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Nanoscale Science. Jack C. Wells Computational Material Science Group Computer Science Division Oak Ridge National Laboratory Research Alliance for Minorities (RAM) Spring '03 Workshop for Faculty and Mentors. G. A. Aramayo ([email protected]) G.P. Brown ([email protected])

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Nanoscale science l.jpg

Nanoscale Science

Jack C. Wells

Computational Material Science Group

Computer Science Division

Oak Ridge National Laboratory

Research Alliance for Minorities (RAM)

Spring '03 Workshop for

Faculty and Mentors


Computational materials science group leader thomas schulthess l.jpg

G. A. Aramayo ([email protected])

G.P. Brown ([email protected])

O.J. Gonzalez ([email protected])

B. C. Hathorn ([email protected])

T. Kaplan ([email protected])

T. Maier ([email protected])

M. A. Majidi ([email protected])

V. Meunier ([email protected])

M. B. Nardelli ([email protected])

D. M. Nicholson ([email protected])

D. W. Noid ([email protected])

P. Nukala ([email protected])

B. Radhakrishnan ([email protected])

G. B. Sarma ([email protected])

W. A. Shelton ([email protected])

A. V. Smirnov ([email protected])

S. Simunovic ([email protected])

B. G. Sumpter ([email protected])

M. Upmanyu ([email protected])

J. C. Wells ([email protected])

L. Zhang ([email protected])

X-G Zhang ([email protected])

J. Zhong ([email protected])

Computational Materials ScienceGroup Leader: Thomas Schulthess


Computational materials science cms l.jpg

Computational Materials Science (CMS)

  • From nano-science to engineering applications.

    • Engineering sciences

    • Nano science

    • Applied mathematics

    • Soft materials (polymers)

    • Surface science (catalysis)

    • Magnetism and magneto transport in nanostructures

    • Light-weight materials

    • Carbon based nanostructures

    • Molecular electronics

  • Intersection of Two Strategic Thrusts

    • Computational Sciences (www.ccs.ornl.gov)

    • Advanced Materials & Nanoscale Science (www.cnms.ornl.gov, www.ssd.ornl.gov/cnms/workshops)


1d qd array synthesis l.jpg

AFM Image

Periodic QD arrays

1D QD Array Synthesis

  • Directed assembly of QDs along engineered DNA.

    • DNA modified with amine groups as binding sites.

    • Covalent QD attachment to DNA.

    • Advantages

      • Particles at desired locations.

      • Achieve desired nanometer-scale periodicity.

      • Long-range order.

      • Stable backbone along the length of duplex DNA.

  • Research Issues:

    • Control site occupation along DNA template.

      • Methylamine blocks excess binding sites.

      • Improved control of chemical binding sites on QD.

K.A. Stevenson, G. Muralidharan, L. Maya, J.C. Wells, J. Barhen, T.G. Thundat,

J. Nanosci. Nanotech. (2002)


Periodicity in qd placement l.jpg

Gold nanoparticles bound to DNA strand with 10 nm spacing.

Small, periodic structures

Periodicity in QD Placement

  • Regular 1D Arrays

  • Method to covalently bond inorganic nanoparticles to duplex DNA in a programmable fashion.

  • Fabrication of nanostructures with nanoscale periodicity.


Transport in qd arrays l.jpg

Electron transport via tunneling

HSGATCTA*CAACGGCTCA*CCAAGATCTA*CAACGGCTCA*CCAAGATCTA*CAACGGCTCA*CCAAGATCTA*CAACGGCTCA*CCAA

TAGTTGCCGAGTAGGTTCTAGATAGTTGCCGAGTAGGTTCTAGATAGTTGCCGAGTAGGTTCTAGATAGTTGCCGAGTAGGTTCTAGASH

Transport in QD Arrays

  • After assembly, DNA can be removed by UV-ozone technique.

  • Current measurement through array.

    • Develop techniques to measure I-V curves.

      • Use AFM / STM, with probe tip acting as electrode.

      • Two electrode measurements.

Electrode

Electrode


Master equation and currents l.jpg

Master Equation and Currents

Tunneling Rates:

  • Fermi’s Golden Rule with approximations,

  • Tunneling between nearest neighbors only,

  • Neglects the effects of co-tunneling,

  • Rk, effective resistance of tunneling junction.

    Master Equation:

  • Time-development of probabilities for charge configurations,

  • Most often solved by Monte-Carlo techniques.

    Current-Voltage Characteristics (Average Current):


The coulomb ladder l.jpg

The Coulomb Ladder

In Collaboration with Dene Farrell, SUNY Brockport


Slide10 l.jpg

Single-Electron Latching Switch

single-

electron

island

Modeling Results:

tunnel

barrier

Vinj

(orthodox theory)

C23/C = 2

C0/C = 1

Q1/e = -0.425

Q2 = 0

Q3/e= -0.2

kBT/(e2/C) = 0.001

3

2

n = 1

C0

1

axon

dendrite

n = 0

Va

Molecular Implementation:

0

0

R

R

R

R’

N

N

S

C

C

C

C

N (2 to 4)

R’’

R”

R’’

0

0

gold

nanowire

gold

nanowire

0

0

R

R

R

R”

R’’

R’’

C

C

S

C

N

N

S

C

C

C

C

C

R

R

R

R

R

R

0

0

SiO2 insulation

p-Si substrate

courtesy: A. Mayr (SBU)


Charging characteristics of monolayer protected clusters l.jpg

Objectives

Elucidate the charging characteristics of monolayer-protected clusters.

Describe ligand-cluster interface in MPC.

Interpret the charging spectrum of MPCs to provide to distinguish between possible structural configurations for the clusters.

Participants

W. Andreoni, IBM-Zurich

A. Curioni, IBM-Zurich

S.A. Shevlin, ORNL/JICS

J.C. Wells, ORNL

Funding

DOE/BES/DMSE

ORNL-IBM CRADA

Charging Characteristics of Monolayer-Protected Clusters

  • Computational Approach

  • Ab-Initio Density-Functional Theory

    • Pseudopotential Plane Wave (PSPW)

      • CPMD, NWChem,

    • Gaussian-type Obitals (LCAO)

      • NWChem


Structure and charge transport in molecular scale electronics l.jpg

Structure and Charge Transport in Molecular-Scale Electronics

Transmission function computed through the electron-molecule-electrode system shown.

  • Objectives

  • Elucidate the role of the atomic structure of the molecule-electrode interface.

  • Role of charging and Coulomb blockade for molecular-scale latching switches.

  • Discrimination of bio-molecules (e.g., proteins, DNA. etc.) by their unique “conductance signature”.

  • Participants

  • D.J. Dean, P.S. Krstic, J. C. Wells, X.-G. Zhang ORNL

  • P.T. Cummings, Y. Leng Vanderbilt

  • D. Keffer, U. Tennessee

  • Funding

  • ARDA/ONR

  • DOE/BES/DMSE

  • ORNL-LDRD

  • Computational Approach

  • Ab-Initio Density-Functional Theory

  • Tight-binding Approach for Physically Realistic Electrode-molecule interface.


Simulation of carbon nanotube nucleation and growth l.jpg

Objectives

Elucidate fundamental catalytic nucleation and growth mechanisms for carbon nanotubes.

Develop expertise in multiscale modeling of carbon nanotube growth processes.

Support ORNL’s experimental program in carbon nanotube growth.

Participants

R.F. Wood, Z. Zhang ORNL/CMSD

D.W. Noid, S. Pannala, B.G. Sumpter, J.C. Wells, ORNL/CSMD

Q. Zhang, U. Texas @ Arlington

Funding

ORNL-LDRD

Simulation of Carbon Nanotube Nucleation and Growth

Decomposition Rates: Dependence on Concentration, Temperature, Composition?

Surface Carbide formation?

How stable is it?

Diffusion pathways? Catalyst clogging? Is diffusion the growth rate-limiting step?

Precipitation of carbon? Is precipitation rate limiting? Control of length, diameter chirality?

  • Computational Approach

  • Continuum Mass and Heat Transfer

  • Ab-Initio Density-Functional Theory

    • Pseudopotential Plane Wave (PSPW)

      • CPMD, NWChem,

    • Gaussian-type Obitals (LCAO)

      • NWChem


Multiscale modeling overview l.jpg

Multiscale Modeling (Overview)

Time and space evolution of carbon concentration in the catalyst

MD Simulations (Dynamic)

Time Scale ~ pico s, Length ~ nm

Mass Diffusion Rates

Rules for Segregation of carbon into the CNT

Growth Interface

2D Continuum Simulations

Time Scale ~ ms-s, Length ~ mm

Single Carbon Atom Addition

(DFT Calculations)


Carbon adsorption on clusters and surfaces l.jpg

3 sites for adsorption on Ni38.

(100), (111) hcp, and (111) fcc.

Localized relaxation of Ni38 at site.

C will remain on cluster surface.

Stable sites:

(100), (110), (111) hcp and fcc.

Adsorption Energetics order in same sequence on surface and Ni38.

(111) fcc

(111) hcp

Interstitial

(110)

fcc (111)

hcp (111)

(100)

(100)

Carbon Adsorption on Clusters and Surfaces

  • Fundamental, new predictions on small NixCy clusters and Ni surfaces.

  • Insight into adsorption, nucleation for large clusters in CVD growth.


Growth of baby tubes on ni 111 surface l.jpg

“Ring”(9 C’s) grows into the tube.

Energy:

Against 9 remote/ separate C’s:-12.69eV

Against 9 adjacent C’s: ~ -9 eV

Reaction-limited growth.

Need to compute Barriers, Dynamics.

Surface diffusion barrier (bridge site) between hcp-fcc hollow:

DE=0.26 eV.

3 different entries for single C:

2 hexagon, DE = -1.26eV

1 pentagon, DE = +0.63eV

Growth of Baby Tubes on Ni(111) Surface

Questions: How are C-atoms incorporated into the tube?

Concerted motion, ring-by-ring growth

Single Atom Addition


2d continuum calculations l.jpg

Yc = 0.03,

Typical Value

Yc = 0.001,

Carbon Activity = 1

dYc /dn= 0,

Zero Flux Condition

Schematic

2D Continuum Calculations

Model


Concluding comments l.jpg

Concluding Comments

  • Diversity of Computational Materials Science Research

  • Favorable collaboration would include RAM student, Faculty Advisor, and ORNL Staff, and remain active outside the constraints of one summer’s project.

  • Challenge of Undergraduate Research

    • Match project to student’s knowledge base

    • More knowledge is better, but we can often “make progress” with limited knowledge/experience.

  • Motivated, enthusiastic, “self-starters” wanted!


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