Halide perovskite solar cells using monocrystalline TiO<sub>2</sub>nanorod arrays as electron transport layers: impact of nanorod morphology
Bibliographic record
Abstract
Abstract This is the first report of a 17.6% champion efficiency solar cell architecture comprising monocrystalline TiO 2 nanorods (TNRs) coupled with perovskite, and formed using facile solution processing without non-routine surface conditioning. Vertically oriented TNR ensembles are desirable as electron transporting layers (ETLs) in halide perovskite solar cells (HPSCs) because of potential advantages such as vectorial electron percolation pathways to balance the longer hole diffusion lengths in certain halide perovskite semiconductors, ease of incorporating nanophotonic enhancements, and optimization between a high contact surface area for charge transfer (good) versus high interfacial recombination (bad). These advantages arise from the tunable morphology of hydrothermally grown rutile TNRs, which is a strong function of the growth conditions. Fluorescence lifetime imaging microscopy of the HPSCs demonstrated a stronger quenching of the perovskite PL when using TNRs as compared to mesoporous/compact TiO 2 thin films. Due to increased interfacial contact area between the ETL and perovskite with easier pore filling, charge separation efficiency is dramatically enhanced. Additionally, solid-state impedance spectroscopy results strongly suggested the suppression of interfacial charge recombination between TNRs and perovskite layer, compared to other ETLs. The optimal ETL morphology in this study was found to consist of an array of TNRs ∼300 nm in length and ∼40 nm in width. This work highlights the potential of TNR ETLs to achieve high performance solution-processed HPSCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".