Synthesis, characterization and an application of graphene oxide nanopowder: methylene blue adsorption and comparison between experimental data and literature data

Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes

This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene under conditions that were determined to be optimal. These conditions included a reaction temperature of 550 °C, a reaction time of 37.3 min, and a gas ratio (H2/C2H2) of 1.0. The experimental data pertaining to MB adsorption on CNTs was found to be extremely well-suited to the Pseudo-second-order model, as evidenced by an R2 value of 0.998, an X2 value of 5.75, a qe value of 163.93 (mg/g), and a K2 value of 6.34 × 10-4 (g/mg min).The MB adsorption system exhibited the best agreement with the Langmuir model, yielding an R2 of 0.989, RL value of 0.031, qm value of 250.0 mg/g. The results of AI modelling demonstrated a remarkable performance using a recurrent neural network, achieving with the highest correlation coefficient of R2 = 0.9471. Additionally, the feed-forward neural network yielded a correlation coefficient of R2 = 0.9658. The modeling results hold promise for accurately predicting the adsorption capacity of CNTs, which can potentially enhance their efficiency in removing methylene blue from wastewater.

Study on the Dye Removal from Aqueous Solutions by Graphene-Based Adsorbents

In the current investigation, the removal efficiency regarding a cationic dye, methylene blue (MB), from three graphene-based materials was investigated. The materials' characterization process involved instrumental methods such as XRD, XPS, SEM, TEM, FTIR, and nitrogen adsorption at 77 K. The survey examined how various process factors influenced the ability of the studied materials to adsorb cationic dyes. These parameters encompassed contact time, initial dye concentrations, solution pH, and temperature. The adsorption procedure was effectively explained through the application of pseudo-second-order and Langmuir models. The maximum adsorption capacity for the best adsorbent at 293 K was found to be 49.4 mg g-1. In addition, the study also determined the entropy, enthalpy, and Gibbs free energy values associated with the removal of MB and showed that the adsorption of MB is endothermic, feasible, and spontaneous. The results also revealed that the studied materials are suitable adsorbents for the removal of cationic dyes.

Industrial applications of immobilized nano-biocatalysts

Immobilized enzyme-based catalytic constructs could greatly improve various industrial processes due to their extraordinary catalytic activity and reaction specificity. In recent decades, nano-enzymes, defined as enzyme immobilized on nanomaterials, gained popularity for the enzymes' improved stability, reusability, and ease of separation from the biocatalytic process. Thus, enzymes can be strategically incorporated into nanostructured materials to engineer nano-enzymes, such as nanoporous particles, nanofibers, nanoflowers, nanogels, nanomembranes, metal-organic frameworks, multi-walled or single-walled carbon nanotubes, and nanoparticles with tuned shape and size. Surface-area-to-volume ratio, pore-volume, chemical compositions, electrical charge or conductivity of nanomaterials, protein charge, hydrophobicity, and amino acid composition on protein surface play fundamental roles in the nano-enzyme preparation and catalytic properties. With proper understanding, the optimization of the above-mentioned factors will lead to favorable micro-environments for biocatalysts of industrial relevance. Thus, the application of nano-enzymes promise to further strengthen the advances in catalysis, biotransformation, biosensing, and biomarker discovery. Herein, this review article spotlights recent progress in nano-enzyme development and their possible implementation in different areas, including biomedicine, biosensors, bioremediation of industrial pollutants, biofuel production, textile, leather, detergent, food industries and antifouling.