The desire for advanced performing renewable materials and friendly to the environment has grown worldwide. Lignocellulosic materials have demonstrated great potential as reinforcing agents in the manufacturing of composite materials. Achieving such materials also depends on the optimization of parameters not limited to particle size, temperature, duration of treatment, manufacturing technique and the type of modification. Laccase enzyme, a biological modification deemed friendly to the environment with the aid of alkali pre-treatment was adopted for removal of hemicellulose and lignin that binds the cellulose fibres together. In this paper, particle sizes between; +100-200 µm, +200-300 µm, +300-425 µm, and +425-710 µm were immersed in 5wt% sodium hydroxide (NaOH) concentration followed by laccase modification. The enzyme activity, temperature and soaking duration were kept constant. The effects resulting from the modifications on the above particle sizes of waste mukwa were studied by scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA) and weight loss assessment. Significant improvements were observed on the untreated fibres. The morphology of modified fibres showed small differences while the crystallinity index revealed noticeable differences between the particle sizes. The surface modifications reduced the weight of the overall particles as impurities were extracted and influenced some functional groups; hydroxyl (-OH), lignin, cellulose, and hemicellulose. An improvement was also observed on the thermal stability of the treated fibres. The modifications were more effective on particle size +100-200 μm followed by +200-300 μm while the coarse particles did not show much improvement.
|Number of pages||6|
|Publication status||Published - 2019|
|Event||2nd International Conference on Sustainable Materials Processing and Manufacturing, SMPM 2019 - Sun City, South Africa|
Duration: Mar 8 2019 → Mar 10 2019
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
- Artificial Intelligence