robert benda 1,3 - inria · multi-scale modelling of nano-sensors based on carbon nanotubes (cnts)...

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Multi-scale modelling of nano-sensors based on carbon nanotubes (CNTs) and conjugated polymers for water quality monitoring Robert BENDA 1,3 Supervisors: Bérengère LEBENTAL 1,2 , Éric CANCES 3 , Gaël Zucchi 1 1 Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR 7647, IPP, Ecole Polytechnique-CNRS, Route de Saclay, 91128 Palaiseau, France 2 Université Paris-Est, IFSTTAR, 14-20 bld Newton, 77447 Marne la Vallée, France 3 CERMICS, Ecole des Ponts , 6 & 8 avenue Blaise Pascal, 77455 Marne-la-Vallée Cedex 2, France and INRIA (MATHERIALS). 20 nm 30 nm Abstract We study resistive chemical sensors based on percolating networks of CNTs functionalized non-covalently by conjugated polymers. A change of resistance is expected in water upon complexation of a target ion by a specifically designed polymer probe. Multi-scale modelling aims at understanding and predicting the response of the sensor. Macroscopic system : Functionalized CNTs ink printed in-between two metallic electrodes : in water under increasing concentration of a target ion under applied voltage (I-V characteristic) Objectives : Find all possible sources of variation of the total sensor resistance. Predict the sensibility of the whole system towards all possible ions in water. Bibliography [1] Benda R., Cancès E. & Lebental B. (2019), Effective resistance of random percolating networks of stick nanowires: Functional dependence on elementary physical parameters. Journal of Applied Physics, 126(4), 044306. [2] Benda R., Zucchi G., Cancès E. & Lebental B., Insights into the π-π interaction driven non-covalent functionalization of carbon nanotubes of various diameters by conjugated fluorene and carbazole copolymers, submitted. [3] K. Malde and al., Automated force field topology builder (ATB) and repository: Version 1.0, Journal of Chemical Theory and Computation , 7(12), 4026, 2011. [4] A. C. T. van Duin and al., ReaxFF : a Reactive Force Field for Hydrocarbons, J. Phys. Chem. A, 105 (41), pp 9396-9409, 2001. [5]. A. K. Rappe and al., Charge equilibration for Molecular Dynamics simulations, The Journal of Physical Chemistry, 95 (8), 3358-3363, 1991. Example : 20 μm x 20 μm channel N random conducting wires of size l * Upper electrode Expected working principle of the sensor at the nanotube segment scale. Similar resistance changes (due to ion/polymer probe interaction) possible at the CNT/CNT junctions and electrode/CNT contacts . In water Conjugated polymer Sensing probe Lower electrode Examples of some scales already studied : CNTs random percolating network (resistance or I-V characteristic at room T) Statistical generalization Conclusion and perspectives The methodology developed across the different scales enables the assisted design of polymer chain structures and sensing probes, thanks to simulations results. Links between the different scales are still to be done (parameters of models at larger scales fed by the outputs of the models at lower scales). How to scale up from (possible) equilibrium geometries of the system derived at classical FF (or QM level), to electronic transport modulation in CNTs ? Monomer probe / target ion interaction (geometry, energy, electronic distribution) Effective resistance of random percolating networks of nanowires [1] Model : graph and adjacency matrix representing the network (nodes duplicated at each CNT/CNT junction) . Equivalent resistance computed numerically : separation of the dependence on geometrical and physical parameters. Relative sensitivity of the sensors to variations of ρ and R c (for instance) depends only on the ratio (, ) (, ) . Example : N=100 (above percolation), L=l=20 μm, l * =5 μm, ρ=6 kΩ/μm, =100 kΩ, , =10 kΩ : = 68 kΩ + 352 kΩ +13 kΩ=433 kΩ. Non-covalent functionalization of CNTs by conjugated polymers [2] Goal : polymer assisted design (sensing purposes). Better understanding of π-π stacking interactions. Systems studied : CNTs (diameters from 1.7 to 9 nm) and polyfluorene polymers or fluorene:carbazole copolymers (varying probes, alkyl chain length and initial geometry). Model : classical force field (ReaxFF) [4], atomic (geometry dependent) partial charges [5]. Method : molecular dynamics simulations (NVT ensemble, T=300 K). Possible adsorbed geometries extracted after adsorption and further minimized (at 0 K). Geometrical and energetical features of polymer adsorption on (DW)CNTs surface analysed. Result : 40 kcal/mol average interaction energy / monomer if no loop, 32 kcal/mol in case of loops / coiled adsorption. Examples of possible adsorption geometries for two 30 monomers long fluorene:carbazole copolymers with different sensing probes on a 9 nm diameter outer shell (right : DWNT) Monomer probe – target ion interaction Goal : understand the selectivity of probes to target ions. Probe assisted design. Systems studied : probe + ions, varying ion initial position. Models compared : GROMOS FF, AMOEBA polarizable FF, QM. Validation of the force field parametrization [3]: Comparison of binding sites and binding energies (in vacuo) for different levels of theory. Perspective : Once parametrization validated : simulations in solvent (free energies, residence times of ions in the binding sites). Cu 2+ -- probe complex minimized at the QM level (DFT, PBE functional, DZVP basis) LUMO orbital HOMO orbital (d character) CU 1.95 Å 22.3° DFT and force fields Polymer / CNT interaction, π-π stacking (geometry, energy) ReaxFF force field Polymer + target ions (interaction probabilities, conformational changes) Monomer / CNT interaction (charge transfer) DFT or wave- function theory CNT or graphene slice + monomer + target ion (geometry, electronic distribution) DFT CNT (pristine or functionalized) : electrical properties (resistance) Semi-classical transport theory (Boltzmann equation ?) Input geometry Input geometry Junction between two CNTs (pristine or functionalized) : electrical properties (resistance or I-V characteristic) Statistical averaging yielding input parameters / resistance probability distributions With / without water With / without water Without water Without water With / without water Without water Without water Monte-Carlo methods. Separate geometrical / (individual component) physical parameters CU linear junctions contacts CU

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Page 1: Robert BENDA 1,3 - Inria · Multi-scale modelling of nano-sensors based on carbon nanotubes (CNTs) and conjugated polymers for water quality monitoring Robert BENDA 1,3 Supervisors:

Multi-scale modelling of nano-sensors based on carbon nanotubes (CNTs) and conjugated polymers for water quality monitoring

Robert BENDA1,3

Supervisors: Bérengère LEBENTAL1,2, Éric CANCES 3, Gaël Zucchi 1

1Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR 7647, IPP, Ecole Polytechnique-CNRS, Route de Saclay, 91128 Palaiseau, France

2Université Paris-Est, IFSTTAR, 14-20 bld Newton, 77447 Marne la Vallée, France

3CERMICS, Ecole des Ponts , 6 & 8 avenue Blaise Pascal, 77455 Marne-la-Vallée Cedex 2, France and INRIA (MATHERIALS).

20 nm

30 nm

Abstract We study resistive chemical sensors based on percolating networks of CNTs functionalized non-covalently by conjugated polymers. A change of resistance is expected in water upon

complexation of a target ion by a specifically designed polymer probe. Multi-scale modelling aims at understanding and predicting the response of the sensor.

Macroscopic system : Functionalized CNTs ink printed in-between two metallic electrodes :

• in water • under increasing concentration of a target ion

• under applied voltage (I-V characteristic)

Objectives : • Find all possible sources of variation of the total

sensor resistance. • Predict the sensibility of the whole system

towards all possible ions in water.

Bibliography

[1] Benda R., Cancès E. & Lebental B. (2019), Effective resistance of random percolating networks of stick nanowires: Functional dependence on elementary physical parameters. Journal of Applied Physics, 126(4), 044306. [2] Benda R., Zucchi G., Cancès E. & Lebental B., Insights into the π-π interaction driven non-covalent functionalization of carbon nanotubes of various diameters by conjugated fluorene and carbazole copolymers, submitted.

[3] K. Malde and al., Automated force field topology builder (ATB) and repository: Version 1.0, Journal of Chemical Theory and Computation , 7(12), 4026, 2011. [4] A. C. T. van Duin and al., ReaxFF : a Reactive Force Field for Hydrocarbons, J. Phys. Chem. A, 105 (41), pp 9396-9409, 2001.

[5]. A. K. Rappe and al., Charge equilibration for Molecular Dynamics simulations, The Journal of Physical Chemistry, 95 (8), 3358-3363, 1991.

Example : 20 μm x 20 μm

channel

N random conducting wires of size l*

Upper electrode

• Expected working principle of the sensor at the nanotube segment scale.

• Similar resistance changes (due to ion/polymer probe interaction) possible at the CNT/CNT

junctions and electrode/CNT contacts .

In water

Conjugated polymer

Sensing probe

Lower electrode

Examples of some scales already studied :

CNTs random percolating network

(resistance or I-V characteristic at room T)

Statistical generalization

Conclusion and perspectives • The methodology developed across the different scales enables the assisted design of polymer chain structures and sensing probes, thanks to simulations results.

• Links between the different scales are still to be done (parameters of models at larger scales fed by the outputs of the models at lower scales). • How to scale up from (possible) equilibrium geometries of the system derived at classical FF (or QM level), to electronic transport modulation in CNTs ?

Monomer probe / target ion interaction (geometry, energy,

electronic distribution)

Effective resistance of random percolating networks of nanowires [1]

• Model : graph and adjacency matrix representing the network (nodes duplicated at each CNT/CNT junction) . • Equivalent resistance computed numerically : separation of the dependence on geometrical and physical parameters.

• Relative sensitivity of the sensors to variations of ρ and Rc (for instance) depends only on the ratio 𝐴(𝑁,

𝐿

𝑙∗)

𝐵(𝑁,𝐿

𝑙∗) .

• Example : N=100 (above percolation), L=l=20 μm, l*=5 μm, ρ=6 kΩ/μm, 𝑅𝑐=100 kΩ, 𝑅𝑚, 𝑤=10 kΩ : 𝑅𝑒𝑞

= 68 kΩ + 352 kΩ +13 kΩ=433 kΩ.

Non-covalent functionalization of CNTs by conjugated polymers [2] • Goal : polymer assisted design (sensing purposes). Better understanding of π-π stacking interactions.

• Systems studied : CNTs (diameters from 1.7 to 9 nm) and polyfluorene polymers or fluorene:carbazole copolymers (varying probes, alkyl chain length and initial geometry). • Model : classical force field (ReaxFF) [4], atomic (geometry dependent) partial charges [5]. • Method : molecular dynamics simulations (NVT ensemble, T=300 K). Possible adsorbed geometries extracted after adsorption and further minimized (at 0 K). • Geometrical and energetical features of polymer adsorption on (DW)CNTs surface analysed. Result : 40 kcal/mol average interaction energy / monomer if no loop, 32 kcal/mol in case of loops / coiled adsorption.

Examples of possible adsorption geometries for

two 30 monomers long fluorene:carbazole

copolymers with different sensing probes on a 9 nm

diameter outer shell (right : DWNT)

Monomer probe – target ion interaction • Goal : understand the selectivity of probes to target ions. Probe assisted design. • Systems studied : probe + ions, varying ion initial position. • Models compared : GROMOS FF, AMOEBA polarizable FF, QM.

• Validation of the force field parametrization [3]: Comparison of binding sites and binding energies (in vacuo) for different levels of theory.

• Perspective : Once parametrization validated : simulations in solvent (free energies, residence times of ions in the binding sites).

Cu2+ -- probe complex minimized at the QM level (DFT, PBE functional, DZVP basis) LUMO orbital HOMO orbital (d character)

CU 1.95 Å

22.3°

DFT and force fields

Polymer / CNT interaction, π-π stacking (geometry, energy)

ReaxFF force field

Polymer + target ions (interaction

probabilities, conformational changes)

Monomer / CNT interaction (charge transfer)

DFT or wave- function theory

CNT or graphene slice + monomer + target ion (geometry, electronic

distribution)

DFT

CNT (pristine or functionalized) : electrical properties (resistance)

Semi-classical transport theory (Boltzmann equation ?)

Input geometry

Input geometry Junction between two CNTs

(pristine or functionalized) : electrical properties (resistance

or I-V characteristic)

Statistical averaging yielding input parameters / resistance probability distributions

With / without water

With / without water

Without water

Without water

With / without water Without water

Without water

Monte-Carlo methods. Separate geometrical / (individual

component) physical parameters

CU

linear junctions contacts

CU