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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".