Textile waste usually ends up in landfills and causes environmental pollution. In this study, pretreatment methods for textile recycling, including autoclaving, freezing alkali/urea soaking, and alkaline pretreatment, were applied to textile waste with various cotton/polyester blending ratios. The best condition for enzymatic hydrolysis was a 60/40 textile waste blend of cotton/polyethylene terephthalate (PET) with a reusable chemical pretreatment (15% NaOH) at 121 °C for 15 min. The hydrolysis of pretreated textile waste by cellulase was optimized using response surface methodology (RSM) based on central composite design (CCD). The optimized conditions were 30 FPU/g of enzyme loading and 7% of substrate loading, which resulted in a maximum observed value of hydrolysis yield at 89.7%, corresponding to the predicted value of 87.8% after 96 h of incubation. The findings of this study suggest an optimistic solution for textile waste recycling.

Nanocomposite film of poly(vinyl alcohol) (PVA) incorporated with bacterial cellulose nanocrystals (BCNCs) and magnetite nanoparticles (Fe₃O₄) is reported in this study. The BCNC-Fe₃O₄ nanoparticles and PVA film was prepared by in situ synthesis technique using chemical co-precipitation. Different concentrations of BCNC-Fe₃O₄ (20%, 40% and 60% w/w) were mechanically dispersed in PVA solution to form the nanocomposite film. Transmission electron microscopy (TEM) analysis of BCNC-Fe₃O₄ nanoparticles showed irregular particle sizes ranging from 4.93 to 30.44 nm with an average size distribution of 22.94 nm. The presence of characteristic functional groups of PVA, BCNC and Fe₃O₄ were confirmed by Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) analysis. Scanning electron microscope (SEM) attached energy dispersive spectroscopy (EDS) and vibrating sample magnetometer (VSM) analysis revealed that, the iron content and magnetic property increased with increasing BCNC-Fe₃O₄ content. The saturation magnetizations (MS) value increased from 5.14 to 11.56 emu/g. The PVA/ BCNC-Fe₃O₄ at 60% showed the highest Young’s modulus value of 2.35 ± 0.16 GPa. The prepared film could be a promising polymeric nanomaterial for various magnetic-based applications and for the design of smart electronic devices.

Textile waste has emerged as a critical global challenge, with improper disposal practices leading to adverse environmental consequences. In response to this pressing issue, there is growing interest in recycling textile waste containing cellulose as an alternative approach to reducing the impact of industrial waste on the environment. The objective of this research is to investigate the extraction and characterization of nanocellulose from polyester–cotton textile waste as a potential solution to address the growing concerns of waste management in the textile industry. To obtain nanocellulose, a comprehensive process involving alkaline sodium hydroxide (NaOH) treatment of the polyester–cotton textile (35% PET and 65% cotton) was employed, resulting in average yield percentages ranging from 62.14% to 71.21%. To achieve the complete hydrolysis of PET polyester in the blends, second hydrolysis was employed, and the optimized condition yield cotton fiber was 65.06 wt%, relatively close to the theoretical yield. Subsequently, the obtained cellulosic material underwent an acid hydrolysis process using 70 percent (v/v) sulfuric acid (H₂SO₄) solution at 45 °C for 90 min, resulting in nanocellulose. Centrifugation at 15,000 rpm for 15 min facilitated the separation of nanocellulose from the acid solution and yielded 56.26 wt% at optimized conditions. The characterization of the nanocellulose was carried out utilizing a comprehensive array of techniques, including absorption, transmission, and reflection spectra, and Fourier transform infrared. The characterization results provide valuable insights into the unique properties of nanocellulose extracted from textile waste. In this research, the obtained nanocellulose was mixed with PVA and silver nanoparticle to form biodegradable film composites as the reinforcement. In comparison, biodegradable film of PVA:nanocellulose 9.5:0.5 with silver nanoparticle 0.3 wt% and glycerol as a plasticizer exhibits better tensile strength (2.37 MPa) and elongation (214.26%) than the PVA film with normal cellulose. The prepared biodegradable film was homogeneous and had a smooth surface without the internal defect confirmed by the CT scan. This result opens avenues for enhancing the quantities of eco-friendly film composites, potentially replacing conventional plastic films in the future.

Cellulose-based paper products derived from agro-industrial waste have attracted considerable interest due to their potential in sustainable material development. In this study, bacterial cellulose (BC) residue from the food and beverage industry was employed as a reinforcing agent to fabricate high-performance paper composites by blending with eucalyptus pulp (EP) at various ratios and basis weights. These papers were coated with a cationic modified starch solution (MS) using a rod coater, followed by hot pressing. Mechanical strengths (TAPPI Standard), water resistance (Cobb test and water contact angle), and air permeability (ASTM D737) were evaluated to assess material performance. The results showed that incorporating 50 wt% BC produced paper with outstanding mechanical performance, characterized by a high tensile index and excellent tear resistance. The application of the MS coating significantly boosted water resistance and air barrier performance, underscoring the effectiveness of this approach in creating high-performance paper materials. The resulting coated composites demonstrated excellent mechanical strength and barrier properties, positioning them as promising candidates for filtration applications such as personal protective face mask membranes.

The success of large-scale deep learning models in remote sensing tasks has been transformative, enabling significant advances in image classification, object detection, and image–text retrieval. However, their computational and memory demands pose challenges for deployment in resource-constrained environments. Knowledge distillation (KD) alleviates these issues by transferring knowledge from a strong teacher to a student model, which can be compact for efficient deployment or architecturally matched to improve accuracy under the same inference budget. In this paper, we introduce Hierarchical Multi-Segment Knowledge Distillation (HIMS_KD), a multi-stage framework that sequentially distills knowledge from a teacher into multiple assistant models specialized in low-, mid-, and high-level representations, and then aggregates their knowledge into the final student. We integrate feature-level alignment, auxiliary similarity-logit alignment, and supervised loss during distillation. Experiments on benchmark remote sensing datasets (RSITMD and RSICD) show that HIMS_KD improves retrieval performance and enhances zero-shot classification; and when a compact student is used, it reduces deployment cost while retaining strong accuracy.