The addition of fillers or additives to improve the mechanical properties of degradable plastics such as sago starch has gained the interest of researchers, scientists, and academicians. This research aims to investigate the addition of calcium silicate as an additive filler on the properties of a sago starch-based degradable plastic. The calcium silicate fillers used were 2, 4, 6, and 8% by weight starch, and the gelatinization process temperature used was 70, 80, and 90 °C. The properties of these plastics were analyzed in terms of their strength, chemical composition, thermal stability, water absorption, and degradation rate. The optimum mechanical characteristics included a tensile strength of 28.04 MPa, 32.55 MPa of elongation at the break, and 70.02% of Young’s modulus obtained with the addition of 8% calcium silicate and a gelatinization temperature of 90oC. Fourier Transform Infrared (FTIR) showed that there were O-H, C-H, and C=O groups that existed at wave numbers of 3795.91 cm-1, 2927.94 cm-1, and 1433.11–1616.35 cm-1; moreover, these groups are hydrophilic, which bind water, so they can be degraded by the microbial activity in the soil. Differential Scanning Calorimetry (DSC) showed that the degradable plastic had a thermogram peak at 271.38 °C; additionally an endothermic peak occurred at 309.30 °C. The maximum swelling value was 64.05% at 2% calcium silicate and a gelatinization temperature of 70oC. The addition of calcium silicate made the plastic more water-resistant. The degradation rate of the degradable plastic ranged from 12–15 days and conformed to the American Standard Testing and Materials (ASTM) D-20.96 (maximum 180 days of decomposition for degradable plastic).
The extraction and utilization of crude oil are fundamental to global energy production, driving economies and fueling countless industries. However, wax deposition in pipelines and equipment creates several challenges, causing issues during the production, transportation, and refining of waxy crude oil. On the other hand, conventional chemicals such as alkylphenol ethoxylates (APEs) and volatile organic compounds (VOCs) used in the treatment have negative environmental and human health effects. Nanocomposites of polymers have emerged as promising solutions to mitigate wax damage. They represent a revolutionary class of nanocomposite hybridized polymer matrices. Moreover, to our knowledge, there has been a lack of comprehensive reviews of researchers who have combined and evaluated the effectiveness of these methods over the last decade. To gain a comprehensive understanding of the current state of knowledge and recognize emerging research trends, in this systematic review, we critically evaluated the published research on the role of polymer nanocomposites in the environmentally friendly management of wax deposition in crude oil systems. This review covers numerous topics, including (1) spatiotemporal distribution of research on polymer nanocomposites, (2) synthesis routes of millennium polymer nanocomposites, (3) reaction mechanisms for wax improvement, (4) common emerging trends in applications, (5) diverse polymer candidates for nanomaterials, (6) trending nanoparticle candidates for polymerization, and (7) future perspectives. However, further progress in understanding the effects of polymer nanocomposites on waxy crude oil is hindered by the lack of comparative studies on their reaction mechanisms and human health toxicity. However, despite these limitations, polymer nanocomposites continue to show great promise in addressing challenges related to waxy crude oil.
Microplastics (MPs) pose a substantial challenge to the environment and have life-threatening implications for organisms, including humans. To overcome this challenge, several investigations have been conducted, including adsorption with a specific absorbent, manual and modified sand filtration columns, and ultrafiltration using polymers. However, microplastic removal using these methods remains limited in certain cases; hence, an optimal method is required to separate MPs from water. The aim of this study was to remove MPs from community water wells in Banda Aceh, Indonesia, using a polyether sulfone (PES) membrane modified with poloxamer surfactants and patchouli oil. Membranes were created using the phase inversion method to form an asymmetrical structure with a top-to-bottom pore distribution. Community well water samples were collected from numerous points in Banda Aceh City. This was followed by analysis before and after filtration using a microscope and FTIR spectroscopy to determine the shape and type of MPs. The results revealed fiber- and film-shaped MPs detected in the well water of each community examined in this study. The FTIR analysis demonstrated that MP contamination was dominated by polyethylene and polypropylene plastics, consistent with the trend observed across Asia. Nonetheless, MP contamination could be eliminated by an ultrafiltration process using a membrane. In this study, the removal of MPs using the membrane delivered significant results. Pure PES membranes can eliminate up to 87.5% of MPs from water samples. However, the PES membrane containing poloxamer and patchouli oil delivered 100% rejection.
Floods have been identified as one of the world’s most common and widely distributed natural disasters over the last few decades. Floods' negative impacts could be significantly reduced if accurately predicted or forecasted in advance. Apart from large-scale spatiotemporal data and greater attention to data from the Internet of Things, the worldwide volume of digital data is increasing. Artificial intelligence plays a vital role in analyzing and developing the corresponding flood mitigation plan, flood prediction, or forecast. Machine learning (ML)-based models have recently received much attention due to their self-learning capabilities from data without incorporating any complex physical processes. This study provides a comprehensive review of ML approaches used in flood prediction, forecasting, and classification tasks, serving as a guide for future challenges. The importance and challenges of applying these techniques to flood prediction are discussed. Finally, recommendations and future directions of ML models in flood analysis are presented.
Patchouli cultivation in Lhokseumawe, Aceh Province, produces a significant amount of biomass, with approximately 96%–98.5% of the raw material ending up as solid residue after the oil distillation process. This waste is often discarded or burned, leading to environmental problems. However, this residue is rich in compounds derived from the breakdown of lignocellulose and can be pyrolyzed to produce liquid smoke. Liquid smoke contains valuable phenols, acids, and carbonyls but may also generate harmful byproducts such as polycyclic aromatic hydrocarbons (PAHs). This study aimed to optimize the production of grade A liquid smoke through purification using a distillation and adsorption process. Pyrolysis of patchouli residue was conducted at 400 °C to produce liquid smoke. Furthermore, the raw liquid smoke was purified by distillation and adsorption. The distillation process was conducted at a temperature range (X1) of 150–200 °C for 30–90 min (X2). The weight of activated biochar used during the adsorption process varied between 5 and 15 g (X3). The experimental design was carried out using Design Expert version 13. Research findings indicate that the optimal condition for liquid smoke was achieved with the equation Y=1.26 + 0.53X1 + 0.0319X2 + 0.8023X3– (4.91X1)2 – (0.0015X2)2 – (0.2147X3)2 – 0. 0644 X1X2 – 0.0327X1X3 + 2.15X2X3. The best results were obtained at a distillation temperature of 175 °C, a duration of 90 minutes, and using 13.4 g of activated carbon, which resulted in a high total phenol yield of 8.06%. The produced liquid smoke was classified as grade A, with a yield of 74.33%, a density of 0.9928 g/m³, a viscosity of 1.6203 cP, and a pH of 3.32. This research highlights an eco-friendly solution for valorizing patchouli waste, transforming it into a product with a wide range of applications as a food preservative, flavor enhancer, and medicinal agent.
Monitoring of river water quality data is crucial to prevent river water pollution. With limited sampling data, the statistical method of kriging interpolation is indispensable. This method can predict unsampled values based on interconnected surrounding values. Two types of kriging methods that can be applied are Machine Learning (ML) kriging and topological kriging (top-kriging). ML kriging is an extension of ordinary kriging by adding a Super Learning (SL) component. Here, we used SL type Support Vector Regression (SVR). Ordinary Kriging and ML Kriging are based on point values. Top-Kriging is defined as the estimation of streamflow-related variables in ungauged catchments and is based on a non-zero catchment area, not a point value. The three methods were applied in Chemical Oxygen Demand (COD) as water river quality in the Special Region of Yogyakarta (DIY), Indonesia. Based on the Mean Square Error (MSE) and Mean Absolute Error (MAE) comparison, Top kriging provided better accuracy that produced the smallest MSE and MAE. This showed that top kriging is suitable for interpolating data with river flow cases. The interpolation result was that the COD value in the upstream area was low, meaning that the level of organic pollution was minimal. Further downstream, after passing through densely populated residential and industrial areas, the COD values were higher.
Environmental, geotechnical, agriculture, and water resources engineers all rely on accurate measurements of soil moisture content. The most widely used technique for determining soil moisture content is the electromagnetic method, which employs dielectric models to relate soil dielectric properties to its moisture levels. This paper introduces an innovative electromagnetic sensor designed to measure the dielectric properties of moist soil. The dielectric properties of seventeen coarse-grain soil samples and seventy-five samples with both coarse and fine grains at varying moisture contents, textures, and densities were measured. The findings were used to evaluate the effectiveness of the existing most common theoretical and empirical models for soil moisture measurement. The results show that all existing models have difficulties with accurately quantifying the soil moisture content. In response, this study developed three new types of dielectric models: a theoretical volumetric model, a general empirical model that addresses the shortcomings of existing models, and an artificial neural network (ANN) model, which demonstrated a higher potential for accurately predicting soil moisture content. The best new theoretical volumetric model was the power model, with a power of 0.9 for the dielectric constant and 1.4 for the loss factor. The best new general empirical model developed in this study considered soil density, texture, and moisture, achieving correlation coefficients of 97.6% for the dielectric constant and 97.2% for the loss factor. The developed ANN models to predict the dielectric properties of moist soil provided high correlation coefficients of more than 98.5%.
This study employed co-integration methodology to explore the fundamental drivers of carbon price in the European Union Emissions Trading System (EU ETS) during the transition from phase III to phase IV, focusing on the interactions between the carbon market, energy sector, and macroeconomic factors. A novel approach of bilaterally modifying dummy variables was used to include the impact of regulatory event announcements on the EU carbon price. Long-term analysis revealed that coal prices exhibited a positive but statistically insignificant impact, while natural gas and oil prices show substantial and significant influences. Short-term dynamics indicated a self-adjusting mechanism, with gas and oil prices playing crucial roles. We found that the European stock market has a dampening effect on short-term carbon prices, whereas the commodity market exerted a counteractive force. In the energy sector, variations in European gas prices and shifts in nonrenewable electricity generation significantly influence short-term carbon price. Regulatory event announcements exhibited an overall negative and statistically significant trend. However, the relatively small magnitude of these coefficients underscored the modest impact of regulatory event announcements on short-term carbon prices within the EU ETS. Additionally, our study estimated the long-term equilibrium relationship between variables using ridge regression, serving as a proxy for the EU’s equilibrium carbon price. We examined the disparities between this equilibrium and market prices. Two distinct phases emerged in the analysis. Before April 2018, the market price consistently lagged behind the equilibrium, suggesting an overestimation of the value of carbon emission rights. Subsequently, the market price consistently surpassed the equilibrium. In particular, the sharp increase in carbon prices from April to June 2020, driven by the COVID-19 pandemic, highlighted the sensitivity of the EU carbon market to external shocks.