kinetic models
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Adel Adly ◽  
Nagwan G. Mostafa ◽  
Abdelsalam Elawwad

Abstract This study investigated removal mechanisms, thermodynamics, and interferences of phosphorus adsorption onto nanoscale zero-valent iron (nZVI)/activated carbon composite. Activated carbon was successfully used as support for nZVI particles to overcome shortcomings of using nZVI include its tendency to aggregate and separation difficulties. A comprehensive characterization was done for the composite particles, which revealed a high specific surface area of 72.66 m2/g and an average particle size of 37 nm. Several adsorption isotherms and kinetic models have been applied to understand the removal mechanisms. Adsorption isotherm is best fitted by Freundlich and Langmuir models, which indicates that the estimated maximum phosphorus adsorption capacity is 53.76 mg/g at pH 4. Adsorption kinetics showed that the chemisorption process behaved according to a pseudo-second-order model. An adsorption mechanism study conducted using the intra-particle diffusion and Boyd kinetic models indicated that the adsorption rate is limited by surface diffusion. A thermodynamic study showed that phosphorus removal efficiency increased as the solution temperature increased from 15 to 37 °C. Finally, the results of an interference study showed that the presence of Ni2+, Cu2+, Ca2+, Na+ cations, nitrate ions (), and sodium acetate improves removal efficiency, while the presence of sulfate ions () and urea reduces removal efficiency.

Vânia Queiroz ◽  
Daniel Souza de Almeida ◽  
Gabriel Henrique de Oliveira Miglioranza ◽  
Evandro Steffani ◽  
Elisa Barbosa-Coutinho ◽  

2022 ◽  
Vol 16 (1) ◽  
pp. 137
Thanabalan Pitchay ◽  
Ali H. Jawad ◽  
Ili Syazana Johari ◽  
Sumiyyah Sabar

Immobilised chitosan on glass plates was used as an adsorbent for metallic ions from aqueous solutions in a batch adsorption system. Experiments were carried out as a function of contact time and initial metallic ions concentration. The adsorption efficiency increased with increasing initial metallic ions concentration (5 – 20 mg L-1) and the observed trend was: Ag2+ > Cu2+ > Ni2+ > Fe3+ > Cd2+ > Zn2+. The experimental data were fitted to pseudo-first, pseudo-second-order, intra-particle, and liquid film diffusion kinetic models. The applicability of the pseudo-second-order kinetic model indicated that the adsorption behaviour was ascribed by chemisorption. Further data analysis by the diffusion kinetic models suggested that the metallic ions adsorption was controlled by more than one step; adsorption at the active sites, intra-particle, and liquid film diffusion.

2022 ◽  
Subham Choudhury ◽  
Michael Moret ◽  
Pierre Salvy ◽  
Daniel Weilandt ◽  
Vassily Hatzimanikatis ◽  

Kinetic models of metabolic networks relate metabolic fluxes, metabolite concentrations, and enzyme levels through well-defined mechanistic relations rendering them an essential tool for systems biology studies aiming to capture and understand the behavior of living organisms. However, due to the lack of information about the kinetic properties of enzymes and the uncertainties associated with available experimental data, traditional kinetic modeling approaches often yield only a few or no kinetic models with desirable dynamical properties making the computational analysis unreliable and computationally inefficient. We present REKINDLE (REconstruction of KINetic models using Deep LEarning), a deep-learning-based framework for efficiently generating large-scale kinetic models with dynamic properties matching the ones observed in living organisms. We showcase REKINDLE's efficiency and capabilities through three studies where we: (i) generate large populations of kinetic models that allow reliable in silico testing of hypotheses and systems biology designs, (ii) navigate the phenotypic space by leveraging the transfer learning capability of generative adversarial networks, demonstrating that the generators trained for one physiology can be fine-tuned for another physiology using a low amount of data, and (iii) expand upon existing datasets, making them amenable to thorough computational biology and data-science analyses. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate novel kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in health, biotechnology, and systems and synthetic biology. REKINDLE is available as an open-access tool.

2022 ◽  
Keunsoo Kim ◽  
Paxton W. Wiersema ◽  
Je Ir Ryu ◽  
Eric Mayhew ◽  
Jacob Temme ◽  

2022 ◽  
Vol 43 (1) ◽  
pp. 2-10
Yongting Chen ◽  
Junxiang Chen ◽  
Shengli Chen

2021 ◽  
pp. 004051752110661
M Khairy ◽  
R Kamal ◽  
MA Mousa

Nanoparticle materials have received increasing attention in the functional modification of textiles. In this work, pure TiO2, Ag-doped TiO2, Fe-doped TiO2, and graphene oxide nanoparticles were used to impart the anti-bacterial and adsorptive properties of nanoparticles to cotton fabric. The treated fabric materials were investigated by X-ray diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy. The obtained treated fabrics were used as adsorbents for the removal of methylene blue from aqueous solution. The functionalized cotton fabrics were tested for their anti-microbial capability against Escherichia coli, Bacillus cereus, and Candida albicans. All the functionalized fabrics have higher anti-microbial activity compared to untreated cotton, especially the fabrics containing silver and Fe-doped TiO2. The optimum conditions of the adsorption process are determined via the study of the effect of the initial concentration of dye, pH, and contact time on the removal efficiency. Langmuir, Freundlich, and Temkin isotherms are applied for the equilibrium adsorption data. GO-Cot and [email protected] samples showed the highest adsorption removal activity. The linear correlation coefficient ( R2) showed that the Temkin model well fitted the data of adsorption in the GO-Cot sample. The analysis of experimental data with different kinetic models showed that the pseudo-second-order kinetic model well fitted the adsorption data better than the other kinetic models of the pseudo-first-order, Elovich, and intra-particle diffusion.

Pharmaceutics ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2100
Luis Castillo-Henríquez ◽  
Pablo Sanabria-Espinoza ◽  
Brayan Murillo-Castillo ◽  
Gabriela Montes de Oca-Vásquez ◽  
Diego Batista-Menezes ◽  

Chronic and non-healing wounds demand personalized and more effective therapies for treating complications and improving patient compliance. Concerning that, this work aims to develop a suitable chitosan-based thermo-responsive scaffold to provide 24 h controlled release of Dexketoprofen trometamol (DKT). Three formulation prototypes were developed using chitosan (F1), 2:1 chitosan: PVA (F2), and 1:1 chitosan:gelatin (F3). Compatibility tests were done by DSC, TG, and FT-IR. SEM was employed to examine the morphology of the surface and inner layers from the scaffolds. In vitro release studies were performed at 32 °C and 38 °C, and the profiles were later adjusted to different kinetic models for the best formulation. F3 showed the most controlled release of DKT at 32 °C for 24 h (77.75 ± 2.72%) and reduced the burst release in the initial 6 h (40.18 ± 1.00%). The formulation exhibited a lower critical solution temperature (LCST) at 34.96 °C, and due to this phase transition, an increased release was observed at 38 °C (88.52 ± 2.07% at 12 h). The release profile for this formulation fits with Hixson–Crowell and Korsmeyer–Peppas kinetic models at both temperatures. Therefore, the developed scaffold for DKT delivery performs adequate controlled release, thereby; it can potentially overcome adherence issues and complications in wound healing applications.

2021 ◽  
Vol 2129 (1) ◽  
pp. 012068
Jillin Soo Ai Lam ◽  
Noor Fazliani Shoparwe ◽  
Nurulbahiyah Ahmad Khairudin ◽  
Lian See Tan ◽  
Kee Quen Lee

Abstract Electrocoagulation (EC) is a reliable technology for wastewater treatment. It has been applied in treating various source of wastewater from tannery, electroplating, dairy, textile processing, oil and oil-in-emulsion. It is crucial to strengthen the fundamental of the EC treatment on oily water sample for further studies. However, in depth studies on the performance of EC treatment on oily water sample is still requires in depth studies. In this research, a series of experiment has been conducted on the performance of EC treatment including effect of the amount of sodium chloride (NaCl), applied voltage and pH to determine the efficiency in oil removal. The EC treatment took placed in room temperature and constantly agitated for 30 minutes meanwhile samples were collected for every 5 minutes for UV–Vis analysis. Then, the efficiency of the treatment was determined followed by simulating the results in kinetic models. The highest efficiency of EC treatment was achieved with 89.26% of oil removal with the addition of 7.5g of NaCl, 4V of applied voltage and at pH 6. In addition, the results have better fitness towards pseudo second order (PSO) which indicates the mechanism of EC treatment is chemisorption.

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