Palmarosa essential oil encapsulated in chitosan nanoparticles by ionotropic gelation: formulation and characterization

In this study, chitosan nanoparticles containing palmarosa essential oil (PEO-CNPs) were formed by ionotropic gelation, consisting of two parts: emulsion preparation followed by ionotropic gelation encapsulation with tripolyphosphate ions (TPP) as a crosslinker. The encapsulation method was optimized by varying three parameters, including chitosan concentration, initial oil loading in the emulsion and TPP concentration. The effects of these parameters on the encapsulation efficiency (EE) and loading capacity (LC) were analyzed. EE had an initial increase followed by a decrease in the range of three parameters. However, LC rose with varying initial oil content while it reduced with changing polymer and TPP concentration. The optimum experiment with the highest EE (10.0 g/L of chitosan, 5.0 g/L of TPP and 30.0 g/L PEO) was chosen to analyze the particle size using Dynamic Light Scanning method (DLS). With DLS measurement, the z-average diameter was 235.3 nm, and the particle size distribution was in the range of 100 – 500 nm.

Adaptive Compression for Online Computer Vision: An Edge Reinforcement Learning Approach

With the growth of computer vision-based applications, an explosive amount of images have been uploaded to cloud servers that host such online computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the de facto compression and encapsulation method for images. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model—for example, the standard quality level of JPEG leads to 50% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models (e.g., InceptionNet and ResNet). Knowing this, designing a better JPEG configuration for online computer vision-based services is still extremely challenging. First, cloud-based computer vision models are usually a black box to end-users; thus, it is challenging to design JPEG configuration without knowing their model structures. Second, the “optimal” JPEG configuration is not fixed; instead, it is determined by confounding factors, including the characteristics of the input images and the model, the expected accuracy and image size, and so forth. In this article, we propose a reinforcement learning (RL)-based adaptive JPEG configuration framework, AdaCompress. In particular, we design an edge (i.e., user-side) RL agent that learns the optimal compression quality level to achieve an expected inference accuracy and upload image size, only from the online inference results, without knowing details of the model structures. Furthermore, we design an explore-exploit mechanism to let the framework fast switch an agent when it detects a performance degradation, mainly due to the input change (e.g., images captured across daytime and night). Our evaluation experiments using real-world online computer vision-based APIs from Amazon Rekognition, Face++, and Baidu Vision show that our approach outperforms existing baselines by reducing the size of images by one-half to one-third while the overall classification accuracy only decreases slightly. Meanwhile, AdaCompress adaptively re-trains or re-loads the RL agent promptly to maintain the performance.

Innovative method for encapsulating highly pigmented biomass from Aspergillus nidulans mutant for copper ions removal and recovery

Biosorption has been considered a promising technology for the treatment of industrial effluents containing heavy metals. However, the development of a cost-effective technique for biomass immobilization is essential for successful application of biosorption in industrial processes. In this study, a new method of reversible encapsulation of the highly pigmented biomass from Aspergillus nidulans mutant using semipermeable cellulose membrane was developed and the efficiency of the encapsulated biosorbent in the removal and recovery of copper ions was evaluated. Data analysis showed that the pseudo-second-order model better described copper adsorption by encapsulated biosorbent and a good correlation (r2 > 0.96) to the Langmuir isotherm was obtained. The maximum biosorption capacities for the encapsulated biosorbents were higher (333.5 and 116.1 mg g-1 for EB10 and EB30, respectively) than that for free biomass (92.0 mg g-1). SEM-EDXS and FT-IR analysis revealed that several functional groups on fungal biomass were involved in copper adsorption through ion-exchange mechanism. Sorption/desorption experiments showed that the metal recovery efficiency by encapsulated biosorbent remained constant at approximately 70% during five biosorption/desorption cycles. Therefore, this study demonstrated that the new encapsulation method of the fungal biomass using a semipermeable cellulose membrane is efficient for heavy metal ion removal and recovery from aqueous solutions in multiple adsorption-desorption cycles. In addition, this reversible encapsulation method has great potential for application in the treatment of heavy metal contaminated industrial effluents due to its low cost, the possibility of recovering adsorbed ions and the reuse of biosorbent in consecutive biosorption/desorption cycles with high efficiency of metal removal and recovery.

Development of Alginate-Montmorillonite-Starch with Encapsulated Trichoderma harzianum and Evaluation of Conidia Shelf Life

Biological control agents, such as Trichoderma harzianum, are widely used in sustainable agriculture. However, commercialisation and mass production of biocontrol products have remained a challenge, especially in viability and efficiency in field application. The encapsulation method has emerged as a sophisticated technique to develop the formulation of T. harzianum. Hence, encapsulation through extrusion was used to prepare T. harzianum beads. The physical characteristics comprising weight, diameter, and swelling ability of the beads were significantly improved when the starch percentage was increased. Alginate-montmorillonite-starch (10%) revealed the lowest shrinkage and the highest swelling ability. The interaction within the functional groups of alginate, montmorillonite, and starch was confirmed by the Fourier-transform infrared spectroscopic (FTIR) study. Furthermore, scanning electron microscopic analysis exposed compatible scattering of montmorillonite particles and starch granules over the alginate linkages. Meanwhile, the X-ray diffraction analysis confirmed the exfoliation between starch and montmorillonite. Storage of T. harzianum beads at 5°C was more suitable than storage at 28°C. At low temperature, the encapsulated T. harzianum beads maintained their viability at 6.59 ± 0.12 log CFU g−1 for an effective threshold value for up to seven months. The current findings indicated that the combination of alginate, montmorillonite, and starch is the best formulation of encapsulated T. harzianum with improved conidia shelf life. © 2021 Friends Science Publishers