Direct determination of the degree of fibrillation of wood pulps by distribution analysis of pixel-resolved optical retardation

Fed-batch treatment attenuates diffusional limitation while preparing high solid microfibrillated cellulose from Gelidium amansii

This study investigates the effects of fed-batch treatment on the fibrillation degree and properties of Gelidium amansii-derived microfibrillated cellulose (MFC). Fed-batch milling was conducted with the initial solid loading of 1 % w/v followed by three stages of feeding to obtain a final solid concentration of 5 % w/v. This process provides a high-solid MFC of around 10 %, while batch milling only provides the maximum solid loading of 4 %. It also reduces approximately 83 % power consumption of batch milling at the same solid loading (4 %). The obtained MFC 5 % has lower fibrils length (14.9 µm) and width (16.46 nm), but higher consistency index (>250 Pa.s) than MFC 1 % (22 µm, 21 nm, 5.88 Pa.s). The crystallinity and maximum decomposition temperatures of both MFCs are comparable, varying at 49-53 % and 318 °C-320 °C. In summary, fed-batch treatment is promising for the techno-economic development of MFC production by lowering energy and maintaining product quality.

Functionalized Wood Veneers as Vibration Sensors: Exploring Wood Piezoelectricity and Hierarchical Structure Effects

Functional wood materials often rely on active additives due to the weak piezoelectric response of wood itself. Here, we chemically modify wood to form functionalized, eco-friendly wood veneer for self-powered vibration sensors. Only the piezoelectricity of the cellulose microfibrils is used, where the drastic improvement comes only from molecular and nanoscale wood structure tuning. Sequential wood modifications (delignification, oxidation, and model fluorination) are performed, and effects on vibration sensing abilities are investigated. Wood veneer piezoelectricity is characterized by the piezoresponse force microscopy mode in atomic force microscopy. Delignification, oxidation, and model fluorination of wood-based sensors provide output voltages of 11.4, 23.2, and 60 mV by facilitating cellulose microfibril deformation. The vibration sensing ability correlates with improved piezoelectricity and increased cellulose deformation, most likely by large, local cell wall bending. This shows that nanostructural wood materials design can tailor the functional properties of wood devices with potential in sustainable nanotechnology.

Statistical genetics concepts in biomass-based materials engineering

With the rise of biomass-based materials such as nanocellulose, there is a growing need to develop statistical methods capable of leveraging inter-dependent experimental data to improve material design, product development, and process optimisation. Statistical approaches are essential given the multifaceted nature of variability in lignocellulosic biomass, which includes a range of different biomass feedstock types, a combinative arrangement of different biomass processing routes, and an array of different product formats depending on the focal application. To account for this large degree of variability and to extract meaningful patterns from research studies, there is a requirement to generate larger datasets of biomass-derived material properties through well-designed experimental systems that enable statistical analysis. To drive this trend, this article proposes the cross-disciplinary utilisation of statistical modelling approaches commonly applied within the field of statistical genetics to evaluate data generated in the field of biomass-based material research and development. The concepts of variance partitioning, heritability, hierarchical clustering, and selection gradients have been explained in their native context of statistical genetics and subsequently applied across the disciplinary boundary to evaluate relationships within a model experimental study involving the production of sorghum-derived cellulose nanofibres and their subsequent fabrication into nanopaper material. Variance partitioning and heritability calculates the relative influence of biomass vs. processing factors on material performance, while hierarchical clustering highlights the obscured similarity between experimental samples or characterisation metrics, and selection gradients elucidate the relationships between characterisation metrics and material quality. Ultimately, these statistical modelling approaches provide more depth to the investigation of biomass-processing-structure-property-performance relationships through outlining a framework for product characterisation, quality evaluation, and data visualisation, not only applicable to nanocellulose production but for all biomass-based materials and products.

Cationic Lignocellulose Nanofibers from Agricultural Waste as High-Performing Adsorbents for the Removal of Dissolved and Colloidal Substances

The accumulation of dissolved and colloidal substances (DCS) in the increasingly closed paper circulating water system can seriously lower the productivity and safety of papermaking machines, and it has been a challenge to develop an adsorbent with low cost, high adsorption efficiency and large adsorption capacity for DCS removal. In this study, cationic lignocellulose nanofibers (CLCNF) were obtained by cationic modification of agricultural waste bagasse in deep eutectic solvents (DES) followed by mechanical defibrillation, and then CLCNF were employed as an adsorbent for DCS model contaminant polygalacturonic acid (PGA) removal. CLCNF was characterized by transmission electron microscopy, Fourier transform infrared, elemental analysis, X-ray diffraction, and thermogravimetric analysis. The analytical results confirmed the successful preparation of CLCNF with 4.6–7.9 nm diameters and 0.97–1.76 mmol/g quaternary ammonium groups. The effects of quaternary ammonium group contents, pH, contact time and initial concentration of PGA on the adsorption were investigated in a batch adsorption study. According to the results, the cationic modification significantly enhanced the adsorption of PGA by CLCNF and the adsorption performance increased with the increase of the quaternary ammonium group contents. The adsorption of PGA on CLCNF followed the pseudo-second-order and the fitted Langmuir isotherm model. The adsorption showed fast initial kinetics and the experimental maximum adsorption capacity was 1054 mg/g, which is much higher than PGA adsorbents previously reported in the literature. Therefore, CLCNF with high cationic group content developed in this paper is a promising adsorbent for DCS removal.