Ionic Liquid Electrolytes for Thermal Energy Harvesting Using a Cobalt Redox Couple
Bibliographic record
Abstract
Waste thermal energy, such as that released from industrial or manufacturing processes, is a promising but as-yet underutilized source of sustainable energy. As an alternative to traditional semi-conductor based thermoelectrics, thermoelectrochemical cells use a redox couple in an electrolyte to directly convert thermal energy to electricity using a very simple device design. The good thermal stability of many ionic liquids (ILs) makes them very promising electrolytes for these devices, but the influence of the nature of the cation and anion on the cell performance is not yet well understood. Here we report measurement of the Seebeck coefficient and the thermoelectrochemical device performance of a cobalt redox couple in a series of ILs, and comparison of the electrolyte performance using a modified figure of merit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".