Energy-Related Materials & Applications

John Perkins

National Renewable Energy Laboratory, Golden, CO USA

Workshop on Combinatorial Approaches to Functional Materials

May 5, 2014

Materials for Energy – A Big Topic

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Focus Journal Issues

Books

Dedicated Journals

Current Conferences

Topics To Discuss

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I. Introduction – Scope of the Energy Challenge

II. High-Throughput Materials Property Measurements

III. Measuring Functionality

IV. High-Throughput Device Optimization

V. Characterizing Interfaces – A Practical Compromise ?

VI. Summary and Challenges

World Energy Usage Increasing

mtoe: millions tons oil equivalent

from BP Statistical Review of World Energy June 2013 13000

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14 % Carbon

Free

CO2 Growth over 50 Thousand Years

Recent CO2 Growth

What will it take to keep CO2 < 450 ppm ?

60 % Low-Carbon Energy Needed by 2050

IPCC “Climate Change 2014: Mitigation of Climate Change" CO2 ≈ 450 ppm

(to keep atmospheric CO2 ≈ 450 ppm)

Currently @ ~ 14% Low-Carbon Energy

mtoe: millions tons oil equivalent

from BP Statistical Review of World Energy June 2013 13000

0

An

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gy U

sage

(m

toe

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14 % Carbon

Free

(incluces Nuclear, Hyrdoelectricity & Renewables)

Example Technologies for Energy

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Thin Film PV

Organic PV (OPV)

Organic LED (OLED)

Electrochromic Window (from Granqvist et al).

Solar Fuel Production (from JCAP)

Hydrogen Fuel Cell (from DOE.gov)

• Lots of Materials • Lots of Interfaces

Development Time Historically Decades

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Can Materials-By-Design Change This ?

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Want Disruptive Development Trajectories

Materials Design via Design Principles

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(as practiced within EFRC Center for Inverse Design)

Close Coupling of - HT-Theory & - HT-Experiment Central

Easy HT-Materials Characterization

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NREL UV/VIS Optical Mapping Optical Property Map

• Cost ≈ 30K in Hardware • Measurement time ≈ 5 sec / spot • Analysis Straight Forward

Everybody Can Have Their Own Taylor et al., Advanced Functional Materials, 18, 3169, (2008)

HT-Structure/Phase Mapping @ NREL

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CoO

NiO ZnO

Growth

Combi Co-Sputtering 2-4 “libraries” / day 50 spots / library

Structure - XRD Mapping

4 libraries / night

Composition – XRF Mapping

8 libraries / night

Structure / Phase Map (20 libraries)

Data Analysis: 1 Grad Student Month

• Medium Throughput Experiments

• Data Analysis Limited

Cost ≈ $700 K

Prototype HT-XRD @ SLAC

Fe Ni

Co

• Libraries on 3 or 4 inch wafers • Concurrent XRD & XRF • Very Fast Data Acquisition in-situ Processing Experiments

X-rays

2D XRD Detector

Fluorescence Detector

Slide Courtesy of Apurva Mehta

XRD w/in-situ Annealing

2D XRD Detector

Fluorescence Detector

• Today: ~ 2000 patterns/day • Future: Scalable to ~ 20-50K/day • Requires a synchrotron – Scarce Resource

Slide Courtesy of Apurva Mehta

Challenges for Big Facility Experiments

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Practical: 1. Standardizing library geometries to enable many users

vs. experimental flexibility ? 2. Remote Experimentation and Sample Throughput

- Mail in analysis with a few day turn around time ? Social: 1. Collaboration vs. Competition ? 2. Dedicated funding of measurement system and staff ?

Data Overload: 1. How do we turn this much data into actionable knowledge ?

(or How Can We Make Good Use of 10K Measurements Per Day ?)

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Spectrum Deconvolution Best Basis Patterns Phase Map

Long et al., Rev. Sci. Instr. (2009) (Takeuchi Group at U. Maryland & NIST)

Non-Negative Matrix Factorization applied to Fe-Pd-Ga Thin Film Alloys

Zarnetta et al., Intermetallics (2012) (used XRD Suite software from Takeuchi Group / NIST)

9 As-Deposited Phases ID’d Phase Evolution During Anneal

500 °C 600 °C 700 °C

Cluster Analysis applied to Phase Evolution in Ni-Cu-Ti Thin Film Alloys

XRD Data to Structure-Phase Maps (Current)

Courtesy of Bruce van Dover

People Helping Computers Faster ?

Example: Al-Li-Fe phase diagram (Synthetic Data) • 28 composition points, 6 phases • 28170 seconds with no human input • 188 seconds with 4.3% of variables set by people (150x Faster!!)

XRD Data to Knowledge: An Ongoing Challenge

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http://www-ssrl.slac.stanford.edu/content/sites/default/files/documents/high_throughput_workshop_technical_summary.pdf

http://www-ssrl.slac.stanford.edu/content/sites/default/files/documents/high_throughput_workshop_technical_summary.pdfhttp://www-ssrl.slac.stanford.edu/content/sites/default/files/documents/high_throughput_workshop_technical_summary.pdfhttp://www-ssrl.slac.stanford.edu/content/sites/default/files/documents/high_throughput_workshop_technical_summary.pdfhttp://www-ssrl.slac.stanford.edu/content/sites/default/files/documents/high_throughput_workshop_technical_summary.pdfhttp://www-ssrl.slac.stanford.edu/content/sites/default/files/documents/high_throughput_workshop_technical_summary.pdfhttp://www-ssrl.slac.stanford.edu/content/sites/default/files/documents/high_throughput_workshop_technical_summary.pdfhttp://www-ssrl.slac.stanford.edu/content/sites/default/files/documents/high_throughput_workshop_technical_summary.pdf

JCA

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SCANNING DROPLET CELL FOR SERIAL (PHOTO)ELECTROCHEMISTRY

1 mm2 sample

Photocurrent measured with sub-1 µA/cm2 sensitivity

Electrocatalysis experiments include collection of full CV at 4 s per sample. Catalytic activity of quaternary spaces are readily mapped.

• The JCAP scanning droplet cell enables serial (photo)electrochemical measurements with data quality rivaling traditional techniques

• Solution flow provides contact to 1 mm2 thin film sample

• 3-electrode cell with low uncompensated resistance

• Continuous solution flow replenishes active solution volume more than once per second

• Gasket-free design allows rapid rastering

. Gregoire, J. M. et al., Scanning Droplet Cell for High Throughput Electrochemical and Photoelectrochemical Measurements. Review of Scientific Instruments 2013, 84 (2), 024102 . JCAP is supported through the Office of Science of the U.S. Department of Energy (Award No. DE-SC0004993)

Slide Courtesy of John Gregoire

JCA

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PARALLEL ELECTROCATALYST SCREEN FOR GAS EVOLVING REACTIONS: BUBBLE IMAGING

Array of 1mm2 catalyst samples

Image of samples in solution

Catalysts held at potential, t=0

Catalysts held at potential, t=30s

Automated bubble identification

• Parallel imaging of evolved gas bubbles for HER and OER catalysts

• Array of catalysts held at operating potential, 10-2s/sample demonstrated

• Independent of solution pH

• Carefully designed geometry and nucleation agents provide registry between catalyst samples and imaged bubbles

. Xiang, C. et al, A High Throughput Bubble Screening Method for Combinatorial Discovery of Electrocatalysts for Water Splitting. ACS Combinatorial Science 2014 . JCAP is supported through the Office of Science of the U.S. Department of Energy (Award No. DE-SC0004993)

Slide Courtesy of John Gregoire

Full Device Optimization ?

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Layered Cu(In,Ga)Se2 (CIGS) Solar Cell

Complex Optimization - 6 Materials

(TCO has 2 layers) - 5 Interfaces

How To Address ?

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Combinatorial Device Optimization (e.g. solar)

Figure: results of JV mapping of 1 row of combinatorial solar cell library

Composition

Real result (test material):

ZnS ZnO

Vertical gradient in absorber thickness e.g. SnS, CuSbS2

Uniform front grid and scribing (e.g. Al, Ag)

Horizontal gradient in n-type front contact = e.g. Zn(O,S), (Zn,Mg)O

Fabrication procedure: (hypothetical materials)

Figure: Schematics of high-throughout PV device fabrication approach

thin

t

hic

k SnS

O rich S rich contact gradient

abso

rber grad

ient

Combinatorial device capabilities are needed to bridge the gap between

materials research and technology development

Fabrication details

- common back contact

by evaporation

- absorber and contact

by co-sputtering

- shadow mask grid by e-

beam

Characterization/analysis details

- JV-curves under 1 Sun (AM1.5) solar simulator, 0.4 cm2 device area

- Automated analysis for PV device parameters VOC, JSC, FF, Eff., n, Rs, Rsh

Slide Courtesy of Andriy Zakutayev

Devices by Design ?

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Substrate

Back Contact

Charge Selective Contact Layer

Absorber

Junction Partner

Charge Selective Contact Layer

Front Contact

Generic Thin Film PV

- 7 Layers - 10 Variants / Layer

Empirical Optimization

107 Combinations!

Devices by Design ?

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Substrate

Back Contact

Charge Selective Contact Layer

Absorber

Junction Partner

Charge Selective Contact Layer

Front Contact

Generic Thin Film PV

- 7 Layers - 10 Variants / Layer

Empirical Optimization

107 Combinations!

Focus on the Interfaces

- 6 Interfaces - 102 Variants / Interface

600 Interfaces

Full Device

Modeling

Bulk Properties - via Theory - via Experiment

Interface Properties - via Theory - via Experiment

Devices By

Design

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Combi Interface Characterization (e.g. band offset)

Combinatorial: thickness wedge (parallel)

- deposit 1 thickness wedge (all at once)

- cool and transfer in vacuum (1 time)

Figure: Schematics of the Thickness-Wedge method

substrate thin film (0.1-10 nm)

thick film (>10 nm) hn

e-

Figure: Schematics of the traditional layer-by-layer method

Traditional: layer-by-layer method (serial method)

thickness

EF-E

VB

0

E

g

FB,p

FB,n

ECB

BE (CL)

ECL

EF-EVB

ΔEVB,CL

FB,n

FB,p Eg

- sequentially deposit 5-10 steps in thickness

- cool and transfer in vacuum to PES (5-10 times)

~5-10x faster parallel thickness wedge methods helps to study/optimize interfaces

- measure XPS/UPS core, VB, SEC energies

- plot vs thickness, determine offsets/barriers

EVB

Slide Courtesy of Andriy Zakutayev

Summary

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• Materials are critical to energy technologies

• High-throughput experiments are key to Materials-by-Design

• Materials are not enough

Need Devices-by-Design

• Challenges are not all technical

Large scale cooperation vs. competition

Funding models to promote such