Overcoming Challenges in Atom Probe Tomography of Carbonate Minerals: Application of <i>In Situ</i> Chromium Coatings for Improved Experiment Yield
Bibliographic record
Abstract
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".