Development of quantitative techniques for lithium compounds for next generation batteries with focused ion beam scanning electron microscopy
Bibliographic record
Abstract
Electric vehicles have started to make their appearance in the transportation industry. At Quebec's scale especially, since electricity is already sustained by an extensive network of dams and reservoir, Hydro-Quebec puts a lot of focus towards green gas emission reduction via transportation electrification. To be widely accepted, batteries used in electric vehicles must have high ranges, long durability, be safe and an economical choice in the long run for its user to abandon gasoline-powered automobiles. In that matter, the company's Center of Excellence in Transportation Electrification and Energy Storage works tirelessly on the development of new generation battery materials using elements available in large quantities, with high performance chemistry. Materials characterization with Scanning Electron Microscopy is one of the most important steps in developing new materials, since it links the microstructure of the material to its fabrication process and properties down to the nanometer scale. This study focuses on the development of quantitative techniques for lithium in battery materials since this light element is the key element in the operation of a battery. In this work energy dispersive X-ray spectroscopy (EDS), electron energy-loss spectroscopy (EELS) and secondary ion mass spectrometry are evaluated in relation to their capabilities to both detect and quantify lithium atoms in battery materials. A portable time-of-flight secondary ion mass spectrometer (TOF-SIMS) that can attach to a standard dual beam microscope(FIB-SEM) was found to fulfill both aspects while allowing high resolution imaging andchemical analysis of the samples. An experimental calibration curve of lithium content in standard nickel cobalt manganese oxide cathodes was built using TOF-SIMS detector. The calibration curve allows identification of lithium content in cathodes with different state of charge and according to different charging rates. TOF-SIMS allows visualization of ionic distributions in material. Furthermore, it can help observe differences in crystallographic orientation with respect to the beam in between primary particles and permits identification of chemical hotspots of lithium.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".