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Record W4412909914 · doi:10.1093/mam/ozaf048.818

Revisiting EELS Fine Structure Analysis in Zircon as a Tool for Interpreting its Structural Evolution into a Disordered System

2025· article· en· W4412909914 on OpenAlexaff
Matthieu Bugnet, Pierre-Marie Zanetta, Gianluigi A. Botton, Anne‐Magali Seydoux‐Guillaume, Guillaume Radtke

Bibliographic record

VenueMicroscopy and Microanalysis · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsZirconMaterials scienceGeologyGeochemistry

Abstract

fetched live from OpenAlex

Zircon (ZrSiO4) is a mineral of singular importance in Earth science, the most widely used mineral in U-Pb geochronology in various geological contexts [1], including high-temperature and high-pressure events (e.g. impact events), and the oldest mineral ever found on Earth. However, the accumulation of radiation damage over time critically affects the physical and chemical properties of zircon crystals [2]. Experimental techniques capable of simultaneously providing structural and chemical information in these complex and tiny damaged areas are highly sought after. Fine chemical analysis using electron energy-loss spectroscopy (EELS) enables to link structure and chemistry in crystalline materials, at the high spatial resolution accessible in the TEM. The near-edge structures arising from core-level excitation in EELS contain such chemical information, since they are very sensitive to chemical bonding and local atomic environment. However, their robust interpretation in terms of orbital hybridizations between atoms requires first-principle simulations, and a correct understanding of the fine structures primarily depends on the agreement between experiments and calculations. In the energy range of EELS, the Si-L2,3 (99 eV) and O-K edges (532 eV) of zircon are easily accessible. Yet, thus far, only few studies have focused on the interpretation of the Si-L2,3 and O-K near-edge fine structures in zircon, using multiple scattering approaches [3-5]. These simulations have shown some agreement with experiments, but further improvements could enhance the understanding of the spectra, which depend on the knowledge of the crystal structure of the chosen sample. Here, instead of multiple scattering, we show that density functional theory reproduces the experimental peaks of the O-K fine structures remarkably well. This DFT approach allows to interpret each peak in terms of molecular orbitals, with a clear distinction between peaks linked to the O-Si and O-Zr hybridizations (see Fig. 1). Notably, the most intense peak is a signature of the O-Si bond. The discussion will be extended to the Si-L2,3 near-edge fine structures, while the low-loss part of the spectrum is also highly structured and rich in information. The experiments were performed using a monochromated probe-corrected FEI Titan and a probe-corrected C-FEG Jeol NeoARM, and the near-edge structure variations were enabled by direct electron detection using a Quantum Detector MerlinEELS. Advanced EELS processing was performed using statistical methods as implemented in Hyperspy [6], allowing spectral variations to be detected and mapped at the nanoscale. The potential and limitations of using EELS fine structure analysis to track zircon's structural evolution will be explored through examples that replicate Earth-like conditions [7]. Experimental and theoretical O-K (a) and Si-L23 (b) near-edge structures of zircon (QE: Quantum Espresso code), which crystallizes in a tetragonal structure with edge-sharing ZrO8 dodecahedra and SiO4 tetrahedra. The main hybridizations from DFT are indicated. (c) The modification of electron density due to core-hole screening highlights the higher degree of covalence of Si compared to Zr.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.004
GPT teacher head0.228
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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