Nonlinear regression for treating adsorption isotherm data to characterize new sorbents: Advantages over linearization demonstrated with simulated and experimental data

Smart Crowding on pH-Induced Elasticity of Weakly Anionic poly(N-Isopropylacrylamide)-Based Semi-Interpenetrating Polymer Networks via Integration of Methacrylic Acid and Linear Polyacrylamide Chains

Weakly anionic semi-interpenetrating polymer networks (semi-IPNs), comprised of copolymer poly(N-isopropylacrylamide-co-methacrylic acid) P(NIPA-MA) and linear poly(acrylamide) (LPA) chains as macromolecular crowding agent, are designed to evaluate pH-induced swelling and elasticity. Uniaxial compression testing after swelling in various pH-conditions is used to analyze the compressive elasticity as a function of swelling pH and LPA-content. The swelling of P(NIPA-MA)/LPA semi-IPNs is strongly pH-dependent due to MA units incorporated into the copolymer network which already exhibits temperature-sensitivity by presence of PNIPA counterpart. Since the behavior of semi-IPNs is a combination of PMA, LPA, and PNIPA moieties, the sensitivity of swelling to external pH can be modified with increasing swelling temperature. At high pH conditions, LPA-doped semi-IPNs show elasticity representing soft and loosely cross-linked structure. Elastic modulus is higher in acidic pH condition due to the less swelling tendency, while in basic pH, the modulus decreases significantly in coordination with swelling. Oscillatory swelling reveals how fast semi-IPNs can respond to environmental pH change (2.1-10.7). By describing adsorption potential of semi-IPNs for cationic methylene blue uptake by pseudo-first-order and Freundlich model, the designed poly(NIPA-MA)/LPA semi-IPNs emerge as promising smart materials in applications requiring rapid response to changes in temperature and pH via diffusional properties.

Metal-organic-framework and walnut shell biochar composites for lead and hexavalent chromium removal from aqueous environments

Extensive research in recent years has explored the realm of porous carbon composites for various applications, including electrochemistry, structural materials, environmental remediation, and more. In particular, the fabrication of porous carbon composites using a metal-organic framework (MOF) and biochar (BC) for aqueous remediation is a fairly new avenue of research. In this study, a MOF-BC composite was synthesized with unmodified and chemically modified BCs using solvothermal synthesis. The composites were used as adsorbents to remediate heavy metals, such as lead (II) and chromium (VI), from aqueous environments. It was verified that the MOF was homogeneously deposited onto the BC's surface using various material characterization techniques. Lead and chromium adsorption studies revealed a high adsorption capacity with greater than 99% removal for lead and ∼65% for chromium, respectively. Impressively, for lead, the highest observed experimental adsorption capacity of the MOF-chemically modified BC composite was 535 mg/g, compared to 240 mg/g for pristine BC. Meanwhile, the adsorption capacity of the same MOF-BC composite for chromium ions was low at 18 mg/g, compared to 80 mg/g for the chemically modified BC. The MOF-BC had a rapid adsorption rate, achieving equilibrium at only 150 min of reaction time for lead ions. MOF-BCs have higher adsorption for cationic lead through physisorption and ion-exchange mechanisms, whereas, for anionic chromium, removal is dominated only by physisorption mechanisms. The outcomes and methodological developments attained in this study offer a novel and compelling approach for synthesizing MOF-BC composites for aqueous remediation applications.

Evaluating the Suitability of Linear and Nonlinear Regression Approaches for the Langmuir Adsorption Model as Applied toward Biomass-Based Adsorbents: Testing Residuals and Assessing Model Validity

Regression analysis is a powerful tool in adsorption studies. Researchers often favor linear regression for its simplicity when fitting isotherm models, such as the Langmuir equation. Validating regression assumptions is crucial to ensure that the model accurately represents the data and allows appropriate inferences. This study provides a detailed examination of assumption checking in the context of adsorption studies while simultaneously evaluating the robustness of linear regression methods for fitting the Langmuir equation to isotherm data from 2,4-dichlorophenol (DCP) adsorption onto various biomass-based adsorbents and activated carbon. Different linearized Langmuir equations (Hanes-Woolf, Lineweaver-Burk, Eadie-Hofstee, and Scatchard) were compared to nonlinear regression, and each method was validated by rigorous residual checking. This included visual plots of residuals as well as statistical tests, including the Durbin-Watson test for autocorrelation (independence), the Shapiro-Wilk test for normality, and the White test for homoscedasticity. Key findings indicate that the Hanes-Woolf (type 1) and Lineweaver-Burk (type 2) linearizations were the best for most biomass adsorbents studied and that Eadie-Hofstee (type 3) and Scatchard (type 4) were generally invalid due to the negative parameters or assumption violations. For activated carbon, all linearization methods were unsuitable due to independence violations. In the case of nonlinear regression, there were no major assumption violations for all of the adsorbents. Symbolic regression identified the Langmuir equation only for activated carbon (AC). This study revealed shortcomings in relying solely on linearized Langmuir models. A proposed workflow recommends using nonlinear or weighted nonlinear regression, starting with Hanes-Woolf or Lineweaver-Burk linearization results as initial values for parameter estimation. If assumptions remain violated with nonlinear techniques, novel methods such as symbolic regression should be employed. This advanced regression technique can improve adsorption models' accuracy and predictive behavior without the stringent need for assumption checking. Symbolic regression can also aid in understanding mechanisms of novel adsorbents.

Metal sequestration by Microcystis extracellular polymers: a promising path to greener water treatment

The problem of heavy metal pollution in water bodies poses a significant threat to both the environment and human health, as these toxic substances can persist in aquatic ecosystems and accumulate in the food chain. This study investigates the promising potential of using Microcystis aeruginosa extracellular polymeric substances (EPS) as an environmentally friendly, highly efficient solution for capturing copper (Cu2+) and nickel (Ni2+) ions in water treatment, emphasizing their exceptional ability to promote green technology in heavy metal sequestration. We quantified saccharides, proteins, and amino acids in M. aeruginosa biomass and isolated EPS, highlighting their metal-chelating capabilities. Saccharide content was 36.5 mg g-1 in biomass and 21.4 mg g-1 in EPS, emphasizing their metal-binding ability. Proteins and amino acids were also prevalent, particularly in EPS. Scanning electron microscopy (SEM) revealed intricate 3D EPS structures, with pronounced porosity and branching configurations enhancing metal sorption. Elemental composition via energy dispersive X-ray analysis (EDAX) identified essential elements in both biomass and EPS. Fourier transform infrared (FTIR) spectroscopy unveiled molecular changes after metal treatment, indicating various binding mechanisms, including oxygen atom coordination, π-electron interactions, and electrostatic forces. Kinetic studies showed EPS expedited and enhanced Cu2+ and Ni2+ sorption compared to biomass. Thermodynamic analysis confirmed exothermic, spontaneous sorption. Equilibrium biosorption studies displayed strong binding and competitive interactions in binary metal systems. Importantly, EPS exhibited impressive maximum sorption capacities of 44.81 mg g-1 for Ni2+ and 37.06 mg g-1 for Cu2+. These findings underscore the potential of Microcystis EPS as a highly efficient sorbent for heavy metal removal in water treatment, with significant implications for environmental remediation and sustainable water purification.

Preparation and study of straw porous biochar with aromatic ring structure for adsorption performance and mechanism toward TNT red water

2,4,6-Trinitrotoluene (TNT) production processes generate a substantial amount of toxic wastewater. Therefore, it is crucial to identify efficient and sustainable methods for treating this wastewater. This paper explores the application of sustainable biomass-derived carbon produced from rice straw for the adsorption of 2,4,6-trinitrotoluene (TNT) red water. The rice straw-derived biochar (SBC) materials were synthesized by two-step reactions through hydrothermal carbonization and chemical activation with KOH. Characterization of the fabricated biochar was conducted using various techniques. Here, the chemical oxygen demand (COD) was used as an evaluation index for adsorption efficiency. The adsorption kinetics showed a good fit with the pseudo-second-order model, and the adsorption equilibrium was achieved in 30 min. The biochar's high surface area (1319 m2/g) and large pore volume (1.058 cm3/g) gave it a large adsorption capacity. The Langmuir model exhibited better correlation for equilibrium data analysis, with a maximum adsorption capacity of 173.9 mg/g at 298 K. The SBC was found to have a high removal effect over a wide pH range (from 1 to 13) and showed remarkable stability after undergoing five desorption-adsorption cycles using ethanol and acetone as eluent. The results provide a simple and low-cost method for the efficient treatment of TNT red water.