Chitosan-Polyaniline (Bio)Polymer Hybrids by Two Pathways: A Tale of Two Biocomposites
Bibliographic record
Abstract
Previous research highlights the potential of polyaniline-based biocomposites as unique adsorbents for humidity sensors. This study examines several preparative routes for creating polyaniline (PANI) and chitosan (CHT) composites: Type 1—in situ polymerization of aniline with CHT; Type 2—molecular association in acidic aqueous media; and a control, Type 3—physical mixing of PANI and CHT powders (without solvent). The study aims to differentiate the bonding nature (covalent vs. noncovalent) within these composites, which posits that noncovalent composites should exhibit similar physicochemical properties regardless of the preparative route. The results indicate that Type 1 composites display features consistent with covalent and hydrogen bonding, which result in reduced water swelling versus Type 2 and 3 composites. These findings align with spectral and thermogravimetric data, suggesting more compact structure for Type 1 materials. Dye adsorption studies corroborate the unique properties for Type 1 composites, and 1H NMR results confirm the role of covalent bonding for the in situ polymerized samples. The structural stability adopts the following trend: Type 1 (covalent and noncovalent) > Type 2 (possible trace covalent and mainly noncovalent) > Type 3 (noncovalent). Types 2 and 3 are anticipated to differ based on solvent-driven complex formation. This study provides greater understanding of structure-function relationships in PANI-biopolymer composites and highlights the role of CHT as a template that involves variable (non)covalent contributions with PANI, according to the mode of preparation. The formation of composites with tailored bonding modalities will contribute to the design of improved adsorbent materials for environmental remediation to versatile humidity sensor systems.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".