Search

A heterogeneous Fenton-like reaction was investigated in this study using Fe3O4 nanoparticles/polyvinylidene fluoride macrospheres for the degradation of methylene blue (MB). MB dye is one of the most common contaminants found in industrial wastewater, and its degradation is considered critical from both an environmental and public health perspectives. In this work, using Response Surface Methodology coupled with the Box-Behnken Design, a systematic investigation on the effects of the initial pH, catalyst loading, and H2O2 dosage on degradation efficiency was performed. The obtained optimal conditions were pH 7, catalyst loading of 10 g/L, and dosage of H2O2 at 10 mM, which resulted a maximum degradation efficiency of 99.94%. The results showed that the efficiency of the Fenton-like reaction was obviously favoured by neutral pH, making a good balance between hydroxyl radical generation and catalyst stability. According to the ANOVA results, initial pH, catalyst loading, and H2O2 dosage are [ABSTRACT FROM AUTHOR]

This study presents the design and evaluation of a deep convolutional neural network (CNN) model for accurately classifying fig ripeness stages. Traditionally, fruit ripeness classification has been conducted manually, which presents several drawbacks, including heavy reliance on human labor and inconsistencies in determining fruit ripeness. By leveraging advanced deep learning techniques, specifically CNNs, this research aims to automate the fig ripeness classification process. The CNN architecture was developed and trained using MATLAB software, targeting three ripeness categories: ripe, half-ripe, and unripe. The methodology involved pre-processing the fig images and configuring the CNN model with multiple convolutional, batch normalization, and max pooling layers specifically for fig classification tasks. The final CNN model achieved an impressive accuracy rate of 94.44%, significantly surpassing results from previously reported studies. The developed model is a promising tool for automating fig ripeness classification, contributing to advancements in precision agriculture and smart farming technologies. [ABSTRACT FROM AUTHOR]

Pharmaceuticals and microplastics have long been identified as water pollutants. Pollutants, including pharmaceutical compounds, have been shown to be transported by microplastics (MPs). In this mini-review, adsorption mechanism between microplastics and emerging contaminants were highlighted. Polyethylene is a non-polar, semi-cystalline microplastic with a density of 240 to 244 kg/m³. Besides, Ibuprofen adsorption onto microplastics is pH dependent. Non-polar or neutral compounds that are homogeneous and extremely hydrophobic in nature interact with non-polar and weakly polar plastics such as Polypropylene and Polyethylene. Furthermore, Molecular dynamic (MD) simulation can be employed to study the mechanism of interaction between MPs and contaminants. As a result, some studies show a complex interaction between polyethylene (PE) and certain contaminants, with no significant differences in adsorption energies, but sulfamethazine molecules effectively adsorbed on the MPs surface. In summary, this mini review shed lights on the insights of adsorption mechanism between these compounds. [ABSTRACT FROM AUTHOR]

Kelantan is a Malaysian state renowned for its beautiful jungles and coastal districts. Kelantan, which is susceptible to monsoons and tidal waves, witnessed a tsunami-like inundation that affected its coastline landscape in 2014. This study does a comparative examination of shoreline alterations between 2013 and 2021 using the Digital Shoreline Analysis System (DSAS) technology. The coastline has grown by approximately 4.03 percent, from 62.1 kilometres to 64.6 kilometres, as a result of the implementation of digitalisation procedures. This highlights the increase in coastline areas between 2013 and 2021. Using GIS and satellite data, the study identifies considerable sedimentation in Pantai Geting and Lagun Jubakar, Tumpat, as well as severe erosion in Pantai Kundur and Pantai Cahaya Bulan, Kota Bharu. The analysis of the Shoreline Change Envelope (SCE) and Net Shoreline Movement (NSM) reveals an accretion rate of 728.44 m/year and a negative distance of -281.91 m/year, which indicates erosion. The paper concludes by emphasising Kelantan's shoreline expansion over the previous decade, stressing the significance of monitoring coastal changes for effective environmental management and catastrophe preparedness. [ABSTRACT FROM AUTHOR]

This study describes the synthesis of silver nanoparticles (AgNPs) using shortleaf spikesedge extract (SSE) to reduce AgNO3. Visual observation, in addition to analyses of UV-vis, EDX, XRD, FTIR, and TEM was employed to monitor the formation of AgNPs. The effects of SSE concentration, AgNO3 concentration, reaction time, pH, and temperature on the synthesis of AgNPs were studied based on the surface plasmon resonance (SPR) band. From the TEM image, highly-scattered AgNPs of quasi-spherical shape with an average particle size of 17.64 nm, were observed. For the catalytic study, the reduction of methylene blue (MB) was evaluated using two systems. A detailed batch study of the removal efficiency (%RE) and kinetics was done at an ambient temperature, various MB initial concentrations, and varying reaction time. Employing the electron relay effect in System 2, the batch study clearly highlighted the significant role of AgNPs in boosting the catalytic activity for MB removal. At 30–100 mg/L initial concentrations, MB was reduced by 100% in a very short reaction time between 1.5 and 5.0 mins. The kinetic data best fitted the pseudo-first-order model with a maximum reaction rate of 2.5715 min−1. These findings suggest the promising application of AgNPs in dye wastewater treatment. The SSE-driven AgNPs were prepared using unwanted dried biomass of shortleaf spikesedge extract (SSE) as a reducing as well as stabilizing agent. Employing the electron relay effect, the batch study clearly highlighted the significant role of SSE-driven AgNPs in boosting the catalytic activity for MB removal. At 30–100 mg/L initial concentrations, MB was reduced by 100% in a very short reaction time between 1.5 and 5.0 mins. In this sense, SSE-driven AgNPs acted as an electron relay point that behaves alternatively as acceptor and donor of electrons. The findings revealed the good catalytic performance of SSE-driven AgNPS, proving their viability for dye wastewater treatment. [ABSTRACT FROM AUTHOR]

This research illustrates the parametric studies for determining the relationship between the operating parameters used in the supercritical fluid extraction (SFE) and the extraction yield of Red Butterfly Wing (RBW) leaves. In this study, SFE used carbon dioxide (CO2) as the main solvent and ethanol as the co-solvent. SFE was operated by manipulating few parameters such as temperature, pressure and also particle size in order to determine the extraction yield. The RBW leaves were purchased from the local supplier and then undergone cleaning, drying, grinding as well as sieving processes. 5 grams of grinded leaves was then run through SFE machine. For the temperature, it was observed that the extraction yield increased starting from 40°C to 50°C and dropped after that. The optimum temperature was 50°C with extraction yield of 2.94%. For the pressure, the trend of the extraction yield was directly proportional to the pressure. As the pressure increased, the extraction yield also increased. The optimum extraction yield was 2.45 % at 375 bar. Meanwhile, the particle size was inversely proportional to the extraction yield. The optimum extraction yield was 3.26 % at 63 μm.

Due to the recent global threat of COVID-19, World Health Organization (WHO) announced a Public Health Emergency of International Concern (PHEIC). The Malaysian government has taken several measures to combat the outbreak, with the goal of finally ceasing it. One of the major initiatives taken by the government was the implementation of the Movement Control Order (MCO). The Malaysian government's enforcement of MCO, which was intended to control the COVID-19 transmission throughout the country was believed to have an impact on the quantity of air pollutants emitted [1]. Several rules were implemented under MCO that greatly limited individuals' activities outside of their homes, such as only permitting critical industries to operate and suspending non-essential ones, which significantly decreased traffic density and the emission of industrial pollutants [2]. This study involved obtaining databases from the Department of Environment Malaysia (DOE), which were then utilised to perform various statistical analyses in OpenAir software to observe the quality of air pollutants emitted across the Seberang Jaya, Penang area. Simultaneously, a statistical analysis was employed using an R-package open-source software to analyse pollutants that were related to meteorological conditions. The main goal of this study was to analyse the overall quality of major air pollutants such as PM10, PM2.5, CO, NO2, SO2, and O3 during MCO 1.0 which began on the 18th of March 2020 to the 31st of May 2020. The data on wind direction and speed from DOE were critical to accomplish this study. In summary, the main pollutants emitted in Seberang Jaya were PM10 and PM2.5, and the locations where the greatest concentration of all pollutants detected were business areas. In the end, this study provided statistical analysis using the OpenAir software tool able to analyse the amounts of various types of air contaminants in this densely populated area of Seberang Jaya. [ABSTRACT FROM AUTHOR]

The ripening stage is a stage where the fruit is ready to be harvested. During ripening, pectin activity is observed to trigger parenchyma cell wall middle lamella dissolution of a fruit. Additionally, the ripening stage also affects the changing appearance of the fruit. Thus, this research aims to develop a classification model based on ANN that can predict or classify the ripening stage based on either pectin activity or fruit appearance. The study will focus specifically on Ficus carica (fig). To achieve the objective, the researchers developed two Multilayer Perceptron (MLP) models: figNN and pectinNN. We trained figNN using features extracted from images of figs with different ripening stages, and pectinNN with a set of the statistical value of pectin activity such as weight (W), brix of sugar (BS), extraction yield (EY), and degree of esterification (ED) from 30 figs with varying degree of ripening. From the result of this research, figNN and pectinNN can distinguish the ripening stage based on either the chemical properties or the images. Furthermore, we can also show that the image-based classification is more accurate than the pectin-based classification. For future work, the study of the correlation between pectin and image features is highly encouraged. [ABSTRACT FROM AUTHOR]