MétaCan
Menu
Back to cohort
Record W4415648425 · doi:10.1093/mam/ozaf095

Overcoming Challenges in Atom Probe Tomography of Carbonate Minerals: Application of <i>In Situ</i> Chromium Coatings for Improved Experiment Yield

2025· article· en· W4415648425 on OpenAlexaff
Renelle Dubosq, Tim M. Schwarz, Aparna Saksena, Christina Bakowsky, Baptiste Gault

Bibliographic record

VenueMicroscopy and Microanalysis · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAtom probeCalciteCarbonateYield (engineering)CoatingChromiumMetalCrystal (programming language)Tomography

Abstract

fetched live from OpenAlex

Carbonate minerals such as calcite cover a significant portion of Earth's ice-free land surface. Beyond their widespread distribution, they play a critical role in geological processes, including the global carbon cycle and various biogeochemical processes. Understanding the crystal chemistry of carbonates is therefore essential for advancing our knowledge of these systems. Atom probe tomography offers promising potential for revealing nanoscale chemical and isotopic processes in minerals; however, its application to carbonates remains technically challenging due to their poor thermal conductivity and absorption. In this study, we apply and adapt an in situ metallic coating technique and optimize atom probe tomography acquisition parameters for calcite. The results demonstrate that applying a Cr coating to calcite specimens significantly improves experimental yield and enhances mass resolution during atom probe analysis. Despite these improvements, the data do not yield stoichiometric proportions for calcite due to the post-ionization dissociation of CxOy molecules into neutrals. These findings provide a framework for extending atom probe tomography methods to other poorly conducting or beam-sensitive materials.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Explore more

Same venueMicroscopy and MicroanalysisSame topicAdvanced Materials Characterization TechniquesFrench-language works237,207