Towards 3D quantitative imaging in FIB-SEM for applications in battery materials
Bibliographic record
Abstract
Background incl.aims The development of more efficient and safer battery materials stems from the commitment to green energy initiatives.To fully understand the failure mechanisms of newly developed battery materials at the micrometer level and below, scanning electron microscopy is a technique of choice.Focused ion beam (FIB) combined with SEM broadens the characterization capabilities of electron microscopy by revealing sub-surface microstructure and phase distributions.The technique permits quantitative analyses from the reconstruction of volumes from collected 2D SEM datasets.Moreover, the collection of Energy-dispersive Xray spectroscopy (EDS) and Electron Backscatter Diffraction (EBSD) maps from the generated x-rays and EBS patterns can help explain further the chemical and structural stability of the materials in 3D.Degradation over time during cycling can originate from alterations of the microstructure of particles, such as the apparition of cracks due to volume expansion, changes in grain morphology or activity at grain boundaries.This work aims to obtain quantitative information on the distributions of the different phases of lithium-based cathodes from field-emission (FE)-SEM datasets of 2D images, including porosity.Porosity is a critical parameter in battery design, since it directly affects electronic and ionic diffusion processes [1,2] and therefore battery performance.An interest will be given towards the changes in the phase distributions as a result of cycling, therefore pristine (uncycled) and cycled specimens will be compared including EDS and EBSD data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".