Processing factors affecting roughness, optical and mechanical properties of nanocellulose films for optoelectronics
Bibliographic record
Abstract
This work aims to understand how nanocellulose (NC) processing can modify the key characteristics of NC films to align with the main requirements for high-performance optoelectronics. The performance of these devices relies heavily on the light transmittance of the substrate, which serves as a mechanical support and optimizes light interactions with the photoactive component. Critical variables that determine the optical and mechanical properties of the films include the morphology of cellulose nanofibrils (CNF), as well as the concentration and turbidity of the respective aqueous suspensions. This study demonstrates that achieving high transparency was possible by reducing the grammage and adjusting the drying temperature through hot pressing. Furthermore, the use of modified CNF, specifically carboxylated CNF, resulted in more transparent films due to a higher nanosized fraction and lower turbidity. The mechanical properties of the films depended on their structure, homogeneity (spatial uniformity of local grammage), and electrokinetic factors, such as the presence of electrostatic charges on CNF. Additionally, we investigated the angle-dependent transmittance of the CNF films, since solar devices usually operate under indirect light. This work demonstrates the importance of a systematic approach to the optimization of cellulose films, providing valuable insight into the optoelectronic field.
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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 | 0.000 |
| 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".