Nanocellulose and Nanochitin Cryogels Improve the Efficiency of Dye Solar Cells
Bibliographic record
Abstract
Abstract Biobased cryogel membranes were applied as electrolyte holders in dye solar cells (DSC) while facilitating carrier transport during operation. They also improved device performance and stability. For this purpose, cellulose nanofibers (CNF), TEMPO-oxidized CNF (TOCNF), bacterial cellulose (BC), and chitin nanofibers (ChNF) were investigated. The proposed materials and protocols for incorporating the electrolyte, via simple casting, avoided the typical problems associated with injection of the electrolyte through filling holes, a major difficulty especially in manufacturing large area cells. Owing to the fact that cryogel membranes did not require any orifice for injection, they were effective in minimizing leakage and in retaining liquid electrolyte. The results indicated the reduction of performance losses compared to conventional electrolyte filling, likely due to the better spatial distribution of electrolyte. DSCs based on BC cryogels had an initially higher performance and similar stability compared to those of the reference cells. When compared to reference cells, CNF and ChNF cryogels produced higher initial performance, but they underwent a faster degradation. The difference in stability was attributed to the effect of residual components, including lignin in CNF and proteins in ChNF, as demonstrated in bleaching experiments. TOCNF indicated a relatively poor performance, most likely because of residual aldehydes. Overall, we offer a comprehensive evaluation based on current–voltage (IV) profiles under simulated sunlight, incident photon-to-charge carrier efficiency (IPCE), electrochemical impedance spectroscopy (EIS), and color image processing, together with accelerated DSC stability tests, to unveil the effects of new membrane-based assembly. Our results give guidelines for future developments related in particular to the effects of the tested biomaterials on device stability.
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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".