Andriy Anta Kacaribu

The discharge of laundry wastewater containing surfactants and phosphates represents a growing environmental concern, particularly in areas where wastewater is released without adequate treatment. This study evaluates sulfuric acid–activated rice husk ash (RHA) as a low-cost adsorbent for removing surfactants and phosphates from real laundry wastewater. FTIR and XRD analyses indicated that the main silica-related functional groups and framework of RHA were largely retained after acid activation, while SEM observations suggested a rougher and more heterogeneous surface morphology. Batch adsorption experiments using real laundry wastewater showed that surfactant and phosphate removal were influenced by adsorbent dosage and contact time. The optimum dosage was 3 g for surfactant removal and 4 g for phosphate removal, while the optimum contact time for both contaminants was 30 min. Controlled isotherm experiments using separate single-solute surfactant and phosphate standard solutions indicated that the adsorption behavior of both contaminants was better described by the Freundlich model, suggesting adsorption on heterogeneous surface sites. Kinetic analysis using real laundry wastewater showed rapid uptake, with the Elovich model providing the best overall fit for both surfactant and phosphate adsorption. These findings suggest that sulfuric acid–activated RHA is a promising, simple, and affordable biomass-derived adsorbent for laundry wastewater treatment, although further studies involving surface area analysis, regeneration, reusability, column operation, and cost performance are required to support practical application.

Introduction: α-Glucosidase inhibitors (AGIs) are essential for controlling postprandial hyperglycemia, with flavonoids representing a major class of natural AGIs. However, conventional quantitative structure–activity relationship (QSAR) models trained on structurally diverse datasets often exhibit limited predictive reliability for natural flavonoids due to chemical space mismatch. This study aimed to develop and optimize Random Forest–based QSAR models derived from ChEMBL AGIs and to evaluate their chemotype-specific applicability to natural flavonoids.Methods: ChEMBL-derived inhibitors were curated into two datasets: (i) a general scaffold-diverse dataset and (ii) a flavonoid-specific dataset. A custom RDKit-based pipeline employing SMARTS pattern recognition and fingerprint similarity automatically classified flavonoid chemotypes while excluding nonphenolic compounds. Molecular descriptors were calculated using Mordred. Regression and classification models were constructed with 10-fold cross-validation. External validation was performed using 15 literature-reported natural flavonoids categorized into flavones/flavonols (aglycones), flavonol glycosides, isoflavones, and flavan-3-ols.Results: The general model (n = 563) demonstrated strong regression performance (R2 = 0.837) and classification accuracy (0.915). The flavonoid-specific model showed moderate regression (R2 = 0.564) and classification accuracy (0.880). However, external validation revealed superior predictive reliability of the flavonoid-specific model, particularly for catechins (MAE = 0.182) and aglycones (MAE = 0.233). Predictive performance decreased for glycosides and isoflavones.Conclusion: Chemotype-focused QSAR modeling enhances predictive reliability for natural flavonoids. The optimized flavonoid-specific model is suitable for predicting pIC50 of flavones and flavonols but should be applied cautiously to glycosylated or structurally divergent subclasses.

Magnetic-activated biocarbon (MAB) composite has been prepared from red algae and applied to remove methylene blue (MB) and Remazol brilliant yellow FG (RBY) dyes from water. Biocarbon prepared from red algae, following synthesized of magnetic biocarbon composites with nickel chloride hexahydrate...

This study developed a colorimetric sensor using an Arduino Uno-based Traction Control System 3200 (TCS3200) color sensor and silver nanoparticles stabilized by polyvinyl-pyrrolidone (AgNPs/PVP) for detecting Hg(II) ions in river water samples from Aceh Selatan Regency. AgNPs/PVP exhibited...

Pump that operates automatically by relying on natural power from water without using great cost and trouble by pumping hydram. Hydram pumps are one of the types of pumps that are driven by kinetic power, it was discovered by a 1796 Montgolfier in Italy. The purpose is to determine the most optimal water speed of the hydram pump by using different numbers of pressure tubes and several check valves. This research uses a factorial Randomised Group Design (RGD) method. The first treatment is valve variation and the second is variation in the number of tubes. The results showed that the average river water flow velocity was 0.154 meters/second with an average water discharge of 2.61 m3/second under normal flow conditions. The average water flow in the intake pipe is 0.717 meters / second and the volume of water in the pipe is 15.19 liters with an average water discharge entering the intake pipe is 10.804 m3 / second. A combination of three valves and three tube treatments consistently produces greater water discharge than other combinations. The highest efficiency of the hydram pump is found in the combination of three checks.