also retained. The total MSD is the sum of the contributions in different directions according to (1.4):

𝑀𝑆𝐷(𝑡) = 𝑀𝑆𝐷_!(𝑡) + 𝑀𝑆𝐷_!(𝑡) + 𝑀𝑆𝐷_!(𝑡) (1.4)

From the total MSD, an average diffusion coefficient is calculated from the Einstein relation (1.5):

𝐷(𝑡) =!"#(!)_!! (1.5)

The combined plots showing the single MSD curves in direction x,y,z provide a rapid insight into the connectedness of the component of interest, discriminating caging effects and free diffusion. Most of these plots are shown in SI.

Scattering Function

We wanted to calculate the SAXS patterns, typically represented as scattered intensity as a function of the magnitude q of the scattering vector Q expressed according to (1.6):

𝑞 = !! !"#(!)

! (1.6)

where 𝜃 is the angle between the incident X-‐ray beam and the detector measuring the

scattered intensity, I, and 𝜆 is the wavelength of the X-‐rays.68 Neglecting inelastic

scattering, the scattered intensity, I, is proportional to the differential cross section, a measure of the fraction of incident photons that emerges in various directions and is proportional to the sum of two factors according to (1.7):

𝐼 ∝ !"

!! !" ∝ 𝑆! 𝑄 + 𝑆! 𝑄 (1.7)

where the first term, 𝑆! 𝑄 , depends purely on the scattering properties of the

Conductive Graphitic Networks: From Atoms to Fuel Cells

Conductive Graphitic Networks:

from Atoms to Fuel Cells

Emanuela NEGRO

Conductive Graphitic Networks:

from Atoms to Fuel Cells

Proefschrift

Emanuela NEGRO

Contents

List of Abbreviations

Introduction

Sustainability and Energy

Structure of the thesis

PART I

Synthesis of Conductive Graphitic Networks

1 Characterization of Dense

Microemulsion Systems

1.1 Introduction

1.2 Methods