This study produced a novel polysulfone (PSF) membrane for dye removal using lemon-derived carbon quantum dots-grafted silver nanoparticles (Ag/CQDs) as membrane nanofiller. The preparation of CQDs was completed by undergoing hydrothermal treatment to carbonize the pulp-free lemon juice into CQD solution. The CQD solution was then coupled with Ag nanoparticles to form Ag/CQDs nanohybrid. The synthesized powders were characterized in terms of morphologies, functional groups and surface charges. A set of membranes was fabricated with different loadings of Ag/CQDs powder using the nonsolvent-induced phase separation (NIPS) method. The modified membranes were studied in terms of morphology, elemental composition, hydrophilicity and pore size. In addition, pure water flux, rejection test and fouling analysis of the membranes were evaluated using tartrazine dye. From the results, 0.5 wt % of Ag/CQD was identified as the optimum loading to be incorporated with the pristine PSF membrane. The modified membrane exhibited an excellent pure water permeability and dye rejection with improvements of 169% and 92%, respectively. In addition, the composite membrane also experienced lower flux decline, higher reversible fouling and lower irreversible fouling. This study has proven that the addition of CQD additives into membrane greatly improves the polymeric membrane's properties and filtration performance.
During membrane filtration, it is inevitable that a membrane will experience physical damage, leading to a loss of its integrity and a decrease in separation efficiency. Hence, the development of a water-responsive membrane capable of healing itself autonomously after physical damage is significantly important in the field of water filtration. Herein, a water-enabled self-healing composite polyethersulfone (PES) membrane was synthesized by coating the membrane surface using a mixed solution composed of poly (vinyl alcohol) and polyacrylic acid (PVA-PAA). The self-healing efficiency of the coated PES membrane was examined based on the changes in water flux at three stages which are pre-damaged, post-damaged, and post-healing. The self-healing process was initiated by the swelling of the water-responsive PVA and PAA, followed by the formation of reversible hydrogen bonds, completing the self-healing process. The coated PES membrane with three layers of PVA-PAA coatings (at 3:1 ratio) demonstrated high water flux and remarkable self-healing efficiency of up to 98.3%. The self-healing capability was evidenced by the morphology of the membrane observed via scanning electron microscope (SEM). The findings of this investigation present a novel architecture approach for fabricating self-healing membranes using PVA-PAA, in addition to other relevant parameters as reported.
Cities are growing geographically in response to the enormous increase in urban population; consequently, comprehending growth and environmental changes is critical for long-term planning. Urbanization transforms naturally permeable surfaces into impermeable surfaces, causing an increase in urban land surface temperature, leading to the phenomenon known as urban heat islands. The urban heat islands are noticeable across Malaysia's rural communities and villages, particularly in Kuala Lumpur. These effects must be addressed to slow, if not halt, climate change and meet the Paris Agreement's 2030 goal. The study posits an application of thermal remote sensing utilizing a space-borne satellite-based technique to demonstrate urban evolution for urban heat island analysis and its relationship to land surface temperature. The urban heat island (UHI) was analyzed by converting infrared radiation into visible thermal images utilizing thermal imaging from remote sensing techniques. The heat island is validated by reference to the characteristics of the normalized difference vegetation index (NDVI), which define the land surface temperature (LST) of distinct locations. Based on the digital information from the satellite, the highest temperature difference between urban and rural regions for a few chosen cities in 2013 varied from 10.8 to 25.5 °C, while in 2021, it ranged from 16.1 to 26.73 °C, highlighting crucial temperature changes. The results from ANOVA test has substantially strengthened the credibility of the significant temperature changes. Some notable reveals are as follows: The Sungai Batu area, due to its rapid development and industry growth, was more vulnerable to elevated urban heat due to reduced vegetation cover; therefore, higher relative vulnerability. Contrary, the Bukit Ketumbar area, which region lies in the woodland region, experienced the lowest, with urban heat islands reading from 2013 at -0.3044 and 0.0154 in 2021. It shows that despite having urban heat islands increase two-fold from 2013 to 2021, increasing the amount of vegetation coverage is a simple and effective way of reducing the urban heat island effect, as evidenced by the low urban heat islands in the Bukit Ketumbar woodland region. The study findings are critical for advising municipal officials and urban planners to decrease urban heat islands by investing in open green spaces.
In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model. The following were the developed best models for their respective study areas: In Peninsular Malaysia, the interactions linear regression model was the best at Pulau Langkawi (RMSE = 19.066), the Matern 5/2 gaussian process regression model at Geting (RMSE = 49.891), and the trilayered artificial neural network at Pulau Pinang (RMSE = 20.026), while the linear regression model was the best at Sandakan in Sabah, East Malaysia (RMSE = 14.054). Other metrics, such as MAE and R-square, were also at their best values, each providing its best values, further substantiating the RMSE respectively, at each of the study areas. These empirical statistics (or metrics) also revealed that despite employing sea level as the sole parameter, results obtained were exceptional better when utilizing a 7-day lag, regardless of the model used. Notably, lag variables with less than a 7-day lag could degrade the model's accuracy in representing ground reality. The study emphasizes the importance of thorough training and testing of ML to aid decision-makers in developing mitigation actions for the climate change phenomena of sea level rise through reliable ML.