Annual General Meeting of ORSO

14 Jun 2021, 09:00 → 18 Jun 2021, 23:59 Europe/Stockholm

Zoom

The third annual meeting of the Open Reflectometry Standards Organisation.

The purpose of the meeting is to discuss topics of interest to the ORSO community. The program will consist of scientific anf technical talks together with discussion forums for each of the working groups

Please note that the timetable is using the time zone: Europe (Stokholm)

Each session has it's own Zoom link, please click on the session blocks in the timetable to find the link.

Monday, 14 June

Plenary: Session 1

Summary of progress since the last meeting
3-4 scientific talks (speakers to be confirmed)

Convener: Thomas Arnold (European Spallation Source ERIC)

1 Introduction

Speaker: Thomas Arnold (European Spallation Source ERIC)

2 Reproducible science in the neutron reflection context

Reproducibility is a broad term that covers many aspects of the scientific endeavour. In the context of a reflectometry experiment, ‘reproducibility’ can be understood to cover the details of sample preparation, data collection, data reduction, and data analysis. Attention to reproducibility is enabling: it allows the experiment to be repeated, the data handling pipeline to be checked, the data to be reanalysed, and the model to be reused.

As data is passed from instrument to instrument scientists to user to journal article it is also transformed from complex formats with rich metadata into lowest-common-denominator formats often with no metadata. While ‘standard’, the data transformations are seldom published and the low fidelity of the transforms with regards metadata frustrates reproducibility. Significant progress has been made in the reproducibility of data analysis, with more publications making use of open source code that has been described in the literature; the specific details of the models including code to reproduce the analysis is now routinely featured in the publications from some groups.

Recent achievements in improving the reproducibility of NR analysis will be illustrated using details from selected publications. Examples of good practice will be discussed, along with challenges (caused by current workflows, software, and file formats) that either prevent or discourage reproducibility.

Speaker: Dr Stuart Prescott (UNSW Chemical Engineering)

3 Spatially resolved neutron reflectometry by computed tomography

Neutron reflectometry (NR) has been employed to analyze the nanometric structure of the surface and interfaces of various materials. In a conventional NR experiment, the illuminated area is on the order of 1 - 10 cm^2; therefore, it has been difficult to use the NR for the sample with in-plane inhomogeneity. This work realized a spatially resolved NR technique by the combination of a "sheet-shaped" neutron beam with a two-dimensional detector and the computed tomography. The depth profile of the neutron scattering length density was analyzed at a local area smaller than 1 mm^2. The current NR tomography method would enable NR measurements for an interface with an inhomogeneous structure.

Speaker: Hiroyuki Aoki

10:15 Coffee Break

4 Resonant x-ray reflectivity: probing the chemical and magnetic profiles

Speaker: Tom Hase

5 Nina-Juliane Steinke

6 The Data Formats Working Group

Speaker: Jochen Stahn (Paul Scherrer Institut)

7 The Education and Outreach Working Group

Speaker: Bridget Murphy

Plenary: Session 2

Summary of progress since the last meeting
3-4 scientific talks (speakers to be confirmed)

Convener: Thomas Arnold (European Spallation Source ERIC)

8 Introduction

Speaker: Thomas Arnold (European Spallation Source ERIC)

9 Reinventing time-of-flight reflectometry—return of a proven approach

T. Charlton, H. Ambaye and A. Huon (ORNL)
M.R. Fitzsimmons (ORNL/University of Tennessee, Knoxville)

In this self-hosted Zoom-chat, I will discuss data acquisition and analysis techniques for neutron reflectometry at continuous wave (CW) sources applied to a pulsed-neutron source. I show data collected using the BL4A reflectometer at the Spallation Neutron Source (ORNL) using the so-called “CW-approach” and their analysis. The approach produces the reflectivity versus wavevector transfer without knowledge of the neutron beam spectrum. The results are compared to measurements of the same sample using the same instrument but with the traditional approach that involves normalization to the neutron beam spectrum typical of time-of-flight reflectometry. We find data acquired and analyzed using the CW-approach is less prone to instrumental artifacts than those obtained with the traditional approach and yields information that is more consistent with that obtained from X-ray reflectometry (at least for the sample studied). I discuss the implications of this study on how reflectometry data are collected at existing and proposed pulsed-neutron sources. I conclude with two parting and provocative questions:
• Why did the time-of-flight reflectometry and SANS communities take such radically different approaches to data acquisition and analysis?
• Given the success of the CW-approach, is a high frequency pulsed-source better suited for reflectometry than a low frequency source?

Work funded by the U.S. Department of Energy under contract No. DE-AC05-00OR22725.

Weblink to software and dataset in support of the presentation:
https://doi.org/10.5281/zenodo.4072377

Speaker: Tim Charlton

10 Free Thiols Regulatation of the Interactions and Self-Assembly of Thiol-Passivated Metal Nanoparticles investigated with X-ray scattering and MD simulations.

Pan Suna,1, Linsey M. Nowack b,1, Wei Bu a, Mrinal K. Bera a, Sean Griesemer b, Morgan Reik b, Joshua Portner b, Stuart A. Riceb,d, Mark L. Schlossman c and Binhua Lin a,b*
a NSF’s ChemMatCARS, University of Chicago, Chicago, IL 60637, USA
b James Franck Institute, University of Chicago, Chicago, IL 60637, USA
c Department of Physics, University of Illinois at Chicago, Chicago, IL 60607, USA
d Department of Chemistry, University of Chicago, Chicago, IL 60637, USA
ABSTRACT: Thiol ligands bound to the metallic core of nanoparticles determine their interactions with the environment and self-assembly. Recent studies suggest that equilibrium between bound and free thiols alters the ligand coverage of the core. Here, X-ray scattering and MD simulations investigate water-supported monolayers of gold-core nanoparticles as a function of the core-ligand coverage that is varied in experiments by adjusting the concentration of total thiols (sum of free and bound thiols). Simulations demonstrate that the presence of free thiols produces a nearly symmetrical coating of ligands on the core. X-ray measurements show that above a critical value of core-ligand coverage the nanoparticle core rises above the water surface, the edge-to-edge distance between neighboring nanoparticles increases, and the nanoparticle coverage of the surface decreases. These results demonstrate the important role of free thiols: they regulate the organization of bound thiols on the core and the interactions of nanoparticles with their surroundings [1].
[1] Pan Sun, Linsey M. Nowack, Wei Bu, Mrinal K. Bera, Sean Griesemer, Morgan Reik, Joshua Portner, Stuart A. Rice, Mark L. Schlossman, and Binhua Lin
Nano Letters 2021 21 (4), 1613-1619
DOI: 10.1021/acs.nanolett.0c04147

Speaker: Binhua Lin

20:15 Coffee Break

11 Why NeXus?

Speaker: Ray Osborn

12 35 years of x-ray reflectivity at NSLS & NSLS II: science hiding in small features and the new kid (instrument) on the block

Speaker: Ben Ocko

13 The Data Analysis Working Group

Speaker: Brian Maranville

14 The Reproducibility Working Group

Speaker: Andrew McCluskey (European Spallation Source ERIC)

Tuesday, 15 June

Data Formats Working Group: Session 1: The present state

Data Formats Working Group: Session 2: Recommendations

Data Formats Working Group: Session 3: A common dictionary for reflectometry

Wednesday, 16 June

Reproducibility Working Group: Session 1: The standard samples and calibrations project

Reproducibility Working Group: Session 2: The reproducible experiment checklist

Thursday, 17 June

Data Analysis Working Group: Session 1: Software Demonstrations/Tutorials

15 RefNX

https://www.github.com/refnx/refnx

Speaker: Andrew Nelson (Australian Nuclear Science and Technology Organisation)

16 GenX

https://aglavic.github.io/genx/index.html

Speaker: Artur Glavic (PSI - Paul Scherrer Institute)

17 BornAgain

http://bornagainproject.org/

Speaker: Joachim Wuttke

14:00 Coffee Break

18 RasCAL2

Speaker: Arwel Hughes

19 Refl1D

https://github.com/reflectometry/refl1d

Speaker: Brian Maranville

20 Analyzer

https://chemmatcars.uchicago.edu/facilities/software/

Speaker: Mrinal Bera

21 Dicussion

Data Analysis Working Group: Session 2: Software Discussion Session

Friday, 18 June

Education and Outreach Working Group: Session 1: The SLD database project

SLD_database_notes.pdf

Education and Outreach Working Group: Session 2: A tutorial paper on the analysis of reflectometry

SLD_database_notes.pdf

Plenary: Session 3

Summary of progress since the last meeting
3-4 scientific talks (speakers to be confirmed)

22 Summary of the working group sessions

23 New Methods in Reflectivity Analysis: Neural Networks and the Fisher Information

Speakers: Joshanial Cooper, James Durant

24 Artificial Intelligence Analysis of Reflectivity Data

Stefan Kowarik1, Alessandro Greco2, David Marecek1, Erich Hüthmair1, Vladimir Starostin2, Alexander Hinderhofer2, Alexander Gerlach2, Maximilian Skoda3, and Frank Schreiber2
— 1Department of Physical Chemistry, University of Graz, Austria
— 2Institute of Applied Physics, University of Tübingen, Germany
— 3Rutherford Appleton Lab, ISIS Neutron and Muon Source, UK
We review the applicability of artificial neural networks to analyse X-ray reflectivity (XRR) and neutron reflectivity data. Compared to standard iterative fitting approaches, the neural network analysis can predict sample parameters without needing a good initial guess of the fit parameters. Also, a neural network analysis copes well with a low signal to noise ratio and a high background signal as long as features such as the total reflection edge are prominent. However, the prediction accuracy is lower than a standard fit with parameter errors around 10 %.
In comparison with iterative fitting the neural network analysis is orders of magnitude faster, which is beneficial e.g. for estimates of parameter errors via quick predictions of multiple ‘leave-one-out’ / jack-knife XRR datasets. The processing speed also offers advantages for (on the fly) batch processing of large datasets e.g. from real-time experiments. Lastly, neural networks can co-refine multiple reflectivity curves, while taking into account prior information. For the example of XRR curves acquired during thin film growth this reduces parameter errors when compared to individual fits and also enables predictions not only of thickness, roughness or density but also of temporal parameters such as growth rate.
[1] Greco et al., J. Appl. Cryst., 52, 1342 (2019)
[2] Greco et al. Mach. Learn.: Sci. Technol. in press https://doi.org/10.1088/2632-2153/abf9b1 (2021)

Speaker: Stefan Kowarik

25 Towards reflectivity profile inversion through artificial neural networks

The goal of specular neutron and x-ray reflectometry is to infer a material's scattering length density (SLD) profile from its experimental reflectivity curves. In this talk I will quickly describe the ill-posed non-invertible problem and an approach to some solutions via the use of artificial neural networks (ANNs). In particular, I will describe a set of tailored numerical experiments with the aim of assessing the applicability of data science and machine learning to the analysis of data generated at large-scale neutron scattering facilities. For this purpose, sample physical models are described under a new paradigm: layer-by-layer descriptions --in terms of SLDs, thicknesses and roughnesses, are replaced by parameter-free curves ρ(z), moving the focus of a priori assumptions towards the sample family to which a given sample belongs (e.g., 'thin film,' 'lamellar structure',etc.). The proposed methodology, when implemented correctly would enable quicker batch analyses of large datasets.

Speaker: Juan Carmona Loaiza