Optimising 3-phenyl-1,4,2-dioxazol-5-one as an electrolyte additive for Lithium-Ion cells
Bibliographic record
Abstract
An effective method to reduce carbon dioxide emissions is to switch to renewables for energy generation and transportation. Since current sources of renewable energy, such as wind and solar, are intermittent, it is essential to find ways to store energy to match supply and demand. If vehicles are to be powered by renewable energy, they need portable energy storage. Currently, lithium-ion batteries are one of the most viable solutions for energy storage. Extending the lifespan of lithium-ion batteries is the goal of this research, carried out with Dr. David Hall of Dr. Jeff Dahn’s research group at Dalhousie University in late 2017. We developed and tested a chemical compound, 3-phenyl-1,4,2-dioxazol-5-one (PDO), which greatly improves the lifespan of lithium-ion batteries. One percent of this by weight in a cell’s electrolyte, along with two percent ethylene sulfate, will extend a battery’s lifespan more than three-fold over those containing conventional vinylene carbonate-containing electrolyte.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".