Adsorption Capacity Prediction and Optimization of Electrospun Nanofiber Membranes for Estrogenic Hormone Removal Using Machine Learning Algorithms DOI Creative Commons
Muhammad Yasir,

Hamza Ul Haq,

Muhammad Nouman Aslam Khan

et al.

Polymers for Advanced Technologies, Journal Year: 2024, Volume and Issue: 35(11)

Published: Nov. 1, 2024

ABSTRACT This study focuses on developing four machine learning (ML) models (Gaussian process regression (GPR), support vector (SVM), decision tree (DT), and ensemble (ELT)) optimized hyperparameters tuned via genetic algorithm (GA) particle swarm optimization (PSO) to analyze predict the adsorption capacity of estrogenic hormones. These hormones are a serious cause fish femininity various forms cancer in humans. Their electrospun nanofibers offers sustainable relatively environmentally friendly solution compared nanoparticle adsorbents, which require secondary treatment. The intricate task is find relationship between input parameters obtain optimum conditions, requires an efficient ML model. GPR integrated GA hybrid model performed most accurate precise results with R 2 = 0.999 RMSE 2.4052e −06 , followed by ELT (0.9976 4.3458e −17 ), DT (0.9586 2.4673e −16 SVM (0.7110 0.0639). 2D 3D partial dependence plots showed temperature, dosage, initial concentration, contact time, pH as vital parameters. Additionally, Shapley's analysis further revealed time dosage sensitive Finally, user‐friendly graphical user interface (GUI) was developed predictor utilizing (GPR‐GA), were experimentally validated maximum error < 3.3% for all tests. Thus, GUI can legitimately work any desired material given conditions efficiently monitor removal concentration simultaneously at wastewater treatment plants.

Language: Английский

Endocrine disruptor (17 β-estradiol) removal by poly pyrrole-based molecularly imprinted polymer: kinetic, isotherms and thermodynamic studies DOI Creative Commons

Samaneh Mohebbi,

Aram Dokht Khatibi,

Davoud Balarak

et al.

Applied Water Science, Journal Year: 2025, Volume and Issue: 15(2)

Published: Feb. 1, 2025

This study focuses on the synthesis and characterization of molecularly imprinted polymer (PPy-MIP) to remove 17β-Estradiol (E2) from aqueous solutions. The MIP was synthesized using a non-covalent procedure, incorporating target compound, E2. To PPy-MIP, mixture 300 μl pyrrole 50 ml distilled water stirred for 30 min. After adding 3 g ferric chloride as an oxidant, solution mixed 2 h stored 48–72 h. capability is compared with non-molecularly (NIP) control. Various factors such pH, contact time, dosage, temperature, concentration were investigated optimize performance PPy-MIP. structure confirmed field emission scanning electron microscopy (FESEM), infrared spectrophotometric spectrum (FTIR), X-ray diffraction (XRD). efficiency PPy-MIP in removing E2 obtained 99.97% at optimum condition; while, NIP achieved removal 69.9%. Adsorption data fitted Langmuir isotherms (R2 0.98) pseudo-second-order kinetics 0.99). selectivity toward similar compounds progesterone cholesterol also examined. understand adsorption process, thermodynamics, kinetics, isotherm studies performed. showed good reproducibility only slight decrease after multiple absorption reabsorption cycles. by followed second-order kinetics. utilized pre-concentrate separate real samples (urine, blood, hospital wastewater, tap water). method shows promise efficient selective

Language: Английский

Citations

1
The Application of Multifunctional Metal–Organic Frameworks for the Detection, Adsorption, and Degradation of Contaminants in an Aquatic Environment DOI Creative Commons

Yachen Liu,

Jinbin Yang,

Junlin Wu

et al.

Molecules, Journal Year: 2025, Volume and Issue: 30(6), P. 1336 - 1336

Published: March 17, 2025

Water pollution poses a severe threat to both aquatic ecosystems and human health, highlighting the crucial importance of monitoring regulating its levels in water bodies. In contrast traditional single-treatment approaches, multiple-treatment methods enable simultaneous detection removal pollutants using single material. This innovation not only offers convenience but also fosters more holistic effective approach remediation. Metal–organic frameworks (MOFs) are versatile porous materials that offer significant potential for use wastewater treatment. article examines latest developments application MOFs multifaceted used removal, or degradation contaminants. Some exhibited different functions contaminants, some showed one function (adsorption detection) than contaminant. All multifunctional facilitate multiple treatment real wastewater. Lastly, existing challenges future outlooks concerning MOF addressed this paper.

Language: Английский

Citations

1
Engineering carbon materials for organic pollutant removal via adsorption and photodegradation: A review DOI
Yu-Chen Huang, Yidan Luo,

Zugen Liu

et al.

Separation and Purification Technology, Journal Year: 2024, Volume and Issue: unknown, P. 130872 - 130872

Published: Dec. 1, 2024

Language: Английский

Citations

4
Simultaneous removal of E1, E2, EE2 and Levonorgestrel from water using TiO2 catalyst anchored on activated carbon: Processes optimization, materials characterization, and assessment of the estrogenicity reduction DOI
Marina Meloni Gória Pastre, Rodrigo Coutinho,

Marina Renno

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 263, P. 120173 - 120173

Published: Oct. 18, 2024

Language: Английский

Citations

1
Adsorption Capacity Prediction and Optimization of Electrospun Nanofiber Membranes for Estrogenic Hormone Removal Using Machine Learning Algorithms DOI Creative Commons
Muhammad Yasir,

Hamza Ul Haq,

Muhammad Nouman Aslam Khan

et al.

Polymers for Advanced Technologies, Journal Year: 2024, Volume and Issue: 35(11)

Published: Nov. 1, 2024

ABSTRACT This study focuses on developing four machine learning (ML) models (Gaussian process regression (GPR), support vector (SVM), decision tree (DT), and ensemble (ELT)) optimized hyperparameters tuned via genetic algorithm (GA) particle swarm optimization (PSO) to analyze predict the adsorption capacity of estrogenic hormones. These hormones are a serious cause fish femininity various forms cancer in humans. Their electrospun nanofibers offers sustainable relatively environmentally friendly solution compared nanoparticle adsorbents, which require secondary treatment. The intricate task is find relationship between input parameters obtain optimum conditions, requires an efficient ML model. GPR integrated GA hybrid model performed most accurate precise results with R 2 = 0.999 RMSE 2.4052e −06 , followed by ELT (0.9976 4.3458e −17 ), DT (0.9586 2.4673e −16 SVM (0.7110 0.0639). 2D 3D partial dependence plots showed temperature, dosage, initial concentration, contact time, pH as vital parameters. Additionally, Shapley's analysis further revealed time dosage sensitive Finally, user‐friendly graphical user interface (GUI) was developed predictor utilizing (GPR‐GA), were experimentally validated maximum error < 3.3% for all tests. Thus, GUI can legitimately work any desired material given conditions efficiently monitor removal concentration simultaneously at wastewater treatment plants.

Language: Английский

Citations

1