Potassium Spraying Preharvest and Nanocoating Postharvest Improve the Quality and Extend the Storage Period for Acid Lime (Citrus aurantifolia Swingle) Fruits.

Citrus fruits are one of the most abundant crops globally in more than 140 countries throughout the world. Acid lime (Citrus aurantifolia swingle) is one of the citrus fruits which popularly has rich nutritional and therapeutic features. The storage period is the important factor that affects the economic and quality properties of this fruit. This study aims to demonstrate the enhancing effect of preharvest spraying with potassium, in addition to the postharvest dipping of fruits in some edible coatings, on the quality and storability of acid lime fruits. Preharvest spraying with organic and mineral forms of potassium, namely, potassium thiosulfate 1.75 g/L (S) and potassium tartrate 2 g/L (T), were carried out at three different times, in May, June, and July. On the other hand, postharvest treatments were carried out via dipping fruits in different types of biopolymers (carboxymethyl cellulose (E2) and gum arabic (E3)) and carboxymethyl cellulose/gum arabic composite (E4) as well as nanocoating formulation based on both biopolymers and doped zinc oxide nanoparticles (ZnONPs) (E1), which were prepared via acid lime peel waste extract. Herein, the physiochemical and morphological characterizations confirmed that the nanocoating was prepared at the nanoscale and doped with green synthesis ZnONPs, with recorded sizes of around 80 and 20 nm, respectively. Preharvest spraying with potassium tartrate enhanced fruit traits (Spraying with potassium tartrate at pre-harvest and nanocoating dipping at post-harvest (TE1), spraying with potassium tartrate at pre-harvest and carboxy methyl cellulose dipping at post-harvest (TE2), spraying with potassium tartrate at pre-harvest and gum arabic dipping at post-harvest (TE3) and spraying with potassium tartrate at pre-harvest and carboxymethyl cellulose/gum arabic composite dipping at post-harvest (TE4)), followed by potassium thiosulfate (spraying with potassium thiosulfate at pre-harvest and nanocoating dipping at post-harvest (SE1), spraying with potassium thiosulfate at pre-harvest and carboxy methyl cellulose dipping at post-harvest (SE2), spraying with potassium thiosulfate at pre-harvest and gum arabic dipping at post-harvest (SE3) and spraying with potassium thiosulfate at pre-harvest and carboxymethyl cellulose/gum arabic dipping at post-harvest (SE4)), compared to control. For postharvest treatments, E1 improved fruit quality, followed by E2, E4, and E3, respectively. The integration between pre- and postharvest treatments showed a clear superiority of TE2, followed by TE4, SE1, and SE2, respectively.

Controlled synthesis of graphene oxide/silica hybrid nanocomposites for removal of aromatic pollutants in water.

The remarkable characteristics of graphene make it a model candidate for boosting the effectiveness of nano-adsorbents with high potential owing to its large surface area, π-π interaction, and accessible functional groups that interact with an adsorbate. However, the stacking of graphene reduces its influence adsorption characteristics and also its practical application. On the other hand, the widespread use of aromatic compounds in the industry has aggravated the contamination of the water environment, and how to effectively remove them has become a research hotspot. Herein, we develop the functionalization of silica nanoparticles on graphene oxide nanosheet (FGS) by a facile, cheap, and efficient synthesis protocol for adsorption of Trypan Blue (TB) and Bisphenol A (BPA). It was demonstrated that chemical activation with KOH at high autoclaving temperature successfully transformed rice husk ash (RHA) into FGS. The graphene oxide layered interlamination was kept open by using SiO2 to expose the interlayers' strong adsorption sites. XRD, EDX, FTIR, Raman spectroscopy, SEM, HR-TEM, and BET surface area are used to investigate the chemical composition, structure, morphology, and textural nature of the as-produced FGS hybrid nanocomposite. The various oxygen-containing functional groups of the hybrid nanocomposites resulted in a significantly increased adsorption capacity, according to experimental findings. In addition, FGS2, the best composite, has a specific surface area of 1768 m2g-1. Based on Langmuir isotherms, the maximal TB dye and BPA removal capacity attained after 30 min were 455 and 500 mg/g, respectively. The Langmuir isotherm model, a pseudo-second-order kinetic model, and an intraparticle diffusion model have all been used to provide mechanistic insights into the adsorption process. This suggests that BPA and TB adsorption on FGS2 is mostly chemically regulated monolayer adsorption. Due to its unique sp2-hybridized single-atom-layer structure, the exposed graphene oxide nanosheets' extremely hydrophobic effect, hydrogen bonding, and strong-electron donor-acceptor interaction contributed to their improved adsorption of BPA and TB. According to adsorption thermodynamics, FGS2 adsorption of TB and BPA is a spontaneous exothermic reaction that is aided by lowering the temperature. For adsorption-based wastewater cleanup, the produced nanocomposites with a regulated amount of carbon and silica in the form of graphene oxide and silica can be used. These findings suggest that functionalized GO/SiO2 hybrid nanocomposites could be a viable sorbent for the efficient and cost-effective removal of aromatic chemicals from wastewater.

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging.

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.