Bibliographic record
Abstract
Even the best lithium-ion cells degrade slightly from one charge-discharge cycle to the next. This degradation, and its origin, can be studied using “delta differential capacity analysis”. Constant-current chronopoteniometry is used to collect voltage (V) versus charge (Q), data as cells are charged and discharged during cycles n, n + 1, n + 2, etc. as V(Q, n) This data is then differentiated, using finite differences, to create differential capacity, dQ/dV(V, n), versus V for the nth measured cycle. “Delta dQ/dV” is calculated as the difference between the differential capacities of the nth and mth cycles, i.e. ΔdQ/dV(V, n, m) = dQ/dV(V, n) – dQ/dV(V, m). Three different battery testers were used to measure ΔdQ/dV(V, n, m) for LiCoO2/graphite commercial Li-ion cells where n and m differed only by a few cycles (2 < n – m < 20). When precision test equipment was used, noise-free ΔdQ/dV(V, n, m) was measured, even when adjacent cycles were used for the calculation (i.e. n − m = 1) and even when very stable cell chemistries were studied. Unfortunately, typical battery test equipment, availably commercially, cannot make such measurements, even when n – m > 20. The best Li-ion cell, that does not degrade from cycle to cycle should have ΔdQ/dV(V, n, mo) = 0 for all V and n, where mo is the number of formation cycles required for a particular cell chemistry. Thus, monitoring ΔdQ/dV(V, n, mo) over just a few cycles can be used as a quality assurance tool for Li-ion cells destined for long lifetime applications, such as in electric vehicles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".