Residual analysis of chitosan-based agronanofungicides as a sustainable alternative in oil palm disease management

Bio- and eco-corona related to plants: Understanding the formation and biological effects of plant protein coatings on nanoparticles

The thriving nano-enabled agriculture facilitates the interaction of nanomaterials with plants. Recently, these interactions and their biological effects are receiving increasing attention. Upon entering plants via leaves, roots, stems, and other organs, nanoparticles adsorb numerous biomolecules inside plants and form bio-corona. In addition, nanoparticles that enter plants through roots may have formed eco-corona with root exudates in the rhizosphere environment before contacting with plant exogenous proteins. The most significant biological effects of plant protein corona include changes in protein structure and function, as well as changes in nanoparticle toxicity and targeting ability. However, the mechanisms, particularly how protein corona affects plant protein function, plant development and growth, and rhizosphere environment properties, require further investigation. Our review summarizes the current understanding of the formation and biological effects of nanoparticle-plant protein corona and provides an outlook on future research.

Modeling and characterization of the electrical conductivity on metal nanoparticles/carbon nanotube/polymer composites

Flexible conductive films have good deformability and conductivity, and are expected to be used in flexible electronic devices. In this paper, four kinds of flexible conductive films were successfully prepared by compounding nano-sized metal (Ni, Cu, Au or AuCu alloy) particles to CNT surface and then dispersing to polydimethylsiloxane matrix. Experiment results show that the conductivity of these prepared films are almost two orders of magnitude higher than that of CNT/polydimethylsiloxane films with the same CNT loadings. A simulation model based on percolation network theory and Monte Carlo technology is introduced to study the influence of nanoparticles on the composite conductivity. Results confirmed that the introduction of nanoparticles effectively reduces the effective resistance of CNT and the tunnelling resistance at CNT junctions. The intrinsic conductivity and the length diameter ratio of CNT, the intrinsic conductivity, the size and the coverage ratio of nanoparticles are the core parameters affecting the conductivity of composite. Compared with CNT/polydimethylsiloxane films, the optimized theoretical conductivity of these nano-sized particles enhanced composites can be further improved.

Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra

The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm⁻¹ is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.