Statistical physical modeling insights for urinary analgesic drug adsorption on carbon nanomaterial derivative.

The presence of phenazopyridine in water is an environmental problem that can cause damage to human health and the environment. However, few studies have reported the adsorption of this emerging contaminant from aqueous matrices. Furthermore, existing research explored only conventional modeling to describe the adsorption phenomenon without understanding the behavior at the molecular level. Herein, the statistical physical modeling of phenazopyridine adsorption into graphene oxide is reported. Steric, energetic, and thermodynamic interpretations were used to describe the phenomenon that controls drug adsorption. The equilibrium data were fitted by mono, double, and multi-layer models, considering factors such as the numbers of phenazopyridine molecules by adsorption sites, density of receptor sites, and half saturation concentration. Furthermore, the statistical physical approach also calculated the thermodynamic parameters (free enthalpy, internal energy, Gibbs free energy, and entropy). The maximum adsorption capacity at the equilibrium was reached at 298 K (510.94 mg g-1). The results showed the physical meaning of adsorption, indicating that the adsorption occurs in multiple layers. The temperature affected the density of receptor sites and half saturation concentration. At the same time, the adsorbed species assumes different positions on the adsorbent surface as a function of the increase in the temperature. Meanwhile, the thermodynamic functions revealed increased entropy with the temperature and the equilibrium concentration.

Experimental and modeling of potassium diclofenac uptake on activated carbon.

The presence of pharmaceuticals in wastewater resulting from human activities has driven researchers to explore effective treatment methods such as adsorption using activated carbon (AC). While AC shows promise as an adsorbent, further studies are essential to comprehend its entire interaction with pharmaceuticals. This article investigates the adsorption of potassium diclofenac (PD) onto AC using experimental and modeling approaches. Batch adsorption studies coupled with Fourier transform infrared spectroscopy (FTIR) were employed to clarify the adsorption mechanism of PD on AC. Various kinetic and isotherm adsorption models were applied to analyze the adsorbent-adsorbate interaction. The kinetics were best described by Avrami's fractional order (AFO) nonlinear model. Also, the intraparticle diffusion (IP) model reveals a three-stage adsorption process. The experimental equilibrium data fitted well with the three-parameter nonlinear Liu model, indicating a maximum adsorption capacity (Qmax) of 88.45 mg g-1 and suggesting monolayer or multilayer adsorption. Thermodynamic analysis showed favorable adsorption (ΔG° < 0), with an enthalpy change (ΔH° = -30.85 kJ mol-1) characteristic of physisorption involving hydrogen bonds and π-π interactions. The adsorption mechanism was attributed to forming a double layer (adsorbate-adsorbent and adsorbate-adsorbate).

Adsorptive features of cyclohexane carboxylic naphthenic acid on a novel cross-linked polymer developed from spent coffee grounds.

Naphthenic acids (NA) are organic compounds commonly found in crude oil and produced water, known for their recalcitrance and toxicity. This study introduces a new adsorbent, a polymer derived from spent coffee grounds (SCGs), through a straightforward cross-linking method for removing cyclohexane carboxylic acid as representative NA. The adsorption kinetics followed a pseudo-second-order model for the data (0.007 g min-1 mg-1), while the equilibrium data fitted the Sips model ( q m = 140.55 mg g-1). The process's thermodynamics indicated that the target NA's adsorption was spontaneous and exothermic. The localized sterical and energetic aspects were investigated through statistical physical modeling, which corroborated that the adsorption occurred indeed in monolayer, as suggested by the Sips model, but revealed the contribution of two energies per site ( n 1 ; n 2 ). The number of molecules adsorbed per site ( n ) was highly influenced by the temperature as n 1 decreased with increasing temperature and n 2 increased. These results were experimentally demonstrated within the pH range between 4 and 6, where both C6H11COO-(aq.) and C6H11COOH(aq.) species coexisted and were adsorbed by different energy sites. The polymer produced was naturally porous and amorphous, with a low surface area of 20 to 30 m2 g-1 that presented more energetically accessible sites than other adsorbents with much higher surface areas. Thus, this study shows that the relation between surface area and high adsorption efficiency depends on the compatibility between the energetic states of the receptor sites, the speciation of the adsorbate molecules, and the temperature range studied.

Machine learning modeling and additive explanation techniques for glutathione production from multiple experimental growth conditions of Saccharomyces cerevisiae.

Glutathione (GSH) production is of great industrial interest due to its essential properties. This study aimed to use machine learning (ML) methods to model GSHproduction under different growth conditions of Saccharomyces cerevisiae, namely cultivation time, culture volume, pressure, and magnetic field application. Different ML and regression models were evaluated for their statistics to select the most robust model. Results showed that eXtreme Gradient Boosting (XGB) was the best predictive performance model. From the best model, additive explanation techniques were used to identify the feature importance of process. According to variable analysis, the best conditions to obtain the highest GSH concentrations would be cultivation times of 72-96 h, low magnetic field intensity (3.02 mT), low pressure (0.5 kgf.cm-2), and high culture volume (3.5 L). XGB use and additive explanation techniques proved promising for determining process optimization conditions and selecting the essential process variables.