Evidence of Bulk Proton Insertion in Nanostructured Anatase and Amorphous TiO2 Electrodes
Bibliographic record
Abstract
Crystalline structures and lattice water molecules are believed to strongly influence the ability of metal oxides to reversibly and rapidly insert protons in aqueous batteries. In the present work, we performed a systematic analysis of the electrochemical charge storage properties of nanostructured TiO 2 electrodes composed of either anatase or amorphous TiO 2 in a mild buffered aqueous electrolyte. We demonstrate that both materials allow reversible bulk proton insertion up to a maximal reversible gravimetric capacity of ~150 mA·h·g -1 . We also show that the TiO 2 crystallinity governs the energetics of the charge storage process, with a phase transition for anatase, while having little effect on either the interfacial charge-transfer kinetics or the apparent rate of proton diffusivity within the metal oxide. Finally, with both TiO 2 electrodes, reversible proton insertion leads to gravimetric capacities as high as 95 mA·h·g -1 at 75 C. We also reveal two competitive reactions decreasing the Coulombic efficiency at low rates, i.e. hydrogen evolution and a non-faradaic self-discharge reaction. Overall, this work provides a comprehensive overview of the proton-coupled electrochemical reactivity of TiO 2 and highlights the key issues to be solved in order to truly benefit from the unique properties of protons as fast charge carriers in metal oxides.
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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 | 0.000 | 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".