Effect of Lattice Interactions on the Structural Evolutions of Cu<sub>2</sub>O Thin Film Model Heterostructures during Solar Energy Conversion Reactions
Bibliographic record
Abstract
Metal oxide heterostructures, formed by interfacing different metal oxide crystals, are utilized in various fields. Such interfaces are typically constructed using different materials and synthesis methods, complicating the isolation of interface-specific effects when fundamental reaction processes. The present study strategically employs Cu 2 O thin films exhibiting two distinct domains formed by weak and strong thin film–substrate lattice interactions to model metal oxide heterostructures with different interfacial interactions. The crystalline and electronic structural evolutions of different domains and planes are studied during gas–solid interactions and photocatalytic energy conversions using three-dimensional (3D) reciprocal space mapping and near-ambient X-ray absorption spectroscopy (XAS). The results reveal that heterostructures with strong lattice interactions experience less pronounced changes in the crystalline and electronic structures during such reactions. The Cu 2 O {200} planes in both domains exhibit more pronounced crystalline distortions compared to the Cu 2 O {111} planes. This study introduces a novel approach to fundamentally studying metal oxide heterostructures and proposes design strategies for optimizing heterostructures used in energy harvesting and conversion applications.
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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".