Breaking and Connecting: Highly Hazy and Transparent Regenerated Networked-Nanofibrous Cellulose Films via Combination of Hydrolysis and Crosslinking
Bibliographic record
Abstract
High optical transparency combined with high optical haze are essential requirements for optoelectronic substrates. Light scattering caused by haze is responsible for increasing light harvesting in photon-absorbing active materials, hence increasing efficiencies. A trade-off between transparency and haze is common in solar substrates with high transparency (~90%) and low optical haze (~20%), or vice versa. In this study, we report a novel, highly transparent film fabricated from regenerated cellulose after controlled acid-hydrolysis of microcrystalline cellulose (MCC). The developed networked-nanofibrous cellulose was chemically crosslinked with glutaraldehyde (GA) and vacuum-cured to facilitate the fabrication of mechanically stable films. The effects of crosslinker concentration, crosslinking time, and curing temperature were investigated. Optimum conditions for fabrication unveils high optical transparency (~94%) and high haze (~60%), using 25% GA for 24 hr with a curing temperature of 25 °C; therefore, conveying an optimal substrate for optoelectronics applications. The high haze arises primarily from the crystalline, networked crystals of cellulose II structure formed within the regenerated cellulose upon hydrolysis. Moreover, the developed crosslinked film presents high thermal stability, water resistance, and good mechanical resilience. This high-performance crosslinked cellulose film can be considered a potential material for new environmentally-friendly optical substrates.
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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".