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Laboratory sources offer a unique advantage compared to synchrotron sources, largely in terms of freedom of operation. This freedom from user obligations, technology and software stacks and legacy decisions make the laboratory a very flexible place to develop and explore new ideas. The unparalleled availability furthermore allows for iterative improvement of instrumentation, sample environments and measurement methodologies to maximise the quality of the data obtained.
This talk will highlight the use of the laboratory as an agile test-bed and development space, by giving examples of some complete and incomplete investigations undertaken in our laboratory over the last years. Furthermore, it will introduce the concept of holistic experimentation, where the laboratory provides broad-ranging support for materials science investigations. This means that we assist in the experimental preparation, perform the measurements, correction and analysis, and follow-up with assistance in interpretation of our analyses in light of the results from other techniques applied to the investigation.
A chemical engineer by training, Brian drifted towards physics and now focuses on a broad spectrum of activities with the aim to improve scientific reproducibility. This includes studies on holistically improving data quality, data collection efficiency and traceability, as well as concomitant laboratory automation for the preparation of consistent, well-documented sample series. The need for pragmatism led to an inexpensive, flexible laboratory automation platform that can be implemented in a modest amount of time. This talk presents that effort.
This presentation highlights ongoing scientific misconduct as found in academic literature. This includes data- and image manipulation, and paper mills. Starting with an expose of examples, it delves deeper into the causes and metrics driving this phenomenon. Finally a range of possible tools is presented, that the young researcher can use to prevent themselves from sliding into the dark scientific methods.
By automatically recording as much information as possible in automated laboratory setups, reproducibility and traceability of experiments are vastly improved. This presentation shows what such an approach means for the quality of experiments in an X-ray scattering laboratory and an automated synthesis set-up.
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.
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 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.
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.
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.
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
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.
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.
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.
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.
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.
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