Filtern
Dokumenttyp
Sprache
- Englisch (7)
Schlagworte
- MOUSE (2)
- Nanostructure quantification (2)
- Round Robin (2)
- Transmission function (2)
- X-ray photoelectron spectroscopy (2)
- Concrete (1)
- Data analysis (1)
- Hackathon (1)
- Intensity scale calibration (1)
- Interlaboratory Study (1)
Organisationseinheit der BAM
- 6 Materialchemie (4)
- 5 Werkstofftechnik (2)
- 5.1 Mikrostruktur Design und Degradation (2)
- 6.1 Oberflächen- und Dünnschichtanalyse (2)
- 6.3 Strukturanalytik (2)
- 6.5 Synthese und Streuverfahren nanostrukturierter Materialien (2)
- 6.6 Physik und chemische Analytik der Polymere (2)
- 8 Zerstörungsfreie Prüfung (1)
- 8.0 Abteilungsleitung und andere (1)
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.
The lead authors failed to name two collaborators as co-authors. The authors listed should include:
Miss Claudia L. Compean-Gonzalez (ORCID:
0000-0002-2367-8450) and Dr. Giacomo Ceccone (ORCID:
0000-0003-4637-0771).
These co-authors participated in VAMAS project A27, provided data that were analyzed and presented in this publication (and supporting information), and reviewed the manuscript before submission.
We report the results of a Versailles Project on Advanced Materials and Standards interlaboratory study on the intensity scale calibration of x-ray photoelectron spectrometers using low-density polyethylene (LDPE) as an alternative material to gold, silver, and copper. An improved set of LDPE reference spectra, corrected for different instrument geometries using a quartz-monochromated Al Kα x-ray source, was developed using data provided by participants in this study. Using
these new reference spectra, a transmission function was calculated for each dataset that participants provided. When compared to a similar calibration procedure using the NPL reference spectra for gold, the LDPE intensity calibration method achieves an absolute offset of ∼3.0% and a systematic deviation of ±6.5% on average across all participants. For spectra recorded at high pass energies (≥90 eV), values of absolute offset and systematic deviation are ∼5.8% and ±5.7%, respectively, whereas for spectra collected at lower pass energies (<90 eV), values of absolute offset and systematic deviation are ∼4.9% and ±8.8%, respectively; low pass energy spectra perform worse than the global average, in terms of systematic deviations, due to diminished count rates and signal-to-noise ratio. Differences in absolute offset are attributed to the surface roughness of the LDPE induced by sample preparation. We further assess the usability of LDPE as a secondary reference material and comment on its performance in the presence of issues such as variable dark noise, x-ray warm up times, inaccuracy at low count rates, and underlying spectrometer problems. In response to participant feedback and the results of the study, we provide an updated LDPE intensity calibration protocol to address the issues highlighted in the interlaboratory study. We also comment on the lack of implementation of a consistent and traceable intensity calibration method across the community of x-ray photoelectron spectroscopy (XPS) users and, therefore, propose a route to achieving this with the assistance of instrument manufacturers, metrology laboratories, and experts leading to an international standard for XPS intensity scale calibration.
We report the results of a Versailles Project on Advanced Materials and Standards (VAMAS) interlaboratory study on the measurement of the shell thickness and chemistry of nanoparticle coatings. Peptide-coated gold particles were supplied to laboratories in two forms: a colloidal suspension in pure water and particles dried onto a silicon wafer. Participants prepared and analyzed these samples using either X-ray photoelectron spectroscopy (XPS) or low energy ion scattering (LEIS). Careful data analysis revealed some significant sources of discrepancy, particularly for XPS. Degradation during transportation, storage, or sample preparation resulted in a variability in thickness of 53%. The calculation method chosen by XPS participants contributed a variability of 67%. However, variability of 12% was achieved for the samples deposited using a single method and by choosing photoelectron peaks that were not adversely affected by instrumental transmission effects. The study identified a need for more consistency in instrumental transmission functions and relative sensitivity factors since this contributed a variability of 33%. The results from the LEIS participants were more consistent, with variability of less than 10% in thickness, and this is mostly due to a common method of data analysis. The calculation was performed using a model developed for uniform, flat films, and some participants employed a correction factor to account for the sample geometry, which appears warranted based upon a simulation of LEIS data from one of the participants and comparison to the XPS results.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.