Current Collector Corrosion in Ca-Ion Batteries
Bibliographic record
Abstract
With significant improvements in electrical energy storage, researchers could change the way energy is generated and used. One emerging approach is to change the cation that shuttles charge from lithium to calcium. Calcium cations, roughly the same size as Na + , have many attributes that make them a desirable charge carrier for energy storage applications, including deposition voltage and a porous passivation layer. However, system level issues, such as corrosion, have yet to be investigated. Corrosion of the current collectors must be considered whenever you change the electrolyte and we show that this is particularly true for calcium based systems. Reversible charge/discharge behavior that is due to corrosion can be seen with stainless steel in electrolytes containing calcium salts. This reversible behavior is similar to what might be expected from materials that are intercalating Ca, making the interpretation of electrochemical data challenging. We have found that this corrosion reaction requires either carbon black and/or a transition metal oxide to catalyze the reaction, making it more difficult to detect. Unlike stainless steel, Graphite foil electrodes do not show high voltage reactions and can be used as a tool for testing Ca-ion cathode materials, although some reactions at low potentials have been observed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".