Utilization of Cobalt Bis(terpyridine) Metal Complex as Soluble Redox Mediator in Li–O<sub>2</sub> Batteries
Bibliographic record
Abstract
Redox mediators hold significant promise in reducing the large overpotentials pervasive upon charging of lithium–oxygen (Li–O 2 ) cells. Cobalt bis(terpyridine) (Co(Terp) 2 ) was investigated as a mediator of the Li–O 2 charging reaction using electrochemical, XRD, and mass spectrometry measurements and benchmarked against tetrathiafulvalene (TTF). Significant reductions in reversible potential versus Li + /Li are measured for Co(Terp) 2 and TTF from diglyme to Pyr 14 TFSI:diglyme to Pyr 14 TFSI, attributable to upward shift in the Li + /Li electrode, due to weakening Li + solvation in this solvent order. Lowering of the reversible potentials has noticeable gains on the kinetics of the charge reaction, and greater reduction in charge overpotential are observed with the cobalt complex. However, using differential electrochemical mass spectrometry reveals that less than 25% of the O 2 consumed on discharge is recovered on charge in the presence of Co(Terp) 2, while TTF enables up to 32% O 2 recovery on charge. CO 2 is a significant charging product at voltages greater than 4.0 V vs Li + /Li because of electrolyte decomposition. Further work is required in order to develop mediators with 100% of oxygen evolution efficiency, and the present finding of the possibility to tune the reversible redox potential of the mediator by changing the solvent will be very useful to achieve this formidable task.
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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 | 0.000 |
| 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".