Optimizing Lead-Free Cs₂NaBiI₆ Perovskite Solar Cells via Mg²⁺ Doping: Enhanced Efficiency and Stability
Bibliographic record
Abstract
Lead-free halide double perovskites are promising alternatives to toxic lead-based counterparts owing to their environmental stability and benign composition.Among these, iodide-based Cs₂NaBiI₆ has attracted attention due to its narrower bandgap (~1.6-1.8 eV) compared to bromide analogues such as Cs₂AgBiBr₆ (~2.0 eV), making it more suitable for solar harvesting.However, limited studies have addressed strategies to enhance its optoelectronic performance.In this work, we systematically investigate the effect of Mg²⁺ incorporation on the structural, morphological, optical, and photovoltaic properties of Cs₂NaBiI₆ thin films and devices.X-ray diffraction (XRD) and FE-SEM analyses reveal that moderate Mg²⁺ doping (3%) improves crystallinity and film compactness, leading to enhanced optical absorption and reduced bandgap (~1.65 eV).Photovoltaic measurements across 100 devices (25 per group) demonstrate a reproducible improvement in power conversion efficiency from 0.62% (pristine) to 1.22% (3% Mg²⁺), primarily due to increased short-circuit current density and fill factor.At higher doping (5%), performance degrades, consistent with structural disorder and morphological irregularities.Although the overall efficiency remains modest compared to Pb-based perovskites, Mg²⁺-doped Cs₂NaBiI₆ exhibits superior environmental stability and reproducibility, suggesting its potential for niche applications such as indoor photovoltaics and long-lifetime devices.These findings highlight the viability of cation doping as an effective approach to tune the optoelectronic properties of iodide-based lead-free double perovskites.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".