Temperature-Dependent Device Characterization of Perovskite Solar Cells Prepared By Different Synthesis Methods
Bibliographic record
Abstract
While a number of synthesis methods have been reported to prepare perovskite solar cells with good device performance, it has not been well studied how they affect the properties of perovskite layers and devices. Here we report on temperature-dependent device characterizations of perovksite solar cells prepared by different synthesis methods. Solar cells with perovkiste absorbers with compact thin film-like morphology are prepared by both one-step and two-step processes, which exhibit comparable efficiencies at room temperature. However, temperature-dependence of device parameters is markedly different between the devices prepared by one-step and two-step processes. The two-step processed sample exhibits the significant collapse of efficiency as temperature is reduced below 300K because of diverging series resistance of the device, but the efficiency recovers as temperature is raised back to 300K. Once the device is heated to 340K, the device suffers from irreversible degradation. By contrast, the one-step processed sample shows a much milder degradation of efficiency at low temperatures and does not experience the irreversible damage at ~340K. We will discuss possible reasons behind the different temperature-dependence of the perovskite devices. Additionally, comparison of different device architectures—standard vs. inverted—focusing on electrical characterization is studied and it reveals that transport of electrons, but not holes, is what limits a total collected photocurrent. Details of the comparison will be presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".