Investigation of Electrochemical and Chemical Processes Occurring at Positive Potentials in “Water-in-Salt” Electrolytes
Bibliographic record
Abstract
Lithium bis(trifluoromethylsulfonyl)imide (LiTFSI) based water-in-salt electrolytes (WiSEs) has recently emerged as a new promising class of electrolytes, primarily owing to their wide electrochemical stability windows (∼3–4 V), that by far exceed the thermodynamic stability window of water (1.23 V). Upon increasing the salt concentration towards superconcentration the onset of the oxygen evolution reaction (OER) shifts more significantly than the hydrogen evolution reaction (HER) does. The OER shift has been explained by the accumulation of hydrophobic anions blocking water access to the electrode surface, hence by double layer theory. Here we demonstrate that the processes during oxidation are much more complex, involving OER, carbon and salt decomposition by OER intermediates, and salt precipitation upon local oversaturation. The positive shift in the onset potential of oxidation currents was elucidated by combining several advanced analysis techniques: rotating ring-disk electrode voltammetry, online electrochemical mass spectrometry, and X-ray photoelectron spectroscopy, using both dilute and superconcentrated electrolytes. The results demonstrate the importance of reactive OER intermediates and surface films for electrolyte and electrode stability and motivate further studies of the nature of the electrode.
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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) | 0.000 | 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 | 0.001 |
| 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".