Multi-Dimensional Electron Microscope
Lead Research Organisation:
University of Cambridge
Department Name: Materials Science & Metallurgy
Abstract
In the past decade or so, there has been something of a revolution in electron microscopy, a technique central to much of materials science, and parts of solid state chemistry and condensed matter physics. That revolution has been based around developments both in hardware, improving electron optics, monochromation, camera sensitivity and spectrometer efficiency, and in software, with code now able to process vast data sets in a robust and speedy fashion to extract the key information. This proposal, for a 'multi-dimensional electron microscope', or MDEM, brings together many of these development into a single instrument that is dedicated to analysing materials at the atomic- and nano-scales in two and three dimensions. The flexibility and power of modern microscopy resides also in the ability to use multiple detectors, cameras and spectrometers simultaneously so that multiple signals can be acquired from a single electron beam position - this is known as 'multi-modal' microscopy and when combined with the MDEM approach leads to a remarkable detailed investigation of structure and composition, crystallography and physico-chemical behaviour.
The MDEM is based around a scanning electron microscope that can operate from low voltages (e.g. 60kV) to high voltages (e.g. 300kV), the former being used for the study of samples with low atomic number and/or of low dimension, such as graphene, where knock-on damage may be predominant, the latter for organic crystals where radiolysis can be hugely detrimental. The MDEM is designed to investigate samples that have previously been considered too beam-sensitive to examine with conventional methods. By using the latest generation of direct electron detectors, with remarkably sensitive and linear response, we are able to record diffraction patterns from organic crystals in just a few milliseconds, before the crystal degrades under the beam. We will apply this method to study the nanoscale defect structure in pharmaceutical crystals, the development of dislocations, stacking faults and twins and importantly the interfaces between dissimilar organic crystals. Remarkably little is known about the microstructure of processed 'semi-crystalline' polymers, especially aliphatic polymers such as polyethylene and related alkanes. By using scanning electron diffraction methods we will use the MDEM to reveal hitherto unseen polymer nano-structure.
Electron tomography, or 3D imaging, can now be extended to a huge range of nanoscale materials and can be combined with diffraction, x-ray and energy loss spectroscopy to provide a full 3D picture of the materials' structure, composition and crystallography. The method is almost universally applicable and the range of materials science enabled by this method is huge. The multi-modal multi-dimensional aspect of the MDEM means we are able to acquire vast amounts of information and new software algorithms will be developed to process the data in a robust, efficient and meaningful fashion. These algorithms use the latest ideas in machine learning and in compressed sensing, where prior information is built into any reconstruction or interpretation of the image, tomogram or spectrum.
There are numerous material systems and devices that will benefit from the MDEM approach and, in addition to those already mentioned, we present a few more examples: perovskite solar cells, nitride semiconductors, engineering alloys such as nano-structured steels and Ni-base superalloys, low-dimensional dichalcogenides , magnetic skyrmionic materials, heterogeneous catalysts, MOFs and metallic glasses.
The MDEM is based around a scanning electron microscope that can operate from low voltages (e.g. 60kV) to high voltages (e.g. 300kV), the former being used for the study of samples with low atomic number and/or of low dimension, such as graphene, where knock-on damage may be predominant, the latter for organic crystals where radiolysis can be hugely detrimental. The MDEM is designed to investigate samples that have previously been considered too beam-sensitive to examine with conventional methods. By using the latest generation of direct electron detectors, with remarkably sensitive and linear response, we are able to record diffraction patterns from organic crystals in just a few milliseconds, before the crystal degrades under the beam. We will apply this method to study the nanoscale defect structure in pharmaceutical crystals, the development of dislocations, stacking faults and twins and importantly the interfaces between dissimilar organic crystals. Remarkably little is known about the microstructure of processed 'semi-crystalline' polymers, especially aliphatic polymers such as polyethylene and related alkanes. By using scanning electron diffraction methods we will use the MDEM to reveal hitherto unseen polymer nano-structure.
Electron tomography, or 3D imaging, can now be extended to a huge range of nanoscale materials and can be combined with diffraction, x-ray and energy loss spectroscopy to provide a full 3D picture of the materials' structure, composition and crystallography. The method is almost universally applicable and the range of materials science enabled by this method is huge. The multi-modal multi-dimensional aspect of the MDEM means we are able to acquire vast amounts of information and new software algorithms will be developed to process the data in a robust, efficient and meaningful fashion. These algorithms use the latest ideas in machine learning and in compressed sensing, where prior information is built into any reconstruction or interpretation of the image, tomogram or spectrum.
There are numerous material systems and devices that will benefit from the MDEM approach and, in addition to those already mentioned, we present a few more examples: perovskite solar cells, nitride semiconductors, engineering alloys such as nano-structured steels and Ni-base superalloys, low-dimensional dichalcogenides , magnetic skyrmionic materials, heterogeneous catalysts, MOFs and metallic glasses.
Planned Impact
MDEM is a research-enabling platform which combines complementary electron microscopy techniques to build a detailed understanding of structure, composition and properties of materials. MDEM's multi-dimensional approach relies on efficient acquisition of images, diffraction patterns and spectroscopic data, that allow to reconstruct 3D, 4D and even 6D data sets, and closely monitor the evolution of specimens under the influence of external stimuli. It will be managed within an existing Cambridge University facility, WEMS, to guarantee access and support to as wide a user-base as possible. User fees, both from internal and external users, will be directed to the maintenance and upgrade of the microscope and ancillary equipment, to ensure continuous and reliable operation throughout the lifetime of the instrument.
Research projects in all areas of Materials Science will benefit from access to MDEM and to the suite of analysis software developed for interpretation and visualisation of multidimensional datasets. Among the most likely academic beneficiaries are the graduates from the CDTs in Graphene Technology and Sustainable and Functional Nano, and researchers involved in advanced materials research or solid state physics and chemistry.
We believe that MDEM will strongly enhance the capability of UK microscopy, through a robust and flexible analytical platform. The characterization techniques available on MDEM are highly relevant to industry, but are not normally accessible within one instrument and therefore are seldom used to their full potential, given that the strict cost/benefit constrains industry faces. Competitive markets require both innovation and strict quality control, and MDEM is ideally placed to support both, and hence to enhance the eco-system. Proactive engagement with industry, in turn, will benefit the academic community, creating opportunities for longer term projects and collaborations.
As well as attracting academic and industrial collaborations, MDEM will generate data that will interest and inspire young scientists, as well as the general public. In line with the Open Data ideal, datasets acquired with MDEM will be shared online, together with analysis software and tutorials aimed at high school and college students, to develop an understanding of Materials Science through practical data analysis.
Research projects in all areas of Materials Science will benefit from access to MDEM and to the suite of analysis software developed for interpretation and visualisation of multidimensional datasets. Among the most likely academic beneficiaries are the graduates from the CDTs in Graphene Technology and Sustainable and Functional Nano, and researchers involved in advanced materials research or solid state physics and chemistry.
We believe that MDEM will strongly enhance the capability of UK microscopy, through a robust and flexible analytical platform. The characterization techniques available on MDEM are highly relevant to industry, but are not normally accessible within one instrument and therefore are seldom used to their full potential, given that the strict cost/benefit constrains industry faces. Competitive markets require both innovation and strict quality control, and MDEM is ideally placed to support both, and hence to enhance the eco-system. Proactive engagement with industry, in turn, will benefit the academic community, creating opportunities for longer term projects and collaborations.
As well as attracting academic and industrial collaborations, MDEM will generate data that will interest and inspire young scientists, as well as the general public. In line with the Open Data ideal, datasets acquired with MDEM will be shared online, together with analysis software and tutorials aimed at high school and college students, to develop an understanding of Materials Science through practical data analysis.
Organisations
Publications
Ashling CW
(2019)
Synthesis and Properties of a Compositional Series of MIL-53(Al) Metal-Organic Framework Crystal-Glass Composites.
in Journal of the American Chemical Society
Bergh T
(2020)
Nanocrystal segmentation in scanning precession electron diffraction data.
in Journal of microscopy
Bergh T
(2021)
Microstructural and mechanical characterisation of a second generation hybrid metal extrusion & bonding aluminium-steel butt joint
in Materials Characterization
Chen Z
(2018)
A heterogeneous single-atom palladium catalyst surpassing homogeneous systems for Suzuki coupling.
in Nature nanotechnology
Chen Z
(2018)
Single-atom heterogeneous catalysts based on distinct carbon nitride scaffolds
in National Science Review
Collins S
(2019)
Scan Strategies for Electron Energy Loss Spectroscopy at Optical and Vibrational Energies in Perylene Diimide Nanobelts
in Microscopy and Microanalysis
Description | The grant was to fund a state of the art electron microscope that exploits the new technique of multi-dimensional electron microscopy. In the last year we have split the time on the microscope approximately 1000 hrs for dedicated user research and 680 to technique development (optimising protocols, etc) and microscope maintenance; in the last 3 months for example ca. 95% of peak hours have been booked highlighting the strong demand for the microscope. The usage is broken down as follows: ca. 60% of user time from within the Materials Department, 20% usage from across the university and 20% external usage (including local industry) - this is in accordance with the suggested usage in the original proposal. Well over 200 hours of Spectra time has been accessed using further funding routes including Royce Institute and EU networks (EXCITE and ESTEEM3). The microscope has been used to study many materials including nickel based superalloys, polymers, catalysis, battery technologies, small molecule crystals, plasmonic materials, perovskites, metal organic frameworks, earth science materials, next generation semiconductor and ferroelectric devices. Techniques utilised has mainly focused on multi-dimensional microscopy (3D-ED and 4D-STEM) and multi-modal microscopy utilising the high resolution nature in EDS, EELS and DPC, and the unique scanning and precession electron diffraction techniques for phase analysis, structure determination and strain mapping. The application of precession to 4D-STEM data sets has the benefit of making the signal more linear in nature and that in turn allows a greater use of (unsupervised) machine learning algorithms which is essential for mining very large multi-dimensional data sets. The microscope has recently been upgraded so that it can operate at 40, 80,120, 200 and 300 kV which enables maximum flexibility for experiments across a multitude of materials. already installed on the instrument. We have also acquired a cryo transfer holder which will enable the study of vitrified samples for soft and organic material examination. We are a beta test site for several manufacturers, Thermo Fisher, Nanomegas and Quantum Detectors (Merlin camera), all delivering new products, technologies and work flows. In June 2024 we will be running a precession electron diffraction workshop showcasing some of those new technologies and workflows. |
Exploitation Route | The key findings are important in two ways. Firstly for academics and non-academics alike the findings illustrate how previously unobtainable microstructural detail can be obtained from soft matter and highly beam sensitive material. Secondly, we have developed new techniques that others will be able to use and apply to other materials, especially in the soft matter community, and especially the examination of organics, hybrids and pharma material. |
Sectors | Aerospace Defence and Marine Chemicals Digital/Communication/Information Technologies (including Software) Energy Healthcare Pharmaceuticals and Medical Biotechnology |
URL | http://pyxem.github.io/pyxem-website/ |
Description | The success of the grant has led to an increasing use of the instrument by users external to Cambridge including two local SMEs. Further industry-based work will be forthcoming. We are a beta test site for several vendors Thermo Fisher, Nanomegas and Quantum Detectors (Merlin camera) all delivering new products, technologies and work flows. In June 2024 we will be running a precession electron diffraction workshop showcasing some of those new technologies and workflows. |
First Year Of Impact | 2023 |
Sector | Aerospace, Defence and Marine,Chemicals,Energy,Pharmaceuticals and Medical Biotechnology,Transport |
Impact Types | Economic |
Description | Multi-Dimensional Electron Diffraction: New Technology and Data Analytics for Improved Pharmaceutical Understanding and Performance |
Amount | £40,000 (GBP) |
Funding ID | EPSRC voucher 210193 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2021 |
End | 09/2025 |
Description | Rich Nonlinear Tomography for advanced materials (LEAD) |
Amount | £635,422 (GBP) |
Funding ID | EP/V007742/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 05/2021 |
End | 05/2024 |
Title | CCDC 2114085: Experimental Crystal Structure Determination |
Description | Related Article: Christopher J. H. Smalley, Harriet E. Hoskyns, Colan E. Hughes, Duncan N. Johnstone, Tom Willhammar, Mark T. Young, Christopher J. Pickard, Andrew J. Logsdail, Paul A. Midgley, Kenneth D. M. Harris|2022|Chemical Science|13|5277|doi:10.1039/D1SC06467C |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | http://www.ccdc.cam.ac.uk/services/structure_request?id=doi:10.5517/ccdc.csd.cc28yw9f&sid=DataCite |
Title | Data for Mapping Short-Range Order at the Nanoscale in Metal-Organic Framework and Inorganic Glass Composites |
Description | Dataset and associated Jupyter Notebook for the paper 'Mapping Short-Range Order at the Nanoscale in Metal-Organic Framework and Inorganic Glass Composites' by Joonatan E. M. Laulainen, Duncan N. Johnstone, Ivan Bogachev, Louis Longley, Courtney Calahoo, Lothar Wondraczek, David A. Keen, Thomas D. Bennett, Sean M. Collins, and Paul A. Midgley. Nanoscale. 2022. Detailed instructions on how to repeat the analysis are included within (in the form of Jupyter Notebooks) as well within the Methods section of the associated publication. X-ray PDF, EDS, and SED datasets are included. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://www.repository.cam.ac.uk/handle/1810/343629 |
Title | Research Data Supporting Visualising Performance-Limiting Nanoscale Trap Clusters at Grain Junctions in Halide Perovskites |
Description | This repository contains data necessary to reproduce figures and results from the associated manuscript. Files included contain data from scanning electron diffraction , photo emission electron microscopy, scanning transmission electron microscopy - energy dispersive X-ray spectroscopy, Kelvin probe force microscopy and photoluminescence measurements. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://www.repository.cam.ac.uk/handle/1810/304634 |
Title | Research data supporting "Local Nanoscale Phase Impurities are Degradation Sites in Halide Perovskites" |
Description | Understanding the nanoscopic chemical and structural changes that drive instabilities in emerging energy materials is essential for mitigating device degradation. The power conversion efficiency of halide perovskite photovoltaic devices has reached 25.7% in single junction and 29.8% in tandem perovskite/silicon cells1,2, yet retaining such performance under continuous operation has remained elusive3. Here, we develop a multimodal microscopy toolkit to reveal that in leading formamidinium-rich perovskite absorbers, nanoscale phase impurities including hexagonal polytype and lead iodide inclusions are not only traps for photo-excited carriers which themselves reduce performance4,5, but via the same trapping process are sites at which photochemical degradation of the absorber layer is seeded. We visualise illumination-induced structural changes at phase impurities associated with trap clusters, revealing that even trace amounts of these phases, otherwise undetected with bulk measurements, compromise device longevity. The type and distribution of these unwanted phase inclusions depends on film composition and processing, with the presence of polytypes being most detrimental for film photo-stability. Importantly, we reveal that performance losses and intrinsic degradation processes can both be mitigated by modulating these defective phase impurities, and demonstrate that this requires careful tuning of local structural and chemical properties. This multimodal workflow to correlate the nanoscopic landscape of beam sensitive energy materials will be applicable to a wide range of semiconductors for which a local picture of performance and operational stability has yet to be established. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://www.repository.cam.ac.uk/handle/1810/342320 |
Title | Research data supporting 'Precipitate nanostructuring that enhances lattice compatibility in a Ti-Fe-Al alloy' |
Description | Data includes: Microscope images and a 2D TEM DP (.tif files), X-ray diffraction data (.xy files), STEM data (both image and EDX) (.ser files), and SED data which is a .zspy file contained within a .zip file. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | https://www.repository.cam.ac.uk/handle/1810/348562 |
Title | Research data supporting Unsupervised machine learning applied to scanning precession electron diffraction data |
Description | The research presented in the publication is an investigation into the the application of the multivariate analysis methods of linear decomposition and data clustering to scanning precession electron diffraction data. Supporting this are several experimental, simulated, and model datasets. The experimental data comprises several scanning precession electron diffraction (SPED) datasets of a nanowire of gallium arsenide. SPED is a TEM technique where a narrow beam is used to collect a diffraction pattern from every point in a sample scan. Optionally, the beam is precessed - rocked about a double cone above and below the sample. Two samples were studied with various precession angles, detailed in the file names and the metadata associated with each file. The simulated data comprises three multislice simulations performed for the gallium arsenide crystal structure at three precession angles. The model data comprises two simple datasets with basic features mimicking diffraction patterns. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://www.repository.cam.ac.uk/handle/1810/332859 |
Title | Scanning precession electron diffraction data of partly overlapping magnesium oxide nanoparticles |
Description | Scanning precession electron diffraction (SPED) data of cubical magnesium oxide (MgO) nanoparticles are provided. The MgO particles in the data are partly overlapping and some share the same orientation. The dataset was used for demonstration of nanocrystal segmentation in SPED data, which is presented in the article entitled "Nanocrystal segmentation in scanning precession electron diffraction data" [1]. In this publication, two methods for nanocrystal segmentation are presented based on; i) virtual dark-field imaging and ii) non-negative matrix factorisation, both incorporating watershed image segmentation. The workflows and code used for the segmentation demonstrated in the article are available open-source [2]. Here, two files are provided based on one raw SPED dataset: - "SPED_MgO_1.hdf5": raw data cropped in navigation space to dimensions (219, 228|144, 144) and exported to hdf5, and - "SPED_MgO.hdf5": the same data binned by 2 in navigation space to yield dimensions (109, 114|144, 144). Adrian Lervik is acknowledged for specimen preparation. [1] Bergh, T., Johnstone, D., Crout, P., Høgås, S., Midgley, P., Holmestad, R., Vullum, P. And Van Helvoort, A. (2019), Nanocrystal segmentation in scanning precession electron diffraction data. Journal of Microscopy. doi:10.1111/Jmi.12850 [2] Duncan N. Johnstone, Phillip Crout, Simon Høgås, Tina Bergh, Joonatan Laulainen, & Stef Smeets. (2019). pyxem/pyxem-demos: pyxem-demos v0.10.0. Zenodo. http://doi.org/10.5281/zenodo.3533670 |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
URL | https://zenodo.org/record/3382873 |
Title | Supporting Data for Hole-limited electrochemical doping in conjugated polymers |
Description | This data set contains all the data presented in the main text figures (for the manuscript titled 'Hole-limited electrochemical doping in conjugated polymers'). The information on how the data was acquired and processed is detailed in the manuscript and supporting information. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | https://www.repository.cam.ac.uk/handle/1810/352791 |
Title | Code supporting Unsupervised machine learning applied to scanning precession electron diffraction data |
Description | The python code supplied here enables the reproduction of the figures presented in the publication as as set of executable python files. Also supplied is a complete static copy of the software used for data clustering, which was written for the publication. |
Type Of Technology | Software |
Year Produced | 2022 |
URL | https://www.repository.cam.ac.uk/handle/1810/332768 |