Secondary Electron Hyperspectral Imaging of Carbons: New Insights and Good Practice Guide
Bibliographic record
Abstract
Energy storage technologies such as lithium-ion batteries (LIBs) incorporate carbon components key to their function. Graphite and carbon binder components in LIB electrodes are engineered to deliver critical electrical and mechanical properties, as are the surface chemistry and morphology of carbon blacks (CBs) in LIBs and catalysts. The challenge of relating surface chemistry to morphology is complicated by the numerous forms of carbon bonding and potential for surface functional groups. Furthermore, materials processing can influence bonding and structure of carbon at multiple length scales, as seen in mechanochemical functionalization of CBs. To understand the nature of carbon surfaces, secondary electron hyperspectral imaging (SEHI) is introduced as a spatially resolved analysis bridging the nano to microscale. The ability to provide novel insights is demonstrated three example applications: observation of nanoscale "satellite" particles of amorphous hydrogenated carbon on graphitic CB particles, differentiation between graphitic and amorphous hydrogenated nano-thickness carbon coatings on particles of lithium iron phosphate, and differentiation between graphitic carbon active material and carbon binder domain in a LIB anode material. SEHI analysis using peak fitting models for graphitic and disordered carbons is developed based on reference materials and standard spectroscopic methods: Raman spectroscopy and X-ray photoelectron spectroscopy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".