Functional Engineering of Perovskite Nanosheets: Impact of Lead Substitution on Exfoliation in the Solid Solution RbCa<sub>2–<i>x</i></sub>Pb<i><sub>x</sub></i>Nb<sub>3</sub>O<sub>10</sub>
Bibliographic record
Abstract
Tuning the chemical composition and structure for targeted functionality in two‐dimensional (2D) nanosheets has become a major objective in the rapidly growing area of 2D materials. In the context of photocatalysis, both miniaturization and extending the light absorption of UV active photocatalysts are major assets. Here, we investigate the solid solution between two photocatalytic systems known from literature to evolve H2 from water/methanol under UV – RbCa2Nb3O10 (Eg = 3.7 eV) – and visible light irradiation – RbPb2Nb3O10 (Eg = 3.0 eV) – by synthesizing hypothetical RbCa2–xPbxNb3O10. While the calcium niobate can easily be exfoliated into individual nanosheets via cation–proton exchange and subsequent treatment with tetra‐n‐butylammonium hydroxide (TBAOH), the lead niobate barely yields nanosheets. Spectroscopic and microscopic analysis suggest that this is caused by volatilization of Pb during synthesis, leading to a local 3D linkage of RbPb2Nb3O10 perovskite units with Pb deficient units. On the one hand, this linkage progressively prevents exfoliation along with an increasing Pb content. On the other hand, introducing Pb into the perovskite blocks successively leads to bandgap narrowing, thus gradually enhancing the light harvesting capability of the solid solution. Finding a compromise between this narrowing of the bandgap and the possibility of exfoliation, visible light sensitized nanosheets can be engineered in good yield for an initial molar ratio of Ca:Pb ≥ 1:1.
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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".