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A comparative study of cobalt ferrite (CoFe2O4) catalysts synthesized via three different methods is presented, aiming to evaluate their catalytic activities in poly(ethylene terephthalate) (PET) chemical depolymerization by glycolysis. The synthesized catalysts have been characterized by X-ray photoelectron spectroscopy, X-ray diffraction, X-ray fluorescence, scanning electron microscopy, and energy dispersive spectroscopy. The conversion of PET into bis(2-hydroxyethyl)terephthalate (BHET) monomer was examined using these cobalt ferrites. The BHET in the reaction mixture was analyzed by FTIR, HPLC, DSC, and TGA and results show the catalyst synthesized by coprecipitation demonstrates the highest BHET yield (95.4%) at a temperature of 200 °C and a pressure of 0.7 bar in 1 h with only 1.0 wt% loading. Hydrothermal synthesized CoFe2O4 and solvent-free synthesized CoFe2O4 both showed lower BHET yields. The catalytic performance of the coprecipitated CoFe2O4 catalyst was influenced by the presence of unintentionally trapped FeCl3 within the catalyst, which resulted in its enhanced activity. The CoFe2O4 catalyst from coprecipitation exhibited acceptable magnetic recoverability and reusability for four consecutive cycles, further highlighting its usefulness and sustainability. [ABSTRACT FROM AUTHOR]

Due to the growing demand for per- and poly fluoroalkyl substances (PFAS) detection, analytical methods are progressively focused on developing facile and efficient techniques for detecting PFAS compounds in water systems to eliminate the need for complex procedures. Here, we developed a nanoscale fluorine-(F), nitrogen-(N)-doped carbon dot-based fluorescence (FL) sensor designed for the sensitive detection of perfluorooctanoic acid (PFOA) in drinking water. To counterbalance the effects of the N/F ratio on fluorophilicity, the synthesis of fluorinated carbon dots (F-CDs) was optimized by examining polyethylenimine (PEI) to tetrafluoroterephthalic acid (TFTA) ratio from 1:3 to 1:10. The highest quantum yield of 10.6% was achieved at a 1:3 ratio. The F-CDs demonstrated a blue fluorescence with optimum excitation and emission wavelengths at 350 and 470 nm, respectively. F-CDs were extensively characterized using fluorine nuclear magnetic resonance (FNMR), Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), X-ray photoelectron Spectroscopy (XPS), dynamic light scattering (DLS), and ζ-potential measurements. ζ-Potential analysis confirmed the surface chemistry of the F, N-CDs, as evidenced by their cationic nature. The interaction with the PFOA led to a noticeable decrease in the surface charge of the CD, as evidenced by electrostatic, and fluorophilic interactions. The sensor exhibited high sensitivity and selectivity toward PFOA within the detection range from 10 to 1660 ppt and a low detection limit of 3 ppt. The sensor's capability to measure PFOA concentrations in groundwaters with high accuracy provides a reliable detection method for safeguarding water resources and public health, making it a valuable tool for environmental monitoring. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorder (ASD) is the fastest‐growing child neuropsychiatric condition. Cognitive dysfunctions such as memory impairments are experienced by patients along with social disturbances and repetitive/stereotypic movements. We have used the radial arm maze (RAM), for measurement of working and reference memory errors in an animal model of autism. In addition, the potential effects of agmatine, an endogenous NMDA antagonist, on RAM performance and autistic‐like behaviors were assessed. Methods: Autism was modeled by valproic acid (VPA) administration at gestational Day 12.5. Autism‐associated behaviors in male offspring were examined in an open field test (OFT) and three‐chambered test (TCT) on postnatal days 50–51. Thereafter, the animals were trained in the RAM (PND 55) until they attained the criteria of 80% correct choices during five consecutive trials. Forty‐eight hours after the acquisition of criteria, agmatine was injected 30 min before subsequent behavioral testing, which included the retention phase of the RAM, OFT, and TCT. Results: VPA‐treated and intact rats showed the same performance in RAM, and acute injection of agmatine rescued social and anxiety‐like behavior induced by VPA without the effect on RAM. Conclusion: In a rat model of autism, spatial learning, and memory did not change. Agmatine rescued social and anxiety‐like behavior in autistic animals. [ABSTRACT FROM AUTHOR]

There is always a need to develop new, fast and non-complicated microextraction methods that consume small amounts of organic solvents. In this study, a new mode of liquid phase microextraction method, termed as continuous sample drop flow-based microextraction (CSDF-ME), was developed for the enrichment and determination of cobalt in water samples. To attain the appropriate conditions for the CSDF-ME, experimental parameters such as pH, type and volume of extraction solvent, concentration of chelating agent, salting effect, sample flow rate and sample volume, needle diameter were examined. The enrichment factor was 167 for 20 mL sample solution. The calibration graph was linear in the concentration range of 5-200 [mu]g/L with the correlation coefficient of 0.999. The limit of detection was 1.3 [mu]g/L. The reliability of the recommended procedure was verified by analysis of real water samples (including tap, mineral and river water) and synthetic sample spiked with known amount of cobalt, and the obtained recoveries of spiked samples have demonstrated accuracy and applicability of the proposed method., Author(s): Mohanna Ezati [sup.1], Soleyman Moinfar [sup.1] [sup.2], Somayeh Mohammadi [sup.1] [sup.3], Gholamreza Khayatian [sup.1] Author Affiliations: (1) grid.411189.4, 0000 0000 9352 9878, Department of Chemistry, Faculty of Science, University [...]

Purpose: Credit risk is the most important risk that banks are facing. Two main variables used for modeling this risk and determining the expected loss from lending are "Probability of Default" and "Loss Given Default." Given the importance of the loss-given-default (LGD) in various applications of credit risk management and credit rating, this study presents a method using the structural model approach to estimate this variable for both secured and unsecured debt, focusing on debt structure and collateral liquidity. The proposed approach has been applied to estimate the LGD for some selected firms active in the Iranian capital market. Method: To estimate the "Expected LGD" and "Downturn LGD" for secured (with both liquid and less liquid collateral) and unsecured debts, a structural model and Monte Carlo simulation approach have been employed. In this structural model, the market value of the firm's total assets follows a Jump-Diffusion process, and each collateral, being among the firm's tangible assets, has a separate but dependent stochastic process relative to the firm's total assets. Additionally, a liquidity penalty is considered for less liquid collateral. Furthermore, in this study, different values are considered for the ratio of tangible assets to total assets in default and non-default conditions (operations). To estimate the LGD for the debts of eleven selected eligible firms, after determining the parameters of the stochastic process for assets of each firm and collateral using data up to the end of the fiscal year 1396 (2017), the Loss Given Default for these firms' debts at the end of the fiscal year 1397 (2018) was estimated using the structural model and Monte Carlo simulation approach. In the Monte Carlo simulation, 500,000 paths for total assets and collaterals were simulated. For the paths where defaults were observed, the expected Loss Given Default values were estimated, first at the debt level and then at the firm level. Additionally, to perform a sensitivity analysis, the mentioned variables were estimated under alternative debt structures for the same firms. Findings: For firms with a debt structure consisting of only one secured debt, where the collateral is liquid and the debt is recovered solely from the collateral, the expected Loss Given Default values for secured debt are similar, ranging from 14% to 14.5%. If the type of collateral changes to the less liquid one, for the same firms, this value ranges from 30% to 33%. For the total LGD variable (where debt recovery comes from both collateral and other remaining assets of the firm), the average expected values for these two debt structures are 9% and 12%, respectively. For firms with a debt structure involving two secured debts, the expected LGD values, where recovery is solely from collateral, are similar to those of firms with only one secured debt with the same type of collateral. However, the average expected total LGD for secured debt with liquid collateral is 10%, and for less liquid collateral, it is 15.8%. Additionally, as the proportion of secured debts with liquid collateral relative to total debts decreases, the expected LGD at the firm level tends to increase on average. Moreover, in the downturn, the LGD for secured debt with liquid collateral is less influenced by the debt structure and is primarily affected by the dynamics of assets and collateral and the values of these two variables under critical conditions. Conclusion: In this study, the LGD for debts was estimated using a structural model and through Monte Carlo simulation for the debts of eleven selected firms listed on the Tehran Stock Exchange. Considering two types of collateral with different liquidity and three common debt structures of companies, it was determined that the liquidity of collateral plays a significant role in reducing the expected LGD for secured loans. The role of debt structure, given the priority of secured debt payments, affects the amount of LGD for unsecured debt, as well as the total LGD for secured debts and the total LGD at the firm level. [ABSTRACT FROM AUTHOR]

Nanomaterials are promising candidates to overcome the water quality issues caused by dye consuming industries. However, their restricted efficiency in water treatment hinders their practical applications, particularly in the case of dispersed nanomaterials used in water systems. This study presents a novel approach for immobilizing ferromagnetic nanocomposites on a polymeric microporous structure. 100 mg L−1 Zn0.5Mn0.5Fe2O4 nanocomposites (NCPs) were embedded into poly(methyl methacrylate) (PMMA), eliminating the need to retrieve the dispersed nanomaterials after water treatment. The developed Zn0.5Mn0.5Fe2O4–PMMA nanocomposite was characterized by scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. The removal of two commercial dyes (Brilliant Blue and Allura Red) at concentration levels of (0, 25, 35, 50, 70, and 100 mg L−1) was examined by batch adsorption studies followed by ultraviolet-visible (UV-vis) spectrophotometry quantification. It was observed that the maximum removal percentage was obtained for the embedded Zn0.5Mn0.5Fe2O4 nanoparticles on the PMMA polymer using 100 mg of Zn0.5Mn0.5Fe2O4 NCPs for all the four dye levels in comparison to dispersed Zn0.5Mn0.5Fe2O4 nanocomposites. It was observed that the maximum adsorption of Brilliant Blue and Allura Red was obtained under alkaline conditions, pH 7.32 and 8.37, respectively. It was suggested that Zn0.5Mn0.5Fe2O4–PMMA can be applied as a sustainable material to enhance water treatment efficiency by merging the advantages of a fast preparation technique and a higher adsorption capacity to remove complex organic contaminants. [ABSTRACT FROM AUTHOR]