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Efforts are rising in opening up science by making data more transparent and more easily available, including the data reduction and evaluation procedures and code. A strong foundation for this is the F.A.I.R. principle, building on Findability, Accessibility, Interoperability, and Reuse of digital assets, complemented by the letter T for trustworthyness of the data. Here, we have used data, which was made available by the Institute Laue-Langevin and can be identified using a DOI, to follow the F.A.I.R.+T. principle in extracting, evaluating and publishing triple axis data, recorded at IN3.
With Direct Laser Writing (DLW) maturing in all aspects as a manufacturing technology a toolset for quality assurance must be developed. In this work we want to introduce a first of its kind test artifact. Test artifacts are standardized 3D models with specific geometric feature to evaluate the performance of writing parameters. Test artifacts are already common in other 3D additive manufacturing technologies e.g. Selective Laser Melting. The test artifact introduced in this work was developed in particular to accommodate 1) the high geometrical resolution of DLW structures and 2) the limited possibilities to examine the resulting structure. Geometric accuracy, surface adhesion as well as confocal raman spectroscopy results were considered when evaluating the design of the test artifact. We will explain the individual features and design considerations of our DLW test artifact. The difference between two slicers, Cura and 3DPoli, and the implications on measured feature sizes and the general shape is quantified. The measured geometries are used to derive a general design guide for a specific combination of photoresist, laser power and scanning speed and to analyse the geometric accuracy of a structure produced using these guidelines.
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).
Filigree structures can be manufactured via two-photon-polymerization (2PP) operating in the regime of non-linear light absorption. For the first time it is possible to apply this technique to the powder processing of ceramic structures with a feature size in the range of the critical defect size responsible for brittle fracture and, thus, affecting fracture toughness of high-performance ceramics. In this way, tailoring of advanced properties can be achieved already in the shaping process. Traditionally, 2PP relies on transparent polymerizable resins, which is diametrically opposed to the usually completely opaque ceramic resins and slurries. Here we present a transparent and photocurable suspension of nanoparticles (resin) with very high mass fractions of yttria-stabilized zirconia particles (YSZ). Due to the extremely well dispersed nanoparticles, scattering of light can be effectively suppressed at the process-relevant wavelength of 800 nm. Sintered ceramic structures with a resolution of down to 500 nm were obtained. Even at reduced densities of 1 to 4 g/cm³, the resulting compressive strength with 4,5 GPa is equivalent or even exceeding bulk monolithic yttria stabilized zirconia. A ceramic metamaterial is born, where the mechanical properties of yttria stabilized zirconia are altered by changing geometrical parameters and gives access to a new class of ceramic materials.
Iron nitride (Fe3N) and iron carbide (Fe3C) nanoparticles can be prepared via sol−gel synthesis. While sol−gel methods are simple, it can be difficult to control the crystalline composition, i.e., to achieve a Rietveld-pure product. In a previous in situ synchrotron study of the sol−gel synthesis of Fe3N/Fe3C, we showed that the reaction proceeds as follows:
Fe3O4 → FeOx → Fe3N → Fe3C. There was considerable overlap between the different phases, but we were unable to ascertain whether this was due to the experimental setup (side-on heating of a quartz capillary which could lead to thermal gradients) or whether individual particle reactions proceed at different rates. In this paper, we use in situ wide- and small-angle X-ray scattering (wide-angle X-ray scattering (WAXS) and small-angle X-ray scattering (SAXS)) to demonstrate that the overlapping phases are indeed due to variable reaction rates. While the initial oxide nanoparticles have a small range of diameters, the size range expands considerably and very rapidly during the oxide−nitride transition. This has implications for the isolation of Rietveld-pure Fe3N, and in an extensive laboratory study, we were indeed unable to isolate phasepure Fe3N. However, we made the surprising discovery that Rietveld-pure Fe3C nanoparticles can be produced at 500 °C with a sufficient furnace dwell time. This is considerably lower than the previous reports of the sol−gel synthesis of Fe3C nanoparticles.
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.
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
he main objective of the nPSize project is to improve the measurement capabilities for nanoparticle size based on both measurement methods traceable to SI units and new reference materials. Two basic approaches have been used in order to develop measurement procedures resulting in traceable results of the nanoparticle size distribution: physical modelling for the methods used in the project (TSEM, SEM, AFM and SAXS) and machine learning.
Physical modelling: In this part, the physical models associated with different shape measurements for the techniques TSEM, SEM, AFM and SAXS have been collected and further developed with the aim to simulate the resulting signal as measured by the individual methods. Uncertainties and traceability associated with each model were investigated and evaluated. In the following, the progress on these physical models is reported for each individual method.
Machine Learning modelling: The aim of this part is to use machine learning to enable automatic measurement of nanoparticle shape from expert a-priori information only. No physical model will be used as a-priori information in this task.
The accuracy and traceability of the size results obtained by each technique will be analyzed and compared with the physical modelling. A machine learning database will then be used to create automatic detection algorithms.
Mechanically stable structures with interconnected hierarchical porosity combine the benefits of both small
and large pores, such as high surface area, pore volume, and good mass transport capabilities. Hence, lightweight micro-/meso-/macroporous monoliths are prepared from ordered mesoporous silica COK-12 by means of spark plasma sintering (SPS, S-sintering) and compared to conventionally (C-) sintered monoliths. A multi-scale model is developed to fit the small angle X-ray scattering data and obtain information on the hexagonal lattice parameters, pore sizes from the macro to the micro range, as well as the dimensions of the silica population. For both sintering techniques, the overall mesoporosity, hexagonal pore ordering, and amorphous character are preserved. The monoliths' porosity (77–49%), mesopore size (6.2–5.2 nm), pore volume (0.50–0.22 g cm-3
), and specific surface area (451–180 m2 g-1) decrease with increasing processing temperature and pressure. While the difference in porosity is enhanced, the
structural parameters between the C-and S-sintered monoliths are largely converging at 900 C, except for the mesopore size and lattice parameter, whose dimensions are more extensively preserved in the
S-sintered monoliths, however, coming along with larger deviations from the theoretical lattice. Their higher mechanical properties (biaxial strength up to 49 MPa, 724 MPa HV 9.807 N) at comparable porosities and ability to withstand ultrasonic treatment and dead-end filtration up to 7 bar allow S-sintered monoliths to reach a high permeance (2634 L m-2 h-1 bar-1), permeability (1.25 x 10^-14 m2), and ability to reduce the chemical oxygen demand by 90% during filtration of a surfactant-stabilized oil in water emulsion, while indicating reasonable resistance towards fouling.
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.