Tunable Ferroionic Properties in CeO<sub>2</sub>/BaTiO<sub>3</sub> Heterostructures
Bibliographic record
Abstract
Ferroionic materials combine ferroelectric properties and spontaneous polarization with ionic phenomena of fast charge recombination and electrodic functionalities. In this paper, we propose the concept of tunable polarization in CeO 2−δ (ceria) thin (5 nm) films induced by built-in remnant polarization of a BaTiO 3 (BTO) ferroelectric thin film interface, which is buried under the ceria layer. Upward and downward fixed polarizations at the BTO thin film (10 nm) are achieved by the lattice termination engineering of the SrO or TiO 2 terminated Nb:SrTiO 3 (NSTO or STN) substrate. We find that the ceria layer punctually replicates the polarization of the BTO interface via a dynamic reconfiguration of its intrinsic defects, i.e., oxygen vacancies and small polarons. Tunable oxidative or reducing properties (redox) also arise at the surface from the built-in polarization. Opposite polarities at the ceria termination tune the chemo-physical dynamics toward water molecule adsorbates. The inversion of the surface potential leads to a modulation of the water adsorption–desorption equilibrium and water ionization (splitting) redox overpotentials within ±400 mV at room temperature, depending on the ceria termination’s charges. Such tunability opens up the perspectives of using ferroionics for wireless electrochemically enhanced catalysis.
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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) | 0.002 | 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".