Impact of Photo-Induced Doping of Spiro-OMeTAD as HTL on Perovskite Solar Cell Hysteresis Dynamics
Bibliographic record
Abstract
The doping strategy employed for enhancing the hole conductivity of spiro-OMeTAD as a hole transport layer (HTL) in perovskite solar cells (PSCs) is rarely specified in the literature. However, as revealed in this work, the specifics of the doping process with respect to the type of storage and its duration can have important consequences for the evolution of hysteresis dynamics in PSCs. The variability in photovoltaic metrics and hysteresis loci in current–voltage ( J–V ) curves are studied before and after light soaking, as well as at different scan rates, to determine operational differences as a result of a given doping process. With the aid of X-ray photoelectron spectroscopy (XPS) results showing the formation of deleterious compounds such as Li 2 O and LiF, next to proposed operational band alignments, the evolution of hysteresis trends for different doping strategies is explained. Finally, utilizing electrochemical impedance spectroscopy (EIS), signatures of HTL bulk and interfacial conductivity modulation are identified. The propagation of a third semicircle in the low-frequency part of the Nyquist plot is ascribed to performance loss caused by unfavorable interfacial electrochemical reactions, while the shrinkage of the high-frequency arc is linked with performance gain due to increased HTL bulk conductivity. This work underlines the important correlation between the spiro-OMeTAD doping strategy with hysteresis evolution as well as loss and gain in PSC performance under operational conditions.
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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".