Preparation and investigation of poly(methylmethacrylate) nano-capsules containing haloxyfop-R-methyl and their release behavior

Abstract

In this study, the preparation and characterization of haloxyfop-R-methyl herbicide loaded in poly(methylmethacrylate) (PMMA) nano-capsules by emulsion polymerization and its release behavior were investigated. The chemical characterizations of PMMA/haloxyfop-R-methyl nano-capsules were confirmed by FT-IR spectroscopy method, and the surface morphology was studied by field emission scanning electron microscopy and transmission electron microscopy. Also, the herbicide loading and encapsulation efficiency were analyzed for the herbicide-loaded nano-capsules. The release rate of PMMA/haloxyfop-R-methyl nano-capsules was determined by UV-visible spectroscopy. The thermal properties and thermal stability of nano-capsules were explored by the thermal gravimetric analysis method. The diameter of the nano-capsules was in the range of 100–300?nm. Increasing the amount of herbicide in nano-formulations significantly affected the surface of the nano-capsules and reduced their surface smoothness. Triton-X100 was identified as the best surfactant for the preparation of nano-capsules, and the sample containing the lowest herbicide content showed the best performance in terms of encapsulation and loading efficiency. This sample showed a steady-state release rate during the six days.     

Degrading a mixture of three textile dyes using photo-assisted electrochemical process with BDD anode and O2–diffusion cathode

In this paper, degradation of a mixture of three azo dyes was studied by the photo-assisted electrochemical process using an O₂-diffusion cathode containing carbon nanotubes and boron-doped diamond (BDD) anode. The concentration of three textile dyes (C.I. Acid Orange 8 (AO8), C.I. Acid Orange 10 (AO10), and C.I. Acid Orange 12 (AO12)) was determined simultaneously despite the severe overlap of their spectra. For this purpose, partial least square (PLS), as a multivariate calibration method, was utilized based on recording UV–Vis spectra during the decolorization process. Moreover, the central composite design was used for the modeling of photo-assisted electrochemical decolorization of the aqueous solutions containing three dyes. The investigated parameters were the initial concentration of three dyes, applied current and reaction time. Analysis of variance (ANOVA) revealed that the obtained regression models match the experimental results well with R (Khataee et al. 2010, Clean-Soil Air Water 38 (1):96–103, 2010) of 0.972, 0.971, and 0.957 for AO8, AO10, and AO12, respectively. Three-dimensional surface and contour plots were applied to describe the relation between experimental conditions and the observed response. The results of TOC analysis confirmed good ability of proposed photo-assisted electrochemical process for degradation and mineralization of textile industry wastewater.     

Using geospatial business intelligence paradigm to design a multidimensional conceptual model for efficient coastal erosion risk assessment

One of the main challenges in Coastal Erosion Risk Assessment (CERA) is integrating and analysis of conflicting data in various time periods and spatial scales through dissimilar environmental, social, and economic criteria. Currently, Geographical Information Systems (GIS) are widely used in risk assessment despite their drawbacks and limitations as transactional systems for multi-scales, multi-epochs, and multi-themes analysis. Hence, an analytical conceptual framework is proposed in this paper based on geospatial business intelligence paradigm to develop a Spatial Multidimensional Conceptual Model (SMCM) to assess coastal erosion risk. The model is designed based on Spatial On-Line Analytical Processing (SOLAP) platform, on the top of both analytical and transactional paradigms, to allow fast synthesis of cross-tabulated data and easy comparisons over space, scales, epochs, and themes. This objective is achieved through a comprehensive integration of multiple environmental, social, and economic criteria as well as their interactions at various scales. It also takes into account multiple elements at risk such as people, infrastructure, and built environment as different dimensions of analysis. Using this solution allows decision makers to benefit from on-demand, interactive, and comprehensive information in a way that is not possible using GIS alone. The developed model can easily be adapted for any other coastal region through the proposed framework to perform risk assessment. The advantages and drawbacks of the proposed framework are also discussed and new research perspectives are presented.     

Risk factors and the psychological impacts of the COVID-19 outbreak: perspectives and experiences of Iranian healthcare workers on the frontline

Frontline healthcare workers (HCWs) looking after COVID-19 patients are at high risk of developing mental health problems. Both preventive and interventional initiatives are essential, therefore, to maintain and improve the mental health of HCWs and ultimately to enhance the quality of care that they provide. This qualitative content analysis study, conducted in Iran between September 2020 and February 2021, involved the gathering of data on the issue through face-to-face interviews with 22 HCWs working directly with COVID-19 patients. Data analysis led to the generation of 163 codes, 45 categories (16 risk factors and 29 consequences), and 9 themes. Overall, the results suggest that caring for a patient with COVID-19 is associated with positive and negative psychological impacts. Measures such as increasing sensitivity to crisis situations, self-adjustment skills training, and identifying signs of job burnout owing to moral conflicts can reduce or eliminate the risk of negative psychological consequences among HCWs.