Browsing by Author "Shojaeiarani, Jamileh"

Now showing 1 - 3 of 3

Cellulose nanocrystal based composites: A review
(2021-07) Shojaeiarani, Jamileh; Bajwa, Dilpreet S.; Chanda, Saptaparni
Cellulose nanocrystals (CNC) have received much attention as renewable, biodegradable, nontoxic, and low-cost nanomaterials with some remarkable properties. Desirable engineering properties of CNC include large surface to volume ratio, high tensile strength (~10 GPa), high stiffness (~110–130 GPa), and high flexibility. They can be chemically modified to tailor their properties for high-end engineering and biomedical applications. Despite their outstanding properties, the wide-scale application is lacking due to their surface characteristics and processing challenges. To achieve their full potential safer extraction methods, improved surface modification and functionalization methods and processing techniques are being researched. This review attempts to access methods for characterizing CNC, and CNC composites as well as their emerging new applications as smart materials. The review is a valuable resource for researchers and scientists working in industry or academia to provide an update on the use of CNC materials and their composites in packaging, biomedical, and high-efficiency energy systems.
Development of spectral indices for identifying glyphosate-resistant weeds
(2020-03) Shirzadifar, Alimohammad; Bajwa, Sreekala G.; Nowatski, John; Shojaeiarani, Jamileh
Glyphosate as the most common and widely-used herbicide in agricultural crops has resulted in the explosion of resistant weeds around the world. An early detection of resistant weeds before observing the visible symptoms of glyphosate application in weeds can contribute to the crop protection in precision agriculture. This paper aims at developing and evaluating spectral weed indices (SWIs) to identify glyphosate-resistant weeds 72 h after herbicide application. A greenhouse experiment was conducted on three common weed species, namely, kochia (Kochia scoparia), ragweed (Ambrosia artemisiifolia L.) and waterhemp (Amaranthus rudis), including resistant and susceptible types to collect canopy spectral reflectance after glyphosate spraying. To generate SWI, the best weighted combination of single wavelength and a normalized wavelength difference in the range of 450–920 nm was established. Relief-F algorithm selected the most discriminative feature wavelengths, and two band normalized differences to differentiate weeds with higher degree of resistance, and changes in the hyperspectral signature caused by glyphosate application. The performance of optimized SWIs on resistant weeds identification was assessed by employing machine learning Random Forest (RF) method. The RF classification model achieved a classification accuracy of 96%, 100% and 97% in detecting resistant kochia, ragweed and waterhemp, respectively. A comparison between introduced SWIs in this study and previously published hyperspectral vegetation indices (VIs) indicated that the SWIs provided better agreement with real data in glyphosate-resistant weeds identification.
Role of Hybrid Nano-Zinc Oxide and Cellulose Nanocrystals on the Mechanical, Thermal, and Flammability Properties of Poly (Lactic Acid) Polymer
(MDPI AG, 2021-02) Bajwa, Dilpreet S.; Shojaeiarani, Jamileh; Liaw, Joshua D.; Bajwa, Sreekala G.
Biopolymers with universal accessibility and inherent biodegradability can offer an appealing sustainable platform to supersede petroleum-based polymers. In this research, a hybrid system derived from cellulose nanocrystals (CNCs) and zinc oxide (ZnO) nanoparticles was added into poly (lactic acid) (PLA) to improve its mechanical, thermal, and flame resistance properties. The ZnO-overlaid CNCs were prepared via the solvent casting method and added to PLA through the melt-blending extrusion process. The composite properties were evaluated using SEM, a dynamic mechanical analyzer (DMA), FTIR TGA, and horizontal burning tests. The results demonstrated that the incorporation of 1.5% nano-CNC-overlaid ZnO nanoparticles into PLA enhanced the mechanical and thermal characteristics and the flame resistance of the PLA matrix. Oxidative combustion of CNC-ZnO promoted char formation and flame reduction. The shielding effect from the ZnO-CNC blend served as an insulator and resulted in noncontinuous burning, which increased the fire retardancy of nanocomposites. By contrast, the addition of ZnO into PLA accelerated the polymer degradation at higher temperature and shifted the maximum degradation to lower temperature in comparison with pure PLA. For PLA composites reinforced by ZnO, the storage modulus decreased with ZnO content possibly due to the scissoring effect of ZnO in the PLA matrix, which resulted in lower molecular weight.