Hydrophobic–Hydrophilic Block Copolymer Mediated Tuning of Halide Perovskite Photosensitive Device Stability and Efficiency
Bibliographic record
Abstract
The polymer additive strategy provides a facile and cost-effective way for passivating defects and trap sites at the grain boundaries and interfaces and acting as a barrier against the external degradation factors in perovskite-based devices. However, limited literature exists discussing the integration of hydrophobic and hydrophilic polymer additives in the form of a copolymer within the perovskite films. The inherent difference in the chemical structure of these polymers and their interaction with perovskite components and the environment leads to critical differences in the respective polymer-perovskite films. The current work utilizes both homopolymer and copolymer strategies to understand the effect of polystyrene (PS) and polyethylene glycol (PEG), two common commodity polymers, over the physicochemical and electro-optical properties of the as-fabricated devices and the distribution of polymer chains across the depth of perovskite films. The hydrophobic PS integrated perovskite devices PS-MAPbI 3, 36 PS- b -1.4-PEG-MAPbI 3, and 21.5 PS- b -20-PEG-MAPbI 3 outperform hydrophilic PEG-MAPbI 3 and pristine MAPbI 3 devices and exhibit higher photocurrent, lower dark currents, and greater stability. A critical difference is also observed in the stability of devices, where rapid decay of performance is observed in the pristine MAPbI 3 films. The deterioration in performance is highly limited for hydrophobic polymer-MAPbI 3 films as they maintain 80% of their initial performance.
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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".