Safety and Quality Issues of Counterfeit Lithium-Ion Cells
Bibliographic record
Abstract
Lithium-ion batteries continue to transform consumer electronics, mobility, and energy storage sectors, and the applications and demands for batteries keep growing. Supply limitations and costs may lead to counterfeit cells in the supply chain that could affect quality, safety, and reliability of batteries. Our research included studies of counterfeit and low-quality lithium-ion cells, and our observations on the differences between these and original ones, as well as the significant safety implications, are discussed. The counterfeit cells did not include internal protective devices such as the positive temperature coefficient or current interrupt devices that typically offer protection against external short circuits and overcharge conditions, respectively, in cells from original manufacturers. Poor-quality materials and lack of engineering knowledge were also evident on analyses of the electrodes and separators from low-quality manufacturers. When the low-quality cells were subjected to off-nominal conditions, they experienced high temperature, electrolyte leakage, thermal runaway, and fire. In contrast, the authentic lithium-ion cells performed as expected. Recommendations are provided to identify and avoid counterfeit and low-quality lithium-ion cells and batteries.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".