Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it