A New Method for Determining the Concentration of Electrolyte Components in Lithium-Ion Cells, Using Fourier Transform Infrared Spectroscopy and Machine Learning
Bibliographic record
Abstract
A new method is introduced for determining unknown concentrations of major components in typical lithium-ion battery electrolytes. The method is quick, cheap, and accurate. Machine learning techniques are used to match features of the Fourier transform infrared (FTIR) spectrum of an unknown electrolyte to the same features of a database of FTIR spectra with known compositions. With this method, LiPF 6 concentrations can be determined with similar accuracy and precision as an inductively coupled plasma optical emission spectrometry (ICP-OES) method. The ratios of organic carbonate solvent species can be determined with more rapidity than gas chromatography (GC). This FTIR method is faster and less expensive than GC and ICP-OES, and has the added benefit of being able to determine LiPF 6 concentration and solvent fractions simultaneously. Application of this tool can facilitate electrolyte analysis of aged lithium-ion cells, and will help elucidate mechanisms for cell degradation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".