Acid Yellow 36, Methyl Red, and Methylene Blue adsorption using ammonia-modified red algae biochar: isotherm,...

This paper examined the removal of Acid Yellow 36 (AY36), Methyl Red (MR), and Methylene Blue (MB) dyes using a novel Ammonia-decorated Red Algae Biochar (RAB-A) synthesized from red algae (Pterocladia capillacea) via a reflux technique in the presence of 25% ammonium hydroxide (NH₄OH). The physicochemical properties of the synthesized RAB-A, including its surface area, morphology, functional groups, elemental composition, and thermal stability, were comprehensively characterized through Brunauer-Emmett-Teller (BET) analysis, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) integrated with energy-dispersive X-ray (EDX) analysis, and thermogravimetric analysis (TGA). RAB-A demonstrated a low specific surface area (3.262 m²/g) and a monolayer adsorption capacity of 0.7495 cm³ (STP)/g. The adsorbent demonstrated an overall pore volume of 0.011 cm³/g, accompanied by an average pore diameter of 13.648 nm. Thermogravimetric analysis revealed an overall mass loss of 40.84% for RAB-A, demonstrating enhanced thermal stability relative to RAB, which showed a weight loss of 51.05%. FTIR analysis confirmed the presence of diverse functional moieties on the surface of RAB-A. Adsorption experiments targeting Acid Yellow 36 (AY36), Methyl Red (MR), and Methylene Blue (MB) were conducted in batch mode by independently adjusting the initial dye concentration (100-200 mg/L), contact time (5-180 min), solution pH (2-12), and adsorbent dosage (0.5-1.5 g/L). The adsorption equilibrium behavior was best described by the Langmuir isotherm, which indicated maximum uptake capacities of 222.22 mg/g for AY36, 192.31 mg/g for MR dye, and 833.33 mg/g for MB dye. Kinetic analyses revealed that the adsorption of all examined dyes was best described by a pseudo-second-order model, thereby demonstrating the high suitability of the synthesized RAB-A for the efficient elimination of dyes from aqueous solutions. Additionally, adsorption was predicted and adjusted utilizing artificial neural networks (ANN).