Synthesis, formulation, and characterization of a bio-based paint derived from TOCN and polypyrrole
Bibliographic record
Abstract
This study presents the development of a biobased paint by integrating TEMPO-oxidized cellulose nanofibers (TOCN) with polypyrrole (PPy) and incorporating polyvinyl alcohol (PVA) as a binder and glycerol as a plasticizer. The TOCN-PPy composite was synthesized via in-situ polymerization, followed by a washing process to ensure compositional purity. High-shear mixing and controlled thermal treatment produced homogeneous and stable formulations. Scanning Electron Microscopy (SEM) revealed a well-dispersed nanofiber network, with increased glycerol content contributing to smoother surface morphology. This may correlate with improved flexibility observed during handling. Raman analysis further confirmed the presence of polypyrrole and revealed spectral shifts associated with enhanced polymer dispersion and hydrogen bonding influenced by PVA and glycerol. Contact angle measurements showed that higher glycerol content increased wettability, reducing hydrophobicity and enhancing adaptability for coating applications. Thermogravimetric Analysis (TGA) revealed multi-stage degradation, with polypyrrole improving thermal stability. The enhanced flexibility observed in glycerol-containing samples is attributed to its plasticizing effect, as evidenced by morphological and handling observations. DSC was also employed for glass transition, melting and thermal decomposition behavior, and thermal stability trends. These findings emphasize the tunability of TOCN-PPy coatings, balancing structural integrity, thermal performance, and wettability for various industrial applications. The results also highlight the potential of TOCN-PPy composites as high-performance, eco-friendly coatings, supporting innovations in green chemistry and the circular bioeconomy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".