Perovskitoid-Driven 1D@3D Dimensionality for High-Performance Perovskite Solar Cells
Bibliographic record
Abstract
Low-dimensional perovskites have emerged as a possible method for enhancing the performance of perovskite solar cells (PSCs). Nevertheless, cation migration across 2D and 3D layers disrupts octahedral networks, resulting in a loss of efficiency over time. This article reports the use of 1D@3D perovskite induced by a large 2-diethylaminoethyl chloride cation template. The one-dimensional solar cell capacitance simulator is employed to propose and calculate the photovoltaic (PV) merits of the 1D@3D-structured PSC with tin oxide (SnO 2 ) and Spiro-OMeTAD charge-transporting materials. The designed FTO/SnO 2 /1D@3D perovskite/Spiro-OMeTAD/Au structure showed a proper energy-level frontier at interfaces with the appropriate material characteristics. The PV parameters of the PSCs are explored by changing the thickness, p -type doping level, and bulk defect density of the perovskite film. In addition, the SnO 2 and Spiro-OMeTAD layers are optimized in terms of doping concentration and effective density of states. Moreover, we have assessed the thermal stability of the proposed PSC by altering the operating temperature. With this approach, high-performance and stable 1D@3D-structured cells with good thermal stability, demonstrating an impressive power conversion efficiency of 28.398% at 280 K, are achieved.
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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".