Standards and Reference Materials for Quantitative Microanalysis: Current Availabilities, Database Status, and Future Avenues with FIGMAS
Bibliographic record
Abstract
High-quality microanalytical reference materials (µRM) are needed to achieve high accuracy quantitative analysis at the (sub-)micron-scale with scanning electron microscopes (SEM) or electron probe microanalyzers (EPMA) equipped with energy and/or wavelength dispersive spectrometers (EDS, WDS) and/or with a soft x-ray emission spectrometer (SXES). For any X-ray detector, independent of matrix correction effects, compositional data accuracy and instrument quality controls highly depend on the quality of the available reference materials. No accuracy can be guaranteed with a “bad” or an “ugly” µRM [1]. The microanalytical community requires “good” µRMs that adhere to the following golden rules: (a) Available in sufficient quantities (>100 g; natural samples) or able to be reliably and reproducibly synthesized. (b) Suitable grain size for microanalysis (ideally > 100 µm). (c) A well-characterized reference composition for major elements with independent certification, along with either a collection location for natural samples or a publicly available recipe for synthesis. (d) Honest assessment of contaminants: trace elements, elemental or mineral impurities, localized yet avoidable heterogeneities, etc. (e) Simple to prepare, polish, and maintain. (f) Homogeneous, non-porous, and stable over time, under vacuum, and under an electron beam (within reason). The problem of availability and reliability has been well documented [1,2] and continues to be frequently discussed among lab managers and at conferences, notably through activities from the Focused Interest Group on Microanalytical Standards (FIGMAS) [3-8]. Several simple materials such as oxides can be easily synthetized in large quantities and at a good homogeneity level near or below 100 ppm (e.g., MgO, Al2O3, SiO2, FexOy). Their distribution and characterization would facilitate development of a community consensus k-ratio database that would reduce the necessity for each laboratory to maintain extensive standard material collections and enable the sharing of k-ratios among labs instead of physical materials [9,10]. However, such simple materials do not cover all necessary elements of the periodic table. For instance, it is not possible to obtain synthetic alkali-rich materials that respect those golden rules, and as such alkali-rich natural materials such as albite and K-feldspar [11], or glasses with their risk of devitrification and inhomogeneity, are still commonly used. Not all µRMs currently available to the microanalytical community are provided with accurate or reliable reference compositions. Commonly observed inaccuracies in provided reference compositions include (a) an assumed perfect stoichiometry of natural minerals, (b) analysis normalized at 100% without the inclusion of H2O in hydrous minerals, (c) variable composition reported by different vendors for the same material, etc. Some issues are already recognized (e.g., surface oxidation of most metals) and some providers take care to acknowledge the limitations of their materials. The Smithsonian collection and its curators deserve particular praise for the honest evaluations of their materials, such as occurrences of amphibole in Fayalite NMNH 85276 [12] or inclusions in Kakanui hornblende [13]. Yet, many discrepancies remain unrevealed. FIGMAS has been working on assessing and addressing the situation since 2016 [3-8]. The initial focus for FIGMAS was to evaluate the situation and develop a database that catalogs the available µRM [14]. Members of FIGMAS may suggest modifications to the database, providing community evaluations of existing materials and sourcing for new µRM’s [3,4]. Additionally, FIGMAS started organizing material mounts for round robins to re-enforce the “good” µRM’s and to characterize potential new natural or synthetic materials [6]. With a new year comes new resolutions, and 2025 may bring several advancements on the FIGMAS side. First, improvements on the web-based database [14] are in the mind of the first author and will hopefully be implemented soon: more µRM entries will be added (*), an option to add uncertainties on µRM chemical data will be introduced. The interface will be simplified, a batch importation process for multiple µRM entries will be added, and discussions will start regarding the idea of a k-ratio database. Ideally, this database would compile a series of agreed-upon k-ratios obtained from pairs of materials, including an uncertainty assessment based on multiple laboratory measurements, with the opportunity for each lab participant to upload their data. This k-ratio database could, in time, eliminate the need for multiple µRM’s within each laboratory. Furthermore, FIGMAS has secured funding for the next several years to develop new µRMs, which will assist in replenishing the collections of many SEM and EPMA laboratories throughout the world. The FIGMAS database may eventually be linked with the Database of Electron Microanalysis [15], facilitating the import and export of µRM data between the two platforms [16]. (*) Any FIGMAS member can enter a new µRM or suggest a modification to an existing one on [14]. If unable to connect to the member section (required to add or modify a µRM entry), follow the website recommendations or contact the first author.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.048 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".