Preparation and Fundamental Characterization of Cellulose Nanocrystal from Oil Palm Fronds Biomass

The objective of this work was to isolate cellulose nanocrystal (CNC) from oil palm fronds (Elaeis guineensis) and its subsequent characterization. Isolation involves sodium hydroxide/anthraquinone pulping with mechanical refining followed by total chlorine free bleaching (includes oxygen delignification, hydrogen peroxide oxidation and peracetic acid treatment) before acid hydrolysis. Bleaching significantly decreased kappa number and increased α-cellulose percentage of fibers as confirmed by Technical Association of the Pulp and Paper Industry standards. Transmission electron microscopy (TEM), X-ray diffraction, Fourier transform infrared spectroscopy and thermogravimetric analysis revealed that acid hydrolysis along with bleaching improved crystallinity index and thermal stability of the extracted nanocrystals. It was observed that CNC maintained its cellulose 1 polymorph despite hydrolysis treatment. Mean diameter as observed by TEM and average fiber aspect ratio of obtained CNC was 7.44 ± 0.17 nm and 16.53 ± 3.52, respectively making it suitable as a reinforcing material for nanocomposite.     

Synergistic Effect of Oil Palm Based Pozzolanic Materials/Oil Palm Waste on Polyester Hybrid Composite

This research work aims to investigate the synergistic effect of pozzolanic materials such as oil palm ash (OPA) and oil palm empty fruit bunch (OPEFB) on the developed hybrid polymer composites. The OPEFB and OPA fillers of different particle sizes (250, 150, and 75 µm) were mixed at OPEFB:OPA ratios of (0:100; 20:80; 40:60; 60:40; 80:20 and 100:0) and incorporated into an unsaturated polyester resin. Furthermore, both mechanical and morphological properties of the composites were analyzed and it was found that tensile, flexural, and impact properties were significantly improved at OPEFB:OPA of 75 µm particle size hybridization of the polymer. The increase of OPEFB to OPA filler ratio up to 80:20 significantly improved the tensile properties of the composites while 40:60 ratio of 75 µm gave the optimum filler ratio to obtain the highest flexural and impact properties of the composites among all studied samples. Scanning electron micrograph images showed strong particle dispersion of the embedded fillers with resin which explained the excellent mechanical strength enhancement of the composite.     

Linking external debt and renewable energy to environmental sustainability in heavily indebted poor countries: new insights from advanced panel estimators

Environmental Science and Pollution Research - There are numerous studies on the linkage between renewable energy and environmental sustainability. These studies have tried to show how renewable...     

Critical Risk Indicators (CRIs) for the electric power grid: a survey and discussion of interconnected effects

The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to diverse factors including solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic, posing novel risks, and presenting new challenges to manage the coupled human–natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may be used to assess risks to electric grid reliability, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domain interconnections. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in interconnected human–natural systems.