the case for materials characterization

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The Case for Materials Characterization Foothill College NANO53

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The case for materials characterization in nanomaterials engineering

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Page 1: The Case for Materials Characterization

The Case for Materials Characterization

Foothill College

NANO53

Page 2: The Case for Materials Characterization

Overview

• The role of characterization

• PNPA model

• Types of information

• Example problems

• Materials analyzed

Page 3: The Case for Materials Characterization

Why Characterize?

• Nanostructures are unknown

• QA/QC of fabrication process

• Failure analysis of products

• Materials characterization

• Process development / optimization

Page 4: The Case for Materials Characterization

PNPA – Nanomaterials Engineering Rubric

• Applications drive requirements

• Requirements inform material selection

• Nanostructured materials engineering

• Process design and optimization

• Characterization tools and approach

Page 5: The Case for Materials Characterization

PNPA – A Rubric for Training Technicians in Nanomaterials Engineering

Page 6: The Case for Materials Characterization

PNPA - Characterization

Processing (P)

Properties (P)

Characterization

(N)Nanostructure

PLOs – Program Learning Outcomes – Integrated Materials Engineering Process

Stru

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oper

ty re

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Fabrication property relationships => <= Nanostructure elucidation

<= Process tools / QA/QC monitoring

Fabr

ication

pro

perty

relati

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ips =

><= Properties determ

ination

Page 7: The Case for Materials Characterization

Nanostructural Information

• Morphology• Composition• Chemistry• Structure• Properties

Novel nanocarbon can store and sieve hydrogen - http://spie.org/x13545.xml?ArticleID=x13545

Page 8: The Case for Materials Characterization

Process Optimization

• Relate structure to properties

• Relate structure to process

• Relate process to properties

• Optimize structure / property process / relationships

• Optimize process parameters for manufacturing / cost / safety etc.

Page 9: The Case for Materials Characterization

Taguchi Methods

• Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently also applied to, engineering, biotechnology, marketing and advertising. Professional statisticians have welcomed the goals and improvements brought about by Taguchi methods, particularly by Taguchi's development of designs for studying variation, but have criticized the inefficiency of some of Taguchi's proposals.[5]

http://en.wikipedia.org/wiki/Taguchi_methods

Page 10: The Case for Materials Characterization

Key Nanomaterials

• Polymers

• Metals/alloys

• Glasses/ceramics

• Nanocarbon

• Thin film coatings

• Silicon

• Particles

Energy of electrons in graphene in the tight-binding model, http://dx.doi.org/10.1103/PhysRev.71.622

Page 11: The Case for Materials Characterization

What we Need to Know

• Surface finish

• Surface composition and chemistry

• Layer thickness

• Bulk composition and chemistry

• Material phase and structure

Page 12: The Case for Materials Characterization

Types of Testing

• Materials characterization

• Process development support

• Failure analysis

• QA/QC

• Authenticity testing

Page 13: The Case for Materials Characterization

Tools

• Image (SEM, AFM, TEM)• Surface (AES, XPS)• Organic (FTIR, Raman,

GC/MS, LC/MS, NMR• Chemical (ICP, XRF, TEM)• Structural (XRD, Raman)• Modeling and simulation

Page 14: The Case for Materials Characterization

AFM Instrumentation

PNI Nano-R AFM Instrumentation as used at Foothill College

Page 15: The Case for Materials Characterization
Page 16: The Case for Materials Characterization

Surface Analysis Tools

SSX-100 ESCA on the left, Auger Spectrometer on the right

Page 17: The Case for Materials Characterization

XPS Spectrum of Carbon

• XPS can determine the types of carbon present by shifts in the binding energy of the C(1s) peak. These data show three primary types of carbon present in PET. These are C-C, C-O, and O-C=O

Page 18: The Case for Materials Characterization

Typical Problems

• Contamination

• Failure

• Process development

• Competitive analysis

• Research (R&D)

http://www.forensicinvestigation.com/

Page 19: The Case for Materials Characterization

Nanocarbon

• Graphitic like structures – CNT, graphene, etc

• Soot that has been annealed (graphitized)

• Graphitic planes are observed by TEM

• No one knows what the 3D structure is

• Electron tomography might be useful

Page 20: The Case for Materials Characterization
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Page 23: The Case for Materials Characterization

Biomedical Stents

• Surface finish is critical to patient outcomes, electropolishing etc.

• Multi-technique analysis– Image analysis– Surface analysis– Depth profiles

Page 24: The Case for Materials Characterization

Identification of Contamination

• Organic contamination• Ionic residues• Cleaning residue• Process residue• Packaging transfer• Environmental

Page 25: The Case for Materials Characterization

Surface Treatment of NiTi

Biomedical Devices and Biomedical Implants – SJSU Guna Selvaduray

Page 26: The Case for Materials Characterization

Biomedical Devices and Biomedical Implants – SJSU Guna Selvaduray

Surface Treatment of NiTi

Page 27: The Case for Materials Characterization

• XPS spectra of the Ni(2p) and Ti(2p) signals from Nitinol undergoing surface treatments show removal of surface Ni from electropolish, and oxidation of Ni from chemical and plasma etch. Mechanical etch enhances surface Ni.

Biomedical Devices and Biomedical Implants – SJSU Guna Selvaduray

Surface Treatment of NiTi

Page 28: The Case for Materials Characterization

Multi-technique Analysis

• Image – surface morphology

• Surface – surface chemistry

• Structural – crystal domain

• Organic – molecular specific identification (separation)

• Chemical – elemental analysis

Page 29: The Case for Materials Characterization

Modeling and Simulation

Page 30: The Case for Materials Characterization

Not Being Blind

• Developing a process with NO characterization tools

• Using properties measurements only

• Not knowing why something is good

• Not knowing if you can do better

• Not having a baseline of quality