Artificial Solids by Design: Assembly and Electron Microscopy Study of Nanosheet-Derived Heterostructures
Bibliographic record
Abstract
Two-dimensional materials do not only attract interest owing to their anisotropic properties and quantum confinement effects but also lend themselves as well-defined building blocks for the rational design of 3D materials with custom-made structures and, hence, properties. Here, we present the bottom-up fabrication of an artificial superlattice derived from positively charged layered double hydroxide (LDH) and negatively charged perovskite layers sequentially assembled by electrostatic layer-by-layer deposition. In contrast to previously employed bulk methods averaging out the elemental distribution within such stacks, we use a combination of HRTEM, STEM, and EEL spectroscopy to elucidate the structure and composition of the multilayer stack with a high spatial resolution on the subnanometer scale. Atomic column resolved STEM coupled with EELS line scans confirms the periodic arrangement of individual nanosheets by evaluation of the Ca-L 2,3 and Mn-L 2,3 edges. Furthermore, HRTEM confirms the formation of up to 100 double layer thick films, thus demonstrating the transition from ultrathin nanosheet assemblies to artificial bulk solids with engineered structures and property profiles. We ascertain the formation of densely packed stacks with a well-ordered layered morphology, while nonidealities such as lack of in-plane layer registry, layer terminations, sheet bending, and contamination by residual ligands are side effects of the solution-based deposition process. In addition, we demonstrate that the packing density of the multilayer system can be tuned by changing the LDH dispersing agent from formamide to water, resulting in porous stacks containing about eight times less LDH and featuring significantly increased interlayer distances.
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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) | 0.000 | 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".