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Nanoscale Science

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Improved control of chemical binding sites on QD. 1D QD Array Synthesis ... covalently bond inorganic nanoparticles to duplex DNA in a programmable fashion. ... – PowerPoint PPT presentation

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Title: Nanoscale Science


1
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

2
(No Transcript)
3
Computational Materials ScienceGroup Leader
Thomas Schulthess
  • G. A. Aramayo (aramayoga_at_ornl.gov)
  • G.P. Brown (browngp_at_ornl.gov)
  • O.J. Gonzalez (gonzalezoj_at_ornl.gov)
  • B. C. Hathorn (hathornb_at_ornl.gov)
  • T. Kaplan (kaplant_at_ornl.gov)
  • T. Maier (maierta_at_ornl.gov)
  • M. A. Majidi (majidima_at_ornl.gov)
  • V. Meunier (meunierv_at_ornl.gov)
  • M. B. Nardelli (buongiornonm_at_ornl.gov)
  • D. M. Nicholson (nicholsondm_at_ornl.gov)
  • D. W. Noid (noiddw_at_ornl.gov)
  • P. Nukala (nukalapk_at_ornl.gov)
  • B. Radhakrishnan (radhakrishnb_at_ornl.gov)
  • G. B. Sarma (sarmag_at_ornl.gov)
  • W. A. Shelton (sheltonwajr_at_ornl.gov)
  • A. V. Smirnov (smirnovav_at_ornl.gov)
  • S. Simunovic (simunovics_at_ornl.gov)
  • B. G. Sumpter (sumpterbg_at_ornl.gov)
  • M. Upmanyu (agor_at_ornl.gov)
  • J. C. Wells (wellsjc_at_ornl.gov)
  • L. Zhang (zhangl_at_ornl.gov)
  • X-G Zhang (zhangx_at_ornl.gov)
  • J. Zhong (zhongjn_at_ornl.gov)

4
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/workshop
    s)

5
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)
6
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.

7
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
8
Master Equation and Currents
  • Tunneling Rates
  • Fermis 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)

9
The Coulomb Ladder
In Collaboration with Dene Farrell, SUNY Brockport
10
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
2
3
n 1
C0
1
axon
dendrite
n 0
Va
Molecular Implementation
0
0
R
R
R
R
S
N
N
C
C
C
C
?N (2 to 4)
R
R
0
R
0
gold nanowire
gold nanowire
0
0
R
R
R
R
R
R
C
C
S
C
C
N
S
N
C
C
C
C
R
R
R
R
R
R
0
0
SiO2 insulation
p-Si substrate
courtesy A. Mayr (SBU)
11
Charging Characteristics of Monolayer-Protected
Clusters
  • 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
  • Computational Approach
  • Ab-Initio Density-Functional Theory
  • Pseudopotential Plane Wave (PSPW)
  • CPMD, NWChem,
  • Gaussian-type Obitals (LCAO)
  • NWChem

12
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.

13
Simulation of Carbon Nanotube Nucleation and
Growth
  • Objectives
  • Elucidate fundamental catalytic nucleation and
    growth mechanisms for carbon nanotubes.
  • Develop expertise in multiscale modeling of
    carbon nanotube growth processes.
  • Support ORNLs experimental program in carbon
    nanotube 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
  • 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 _at_ Arlington
  • Funding
  • ORNL-LDRD

14
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)
15
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.
  • 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.

16
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
  • Surface diffusion barrier (bridge site) between
    hcp-fcc hollow
  • DE0.26 eV.
  • 3 different entries for single C
  • 2 hexagon, DE -1.26eV
  • 1 pentagon, DE 0.63eV
  • Ring(9 Cs) grows into the tube.
  • Energy
  • Against 9 remote/ separate Cs-12.69eV
  • Against 9 adjacent Cs -9 eV
  • Reaction-limited growth.
  • Need to compute Barriers, Dynamics.

17
2D Continuum Calculations
Model
18
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
    summers project.
  • Challenge of Undergraduate Research
  • Match project to students 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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