Top-Down Approaches Towards Single Crystal Perovskite Solar Cells
Bibliographic record
Abstract
Solar cells employing hybrid perovskites have proven to be a serious contender versus established thin-film photovoltaic technologies. Typically, current photovoltaic devices are built up layer by layer from a transparent substrate (bottom-up approach), while the deposition of the perovskite layer itself comes with many challenges including the control of crystal size, nucleation density and growth rate. On the other hand, single crystals have been used with great success for studying the fundamental properties of this new class of optoelectronic materials. However, optoelectronic devices fabricated from single crystals often employ different materials than in their thin film counterparts. Here, we demonstrate various top-down approaches for low-temperature processed organic-inorganic metal halide perovskite single crystal devices. Our approach uses common and well-established material combinations that are often used in polycrystalline thin film devices. The use of a polymer bezel allows easier processing of small crystals and the fabrication of solution-processed, free-standing perovskite single crystal devices. All in all these approaches can supplement other measurements of more fundamental material properties often requiring perovskite single crystals by rendering a photovoltaic characterization possible on the very same crystal with comparable material combinations as in thin film devices.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".