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Enregistrement W4412908517 · doi:10.1093/mam/ozaf048.257

Standards and Reference Materials for Quantitative Microanalysis: Current Availabilities, Database Status, and Future Avenues with FIGMAS

2025· article· en· W4412908517 sur OpenAlexaff
Julien Allaz, Anette von der Handt, Owen K. Neill, E. S. Bullock, William O. Nachlas, Abigail P. Lindstrom, Andrew Mott

Notice bibliographique

RevueMicroscopy and Microanalysis · 2025
Typearticle
Langueen
DomaineEngineering
ThématiqueAdvanced Materials Characterization Techniques
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMicroanalysisCurrent (fluid)Analytical Chemistry (journal)Materials scienceData scienceEnvironmental chemistryComputer scienceEngineeringChemistryElectrical engineering

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,023
score de la tête « metaresearch » (Gemma)0,028
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,052
Score d'incertitude au seuil0,174

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0230,028
Méta-épidémiologie (sens strict)0,0030,002
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0160,009
Études des sciences et des technologies0,0040,003
Communication savante0,0050,006
Science ouverte0,0100,003
Intégrité de la recherche0,0060,004
Charge utile insuffisante (le modèle a refusé de juger)0,0520,048

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,293
Écart entre enseignants0,282 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentnon

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