Conventional Technology and Nanotechnology in Wood Preservation: A Review

Using a sedimentation method, the extracted starch from Dioscorea hispida tuber was characterized for their morphological, thermal and other physicochemical properties such as total starch content, moisture content, pH, water binding capacity (WBC), solubility and swelling power. The starch content extracted from the tuber was 11.46% with the WBC and moisture content were 107% and 249% respectively. Results showed the starch solubility and swelling power gradually increased with the increasing of temperature ranging from 65°C to 95°C. Under a scanning electron microscope (SEM), it was observed the starch granules have polyhedral shapes and their size between 1.3 ?m to 4.3 ?m. The results obtained from differential scanning calorimeter (DSC) showed the starch transition temperature (To, Tp and Tc) were 74.54°C, 79.35°C and 83.36°C whereas, enthalpy of gelatinization (?Hgel) was 4.12 Jg-1. This research identify that starch from D. hispida has unique properties which can be used as a new starch source for various applications.

A new cellulose nanocrystal–reduced graphene oxide (CNC–rGO) nanocomposite was successfully used for mediatorless electrochemical sensing of methyl paraben (MP). Fourier-transform infrared spectroscopy (FTIR) and field-emission scanning electron microscopy (FESEM) studies confirmed the formation of the CNC–rGO nanocomposite. Cyclic voltammetry (CV) studies of the nanocomposite showed quasi-reversible redox behavior. Differential pulse voltammetry (DPV) was employed for the sensor optimization. Under optimized conditions, the sensor demonstrated a linear calibration curve in the range of 2 × 10⁻⁴–9 × 10⁻⁴ M with a limit of detection (LOD) of 1 × 10⁻⁴ M. The MP sensor showed good reproducibility with a relative standard deviation (RSD) of about 8.20%. The sensor also exhibited good stability and repeatability toward MP determinations. Analysis of MP in cream samples showed recovery percentages between 83% and 106%. Advantages of this sensor are the possibility for the determination of higher concentrations of MP when compared with most other reported sensors for MP. The CNC–rGO nanocomposite-based sensor also depicted good reproducibility and reusability compared to the rGO-based sensor. Furthermore, the CNC–rGO nanocomposite sensor showed good selectivity toward MP with little interference from easily oxidizable species such as ascorbic acid.

Agarwood is an important agricultural product widely used in fragrance industries. It can be found in various parts of ASEAN countries. The price of the Agarwood is determined according to its quality, which is generally decided based on certain grade. This paper proposes an intelligent grading technique for the wood using advanced signal processing of E-nose measurements. Agarwoods from Malaysia and Indonesia are classified into either high or low grade using artificial neural network. Thirty two sensor readings of the E-nose are used as the inputs of the artificial neural network. The experimental results show that the proposed technique, employing feed forward artificial neural network defined by 32-8-1 architecture and trained via Levenberg-Marquardt back propagation (LMBP) algorithm, successfully grade the Agarwood with a 100% classification rate.