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.

Surface defects are frequently observed in calendered polyvinyl chloride (PVC) films. Their evaluation in production environments is typically qualitative and operator dependent. Using the common gas entrapment defects as the test case, the present study develops a four-step image-processing workflow that converts scanned film images into pixel intensity matrices and groups defect pixels using density-based clustering (DBSCAN). The procedure provides quantitative measures of defect count, size, and spatial distribution without manual labeling. The effects of roll gap, calendering speed, upstream mixing time, and plasticizer type were examined under controlled conditions. Larger roll gaps and higher speeds reduced degassing efficiency and increased both defect number and defect area. Short mixing times led to incomplete gelation and higher defect frequency. Among the tested plasticizers, TOTM produced the lowest defect counts, followed by DEHP and ESBO. Design-of-experiments analysis ranked parameter sensitivity and identified operating ranges that limit defect formation. The method provides a practical basis for routine surface inspection and supports process adjustment using measurable defect metrics rather than visual judgment alone.