The Effect of Trimethoxyboroxine on Carbonaceous Negative Electrodes for Li-Ion Batteries
Bibliographic record
Abstract
The effects of trimethoxyboroxine (TMOBX), a useful electrolyte additive, on the electrochemical properties of carbonaceous negative electrode materials for Li-ion batteries (graphite and petroleum coke) were studied. TMOBX was added to electrolyte to examine the impact on parasitic reaction rate and electrode impedance. In order to test parasitic reaction rates with TMOBX containing electrolytes: 1) graphite/Li half cells were constructed and cycled on the High Precision Charger at Dalhousie University (HPC) to accurately measure the coulombic efficiency at different temperatures and 2) Coke/Li cells were constructed for storage experiments at different temperatures and potentials. The results of the HPC cycling showed that adding TMOBX decreases the coulombic efficiency compared to control cells. The storage experiments on the coke electrodes agree well with the HPC results in that the rate of voltage increase was accelerated by the addition of TMOBX. Electrochemical impedance spectroscopy measurements were conducted on coke/coke symmetric cells and while small concentrations (0.3 and 1%) of TMOBX may slightly decrease the cell impedance, higher concentrations (1.5 and 2%) of TMOBX in the electrolyte substantially increase impedance. This work suggests that TMOBX increases the rate of reactions on carbonaceous negative electrodes and at high concentrations can result in higher electrode impedance. These results help explain why TMOBX must be used in combination with another additive, like vinylene carbonate, to provide a significant benefit to Li-ion cells.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".