Fixed bed column and artificial neural network model to predict heavy metals adsorption dynamic on surfactant decorated graphene

Machine learning for the adsorptive removal of ciprofloxacin using sugarcane bagasse as a low-cost biosorbent: comparison of analytic, mechanistic, and neural network modeling

Contamination with traces of pharmaceutical compounds, such as ciprofloxacin, has prompted interest in their removal via low-cost, efficient biomass-based adsorption. In this study, classical models, a mechanistic model, and a neural network model were evaluated for predicting ciprofloxacin breakthrough curves in both laboratory- and pilot scales. For the laboratory-scale (d = 2.2 cm, Co = 5 mg/L, Q = 7 mL/min, T = 18 °C) and pilot-scale (D = 4.4 cm, Co = 5 mg/L, Q = 28 mL/min, T = 18 °C) setups, the experimental adsorption capacities were 2.19 and 2.53 mg/g, respectively. The mechanistic model reproduced the breakthrough data with high accuracy on both scales (R2 > 0.4 and X2 < 0.15), and its fit was higher than conventional analytical models, namely the Clark, Modified Dose-Response, and Bohart-Adams models. The neural network model showed the highest level of agreement between predicted and experimental data with values of R2 = 0.993, X2 = 0.0032 (pilot-scale) and R2 = 0.986, X2 = 0.0022 (laboratory-scale). This study demonstrates that machine learning algorithms exhibit great potential for predicting the liquid adsorption of emerging pollutants in fixed bed.

The use of artificial neural network for modelling adsorption of Congo red onto activated hazelnut shell

Activated hazelnut shell (HSAC), an organic waste, was utilized for the adsorptive removal of Congo red (CR) dye from aqueous solutions, and a modelling study was conducted using artificial neural networks (ANNs). The structure and characteristic functional groups of the material were examined by the FTIR method. The BET surface area of the synthesized material, named HSAC, was 812 m2/g. Conducted in a batch system, the adsorption experiments resulted in a notable removal efficiency of 87% under optimal conditions. The kinetic data for hazelnut shell activated carbon (HSAC) removal of CR were most accurately represented by the pseudo-second-order kinetic model (R2 = 0.998). Furthermore, the equilibrium data demonstrated a strong agreement with the Freundlich model. The maximum adsorption capacity of HSAC for CR was determined to be 34.8 mg/g. The optimum adsorption parameters were determined to be pH 6, contact time of 60 min, 10 g/L of HSAC, and a concentration of 400 mg/L for CR. Considering the various experimental parameters influencing CR adsorption, an artificial neural network (ANN) model was constructed. The analysis of the ANN model revealed a correlation of 98%, indicating that the output parameter could be reliably predicted. Thus, it was concluded that ANN could be employed for the removal of CR from water using HSAC.

ASPAD dynamic simulation and artificial neural network for atenolol adsorption in GGSWAC packed bed column

This study aimed to assess the dynamic simulation models provided by Aspen adsorption (ASPAD) and artificial neural network (ANN) in understanding the adsorption behavior of atenolol (ATN) on gasified Glyricidia sepium woodchips activated carbon (GGSWAC) within fixed bed columns for wastewater treatment. The findings demonstrated that increasing the bed height from 1 to 3 cm extended breakthrough and exhaustion times while enhancing adsorption capacity. Conversely, higher initial ATN concentrations resulted in shorter breakthrough and exhaustion times but increased adsorption capacity. Elevated influent flow rates reduced breakthrough and exhaustion times while maintaining constant adsorption capacity. The ASPAD software demonstrated competence in accurately modeling the crucial exhaustion points. However, there is room for enhancement in forecasting breakthrough times, as it exhibited deviations ranging from 6.52 to 239.53% when compared to the actual experimental data. ANN models in both MATLAB and Python demonstrated precise predictive abilities, with the Python model (R2 = 0.985) outperforming the MATLAB model (R2 = 0.9691). The Python ANN also exhibited superior fitting performance with lower MSE and MAE. The most influential factor was the initial ATN concentration (28.96%), followed by bed height (26.39%), influent flow rate (22.43%), and total effluent time (22.22%). The findings of this study offer an extensive comprehension of breakthrough patterns and enable accurate forecasts of column performance.

Recent Advances in the Treatment of Industrial Wastewater from Different Celluloses in Continuous Systems

There are numerous studies on water care methods featured in various academic and research journals around the world. One research area is cellulose residue coupled with continuous systems to identify which are more efficient and easier to install. Investigations have included mathematical design models that provide methods for developing and commissioning industrial wastewater treatment plants, but nothing is provided on how to size and start these treatment systems. Therefore, the objective is to determine recent advances in the treatment of industrial wastewater from different celluloses in continuous systems. The dynamic behavior of the research results with cellulose biomasses was analyzed with the mass balance model and extra-particle and intraparticle dispersion, evaluating adsorption capacities, design variables, and removal constants, and making a size contribution for each cellulose analyzed using adsorption capacities. A mathematical model was also developed that feeds on cellulose reuse, determining new adsorption capacities and concluding that the implementation of cellulose waste treatment systems has a high feasibility due to low costs and high adsorption capacities. Furthermore, with the design equations, the companies themselves could design their systems for the treatment of water contaminated with heavy metals with cellulose.

Green composite aerogel based on citrus peel/chitosan/bentonite for sustainable removal Cu(II) from water matrices

Here, we propose a green and sustainable 3D porous aerogel based on citrus peel (CP), chitosan (CS), and bentonite (BT). This aerogel is prepared through a simple sol-gel and freeze-drying process and is designed for efficient capture of Cu(II) ions from water matrices. CCBA-2, with its abundance of active binding sites, exhibits an impressive Cu(II) adsorption yield of 861.58 mg/g. The adsorption isotherm and kinetics follow the Freundlich and pseudo-second-order models, respectively. In the presence of coexisting mixed-metal ions, CCBA-2 demonstrates a significantly higher selectivity coefficient (KdCu = 1138.5) for removing Cu(II) ions compared to other toxic metal ions. Furthermore, the adsorption of Cu(II) ions by CCBA-2 is not significantly affected by coexisting cations/anions, ionic strength, organic matter, or different water matrices. Dynamic fixed-bed column experiments show that the adsorption capacity of Cu(II) ions reaches 377.4 mg/g, and the Yoon-Nelson model accurately describes the adsorption process and breakthrough curve. Through experiments, FTIR, and XPS analyses, we propose a reasonable binding mechanism between CCBA-2 and metal cations, involving electrostatic attraction and chemical chelation between Cu(II) and the functional groups of the aerogel. CCBA-2 saturated with Cu(II) ions can be successfully regenerated by elution with 1 M HNO3, with only a slight decrease in adsorption efficiency (5.3%) after 5 adsorption-desorption cycles. Therefore, CCBA-2 offers a cost-effective and environmentally friendly material that can be considered as a viable alternative for the green and efficient removal of toxic Cu(II) ions from wastewater.

Functionalized Carbon Nanotubes Encapsulated Alginate Beads for the Removal of Mercury Ions: Design, Synthesis, Density Functional Theory Calculation, and Demonstration in a Batch and Fixed-Bed Process

Various nanomaterials have been envisaged mainly through batch studies for environmental remediation application. The real utilization of these new generation adsorbents in large scale pose a difficulty due to its low density and small size which makes it difficult for isolation after application. In this context, nanoadsorbents polymer composite beads can be seen as a way out. Here, functionalized CNTs (carbon nanotubes) have been fabricated into micro beads with sodium alginate. The alginate-functionalized CNT (Alg-f-CNT) beads were then comprehensively evaluated for batch and fixed-bed column separation of divalent mercury ions from an aqueous medium. The effects of process parameters such as pH, contact time, feed Hg2+ concentration, and temperature were studied. Simulation of the experimental data suggested that adsorption is an endothermic spontaneous process which follows the pseudo-second-order kinetic and Langmuir isotherm model. The desorption of the Hg2+ ion from used adsorbent was possible with 1 M HNO3. The breakthrough curves at different process parameters were investigated during fixed-bed column separation and found to be in good agreement with Thomas model. The regeneration and reusability of the adsorbent were tested up to five cycles without a significant decrease in the removal performance. Density functional theory studies revealed stronger interaction of Alg-f-CNT with Hg compared to free alginic acid and established the role of carboxyl and oxo groups present in the adsorbent in the coordination of the Hg2+ ions. The experimental results demonstrate that functionalized CNT-encapsulated alginate beads are a promising alternate material, which can be used to remove mercury in the fixed-bed column mode of the operation.

Extensive sorption of Amoxicillin by highly efficient carbon-based adsorbent from palm kernel: Artificial neural network modeling

In the present study, a new sorbent was fabricated from Palm kernel (PK) by dry thermochemical activation with NaOH and characterized by FTIR, X-ray diffraction, FE-SEM and BET, which was used for the Amoxicillin (AMX) sorption from aqueous solution. The influence of effective parameters such as pH, reaction time, adsorbent dosage, AMX concentration and ionic strength on the sorption efficacy of AMX removal were evaluated. The main functional groups on the surface of the magnetic activated carbon of Palm Kernel (MA-PK) were C-C, C-O, C[bond, double bond]O and hydroxyl groups. The specific surface of char, activated carbon Palm Kernel (AC-PK) and MA-PK were 4.3, 1648.8 and 1852.4 m2/g, respectively. The highest sorption of AMX (400 mg/L) was obtained by using 1 g/L of sorbent at solution pH of 5 after 60 min contact time, which corresponding to 98.77%. Non-linear and linear models of isotherms and kinetics models were studied. The data fitted well with Hill isotherm (R2 = 0.987) and calculated maximum sorption capacity were 719.07 and 512.27 mg/g from Hill and Langmuir, respectively. A study of kinetics shows that the adsorption of AMX follows the Elovich model with R2 = 0.9998. Based on the artificial neural network (ANN) modeling, the MA-PK dosage and contact time showed the most important parameters in the removal of AMX with relative importance of 36.5 and 25.7%, respectively. Lastly, the fabricated MA-PK was successfully used to remove the AMX from hospital wastewater.

Removal of boron by a modified resin in fixed bed column: Breakthrough curve analysis using dynamic adsorption models and artificial neural network model

Continuous removal of toxic element boron from aqueous solution was investigated with new phenolic hydroxyl modified resin (T-resin) using a fixed bed column reactor operated under various flow rates, bed height and influent concentrations. The breakthrough time, exhaustion time and uptake capacity of the column bed increased with increasing column bed height, whereas decreased with increasing influent flow rate. The breakthrough time and exhaustion time decreased, but uptake capacity increased with increasing influent concentration, and actual uptake capacity was obtained as 6.52 mg/g at a concentration of 7.64 mg/L. The three conventional models of bed depth service time (BDST), Thomas and Yoon-Nelson were used to appropriately predict the whole breakthrough behavior of the column and to estimate the characteristic model parameters for boron removal. However, artificial neural network (ANN) model was more accurate than the conventional models with the least relative error and the highest correlation coefficients. By the relative importance of the operational parameters obtained from ANN model, the sequence is as follows: total effluent time > initial concentration > flow rate > column height. The adsorption capacity of boron was changed between 5.24 and 1.74 mg/g during the five time regeneration. From the life factor calculation, it is suggested that the column bed could avoid the breakthrough time of t = 0 for 6.8 cycles, whereas, the uptake capacity would be zero after 7.8 cycles.

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

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.

Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System

This study investigates chromium removal onto modified maghemite nanoparticles in batch experiments based on a central composite design. The effect of modified maghemite nanoparticles on the adsorptive removal of chromium was quantitatively elucidated by fitting the experimental data using artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modeling approaches. The ANN and ANFIS models, relating the inputs, i.e., pH, adsorbent dose, and initial chromium concentration to the output, i.e., chromium removal efficiency (RE), were developed by comparing the predicted value with that of the experimental values. The RE of chromium ranged from 49.58% to 92.72% under the influence of varying pH (i.e., 2.6–9.4) and adsorbent dose, i.e., 0.8 g/L to 9.2 g/L. The developed ANN model fits the experimental data exceptionally well with correlation coefficients of 1.000 and 0.997 for training and testing, respectively. In addition, the Pearson’s Chi-square measure (χ2) of 0.0004 and 0.0673 for the ANN and ANFIS models, respectively, indicated the superiority of ANN over ANFIS. However, a small discrepancy in the predictability of the ANFIS model was observed owing to the fuzzy rule-based complexity and overtraining of data. Thus, the developed models can be used for the online prediction of RE onto synthesized maghemite nanoparticles with different sets of input parameters and it can also predict the operational errors in the system.

A State-of-the-Art Review on Innovative Geopolymer Composites Designed for Water and Wastewater Treatment

There is nothing more fundamental than clean potable water for living beings next to air. On the other hand, wastewater management is cropping up as a challenging task day-by-day due to lots of new additions of novel pollutants as well as the development of infrastructures and regulations that could not maintain its pace with the burgeoning escalation of populace and urbanizations. Therefore, momentous approaches must be sought-after to reclaim fresh water from wastewaters in order to address this great societal challenge. One of the routes is to clean wastewater through treatment processes using diverse adsorbents. However, most of them are unsustainable and quite costly e.g. activated carbon adsorbents, etc. Quite recently, innovative, sustainable, durable, affordable, user and eco-benevolent Geopolymer composites have been brought into play to serve the purpose as a pretty novel subject matter since they can be manufactured by a simple process of Geopolymerization at low temperature, lower energy with mitigated carbon footprints and marvellously, exhibit outstanding properties of physical and chemical stability, ion-exchange, dielectric characteristics, etc., with a porous structure and of course lucrative too because of the incorporation of wastes with them, which is in harmony with the goal to transit from linear to circular economy, i.e., "one's waste is the treasure for another". For these reasons, nowadays, this ground-breaking inorganic class of amorphous alumina-silicate materials are drawing the attention of the world researchers for designing them as adsorbents for water and wastewater treatment where the chemical nature and structure of the materials have a great impact on their adsorption competence. The aim of the current most recent state-of-the-art and scientometric review is to comprehend and assess thoroughly the advancements in geo-synthesis, properties and applications of geopolymer composites designed for the elimination of hazardous contaminants viz., heavy metal ions, dyes, etc. The adsorption mechanisms and effects of various environmental conditions on adsorption efficiency are also taken into account for review of the importance of Geopolymers as most recent adsorbents to get rid of the death-defying and toxic pollutants from wastewater with a view to obtaining reclaimed potable and sparkling water for reuse offering to trim down the massive crisis of scarcity of water promoting sustainable water and wastewater treatment for greener environments. The appraisal is made on the performance estimation of Geopolymers for water and wastewater treatment along with the three-dimensional printed components are characterized for mechanical, physical and chemical attributes, permeability and Ammonium (NH4+) ion removal competence of Geopolymer composites as alternative adsorbents for sequestration of an assortment of contaminants during wastewater treatment.

Artificial neural network (ANN) approach for prediction and modeling of breakthrough curve analysis of fixed-bed adsorption of iron ions from aqueous solution by activated carbon from Limonia acidissima shell

The present research article explored the potential of activated carbon prepared from Limonia acidissima shell to adsorb total Fe ions from aqueous solution in a packed bed up-flow column. The effect of essential factors such as bed height (3–5 cm), initial concentration (30–50 mg/L), and flow rate (3.32–5.4 mL/min) on the performance of the column bed was investigated. The adsorption capacity augmented with an increase in bed height and initial adsorbate concentration but declined with an increase in flow rate. The maximum uptake capacity of 209.6 mg/g was achieved at 5 cm bed height, 3.32 mL/min, and 50 mg/L initial concentration. The bed depth service time (BDST) model was used to analyze the experimental data and determine the characteristic parameters of the packed bed reactor suitable for designing large-scale column studies. The Adams–Bohart, Thomas, and Yoon–Nelson models were applied to the experimental data to predict breakthrough curves using non-linear regression. The artificial neural network (ANN) based model was able to efficaciously predict the column performance using the Levenberg–Marquardt (LM) algorithm. A comparison between the experimental data and model results contributed to a high degree of correlation, specifying that the preliminary information was in good agreement with the ANN predicted data.

A multilayer perceptron-based prediction of ammonium adsorption on zeolite from landfill leachate: Batch and column studies

In this study, multilayer perceptron (MLP) artificial neural network was used to predict the adsorption rate of ammonium on zeolite. pH, inlet ammonium concentration, contact time, temperature, dosage of adsorbent, agitation speed, and particle size in the batch experiments were used as independent variables while flow rate and particle size in column mode were investigated. In MLP application, different architecture structures were tried and the architecture structures with the highest predictive performance were determined. To comparatively evaluate the predictive capabilities of MLP based prediction tool, Response Surface Methodology (RSM) was utilized. When the results were evaluated with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values (<1%) for almost all experiments, it was seen that MLP-based prediction tool produces better predictions than RSM. The scatter plots showed that predictions and actual values were quite compatible. Both regression and determination coefficients were interpreted by creating a regression of the predictions against the actual values and these coefficients were obtained as pretty close to 1. The outstanding performance of MLP in out-of-sample data sets without the need for additional experiment demonstrate that MLP can be effectively and reliably used in cases where experimental setups are difficult or costly.

gPROMS-driven modeling and simulation of fixed bed adsorption of heavy metals on a biosorbent: benchmarking and case study

Adsorptive separation of heavy metals from wastewater is a viable approach to reuse it and avoid environmental pollution. The productive employment of adsorptive separation at a commercial scale, however, relies on the optimized conditions of an adsorber bed holding maximum and selective isolation of the heavy metals. The experimental route includes a significant trial and error approach, is time-consuming, involves operating cost, and remains economically unattractive. Contrarily, simulation of a mathematical model mimicking the adsorption system along with experimental validation can significantly minimize optimization efforts and suggests the best conditions of separation. In this work, a convective-dispersive model and adsorption model for fixed bed adsorption of copper (Cu), chromium (Cr), and cadmium (Cd) metals over wheat bran biosorbent are simulated using the gPROMS tool for benchmarking. The influence of feed flow rate, bed height, and metal concentration is studied, and breakthrough profiles of all heavy metals are predicted and matched with the literature. The error values (R² and RMSE) and Chi-squared values determined from gPROMS simulations matched well with the previously available MATLAB-simulated data. After a successful benchmarking, we modeled pilot-scale adsorption of Cr on coconut coir (or Biosorbent) in a gPROMS simulation environment. A detailed method and algorithm of gPROMS simulation for Cr isolation is provided. The influence of feed flow rate, bed height, and initial metal concentration is studied on the breakthrough curves of the Cr. The optimum operating condition for the pilot-scale isolation of Cr from the water is suggested. The parameters, such as the axial dispersion coefficient and distribution coefficient, are determined.

Experimental and Modeling of Dicamba Adsorption in Aqueous Medium Using MIL-101(Cr) Metal-Organic Framework

Drift deposition of emerging and carcinogenic contaminant dicamba (3,6-dichloro-2-methoxy benzoic acid) has become a major health and environmental concern. Effective removal of dicamba in aqueous medium becomes imperative. This study investigates the adsorption of a promising adsorbent, MIL-101(Cr) metal-organic framework (MOF), for the removal of dicamba in aqueous solution. The adsorbent was hydrothermally synthesized and characterized using N2 adsorption-desorption isotherms, Brunauer, Emmett and Teller (BET), powdered X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR) and field emission scanning electron microscopy (FESEM). Adsorption models such as kinetics, isotherms and thermodynamics were studied to understand details of the adsorption process. The significance and optimization of the data matrix, as well as the multivariate interaction of the adsorption parameters, were determined using response surface methodology (RSM). RSM and artificial neural network (ANN) were used to predict the adsorption capacity. In each of the experimental adsorption conditions used, the ANN gave a better prediction with minimal error than the RSM model. The MIL-101(Cr) adsorbent was recycled six times to determine the possibility of reuse. The results show that MIL-101(Cr) is a very promising adsorbent, in particular due to the high surface area (1439 m2 g−1), rapid equilibration (~25 min), high adsorption capacity (237.384 mg g−1) and high removal efficiency of 99.432%.

Removal of 4-chloro-2-methylphenoxyacetic acid from water by MIL-101(Cr) metal-organic framework: kinetics, isotherms and statistical models

Effective removal of 4-chloro-2-methylphenoxyacetic acid (MCPA), an emerging agrochemical contaminant in water with carcinogenic and mutagenic health effects has been reported using hydrothermally synthesized MIL-101(Cr) metal-organic framework (MOF). The properties of the MOF were ascertained using powdered X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, thermal gravimetric analysis (TGA), field emission scanning electron microscopy (FESEM) and surface area and porosimetry (SAP). The BET surface area and pore volume of the MOF were 1439 m2 g-1 and 0.77 cm3 g-1, respectively. Artificial neural network (ANN) model was significantly employed for the accurate prediction of the experimental adsorption capacity (qe ) values with minimal error. A rapid removal of the pollutant (99%) was recorded within short time (approx. 25 min), and the reusability of the MOF (20 mg) was achieved up to six cycles with over 90% removal efficiency. The kinetics, isotherm and thermodynamics of the process were described by the pseudo-second-order, Freundlich and endothermic adsorption, respectively. The adsorption process is spontaneous based on the negative Gibbs free energy values. The significant correlation between the experimental findings and simulation results suggests the great potential of MIL-101(Cr) for the remediation of MCPA from water matrices.

Surface functionalization of recycled polyacrylonitrile fibers with ethylenediamine for highly effective adsorption of Hg(II) from contaminated waters

In this research, recycled polyacrylonitrile fibers (PANFs) acquired from the textile recycling process were amino-functionalized in one simple step by means of ethylenediamine (EDA). The amino-functionalized polyacrylonitrile fibers (AF-PANFs) were utilized for adsorption of Hg(II) ions from aquatic media. Temperature and contact time during the synthesis were optimized by the Central Composite Design (CCD) method. FE-SEM, EDS, BET, and FT-IR analysis, and pH_ZPC measurement were conducted to characterize the features of the AF-PANFs. The average diameter of raw fiber was 20 μm, which increased 20 percent after functionalizing. The impact of independent parameters on the adsorption process was investigated using the Box-Behnken Design (BBD) method during the batch experiments. The column tests were conducted in a semi-continuous system with the removal efficiency of over 99% for various initial concentrations after specific cycles. Freundlich, Langmuir, UT, Redlich-Peterson, and Temkin isotherm models were employed to analyze the relation between the final concentration of Hg(II) (C_o) and the equilibrium adsorption capacity (q_e) of the AF-PANFs. According to the isotherm models and experimental results, the maximum q_e of the AF-PANFs was 1116 mg g^-1 at initial Hg(II) concentration of 850 mg L^-1, contact time of 120 min, solution pH of 6, and at 40 °C. Kinetic and thermodynamic studies illustrated the approximate equilibrium time and endothermicity or exothermicity of the process. Regeneration of the AF-PANFs was accomplished for seven times without efficiency drop. The superb performance of the AF-PANFs in the presence of co-existing ions did not decline.