Impact of reversible proton insertion on the electrochemistry of electrode materials operating in mild aqueous electrolytes: a case study with TiO2
Bibliographic record
Abstract
Near-neutral aqueous electrolytes are to be preferred for the development of sustainable electrochemical energy conversion and storage devices. Protons are inherent to these electrolytes and their reactivity towards the electrode material extends beyond their own reduction, especially when reversible proton insertion takes place in the bulk electrode material from acidic or buffered electrolytes. However, a still burning question regards whether reversible proton insertion persists when working in unbuffered mild aqueous electrolytes, and if so, with which consequences on the functioning of the electrode material. Here, we address this issue by examining TiO2 as a model insertion electrode in a range of mild aqueous electrolytes. Through a combination of experiments, modelling and multiphysics simulations, we demonstrate that, in a KCl-based electrolyte, water acts as proton donor to support reversible insertion of protons in TiO2, while in a NH4Cl-based aqueous electrolyte, the proton donor is NH4+. Moreover, we establish that strong pH gradients develop at the electrode interface during proton insertion/disinsertion, highlighting their dependence on the proton donor/acceptor and rationalizing their impact on the electrode voltage. Overall, this work provides a comprehensive framework of proton-insertion coupled electron transfer (PICET) that can be easily generalised to other electrode materials.
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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 | 0.001 | 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".