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Nanoscience, SAXS and you
(2017)
Excelling in brevity but lacking in applicability, the 2011 EU nanomaterial definition has become a source of anguish for scientists and industry alike. Repeated pleas and discussions with our own envoy have demonstrated the strength of their resolve: this definition is unlikely to change. Manufacturers of many materials (cosmetics, pigments, foodstuffs, etc.) will have to characterise and label all their products accordingly, a task still impossible for lack of a clear metrological approach towards this goal. Therefore, the onus has fallen on the scientists to come up with a practicable measurement technique allowing inexpensive classification covering large swathes of the material landscape. Small-angle X-ray Scattering (SAXS) probes the size range in question, and can - with due care - deliver a bulk-averaged volume-weighted size distribution. Like any other real-world measurement method, however, it is not (and can never be) a universal solution. This presentation will clarify the SAXS technique, provide several application examples for nanomaterial characterisation, and will detail the limitations and pitfalls that accompany its abilities. At the end of this presentation, you will have the information to judge whether the technique is amenable to your materials or not.
A brief introduction is given into our data collection and organization procedure, and why we have settled on the HDF5-based NeXus format for describing experimental data.
The links between NeXus and the SciCat data catalog is also provided, showing how the NeXus metadata is automatically added as searchable metadata in the catalog.
Glimpses of the Future ✨: Advancing X-ray Scattering in an Automated Materials Research Laboratory
(2023)
In our (dramatically understaffed) X-ray scattering laboratory, developing a systematic, holistic methodology1 let us provide scattering and diffraction information for more than 2100 samples for 200+ projects led by 120+ collaborators. Combined with automated data correction pipelines, and our analysis and simulation software, this led to more than 40 papers2 in the last 5 years with just over 2 full-time staff members.
This year, our new, modular synthesis platform has made more than 1000 additional samples for us to analyse and catalogue. By virtue of the automation, the synthesis of these samples is automatically documented in excruciating detail, preparing them for upload and exploitation in large-scale materials databases. Having developed these proof-of-concepts, we find that materials research itself is changed dramatically by automating dull tasks in a laboratory.
This talk is intended to spark ideas and invite collaborations by providing an overview of: 1) the current improvements in our wide-range X-ray scattering laboratory methodology, 2) Introduce some of our open-source analysis and simulation software, touching on scattering, diffraction and PDF, and 3) introducing our open, modular robotic platform for systematic sample preparation. Finally, the remaining bottlenecks and points of attention across all three are highlighted.
In our (dramatically understaffed) X-ray scattering laboratory, developing a systematic, holistic methodology let us provide scattering and diffraction information for more than 2100 samples for 200+ projects led by 120+ collaborators. Combined with automated data correction pipelines, and our analysis and simulation software, this led to more than 40 papers in the last 5 years with just over 2 full-time staff members.
This year, our new, modular synthesis platform has made more than 1000 additional samples for us to analyse and catalogue. By virtue of the automation, the synthesis of these samples is automatically documented in excruciating detail, preparing them for upload and exploitation in large-scale materials databases.
This talk is intended to spark ideas and invite collaborations by providing an overview of: 1) the current improvements in our wide-range X-ray scattering laboratory methodology, and 2) introducing our open, modular robotic platform for systematic sample preparation.
The second talk for the Swiss Society for Crystallography (SSCr) workshop on SAXS will highlight the data processing challenges, holistic experimental workflow developments, and the pitfalls. In particular, the following items will be addressed:
- The importance of data processing and estimating uncertainty
- A universal correction pipeline – away with the headaches, at least for this step!
- Experiment planning part 2, some tips and advice to improve your corrected data.
- Sample preparation, background selection, some tips and advice to improve your corrected data.
- Automate for your mental well-being; electronic logbooks, measurement catalogs and workflow management software
- Life on the edge: several pitfalls to avoid…
This talk for the Swiss Society for Crystallography (SSCr) workshop on SAXS will introduce scattering from various angles, focusing in particular on:
- Information content of X-ray scattering experiments, three entry points…
- An introduction to Fourier Transforms
- Sample criteria, compatibility, and selection
- Key indicators of a measurement – where is the information?
- Key indicators of measurement quality
- Experiment planning, the basics
Trinamic TMCL IOC is a Python package designed for controlling stepper motors connected to a Trinamic board using the TMCL language (all boards supported by PyTrinamic should now work, has been tested on the TMCM 6110 and the TMCM 6214). Since it is implementing the TMCL protocol, it should be easy to adapt to other Trinamic motor controller boards. This package assumes the motor controller is connected over a machine network via a network-to-serial converter, but the underlying PyTrinamic package allows for other connections too.
This allows the control of attached motors via the EPICS Channel-Access virtual communications bus. If EPICS is not desired, plain Pythonic control via motion_control should also be possible. An example for this will be provided in the example.ipynb Jupyter notebook.
This package leverages Caproto for EPICS IOCs and a modified PyTrinamic library for the motor board control, and interfaces between the two via an internal set of dataclasses. Configuration for the motors and boards are loaded from YAML files (see tests/testdata/example_config.yaml).
The modifications to PyTrinamic involved extending their library with a socket interface. This was a minor modification that should eventually find its way into the official package (a pull request has been submitted).
An introductory lecture on the Dark Side of Science; what it is, why it exists, and what can be done to fight it. This lecture illuminates the increasing prevalence of fraudulent scientific work (e.g. faked data, manipulated images, paper mills) with plenty of examples and sources. The second section expands on the driving forces that caused this phenomenon to emerge, largely driven by pressures from management, peers and the researcher themselves. The third section expands on methods and tools that can be used to educate and arm oneself against this phenomenon. The 2023 edition includes new examples of larger fraudulent bodies of work emerging, and the problems posed by the arrival of LLMs.
Recorded at the Better with Scattering workshop 2020, this talk highlights the complete set of data correction steps that we do for the MAUS, and how they can be used elsewhere too. This links well with the talk in this series by Dr. Tim Snow, and also highlights the details of the background subtraction that needs to be done.
In contrast to the crisp, clear images you can get from electron microscopy, small-angle X-ray scattering (SAXS) patterns are rather featureless. These patterns, however, contain averaged structural information of all of the finest material structures that were illuminated by the X-ray beam. With careful and precise investigation, and supplementary information from complementary techniques, this bulk material structure can be quantified to reveal structural information spanning four or even five decades in size. Additionally, while the data correction and analysis is complex, sample preparation is very straightforward, also allowing for in-situ and operando measurements to be performed without breaking a sweat. In the right hands, then, this technique can be the most powerful tool in your analytical arsenal.
The dark side of science
(2020)
After a colleague sent me a more useful measurement from a laboratory instrument than what I could get from the beamline, I knew it was time to reassess my life's choices. Over the course of several subsequent post-doc and permanent positions around lab instruments, I managed to refine a flexible, comprehensive methodology for data collection, correction and analysis which can be applied to many X-ray scattering investigations at the lab and at the synchrotron. With the help of friends at round places, this methodology was implemented and put into production, and has been delivering high-quality data since then. Now, we have almost all possible data corrections (for X-ray scattering) implemented, and are improving the hardware to deliver higher-quality metadata to enable the corrections to be performed to a higher accuracy.
Simultaneously, we have set up a mini-large facility at BAM with the MAUS, the Multi-scale Analyzer for Ultrafine Structures. The MAUS combines the freedom of a laboratory instrument, with the spectrum of users of a beamline: besides measuring our own samples, and performing our own machine and methodology developments, we have opened this instrument for collaboration with fellow scientists from within BAM and from external institutes and universities. Here, we provide a comprehensive support for these collaborations, guiding the user from concept to sample selection, to interpretation and analysis. In 2019, we have supported over 30 different projects this way, leading to seven co-authored publications involving the MAUS in that year alone. As the MAUS uses the latest iteration of our comprehensive measurement methodology, the data quality is unmatched by any other lab instrument, and fully traceable to boot.
The freedom of the laboratory allows for more proof-of-principle experimentation than what is possible at the synchrotron. Therefore, the MAUS provides a good first (and sometimes final) step towards many experimental materials science investigations, nicely complementing the capabilities of the synchrotron. If and when more flux is needed, the step to the synchrotron is now smaller than ever, in particular with the same method
X-ray generation
(2020)
Today's speaker is a young scientist whose research on all aspects of small-angle scattering has taken him from his birthplace in Netherlands, to Denmark, Japan and now Germany. His research has led to a new method and software for scattering pattern analysis, a comprehensive set of data corrections together with the Diamond Light Source, and a new ultra-SAXS plug-in instrument.
For the last few years, he has been working on a comprehensive and universal methodology to get high-quality X-ray scattering measurements for any sample, using his new instrument at the institute. This instrument has now been heavily modified both in hardware and software, so that it can deliver better data.
These developments are always driven by interesting collaborations with materials researchers and other scientists. As a joint member he has published works on a wide variety of materials, including self-assembled structures in liquids, composite materials and porous carbon catalysts.
He has also been very active in outreach, for example by co-organizing an online lecture series called ‘#the Light Stuff’ on scattering and diffraction, running the ‘looking at nothing’ weblog, hosting a yearly introductory scattering course, and he has many scattering-related lectures available on YouTube.
Our distinguished speaker is Dr. Brian Richard Pauw from the Federal Institute for Materials Research and Testing in Germany. I proudly invite Dr. Pauw to begin his talk
The Dark Side of Science
(2020)
Today's speaker is a young scientist whose research on all aspects of small-angle scattering has taken him from his birthplace in Netherlands, to Denmark, Japan and now Germany. His research has led to a new method and software for scattering pattern analysis, a comprehensive set of data corrections together with the Diamond Light Source, and a new ultra-SAXS plug-in instrument.
For the last few years, he has been working on a comprehensive and universal methodology to get high-quality X-ray scattering measurements for any sample, using his new instrument at the institute. This instrument has now been heavily modified both in hardware and software, so that it can deliver better data.
These developments are always driven by interesting collaborations with materials researchers and other scientists. As a joint member he has published works on a wide variety of materials, including self-assembled structures in liquids, composite materials and porous carbon catalysts.
He has also been very active in outreach, for example by co-organizing an online lecture series called ‘#the Light Stuff’ on scattering and diffraction, running the ‘looking at nothing’ weblog, hosting a yearly introductory scattering course, and he has many scattering-related lectures available on YouTube.
Our distinguished speaker is Dr. Brian Richard Pauw from the Federal Institute for Materials Research and Testing in Germany. I proudly invite Dr. Pauw to begin his talk.
In our (dramatically understaffed) X-ray scattering laboratory, developing a systematic, holistic methodology let us provide scattering and diffraction information for more than 2100 samples for 200+ projects led by 120+ collaborators. Combined with automated data correction pipelines, and our analysis and simulation software, this led to more than 40 papers in the last 5 years with just over 2 full-time staff members.
This year, our new, modular synthesis platform has made more than 1000 additional samples for us to analyse and catalogue. By virtue of the automation, the synthesis of these samples is automatically documented in excruciating detail, preparing them for upload and exploitation in large-scale materials databases. Having developed these proof-of-concepts, we find that materials research itself is changed dramatically by automating dull tasks in a laboratory.
This talk is intended to spark ideas and collaborations by providing an overview of: 1) the current improvements in our scattering laboratory methodology, 2) introducing our open, modular robotic platform that is used for systematic sample preparation, and 3) demonstrating the data structure of the synthesis logs and measurements. Finally, the remaining bottlenecks and points of attention across all three are highlighted.
While the synthesis of Metal-Organic Framework (MOF) particles can be as easy as adding two solutions together, reproducibly obtaining the same particles, time and time again, is a lot harder. As laboratory-independent reproducibility is a cornerstone of the scientific method, we must put effort into finding and controlling all necessary parameters to achieve this.
An open-source Python/EPICS-controlled robotic platform (see picture) was adapted to systematically explore this for a 20 ml MOF synthesis of the Zeolitic Imidazole Framework-8 (ZIF-8) chemistry in methanol. Parameters that were explored included: 1) addition sequence, 2) addition speeds, 3) reaction times, 4) source chemicals, 5) stirring speeds, 6) stirring bar choice, 7) starting concentrations, and 8) workup methodologies. It was found that, by controlling these parameters, highly reproducible syntheses are obtained. Secondly, the variation of these parameters alone led to a dramatic difference in volume-weighted particle size means, which exceeds an order of magnitude as investigated by our in-house X-ray scattering instrument [1].
The syntheses are thoroughly documented in an automated fashion, and the synthesis libraries as well as analyses libraries will become available in batches soon. With this library, it will be possible to extract previously unknown correlations, and other laboratories can produce specific particles by following the exact procedures of the particles of their choice.
McSAS3 is a refactored software package for fitting large batches of (X-ray or Neutron) scattering data. It uses a Monte-Carlo acceptance-rejection algorithm to optimize model parameters - ideal for analysis of size-disperse scatterers.
The refactored code can exploit multiprocessing, traceably stores (multiple) results in the output file, and allows for re-histogramming of previous optimizations. Besides analysis of large batches, it can also be integrated in automated data processing pipelines.
The live demonstration will show how to use the software, what its limitations are, and what outcomes can look like for batches of results.
This talk introduces the expanded view that comes from wide-range X-ray scattering investigations.
Compared to X-ray diffraction studies alone, the additional angular range of this technique provides information on the larger structural dimensions present in your samples. This allows for the extraction of information on the size and size distribution of nanostructural components, such as nanoparticles, nanovoids, and any other structure exhibiting an electron density contrast.
The talk introduces the technique, the MOUSE instrument used for these investigations, and provides several real-world examples of its uses. The audience is invited to choose which examples captures their interest from a range of options, in the latter segment of the talk.
This talk highlights a proof-of-concept that demonstrates the ability to calculate high-resolution Fourier transforms. These can be combined with multi-scale modeling to simulate scattering over a wide range, from small-angle scattering to XRD and PDF.
The preprint documenting this is available on the ArXiv here:
https://doi.org/10.48550/arXiv.2303.13435
The Jupyter notebook, VASP calculation details and MOUSE measured scattering patterns are available from this Zenodo repository: https://dx.doi.org/10.5281/zenodo.7764045
In our (dramatically understaffed) X-ray scattering laboratory, developing a systematic, holistic methodology1 let us provide scattering and diffraction information for more than 2100 samples for 200+ projects led by 120+ collaborators over the last five years. Combined with universal, automat-ed data correction pipelines, as well as our analysis and simulation software, this led to more than 40 papers2 in the last 5 years with just over 2 full-time staff members.
While this approach greatly improved the consistency of the results, the consistency of the samples and sample series provided by the users was less reliable nor necessarily reproducible. To address this issue, we built an EPICS-controlled, modular synthesis platform to add to our laboratory. To date, this has prepared over 1200 additional (Metal-Organic Framework) samples for us to meas-ure, analyse and catalogue. By virtue of the automation, the synthesis of these samples is automat-ically documented in excruciating detail, preparing them for upload and exploitation in large-scale materials databases alongside the morphological results obtained from the automated X-ray scat-tering analysis.
Having developed these proof-of-concepts, we find that the consistency of results are greatly im-proved by virtue of their reproducibility, hopefully adding to the reliability of the scientific findings as well. Additionally, the nature of the experiments has changed greatly, with much more emphasis on preparation and careful planning. This talk will discuss the advantages and disadvantages of this highly integrated approach and will touch upon upcoming developments.
In this talk, the importance of metadata is underscored by real-world examples.
Metadata is essential to alleviating the reproducibility crises in science. This imples that a wide range of metadata must be collected, with a heavy emphasis on the automated collection of such metadata. This must subsequently be organized in an intelligible, archival structure, when possible with units and uncertainties.
Such metadata can aid in improving the usage efficiency of instrumentation, as is demonstrated on the MOUSE instrument. This metadata can now be used to connect the various aspects of the holistic experimental procedure to gain better insights on the materials structure.
A second example shows the extraction and organization of such metadata from an automated materials development platform, collected during the synthesis of 1200 samples. These metadata from the synthesis can then be linked to the results from the analysis of these samples, to find direct correlations between the synthesis parameters and the final structure of the materials.
Measuring an X-ray scattering pattern is relatively easy, but measuring a steady stream of high-quality, useful patterns requires significant effort and good laboratory organization.
Such laboratory organization can help address the reproducibility crisis in science, and easily multiply the scientific output of a laboratory, while greatly elevating the quality of the measurements. We have demonstrated this for small- and wide-angle X-ray scattering in the MOUSE project (Methodology Optimization for Ultrafine Structure Exploration).
With the MOUSE, we have combined a comprehensive and highly automated laboratory workflow with a heavily modified X-ray scattering instrument. This combination allows us to collect fully traceable scattering data, within a well-documented, FAIR-compliant data flow (akin to what is found at the more automated synchrotron beamlines). With two full-time researchers, our lab collects and interprets thousands of datasets, on hundreds of samples, for dozens of projects per year, supporting many users along the entire process from sample selection and preparation, to the analysis of the resulting data.
This talk will briefly introduce the foundations of X-ray scattering, present the MOUSE project, and will highlight the proven utility of the methodology for materials science. Upgrades to the methodology will also be discussed, as well as possible avenues for transferring this holistic methodology to other instruments
This is a set of drawings accompanying the submitted paper entitled "Extending Synchrotron SAXS instrument ranges through addition of a portable, inexpensive USAXS module with vertical rotation axes". The parts described herein will combine with commercial off-the-shelf components to build a high precision pair of rotation stages for accurate measurement of scattering angles with a sub-microradian precision.
McSAS3
(2023)
McSAS3 is a refactored version of the original McSAS (see DOI 10.1107/S1600576715007347). This software fits scattering patterns to obtain size distributions without assumptions on the size distribution form. The refactored version has some neat features:
- Multiprocessing is included, spread out over as many cores as number of repetitions!
- Full state of the optimization is stored in an organized HDF5 state file.
- Histogramming is separate from optimization and a result can be re-histogrammed as many times as desired.
- SasModels allow a wide range of models to be used
- If SasModels does not work (e.g. because of gcc compiler issues on Windows or Mac), an internal sphere model is supplied
- Simulated data of the scattering of a special shape can also be used as a McSAS fitting model. Your models are infinite!
- 2D fitting also works.
Simulates X-ray and Neutron scattering patterns from arbitrary shapes defined by STL files.
Features:
- Uses multithreading to compute a number of independent solutions, then uses the variance of the results to estimate an uncertainty on the output.
- Can be launched from the command line using an excel sheet to define settings, or from a jupyter notebook.
- Outputs scattering patterns in absolute units if the contrast is set.
- A Gaussian size distribution is available, where the relative scaling of objects for each repetion can be varied. Recommended to be used with limited width (max. 10%) to avoid artefacts.
- Writes results with settings to an archival HDF5 file.
Application examples:
This software has been used in several studies to date. For example, it has been used here to simulate a model scattering pattern for a cuboid shape, which was then fed forward into the McSAS3 analysis program for analyzing scattering patterns of polydisperse cuboids. A second use is here, where it was used for the modeling of flattened helices. In this paper, scattering pattern features could be matched with particular morphological changes in the structure. Lastly, this paper has an example where it was used to validate the analytical analysis model, and explore the realistic limits of application of the analytical model.
How much do we, the small-angle scatterers, influence the results of an investigation? What uncertainty do we add by our human diversity in thoughts and approaches, and is this significant compared to the uncertainty from the instrumental measurement factors?
After our previous Round Robin on data collection, we know that many laboratories can collect reasonably consistent small-angle scattering data on easy samples1. To investigate the next, human component, we compiled four existing datasets from globular (roughly spherical) scatterers, each exhibiting a common complication, and asked the participants to apply their usual methods and toolset to the quantification of the results https://lookingatnothing.com/index.php/archives/3274).
Accompanying the datasets was a modicum of accompanying information to help with the interpretation of the data, similar to what we normally receive from our collaborators. More than 30 participants reported back with volume fractions, mean sizes and size distribution widths of the particle populations in the samples, as well as information on their self-assessed level of experience and years in the field.
While the Round Robin is still underway (until the 25th of April, 2022), the initial results already show significant spread in the results. Some of these are due to the variety in interpretation of the meaning of the requested parameters, as well as simple human errors, both of which are easy to correct for. Nevertheless, even after correcting for these differences in understanding, a significant spread remains. This highlights an urgent challenge to our community: how can we better help ourselves and our colleagues obtain more reliable results, how could we take the human factor out of the equation, so to speak?
In this talk, we will introduce the four datasets, their origins and challenges. Hot off the press, we will summarize the anonymized, quantified results of the Data Analysis Round Robin. (Incidentally, we will also see if a correlation exists between experience and proximity of the result to the median). Lastly, potential avenues for improving our field will be offered based on the findings, ranging from low-effort yet somehow controversial improvements, to high-effort foundational considerations.
An extremely brief summary of what X-ray scattering can do for you (X-ray scattering encompasses small-angle X-ray scattering (SAXS), and wide-angle X-ray scattering (WAXS/XRD), amongst others). See my other videos for more detailed explanations on sample selection, data correction, data analysis, etc.
A tool for merging and/or rebinning single or multiple datasets to achieve a lower point density with best possible statistics. highly scriptable, CLI, no GUI
Version 0.1: works but could do with a cleanup. Weighting by uncertainty currently always on, but should be optional for use as an azimuthal or radial averager
This is a remote presentation I gave at the 2022 Small-angle Scattering conference in Campinas, Brazil. The video has been obtained from the conference organisers with their explicit permission for use on YouTube. I've tried to spruce up the audio from the remote recording the best I could.
The conference abstract for this talk was:
"How much do we, the small-angle scatterers, influence the results of an investigation? What uncertainty do we add by our human diversity in thoughts and approaches, and is this significant compared to the uncertainty from the instrumental measurement factors?
After our previous Round Robin on data collection, we know that many laboratories can collect reasonably consistent small-angle scattering data on easy samples[1]. To investigate the next, human component, we compiled four existing datasets from globular (roughly spherical) scatterers, each exhibiting a common complication, and asked the participants to apply their usual methods and toolset to the quantification of the results (https://lookingatnothing.com/index.ph....
Accompanying the datasets was a modicum of accompanying information to help with the interpretation of the data, similar to what we normally receive from our collaborators. More than 30 participants reported back with volume fractions, mean sizes and size distribution widths of the particle populations in the samples, as well as information on their self-assessed level of experience and years in the field.
While the Round Robin is still underway (until the 25th of April, 2022), the initial results already show significant spread in the results. Some of these are due to the variety in interpretation of the meaning of the requested parameters, as well as simple human errors, both of which are easy to correct for. Nevertheless, even after correcting for these differences in understanding, a significant spread remains. This highlights an urgent challenge to our community: how can we better help ourselves and our colleagues obtain more reliable results, how could we take the human factor out of the equation, so to speak?
In this talk, we will introduce the four datasets, their origins and challenges. Hot off the press, we will summarize the anonymized, quantified results of the Data Analysis Round Robin. (Incidentally, we will also see if a correlation exists between experience and proximity of the result to the median). Lastly, potential avenues for improving our field will be offered based on the findings, ranging from low-effort yet somehow controversial improvements, to high-effort foundational considerations."
Introduction
A good laboratory organization can help address the reproducibility crisis in science, and easily multiply the scientific output of a laboratory, while greatly elevating the quality of the measurements. We have demonstrated this for small- and wide-angle X-ray scattering in the MOUSE project (Methodology Optimization for Ultrafine Structure Exploration). In the MOUSE, we have combined: a) a comprehensive laboratory workflow with b) a heavily modified, highly automated X-ray scattering instrument. This combination allows us to collect fully traceable scattering data, with a well-documented data flow (akin to what is found at the more automated beamlines). With two full-time researchers, the lab collects and interprets thousands of datasets, on hundreds of samples for dozens of projects per year, supporting many users along the entire process from sample selection and preparation, to the analysis of the resulting data.
While these numbers do not light a candle to those achieved by our hardworking compatriots at the synchrotron beamlines, the laboratory approach does allow us to continually modify and fine-tune the integral methodology. So for the last three years, we have incorporated e.g. FAIR principles, traceability, automated processing, data curation strategies, as well as a host of good scattering practices into the MOUSE system. We have concomitantly expanded our purview as specialists to include an increased responsibility for the entire scattering aspect of the resultant publications. This ensures full exploitation of the data quality, whilst avoiding common pitfalls.
Talk scope
This talk will present the MOUSE project as implemented to date, and will introduce foreseeable upgrades and changes. These upgrades include better pre-experiment sample scattering predictions to filter projects on the basis of their suitability, exploitation of the measurement database for detecting long-term changes and automated flagging of datasets, extending the measurement range through an Ultra-SAXS module, and enhancing MC fitting with sample scattering simulations for better matching of odd-shaped scatterers.
Compared to the clear, real-space images you can get from electron microscopy, X-ray scattering patterns are rather featureless. These patterns, however, contain structural information from all of the material structure illuminated by the X-ray beam. With this technique, you can measure nanoparticle dispersions, catalysts, composites, MOF powders, battery materials, light metal alloys and gels to reveal information on the structural features found within these materials. We have even measured many such materials for several research groups from the University of Birmingham, revealing structure features in the sub-nm to the micrometer range.
Measuring an X-ray scattering pattern is relatively easy, but measuring a high-quality, useful pattern requires significant effort and good laboratory organization. Such laboratory organization can help address the reproducibility crisis in science, and easily multiply the scientific output of a laboratory, while greatly elevating the quality of the measurements. We have demonstrated this for small- and wide-angle X-ray scattering in the MOUSE project (Methodology Optimization for Ultrafine Structure Exploration) [1]. With the MOUSE, we have combined: a) a comprehensive and highly automated laboratory workflow with b) a heavily modified X-ray scattering instrument. This combination allows us to collect fully traceable scattering data, within a well-documented, FAIR-compliant data flow (akin to what is found at the more automated synchrotron beamlines). With two full-time researchers, our lab collects and interprets thousands of datasets, on hundreds of samples, for dozens of projects per year, supporting many users along the entire process from sample selection and preparation, to the analysis of the resulting data.
This poster deals with improvements and characteriztion of small-angle scattering limitations, by looking at the trifecta of Data collection and uncertainty propagation, data analysis methodologies, and real-world tests. It is found that - with appropriate care and instrumentation - accuracies of 1% on mean nanomaterial sizes, and 10% on the size distribution width as well as the volume fraction can be achieved.
Everything SAXS
(2019)
The Dark Side of Science
(2019)
The Joint Summer School of the two Marie Skłodowska-Curie Innovative Training Networks (ITN) “BioCapture” and “GlycoImaging”, funded by the EU within the Horizon 2020 framework programme, which are both devoted to the development of new methods for cancer biomarker and cancer cell detection, will take place at the Adlershof Campus of BAM. 19 Early stage researchers of both projects will convene, discuss their own science and plan future collaborative research. Training in scientific writing (instructor: Luita Spangler, Free University of Berlin), an employability workshop (Antti Kapanen, University of Applied Sciences Berlin) and first contacts with the “dark side of science” (Brian R. Pauw, BAM) will complement the programme of the summer school.
Everything SAXS
(2019)
Experimenting on MAUS
(2019)
No matter whether you are doing X-ray diffraction or scattering, at wide or small angles, in a lab or at a synchrotron, you will need a bespoke sequence of up to twenty different corrections before it gets about right (at our latest count). Our library now does this automatically and reliably to get you the data you deserve – in absolute units and with uncertainty estimates – without the pain. This talk will highlight the development of the modular library, the sequence and its foundation, show its applicability to real-world datasets, and highlight a possible way forward
This paper presents the first worldwide inter-laboratory comparison of small-angle X-ray scattering (SAXS) for nanoparticle sizing. The measurands in this comparison are the mean particle radius, the width of the size distribution and the particle concentration. The investigated sample consists of dispersed silver nanoparticles, surrounded by a stabilizing polymeric shell of poly(acrylic acid). The silver cores dominate the X-ray scattering pattern, leading to the determination of their radius size distribution using (i) the generalized indirect Fourier transformation method, (ii) classical model fitting using SASfit and (iii) a Monte Carlo fitting approach using McSAS. The application of these three methods to the collected data sets from the various laboratories produces consistent mean number- and volume-weighted core radii of Rn = 2.76 (6) nm and Rv = 3.20 (4) nm, respectively. The corresponding widths of the lognormal radius distribution of the particles were σn = 0.65 (1) nm and σv = 0.71 (1) nm. The particle concentration determined using this method was 3.0 (4) g l−1 or 4.2 (7) × 10−6 mol l−1. These results are affected slightly by the choice of data evaluation procedure, but not by the instruments: the participating laboratories at synchrotron SAXS beamlines, commercial and in-house-designed instruments were all able to provide highly consistent data. This demonstrates that SAXS is a suitable method for revealing particle size distributions in the sub-20 nm region (at minimum), out of reach for most other analytical methods.
We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is on the same scale as the scattering intensity, may offer a novel pathway for determining the degree of crystallinity.
The "Ultima Ratio" strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from Q < 0.01 1/nm up to Q < 150 1/nm, with a resolution of 0.16 Angstrom. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to 8000^3 voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-Q behaviour. The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder. While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts.
## Summary:
This notebook and associated datasets (including VASP details) accompany a manuscript available on the ArXiv (https://doi.org/10.48550/arXiv.2303.13435) and hopefully soon in a journal as short communication as well. Most of the details needed to understand this notebook are explained in that paper with the same title as above. For convenience, the abstract is repeated here:
## Paper abstract:
We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is presented coupled to the scattering intensity, may offer a novel pathway for determining the degree of crystallinity.
The ``Ultima Ratio'' strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from $Q<0.01$\,$\mathrm{nm}^{-1}$ up to $Q\approx150$\,$\mathrm{nm}^{-1}$, with a resolution of 0.16\,\AA. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to $8000^3$ voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-$Q$ behaviour.
The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder.
While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts.
Technical drawings and documents for building a compact, heated, vacuum compatible flow-through sample holder. This holder is in use at the BAM MOUSE instrument as well as at the I22 beamline at the Diamond Light Source (see references for instrument details).
This holder has several features:
- The holder can be used in vacuum environments as well as in atmosphere
- It has two G 1/4" UNF fittings to attach HPLC tubing for (optionally) flowing a medium through the sample cell
- There are two additional (unflowed) sample positions for backgrounds and calibrants, held at the same temperature
- The low-mass design coupled with a 250W heating element can achieve heating rates of 1 degree C per second, when coupled (for example) with an Omron E5CC PID controller.
- The sample holder insert can be made from various materials depending on the application. Sealing the sample from the vacuum can be achieved using kapton, teflon or Magic tape, depending on the temperature requirements. The inlet and outlet holes will need to be punctured with a needle to enable flow.
- Large exit cones ensure a clear exit angle of at least 45 degrees two theta.
- It has been tested with temperatures up to 400 degrees C.
- Compression area has been raised and polished to ensure a good vacuum seal.
The SPONGE
(2020)
This dataset is a complete set of raw, processed and analyzed data, complete with Jupiter notebooks, associated with the manuscript mentioned in the title.
In the manuscript, we provide a "systems architecture"-like overview and detailed discussions of the methodological and instrumental components that, together, comprise the "MOUSE" project (Methodology Optimization for Ultrafine Structure Exploration). Through this project, we aim to provide a comprehensive methodology for obtaining the highest quality X-ray scattering information (at small and wide angles) from measurements on materials science samples.
Electric Safety Interlock
(2023)
This interlock is designed to prevent electrical shock from high voltage (>60V) equipment.
While the general safety interlock can be generically applied, this particular example employs an external vacuum-activated switch. It is for safeguarding human operations inside a vacuum sample chamber while the chamber doors are open. The circuit is closed (output is active) when a sufficient level of vacuum is reached, i.e. when all accessible openings are necessarily closed.
The initial application is to interrupt power to a 220V, 250W heating cartridge (itself mounted inside a small sample holder with potentially exposed contacts) when the sample chamber is open.
The external circuit can be modified to use different interlock mechanisms as needed. Note that the external interlock circuit is only a single circuit (with two signal lines) and thus is not protected against external shorts.
To accomodate a range of safety interlocks, the 4-pin M12 connector is wired as follows: Pin 1 (Brown): +24V for power supply, max current 0.6A Pin 2 (White): Safety interlock system signal 1 (0 or 24V) Pin 3 (Blue) : Safety interlock system signal 2 (0 or 24V) Pin 4 (Black): 0V for power supply
The safety is interlocked (output active) when both signal pins are set high (24V), with sufficient current to activate the two relays. Pin 1 and 4 can be used to power safety hardware (such as light curtains or proximity detectors) with 24VDC up to a current of 0.6A. A larger power supply can be installed when higher currents are needed, while staying within the current limits imposed by the wiring cross-section.
Wide-range X-ray scattering datasets and analyses for all samples described in the 2020 publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup". These datasets are composed by combining multiple small-angle x-ray scattering and wide-angle x-ray scattering curves into a single dataset. They have been analyzed using McSAS to extract polydispersities and volume fractions. They have been collected using the MOUSE project (instrument and methodology).
These are four datasets that were made available to the participants of the Small-angle Scattering data analysis round robin. The intent was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.
In this repository, there are:
1) a PDF document with more details for the study,
2) the datasets for people to try and fit,
3) an Excel spreadsheet to document the results.
Datasets 1 and 2 were modified from: Deumer, Jerome, & Gollwitzer, Christian. (2022). npSize_SAXS_data_PTB (Version 5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5886834
Datasets 3 and 4 were collected in-house on the MOUSE instrument.
A round-robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions and two from powders. Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5% and half of the population width entries within 40%. Due to the added complexity of the structure factor, far fewer people submitted answers on the powder datasets. For those that did, half of the entries for the means and widths were within 44 and 86%, respectively. This round-robin experiment highlights several causes for the discrepancies, for which solutions are proposed.
A Round Robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions, and two from powders.
Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5 % and half of the population width entries within 40 %, respectively. Due to the added complexity of the structure factor, much fewer people submitted answers on the powder datasets.
For those that did, half of the entries for the means and widths were within 44 % and 86 % respectively. This Round Robin experiment highlights several causes for the discrepancies, for which solutions are proposed.
This is a set of use examples for the HDF5Translator framework. This framework lets you translate measurement files into a different (e.g. NeXus-compatible) structure, with some optional checks and conversions on the way. For an in-depth look at what it does, there is a blog post here.
The use examples provided herein are each accompanied by the measurement data necessary to test and replicate the conversion. The README.md files in each example show the steps necessary to do the conversion for each.
We encourage those who have used or adapted one or more of these exampes to create their own conversion, to get in touch with us so we may add your example to the set.
Small-angle scattering data analysis round robin - Anonymized results, figures and Jupyter notebook
(2023)
The intent of this round robin was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.
This zip file contains the anonymized results and the jupyter notebook used to do the data processing, analysis and visualisation. Additionally, TEM images of the samples are included.
Ultra-SAXS can enhance the capabilities of existing synchrotron SAXS/WAXS beamlines. A compact ultra-SAXS module has been developed, which extends the measurable q-range with 0.0015 ≤ q (nm−1) ≤ 0.2, allowing structural dimensions in the range 30 ≤ D (nm) ≤ 4000 to be probed in addition to the range covered by a high-end SAXS/WAXS instrument. By shifting the module components in and out on their respective motor stages, SAXS/WAXS measurements can be easily and rapidly interleaved with USAXS measurements. The use of vertical crystal rotation axes (horizontal diffraction) greatly simplifies the construction, at minimal cost to efficiency. In this paper, the design considerations, realization and synchrotron findings are presented. Measurements of silica spheres, an alumina membrane, and a porous carbon catalyst are provided as application examples.
Ultra-SAXS can enhance the capabilities of existing SAXS/WAXS beamlines and laboratory instruments. A compact Ultra-SAXS module has been developed, which extends the measurable q-range with 0:0015 < q 1/nm) < 0:2, allowing structural dimensions between 30 < D(nm) < 4000 to be probed in addition to the range covered by a high-end SAXS/WAXS instrument. By shifting the module components in and out on their respective motor stages, SAXS/WAXS measurements can be easily and rapidly interleaved with USAXS measurements.
Data correction is probably the least favourite activity amongst users experimenting with small-angle X-ray scattering: if it is not done sufficiently well, this may become evident only during the data analysis stage, necessitating the repetition of the data corrections from scratch. A recommended comprehensive sequence of elementary data correction steps is presented here to alleviate the difficulties associated with data correction, both in the laboratory and at the synchrotron. When applied in the proposed order to the raw signals, the resulting absolute scattering cross section will provide a high degree of accuracy for a very wide range of samples, with its values accompanied by uncertainty estimates. The method can be applied without modification to any pinhole-collimated instruments with photon-counting direct-detection area detectors.
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.
Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet.
Protein folding, unfolding and misfolding have become critically important to a range of health and industry applications. Increasing high temperature and high pressure are used to control and speed up reactions. A number of studies have indicated that these parameters can have a large effecton protein structure and function. Here we describe the additive effects of these parameters on the small angle scattering behaviour of ribonuclease A. We find that alternate unfolded structures can be obtained with combined high pressure and temperature treatment of the protein.
Herein, we provide a "systems architecture"-like overview and detailed discussions of the methodological and instrumental components that, together, comprise the "MOUSE" project (Methodology Optimization for UltrafineStructure Exploration). The MOUSE project provides scattering information on a wide variety of samples, with traceable dimensions for both the scattering vector (q) and the absolute scattering cross-section (I). The measurable scattering vector-range of 0.012≤ q (nm-1) ≤ 92, allows information across a hierarchy of structures with dimensions ranging from ca. 0.1 to 400 nm. In addition to details that comprise the MOUSE project, such as the organisation and traceable aspects, several representative examples are provided to demonstrate its flexibility. These include measurements on alumina membranes, the tobacco mosaic virus, and dual-source information that overcomes fluorescence limitations on ZIF-8 and iron-oxide-containing carbon catalyst materials.
OpenSCAD, STL and technical drawings for the Modular sample holder designed primarily for use with The MOUSE instruments.
This modular sample holder can be used in conjunction with the following:
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Laser-cut sample holder (10.5281/zenodo.7499437)
OpenSCAD, STL and technical drawings for the capillary flow-through cell designed primarily for use with The MOUSE instruments.
This flow-through cell can be used in conjunction with:
- Modular sample holder (10.5281/zenodo.7499416)
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Laser-cut sample holder (10.5281/zenodo.7499437)
OpenSCAD, STL and technical drawings for the solid sample rack designed primarily for use with The MOUSE instruments.
This solid sample rack can be used in conjunction with:
- Laser-cut sample holder (10.5281/zenodo.7499437)
- Modular sample holder (10.5281/zenodo.7499416)
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
PDF file for the laser-cut sample holder designed primarily for use with The MOUSE instruments.
This sample holder can be used in conjunction with:
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Modular sample holder (10.5281/zenodo.7499416)
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
X-ray scattering datasets for samples described in the 2022 publication "Molecular Mobility of Polynorbornenes with Trimethylsiloxysilyl side groups: Influence of the Polymerization Mechanism". This dataset includes both raw and processed X-ray scattering data for samples APTCN and MPTCN, alongside background measurements files (BKG).
X-ray scattering datasets for samples described in the 2022 publication "Side chain length dependent dynamics and conductivity in self assembled ion channels". This dataset includes both raw and processed X-ray scattering data for samples ILC8, ILC10, ILC12, ILC14 and ILC16 alongside background measurement files (BKG).
X-ray scattering datasets for samples described in the 2020 publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation". This dataset includes both raw and processed X-ray scattering data for samples PTCHSiO-Pr, Bu, Hx, Oc and De, alongside background measurements files (BKG). This data was collected using the MOUSE project (instrument and methodology).
In recent years, we have come to appreciate the astounding intricacies associated with the formation of minerals from ions in aqueous solutions. In this context, a number of studies have revealed that the nucleation of calcium sulfate systems occurs nonclassically, involving the aggregation and reorganization of nanosized prenucleation species. In recent work, we have shown that this particle-mediated nucleation pathway is actually imprinted in the resultant micrometer-sized CaSO4 crystals. This property of CaSO4 minerals provides us with the unique opportunity to search for evidence of nonclassical nucleation pathways in geological environments.
In particular, we focused on large anhydrite Crystals extracted from the Naica Mine in Mexico. We were able to shed light on this mineral's growth history by mapping defects at different length scales. Based on this, we argue that the nanoscale misalignment of the structural subunits, observed in the initial calcium sulfate crystal seeds, propagates through different length scales both in morphological, as well as in strictly crystallographic aspects, eventually causing the formation of large mesostructured single crystals of anhydrite. Hence, the nonclassical nucleation mechanism introduces a “seed of imperfection,” which leads to a macroscopic “single” crystal whose fragments do not fit together at different length scales in a self-similar manner. Consequently, anisotropic voids of various sizes are formed with very welldefined walls/edges. However, at the same time, the material retains in part its single crystal nature.
The complex effects of nanoparticles on a thermosetting material based on an anhydride cured DGEBA/Boehmite nanocomposite with different particle concentrations is considered. A combination of X-ray scattering, calorimetry, including fast scanning calorimetry as well as temperature modulated calorimetry and dielectric spectroscopy, was employed to study the structure, the vitrification kinetics and the molecular dynamics of the nanocomposites. For the first time in the literature for an epoxy-based composite a detailed analysis of the X-ray data was carried out. Moreover, the unfilled polymer was found to be intrinsically heterogeneous, showing regions with different crosslinking density, indicated by two separate dynamic glass transitions. The glass transition temperature decreases with increasing nanoparticle concentration, resulting from a change in the crosslinking density. Moreover, on the one hand, for nanocomposites the incorporation of nanofiller increased the number of mobile segments for low nanoparticle concentrations, due to the altered crosslinking density. On the other hand, for higher loading degrees the number of mobile segments decreased, resulting from the formation of an immobilized interphase (RAF). The simultaneous mobilization and immobilization of the segmental dynamics cannot be separated unambiguously. Taking the sample with highest number of mobile segments as reference state it was possible to estimate the amount of RAF.
A detailed calorimetric study on an epoxy-based nanocomposite system was performed employing bisphenol A diglycidyl ether (DGEBA) cured with diethylenetriamine (DETA) as the polymer matrix and taurine-modified MgAL layered double hydroxide (T-LDH) as nanofiller. The -NH2 group of taurine can react with DGEBA improving the interaction of the polymer with the filler. The combined X-ray scattering, and electron microscopy data showed that the nanocomposite has a partially exfoliated morphology. Calorimetric studies were performed with conventional DSC, temperature modulated DSC (TMDSC) and fast scanning calorimetry (FSC) in the temperature modulated approach (TMFSC) to investigate the vitrification and molecular mobility in dependence of the filler concentration. First, TMDSC and NMR were used to estimate the amount of the rigid amorphous fraction which consists of immobilized polymer segments at the nanoparticle surface. It was found to be 40 wt% for the highest filler concentration, indicating that the interface dominates the overall macroscopic properties and behavior of the material to a great extent. Second, the relaxation rates of the α-relaxation obtained by TMDSC and TMFSC were compared with the thermal and dielectric relaxation rates measured by static FSC. The investigation revealed that the system shows two distinct α-relaxation processes. Furthermore, also two separate vitrification mechanisms were found for a bulk network-former without geometrical confinement as also confirmed by NMR. This was discussed in terms of the intrinsic spatial heterogeneity on a molecular scale, which becomes more pronounced with increasing nanofiller content.
Assembly of permanently porous metal–organic polyhedra/cages (MOPs) with bifunctional linkers leads to soft supramolecular networks featuring both porosity and processability. However, the amorphous nature of such soft materials complicates their characterization and thus limits rational structural control. Here we demonstrate that aging is an effective strategy to control the hierarchical network of supramolecular gels, which are assembled from organic ligands as linkers and MOPs as junctions. Normally, the initial gel formation by rapid gelation leads to a kinetically trapped structure with low controllability. Through a controlled post-synthetic aging process, we show that it is possible to tune the network of the linked MOP gel over multiple length scales. This process allows control on the molecular-scale rearrangement of interlinking MOPs, mesoscale fusion of colloidal particles and macroscale densification of the whole colloidal network. In this work we elucidate the relationships between the gel properties, such as porosity and rheology, and their hierarchical structures, which suggest that porosity measurement of the dried gels can be used as a powerful tool to characterize the microscale structural transition of their corresponding gels. This aging strategy can be applied in other supramolecular polymer systems particularly containing kinetically controlled structures and shows an opportunity to engineer the structure and the permanent porosity of amorphous materials for further applications.
The phase behavior and molecular ordering of hexakishexyloxy triphenylene (HAT6) DLC under cylindrical nanoconfinement is studied utilizing differential scanning calorimetry (DSC) and dielectric spectroscopy (DS), where cylindrical nanoconfinement is established through embedding HAT6 into the nanopores of anodic aluminum oxide membranes (AAO), and a silica membrane with pore diameters ranging from 161 nm down to 12 nm. Both unmodified and modified pore walls were considered, and in the latter case the pore walls of AAO membranes were chemical treated with n octadecylphosphonic acid (ODPA) resulting in the formation of a 2.2 nm thick layer of grafted alkyl chains. Phase transition enthalpies decrease with decreasing pore size, indicating that a large proportion of the HAT6 molecules within the pores has a disordered structure, which increases with decreasing pore size for both pore walls. In the case of the ODPA modification the amount of ordered HAT6 is increased compared to the unmodified case. The pore size dependencies of the phase transition temperatures were approximated using the Gibbs Thomson equation, where the estimated surface tension is dependent on the molecular ordering of HAT6 molecules within the pores and upon their surface. DS was employed to investigate the molecular ordering of HAT6 within the nanopores. These investigations revealed that with a pore size of around 38 nm, for the samples with the unmodified pore walls, the molecular ordering changes from planar axial to homeotropic radial. However, the planar axial configuration, which is suitable for electronic applications, can be successfully preserved through ODPA modification for most of the pore sizes.
Superglassy polymers with a large fractional free volume have emerged as novel materials with a broad range of applications, especially in the field of membrane separations. Highly permeable addition-type substituted polynorbornenes with high thermal resistance and chemical stability are among the most promising materials. The major obstacle for extending the practical membrane application is their strong tendency to physical aging, leading to a partial decline in their superior transport performance over time. In the present study, broadband dielectric spectroscopy with complementary X-ray scattering techniques were employed to reveal changes in microporous structure, molecular mobility, and conductivity by systematic
comparison of two polynorbornenes with different numbers of trimethylsilyl side groups. Their response upon heating (aging) was compared in terms of structure, dynamics, and charge transport behavior. Furthermore, a detailed analysis of the observed
Maxwell−Wagner−Sillars polarization at internal interfaces provides unique information about the microporous structure in the
solid films. The knowledge obtained from the experiments will guide and unlock potential in synthesizing addition-type polynorbornenes with versatile properties.
Polymers with intrinsic microporosity are promising candidates for the active separation layer in gas separation membranes. Here, the vibrational density of states (VDOS) for PIM-1, the prototypical polymer with intrinsic microporosity, is investigated by means of inelastic neutron scattering. The results are compared to data measured for a more conventional high-performance polyimide used in gas separation membranes (Matrimid). The measured data show the characteristic low frequency excess contribution to VDOS above the Debye sound wave level, generally known as the Boson peak in glass-forming materials. In comparison to the Boson peak of Matrimid, that of PIM-1 is shifted to lower frequencies. This shift is discussed considering the microporous, sponge-like structure of PIM-1 as providing a higher compressibility at the molecular scale than for conventional polymers. For an annealed PIM-1 sample, the Boson peak shifts to higher frequencies in comparison to the un-annealed sample. These changes in the VDOS of the annealed PIM-1 sample are related to changes in the microporous structure as confirmed by X-ray scattering.