University COVID-19 update

Visit the University's Coronavirus Information website for more information.

The Waterloo Institute for Nanotechnology main office (QNC 3606) is closed until further notice. If you are a student trying to pick up or return a lab/office key, please email asomel@uwaterloo.ca for assistance. All other inquires can be directed to win-office@uwaterloo.ca. For emergencies, contact Campus Police.

WIN Seminar Series: New Approaches for Powder Diffraction Export this event to calendar

Friday, July 20, 2018 — 2:00 PM to 3:00 PM EDT

In this WIN Seminar Series, Dr. Peter Khalifah will discuss his seminal research in the area of battery materials. The Khalifah group carries out the design, synthesis, structural characterization, and properties characterization of functional materials, primarily targeting compounds with applications for batteries, solar water splitting, electrocatalysts, and other emerging energy technologies.

New approaches for powder diffraction that extend the spatial resolution, temporal resolution, and sensitivity to defects in studies of battery materials

Although the Rietveld refinement of powder diffraction data from synchrotron and neutron sources can in theory be used to determine structural models with exquisite sensitivity to crystallographic parameters based on the estimated standard deviations for these parameters, in practice it is found that there are very large systematic errors in the determination of some key parameters, especially in site occupancies and atomic displacement parameters. These problems are most obvious when comparisons are made between the two structural models that are obtained from a single sample when neutron and synchrotron powder diffraction data from the sample are independently utilized for refinements. In this work, it will be shown that with the proper methods for analyzing data, superb agreement (~0.1% absolute) in occupancy-related parameter values can be obtained between structural models refined using neutron and synchrotron data. It is found that the powder diffraction data from modern user facility beamlines is of sufficient quality to allow systematic problems in the standard neutral atomic form factors used in X-ray diffraction data to be discerned. This discrimination is best accomplished using a new intuitive method of visualizing diffraction parameter space that we have developed. These methods have been applied to identify the nature of and to quantify the amount of occupancy defects in layered NMC cathodes with nominal stochiometries of Li(NixMnyCoz)O2, providing new general insights into the behavior of defects in this class of materials.

The high flux of modern user facilities beamlines can also be applied to carry out high-throughput measurements of samples where 103 – 105 high quality diffraction patterns can be collected during ex situ or operando measurements of full battery cells. In the case of large-format pouch cells, lateral mapping studies are of great value in assessing the homogeneity of the as-prepared cells as well as their failure mechanism after cycling. For the alternative coin cell geometry, depth-profiling studies using a narrow beam have been carried out which allow the vertical inhomogeneity within a single cathode layer to be followed as operando cells are cycled, proving insight into the transport limitations perpendicular to the plane of cathode films.

Dr. Peter Khalifah is an Associate Professor of Chemistry at Stony Brook University with a joint appointment at Brookhaven National Laboratory. His research interests include materials design, synthesis, and characterization. In addition, his research focuses on novel battery electrode materials, design of semiconductors for efficient solar water splitting, transition metal oxides for fuel cell electrocatalysis, and new classes of oxide thermoelectric materials.

Location 
QNC - Quantum Nano Centre
Room 1501
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

S M T W T F S
28
29
30
31
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
1
  1. 2021 (4)
    1. April (1)
    2. February (3)
  2. 2020 (29)
    1. November (2)
    2. October (3)
    3. September (2)
    4. August (1)
    5. July (6)
    6. June (7)
    7. May (1)
    8. April (1)
    9. March (1)
    10. February (3)
    11. January (2)
  3. 2019 (30)
  4. 2018 (21)
  5. 2017 (32)
  6. 2016 (31)
  7. 2015 (2)
  8. 2012 (1)