Embedding of L–Arginine into graphene oxide (GO) for endotoxin removal from water: Modeling and optimization approach

Tapouk, Fahim Amini; Nabizadeh, Ramin; Nasseri, Simin; Mesdaghinia, Alireza; Khorsandi, Hassan; Yousefi, Mahmood; Alimohammadi, Mahmood; Khoobi, Mehdi

doi:10.1016/j.colsurfa.2020.125491

Cited by 30 publications

(6 citation statements)

References 67 publications

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“…Apart from natural ligands, several synthetic materials have also been developed. Poly(1-vinylimidazole), allantoin crystals, chitosan, − poly-ε-caprolactone, imidazolium-type polyurethane, and amino acids − have been reported for LPS clearance. Unfortunately, although these synthetic materials exhibited LPS clearance capability to a certain extent, they generally suffer from poor selectivity toward LPSs when it comes to complicated biosamples, severely limiting their further applications in blood purification.…”

Section: Introductionmentioning

confidence: 99%

Broad-Spectrum Clearance of Lipopolysaccharides from Blood Based on a Hemocompatible Dihistidine Polymer

Chen,

Shi,

Yang

et al. 2023

ACS Appl. Mater. Interfaces

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Blood infection can release toxic bacterial lipopolysaccharides (LPSs) into bloodstream, trigger a series of inflammatory reactions, and eventually lead to multiple organ dysfunction, irreversible shock, and even death, which seriously threatens human life and health. Herein, a functional block copolymer with excellent hemocompatibility is proposed to enable broad-spectrum clearance of LPSs from whole blood blindly before pathogen identification, facilitating timely rescue from sepsis. A dipeptide ligand of histidine–histidine (HH) was designed as the LPS binding unit, and poly[(trimethylamine N-oxide)-co-(histidine–histidine)], a functional block copolymer combining the LPS ligand of HH and a zwitterionic antifouling unit of trimethylamine N-oxide (TMAO), was then designed by reversible addition–fragmentation chain transfer (RAFT) polymerization. The functional polymer achieved effective clearance of LPSs from solutions and whole blood in a broad-spectrum manner and had good antifouling and anti-interference properties and hemocompatibility. The proposed functional dihistidine polymer provides a novel strategy for achieving broad-spectrum clearance of LPSs, with potential applications in clinical blood purification.

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Section: Introductionmentioning

confidence: 99%

Broad-Spectrum Clearance of Lipopolysaccharides from Blood Based on a Hemocompatible Dihistidine Polymer

Chen,

Shi,

Yang

et al. 2023

ACS Appl. Mater. Interfaces

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“…Bingöl et al [121] carried out a comparison between the multiple linear regression and the adaptive neuro-fuzzy inference system (ANFIS) for the cadmium adsorption with date palm seeds. This analysis considered 20 experiments to 22 Adsorption Science & Technology assess the effect of adsorbent mass (0.05-0.5 g), initial adsorbate concentration (5-100 mg/L), and pH (2-6) on the adsorption capacity (0.01-4.18 mg/g). ANFIS was trained with 50% of the experimental data, and the remaining 50% was utilized in testing.…”

Section: °Cmentioning

confidence: 99%

“…Experimental studies with batch adsorbers allow to quantify the adsorption kinetics and isotherms as well as other important thermodynamic parameters associated to the adsorbent performance. Batch adsorbers are useful for establishing the maximum adsorption capacities for the adsorbate(s)-adsorbent system under ideal and controlled conditions since the experimental data are obtained at the thermodynamic equilibrium where the mass transfer resistances are reduced [21, 22]. Note that the adsorption processes in batch condition are not commonly employed for the treatment of real fluids at large scale since equipment with significant dimensions and long operating times is required.…”

Section: Introductionmentioning

confidence: 99%

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

Reynel-Ávila

Aguayo‐Villarreal

Díaz-Muñoz

et al. 2022

Adsorption Science & Technology

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The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

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“…The combination of graphene-based materials with TiO 2 appears to be a promising candidate since it can serve as a buffer against particle aggregation as well as facilitate the transfer of electron-hole pairs generated by photons [ 23 ]. As a result of its high surface area, low mass, high electrical conductivity, and a large number of functional groups, such as carboxyls, hydroxyls, and epoxy groups, graphene oxide (GO) is a very attractive compound because of its excellent properties [ 24 , 25 ]. However, after the photocatalyst process, the used TiO 2 is commonly recovered through tedious filtration or high-speed centrifugation in order to prevent secondary pollution in the source, which is highly inconvenient, not economically feasible, and difficult to operate.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and characterization of rGO/Fe0/Fe3O4/TiO2 nanocomposite and application of photocatalytic process in the decomposition of penicillin G from aqueous

Mehralipour¹,

Bagheri²,

Gholami³

2023

Heliyon

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Embedding of L–Arginine into graphene oxide (GO) for endotoxin removal from water: Modeling and optimization approach

Cited by 30 publications

References 67 publications

Broad-Spectrum Clearance of Lipopolysaccharides from Blood Based on a Hemocompatible Dihistidine Polymer

Broad-Spectrum Clearance of Lipopolysaccharides from Blood Based on a Hemocompatible Dihistidine Polymer

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

Synthesis and characterization of rGO/Fe0/Fe3O4/TiO2 nanocomposite and application of photocatalytic process in the decomposition of penicillin G from aqueous

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