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Development of a High-Throughput IonExchange Resin Characterization Workflow Chun Liu, Daniel Dermody, Keith Harris, Tom Boomgaard, Jeff Sweeney, Daryl Gisch, and Bob Goltz ACS Comb. Sci., Just Accepted Manuscript • Publication Date (Web): 10 May 2017 Downloaded from http://pubs.acs.org on May 16, 2017

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ACS Combinatorial Science

Development of a High-Throughput Ion-Exchange Resin Characterization Workflow Chun Liu*,1, Daniel Dermody1, Keith Harris1, Thomas Boomgaard2, Jeff Sweeney3, Daryl Gisch4, Bob Goltz4 1

Core R&D Formulation Science, The Dow Chemical Company, Midland, MI 48674, United

States;

2

Core R&D Information Research, The Dow Chemical Company, Midland, MI 48674,

United States;

3

R&D Statistics, The Dow Chemical Company, Midland, MI 48674, United

States; 4 Dow Water & Process Solutions, 1801 Larkin Center Drive, Midland, MI 48674, United States. KEYWORDS: High Throughput, Ion Exchange Resin, Design of Experiment, Response Surface Model, Model Visualization

ABSTRACT

A novel high throughout (HTR) ion-exchange (IEX) resin workflow has been developed for characterizing ion exchange equilibrium of commercial and experimental IEX resins against a range of different applications where water environment differs from site to site. Owing to its much higher throughput, Design Of Experiment (DOE) methodology can be easily applied for studying the effects of multiple factors on resin performance. Two case studies will be presented

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to illustrate the efficacy of the combined HTR workflow and DOE method. In case study one, a series of anion exchange resins have been screened for selective removal of NO3-, NO2- in water environments consisting of multiple other anions, varied pH and ionic strength. The Response Surface Model (RSM) is developed to statistically correlate the resin performance with the water composition and predict the best resin candidate. In case study two, the same HTR workflow and DOE method have been applied for screening different cation exchange resins in terms of the selective removal of Mg2+, Ca2+ and Ba2+ from high Total Dissolved Salt (TDS) water. A master DOE model including all of the cation exchange resins is created to predict divalent cation removal by different IEX resins under specific conditions, from which the best resin candidates can be identified. The successful adoption of HTR workflow and DOE method for studying the ion exchange of IEX resins can significantly reduce the resources and time to address industry and application needs.

INTRODUCTION The combinatorial approach of developing and characterizing materials has revolutionized the pharmaceutical, catalytic and consumer industries.1-4 In the last two decades, The Dow Chemical Company has invested extensively in high throughput (HTR) research capability within Core R&D.5-7 These HTR capabilities have included tools/robots for rapid and parallel polymer synthesis, material characterization and workflows targeting specific applications such as liquid formulations, coatings, adhesives etc. All HTR tools/robots within Dow are integrated into a central, global database designed to accommodate the flow of data into and out of the database. Multiple programs are included in the database package such as Library Studio, Epoch, Automation Studio, Spectra Studio, Data Browser and PolyView as well as several specialized


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software routines created by Dow personnel. The versatility of these programs is expanded by allowing the formulator to create user-defined parameters. These parameters can be used to calculate component additions, agitation times or temperatures ramping in a well-by-well fashion using the parameter feature. These capabilities have been successfully applied for process optimization, fundamental understanding, and most of all, accelerated product breakthrough and technologies discovery. Owning to the much higher sample throughput of the HTR tools/robots than that of traditional manual methods, the effects of key factors on formulation performances can be experimentally screened in a relatively short period of time. However, in the case of multiple factors (> 5 factors) with ≥ 3 levels, a huge amount of data could be generated, which is challenging to analyze for the structure-performance relationship. To address this challenge, Design of Experiment (DOE) methodology has been widely applied for developing statistical models that can predict the performance of formulation systems. Here, DOE is a systematic method to determine the relationship between factors that affect a process and the output of that process. 8-11 This is done by application of geometric principles to statistical samplings for obtaining desired results through regression. It is normally used to find cause-and-effect relationship, which information is needed to manage process inputs in order to optimize the output. Furthermore, DOE can optimize and reduce the number of experiments to accelerate the research progress. The combination of HTR capability and DOE methodology has help research scientists successfully reduce the development and response time to address industry and application needs. The reduced R&D investment coupled with existing market development and commercial-scale manufacturing capabilities within each business allows development of differentiated products on a customer or application basis.


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Over the past several decades, Dow Water & Process Solutions (DW&PS) business has developed a variety of ion exchange resins for different applications of water treatment.12 For example, nitrates are extremely soluble in water and can move easily through soil into the drinking water supply.13-15 The presence of NO3-/NO2- above 44.3 ppm in drinking water is unsafe and can cause methemoglobinemia or “blue baby” syndrome in infants. A nitrate selective resin can bind and reduce the levels of NO3-/NO2- to meet the regulatory drinking water standards set by the World Health Organization (WHO). In addition, the presence of excessive NO3-/NO2- in drinking water is often an indicator of other contaminants, thus requiring disinfection to counter other health concerns. The preferred technology for nitrate removal from drinking water is through ion exchange using IEX resins, a low-cost process operated in almost the same manner as a common water softener.16,17 Thus, it becomes necessary to identify “nitrate selective” resins for selectively removing NO3-/NO2- against other anions under a range of different environmental conditions.18 Recently several nitrate selective resins have been developed. Nitrate specific resins have been proven to have affinity for the following ions in decreasing order: NO3->SO42->Cl->HCO3-.19 In another example, the presence of Mg2+ and Ca2+ divalent cations in ground water causes water hardness that produces a scum or curd with soap, and forms a hard scale in piping, water heaters, steam irons, and even pots and pans. Thus, natural waters often need to be softened prior to home and industrial use. One widely used technology for water softening is the sodium cycle operation in which Mg2+ and Ca2+ in water are replaced by Na+ as hard water passes through a bed of IEX materials. In some cases, the Total Dissolved Salts (TDS), mostly sodium salts, can be very high and thus require an IEX resin that has a greater affinity for divalent cations (e.g. Mg2+, Ca2+, Ba2+) than monovalent cations (e.g. Na+, K+). However, one challenge for quickly


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evaluating resin performance against removing different ions comes from the complexity of the water matrix (multiple cations and anions, varied pH and ionic strength, presence of organic impurities etc.). In this paper, we report on the development of a HTR IEX resin characterization workflow that can be applied to provide extensive ion exchange equilibrium data against a range of different applications where the water environment is complex and differs from site to site. The developed HTR IEX resin characterization workflow consists of resin pre-processing, resin-in-solution formulations, resin-in-solution post-processing and ion concentration analysis using ion chromatography (IC) for anions and inductively coupled plasma atomic emission spectroscopy (ICP-AES) for cations. The efficacy of HTR workflow was quickly evaluated via screening a series of anion exchange resins towards selective nitrate removal from a simple water matrix consisting of only NO3- and SO42-, from which promising candidates can be identified. Besides HTR tools, DOE modeling is applied for predicting the resin performances and identifying the best resin candidates. Two case studies will be presented here to illustrate the applications of the HTR IEX resin workflow and DOE methodology. In case study one, some promising anion exchange resins were screened for the selective removal of NO3-/NO2- from a complex water environment consisting of multiple competing anion species (SO42-, Cl-, HCO3-/CO32-), varying pH and ionic strength. The DOE modeling was applied to generate a statistical model from which the best resin candidate can be predicted based upon the specific water environment. In case study two for high TDS water softening, the DOE modeling was applied throughout the study to screen the performance of each cation exchange resin towards the removal of Mg2+, Ca2+ and Ba2+ divalent cations in presence of high Na+ level. A master DOE model including all


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the cation exchange resins was created to better understand the influence of the resin structures on IEX performance and identify the most promising IEX candidate for specific conditions. SUMMARY OF HTR IEX RESIN CHARACTERIZATION WORKFLOW The developed HTR IEX resin characterization workflow consists of resin pre-processing, resinin-solution formulations, resin-in-solution post-processing and ion concentration analysis, which utilize a combination of different HTR tools. The overall workflow is schematically illustrated in Figure 1. All the HTR tools were evaluated for their applicability for quickly dispensing resins, formulating solutions and analyzing ion concentrations.

Figure 1 Schematic illustration of HTR IEX resin characterization workflow.


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Resin Pre-processing: IEX resin samples are conditioned using the following methods. For “wet” resin beads immersed in de-ionized (D.I.) water, the beads are filtered using a VacMaster™ 20 Biotage station at a low vacuum of 40 ± 5 mm Hg for 10 minutes to remove excessive water. To prevent water loss from the interior of the resin pores, a tube is connected to the top of a filtration syringe to introduce a small amount of water vapor-saturated nitrogen flow. The Biotage station is capable of filtrating up to 20 resin samples simultaneously. For resin beads in the “dry” form, the beads are used directly without any re-saturation and filtration. Resin Dispensing: A One-To-Many (OTM) Powdernium (Symyx, Santa Clara CA) solids handling robot is used to dispense the IEX resins into each 4 mL plastic vial. The weight accuracy and dispensing time of the Powdernium was examined using DOWEX Marathon A resin which is more difficult to dispense due to its higher stickiness. Here, the weight accuracy and dispensing time using a Powdernium are inter-correlated with each other via the operation parameter: weight tolerance. To assess this, the OTM Powdernium was programmed to dispense 80 mg DOWEX Marathon A resin beads into each of 24 vials. The experiments were carried out at weight tolerances of ± 0.5 mg and ± 5.0 mg, respectively. As summarized in Table S1, the Powdernium needs longer dispensing time (108s each) and tends to overshoot the weight frequently (9 out of 24 vials have > 86 mg resins) for weight tolerance of ± 0.5 mg due to the nature of stepwise weight increase when approaching the target weight. On the other hand, it takes ca. 40% less time (60s each) to dispense the same amount of resin beads using ± 5.0 mg weight tolerance, and the weight overshooting is much less frequent as well (1 out of 24 vials has > 86 mg resins). Overall, ± 5.0 mg tolerance gives the average weight closer to the target weight with less variation as well as shorter dispensing time. Thus, it is preferred to use large enough weight tolerance (e.g., ~ 5% of target weight) when dispensing relatively "stickier" resin beads.


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Based on the results presented here, the OTM Powdernium is capable of accurately dispensing and weighing the IEX resins. Resin-in-Solution Formulation: A Tecan 150A liquid dispensing robot is used for formulating multicomponent solutions for testing IEX resin performance. Since the viscosity of the dilute aqueous solutions are low, it is expected that Tecan 150A can deliver the desirable volume more accurately than OTM Powdernium can. To prove this, we examined the use of the Tecan 150A to transfer both a large volume (> 1 mL) of water in multiple times and a small volume of 1.0N Na2SO4 aqueous solution from 0.05 to 0.50 mL in a single transfer. As expected, the results give a perfect linear fit of measured mass vs. delivered volume with the slopes closer to the solution density (Figure S1). Resin-in-Solution Post-processing: After equilibrating the resin-in-solution formulations under a low speed horizontal shaking at room temperature for 9 hrs, a 50 µL aliquot of each solution is taken by the Tecan 150A and transferred to a second plastic vial for dilution with water prior to analysis of the equilibrium ion concentrations. The initial and final pH values of the solutions are monitored using a custom-built HTR pH robot.20 Ion Concentration Analysis: A Dionex ICS-2000 IC equipped with an auto-sampler is employed as a HTR tool for analyzing anion concentrations in matrix solutions after exchanging with the resin samples.21 The instrument can run 24/7 automatically following the designated operating conditions, enabled by the use of an auto-sampler. The supplied Chromeleon software v 6.80 can then be used to automatically extract the anion concentrations. The estimated error of the analysis is ± 2% relative. A representative chromatogram of the resin sample solution is shown in Figure S2. As for the cations, their concentrations are analyzed using a Perkin Elmer


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7300 DV ICP-AES equipped with an auto-sampler. The calibration curves of ICP-AES show good consistency between calculated and measured concentration for cations (Figure S3). As mentioned before, all the HTR tools/robots within Dow are integrated into a central, global database accessible by user-friendly software. For the HTR IEX resin characterization workflow, Library Studio is used to operate the OTM Powdernium and Tecan 150A for achieving desirable formulations. As shown in Figure 2, the resin masses from the OTM Powdernium are used as inputs for the Tecan 150A to calculate and deliver the required amounts of solutions. Thus, all the steps for resin-in-solution formulations and dilutions can be included in a single Library, which significantly simplify the work process.

Figure 2 Example Library for Dow MARATHON A formulations showing the calculations of required nitrate solutions for a 24-well destination plate based on actual resin masses using a constant resin-to-nitrate ratio. MATERIALS AND METHODS Materials: The IEX resin samples were provided by Dow Water & Process Solutions (DW&PS) business and conditioned to remove excessive water prior to use. The IEX resin properties are given in Table 1 and 2 for anion and cation exchange resins, respectively.


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Table 1 Anion exchange resin properties

Resin

TEC (meq/L)a

MHC (%)b

Functionality

Matrix Type

TEA-XUR-081

0.7

32.7

Triethylamine

Gel

TEA-XUR-082

0.8

38.7

Triethylamine

Gel

TEA-XUR-083

0.9

57.8

Triethylamine

Macroporous

TEA-XUR-084

0.9

54.4

Triethylamine

Macroporous

DOWEX NSR-1

0.9

53-63

Triethylamine

Macroporous

Amberlite PWA-5

1.1

52-58

Triethylamine

Macroporous

DOWEX-1

1.4

43-48

Trimethylamine

Gel

DOWX PSR-2

0.65

40-47.5

Tri-n-butylamine

Gel

Amberlite PWA-7

≥ 1.9

58-68

N/A

Cross-linked

DOWEX MSA-2

1.0

45-56

DOWEX PSR-3

0.6

50-65

Tri-n-butylamine

Macroporous

DOWEX MARATHON A

1.3

56-60

Trimethylamine

Gel

DOWEX MARATHON A2

1.2

45-54

Dimethylethanolamine

Gel

a

Dimethylethanolamine Macroporous

TEC: The Effective Capacity; b MHC: Moisture Holding Content.

Table 2 Cation exchange resin properties

Resin ID

Type c

Matrix Type d

Functional group

TEC (eq/l)

MHC (%)

DOWEX MAC 3

WAC

Polyacrylic-DVB, Macroporous

Carboxylic acid

3.8

65.6

DOWEX MARATHON C

SAC

PS-DVB, UPS Geltype

Sulfonic acid

2.0

45.1


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Lewatit CNP 80

WAC

Polyacrylic-DVB, Macroporous

Carboxylic acid

4.3

59.4

Lewatit Monoplus S108H

SAC

PS-DVB, UPS Geltype

Sulfonic acid

1.8

45.1

Lewatit S1667

SAC

PS-DVB, UPS

Sulfonic acid

2.1

42.8

Purolite C100E

SAC

PS-DVB, Gel-type

Sulfonic acid

1.9

48.8

Purolite PFC100

SAC

PS-DVB, UPS Geltype

Sulfonic acid

2.0

47.8

Purolite C104

WAC

Polyacrylic-DVB, Gel-type

Carboxylic acid

3.8

57.6

Amberlite IRC748

WAC chelating

PS-DVB, Macroporous

Iminodiacetic acid

1.35

64.9

c

WAC: Weak Acid Cation resins in the H+ form, SAC: Strong Acid Cation resin resins in the

Na+ form; d UPS: Uniform Particle Size NaNO3 (certified ACS grade), NaSO4 (certified ACS grade), NaCl (USP/FCC) and NaHCO3 (certified A.C.S. grade) were purchased from Fischer Chemical, and NaNO2 (certified ACS. grade) was purchased from J. T. Baker Chemicals. 0.1 N standard and 37 % concentrated HCl solutions (ACS reagent) were purchased from Sigma-Aldrich. Ultra-pure D.I. water (>18MΩ) was used to prepare all the solutions and formulations. NaNO3, NaNO2, NaSO4, NaCl and NaHCO3 solids were directly dissolved in D.I. water to make 0.5 N NaNO3, 0.5 N NaNO2, 1.0 N NaSO4, 1.0 N NaCl and 0.5 N NaHCO3 stock solutions, respectively. 0.01 N and 1.0N HCl stock solutions were prepared by diluting the 0.1 N and 37% HCl solutions 10 and 12 times, respectively. NaCl (AR@ACS), MgCl2·6H2O (Certified ACS), CaCl2 and BaCl2·2H2O (99% ACS) were purchased from Mallinckrodf, Fisher, VWR and Aldrich Chemicals, respectively. 0.1 N standard solution of NaOH was purchased from Aldrich-Sigma. NaH2PO4 (99% ReagentPlus), tris(hydroxymethyl) aminomethane (Tris, C4H11NO3, ≥ 99%) and glycine (NH2CH2COOH,


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Certified crystalline) were obtained from Aldrich, Sigma and Fisher Chemicals, respectively. NaCl (25000 ppm Na+), h-MgCl2 (5000 ppm Mg2+), h-CaCl2 (5000 ppm Ca2+), h-BaCl2 (2000 ppm Ba2+) stock solutions were prepared by directly dissolving the corresponding salts in D.I. water. l-MgCl2 (50 ppm Mg2+), l-CaCl2 (50 ppm Ca2+) and l-BaCl2 (20 ppm Ba2+) stock solutions were prepared by diluting h-MgCl2, h-CaCl2 and h-BaCl2 stock solutions 100 times with D.I. water, respectively. 0.5 N pH=5.5 NaH2PO4, pH=8.0 Tris, and pH=10 glycine-NaOH buffer solutions were prepared by directly dissolving the respective salts into D.I. water, followed by the addition of 0.1 N NaOH, 1.0 N HCl solution and 1.0 N NaOH solution, respectively. The detailed recipes for pH buffer solutions are given Table S2. Evaluation of HTR IEX Resin Characterization Workflow: The developed HTR workflow was first evaluated for IEX resin characterization by screening a variety of anion exchange resins in a water matrix consisting of NO3- and SO42- at varied pHs. Due to the simplicity of the matrix environment, it is possible to systematically vary the SO42-/NO3- ratios and pH without DOE. Promising resin candidates were then identified for the study of selective nitrate removal. Case Study 1 Selective Nitrate Removal: The 4 promising resins identified above were further evaluated in a complex water matrix consisting of NO3-, NO2-, SO42-, Cl-, HCO3-/CO32- with varied concentrations at varied pHs (pH=5.5, 7.0, 8.5). Due to the presence of multiple anions at varied levels, the number of formulations can increase dramatically up to 2,916 samples assuming three levels for each anion (4×3×3×3×3×3×3=2,916). Thus, it becomes necessary to introduce DOE method to reduce the number of formulations, but still develop a statistical model to predict resin performance. The detailed DOE considerations are summarized in Table 3. The DOE was custom designed using JMP® software (SAS Institute, Cary NC). Optimal design was used here which not only allows model to be estimated with fewer experiments but also


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optimizes the constrained design space.22 JMP automatically select the type of optimality (D or I) to use. D optimality seeks to maximize the determinant of the information matrix X`X of the design; while I optimality seeks to minimize the average prediction variance over the design space. The quality of the proposed DOE can be assessed in terms of “Prediction Variance Profile” and “G-Efficiency”. The maximum “Prediction Variance” should be less than 1 and absolutely not greater than 1.5. Otherwise, the uncertainty associated with the model ability to predict in various conditions based on this design will be unacceptably high. G-Efficiency measures the design to that of an ideal orthogonal design, which ranges from 0 to 100% with 100% being the best. A rule of thumb here is that G-Efficiency needs to be greater than 70%. A design is optimal when it meets the above two criteria. Since 4×6 plate was used in HTR workflow, a special statistical DOE often referred to as a “Split-Plot” design was utilized and the number of experiments without replicates is fixed to be the multiple of 20 (n×20). A Split-Plot DOE is used in situations where groups of experiments with the same level of a primary factor will be completed together and the order of the individual experiment cannot be completely randomized within the available experimental unit positions (i.e. positions on each 4×6 plate). A Split-Plot DOE is developed to ensure that this grouping of the experiments (with the same resin type in a single column) would be handled in the most statistically friendly way and that the potential for biases would be minimized. The other advantage of applying a Split-Plot DOE for this experimental situation is to ensure that the analysis of the resulting data takes into account that the experiments were completed in these groups (sometimes referred to as “whole plots”). This will ensure that the most correct estimates of experimental errors are utilized in the analysis for testing the importance of each primary factor effect. The minimum number of experiments to meet both “Maximum Prediction Variance” < 1.5 and “G-Efficiency > 70%” was found to be


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100. In addition, 4 replicates are strategically added per 20 experiments (or 4 replicates per 4×6 plate) to ensure that reasonable estimates of reproducibility could be obtained from the resulting data. This gives a final number of 120 formulations, a small fraction (< 5 %) of the initial 2,916 total formulations. The detailed DOE design is provided in Table S3. The DOE statistical model can then be developed from these 120 formulations, and the best resin candidates identified using model prediction profiles. Table 3 Summary table on DOE factors, constraint, responses for selective nitrate removal

Primary factors (*: relative to 40 mM): 1) Resin types: Resin #1, Resin #2, Resin #3, Resin #4. 2) Initial pH: 5.5, 7.0, 8.5. 3) [NO3-]0+[NO2-]0*: 0.50, 1.25, 2.0. 4) [NO3-]0/([NO3-]0+[NO2-]0): 0.20, 0.50, 0.80. 5) [SO42-]0*: 0.50, 1.25, 2.0. 6) [Cl-]0*: 0.50, 1.25, 2.0. 7) [HCO3-]0*: 0.25, 0.625, 1.00. Constraint: pH + 6× [Cl-]0 – 6 × [HCO3-]0 ≥ 5.5 Responses: 1) NO3-+NO2- removal capacity 2) (NO3-+NO2-)/SO42- removal selectivity 3) NO3-/(NO3-+NO2-) removal selectivity

Case Study 2 High TDS Water Softening: Similar to selective nitrate removal in a complex water matrix, we have utilized a similar DOE method to reduce/optimize the sample size of resin-in-solution formulations as well as develop detailed DOE models for predicting resin performance. The high TDS water matrix consists of Na+, Mg2+, Ca2+ and Ba2+ cations under varied pH's. In this case, only one resin will be studied per DOE. Thus, only those factors relevant to the matrix solutions were considered when designing DOE experiments (Table 4).


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The entire formulation consists of 50 mg IEX resin and 5 mL pH-buffered aqueous solution of Na+, Mg2+, Ca2+ and Ba2+ cations at the concentrations designated by the DOE design. Following the same procedure, the developed DOE plan requires 48 formulations per resin, which is less than 20% of all possible 243 formulations (3×3×3×3×3=243). The detailed formulation recipes are shown in Table S4. By combining each DOE model together, it is possible to develop a master DOE model to study the dependence of resin performance on the structures and predict the best resin candidate at different conditions. Table 4 Summary table on DOE factors and responses for high TDS water softening

Primary factors: 1) 2) 3) 4) 5)

Initial pH: 5.5, 7.0, 8.5. [Na+]0: 1500, 5000, 15000 ppm [Mg2+]0: 5, 250, 500 ppm [Ca2+]0: 5, 250, 500 ppm [Ba2+]0: 2, 100, 200 ppm

Responses: 1) Equilibrium concentrations: [Mg2+]t, [Ca2+]t, [Ba2+]t. 2) The Effective Capacity (TEC: ,eq/g resin): Mg2+ TEC, Ca2+ TEC, Ba2+ TEC.

Alternatively, we can accelerate the above two case studies by limiting ion concentrations and pH to two levels (highest and lowest). This reduces the total number of experiments without replicates to 256 (4×2×2×2×2×2×2=256) and 32 (2×2×2×2×2=32) for selective nitrate removal and high TDS water softening study, respectively. By applying the optimal DOE design, these numbers are further reduced to 52 and 22. However, the 2-level design only allows a linear prediction between the points. Therefore, it is less preferred and not applied here.


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Tableau Software for Model Visualization: Tableau software (Tableau Software, Seattle WA) was utilized as a visualization tool to demonstrate the model-predicted resin performance in a more easy-to-understand manner. The predicted values were first generated using "Prediction Formula" obtained from the DOE statistical models in JMP. These values were then imported into the Tableau software and plotted versus different factors in separated worksheets. RESULTS AND DISCUSSION Evaluation of HTR IEX Resin Characterization Workflow The efficacy of HTR IEX resin characterization workflow was evaluated by screening 14 Dow IEX resins towards selective nitrate removal from a simple matrix consisting of NO3- and SO42at varied pHs. The selective nitrate removal can be summarized in two types of graphs: 1) anion removal capacity versus initial concentration ratio ([SO42-]0/[NO3-]0), 2) the equilibrium concentrations (y[SO42-]t, y [NO3-]t) in resins versus those in solutions. For example, Amberlite PWA-5 resin shows weak pH dependence, except for the fluctuation of SO42- removal capacities at high [SO42-]0/[NO3-]0 (Figure 3). This is consistent with the fact that Amberlite PWA-5 is a strong-base anion-exchange resin. In addition, Amberlite PWA-5 shows weak decrease of the NO3- capacities and small increase of the SO42- capacities with increasing [SO42-]0/[NO3-]0 ratio. The critical [SO42-]0/ [NO3-]0 ratio at which the SO42- capacity surpasses the NO3- capacity is estimated to be ~12 for Amberlite PWA-5. At equilibrium, the plot of y(NO3-)t vs. x(NO3-)t is well above the diagonal line (y=x), indicating that the NO3- molar ratio in resin phase (y[NO3-]t) is significantly higher than that in aqueous phase (x[NO3-]t); meanwhile in the case of the SO42distribution, an exact opposite trend is observed. This again indicates that Amberlite PWA-5 has a high NO3- removal selectivity under studied conditions.


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Figure 3 Amberlite PWA-5 resin performance at varied pHs: a) NO3- removal capacity, b) SO42removal capacity, c) NO3- equilibrium concentration, d) SO42- equilibrium concentration. Similar analysis can be applied for all of the other 13 anion exchange resins for selective nitrate removal and the results are summarized in Table S5. The overall resin performance can be ranked in terms of critical [SO42-]0/[NO3-]0 ratio and average NO3- removal capacity (Figure 4). Here, the average value and standard deviation of NO3- removal capacity for each IEX resin are calculated from values of NO3- removal capacities at different pHs and [SO42-]0/[NO3-]0 ratios.


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Since most resins have weak pH dependence except for Amberlite PWA-7, the standard deviation in the NO3- removal capacity originates mainly from the [SO42-]0/[NO3-]0 dependence (Table S5). The higher standard deviation normally indicates that the NO3- capacities decrease more rapidly with [SO42-]0 /[NO3-]0 ratio. Those resins at the upper right corner have high NO3removal capacity (> 0.5 mmol/g) and selectivity (critical [SO42-]0 /[NO3-]0 ratio > 10), thus categorized as "Type A Resin"; while those at the lower right corner have high NO3- removal capacity (> 0.5 mmol/g) but low selectivity (critical [SO42-]0 /[NO3-]0 ratio < 10), thus categorized as "Type B Resin". Only Amberlite PWA-7 is at the lower left corner and has both low NO3- removal capacity (< 0.5 mmol/g) and selectivity (critical [SO42-]0 /[NO3-]0 ratio < 10), thus categorized as "Type C Resin". Clearly, in order to achieve good NO3- selective removal, it is preferred to use "Type A Resin" such as DOWEX PSR-2, TEA-XUR-1525-L09-085, TEAXUR-1525-L09-083, TEA-XUR-1525-L09-081, DOWEX PSR-3 and Amberlite PWA-5.

Figure 4 Phase diagram of resin performance on NO3- selective removal: 1)=DOWEX PSR-2, 2)=TEA-XUR-1525-L09-085,

3)=TEA-XUR-1525-L09-083,

4)=TEA-XUR-1525-L09-081,


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5)=DOWEX PSR-3, 6)=Amberlite PWA-5, 7)=TEA-XUR-1525-L09-084, 8)=DOWEX NSR-1, 9)=DOWEX MSA-1, 10)=DOWEX-1, 11)=DOWEX MARATHON A, 12)=TEA-XUR-1525L09-082, 13)=DOWEX MARATHON A2, 14)=Amberlite PWA-7. Case Study 1 Selective Nitrate Removal Three Type A resins (Amberlite PWA-5, TEA-XUR-1525-L09-081, TEA-XUR-1525-L09-083) and one Type B resin (DOWEX NSR-1) were selected for further evaluation of selective nitrate removal from a complex water environment. Due to potential interactions between various factors, we could not detect any relationship between nitrates removal capacities/selectivities and different factors as most data distribute randomly across the space due to the nature of DOE method (Figure S4 to S6). The DOE modeling thus becomes essential here to help detect significant interactions, and further understand the above relationship, and make predictions when testing new resins in new environments. Because the DOE plan typically calls for completing only a small fraction of the total possible experiments, the corresponding DOE modeling provides the ability to predict all of the possible experimental conditions whether they were completed as part of the DOE plan or not. This can provide an efficient approach to experimentation by not requiring every possible experiment to be completed. And DOE model can provide useful predicted results and identify strategic follow-up experiments. Since each DOE factor has three levels, Response Surface Methodology (RSM) was used to model the data using resin TEC in place of resin name with no model reduction. Figure 5 summarizes the quality of the RSM models for the nitrates removal capacity and selectivity in terms of p value, Rsq, RMSE (root-mean-square error) and distribution of residuals. Because any model can be fit to any data, we really want to carefully assess how well the model fits and


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whether it appears to be trustworthy in terms of the predictions that it can provide. The first step is to assess if the model is significant (i.e. should the DOE model be considered at all?). Since all three p values are far less than 0.05 (the significance level that corresponds to 95% confidence), we conclude that the models are significant. Furthermore, all three "Residual vs. Predicted" plots show that model residuals are randomly scattered which suggests that the model is NOT missing other obvious effects. Therefore, by using the HTR method to generate data and applying sound empirical modeling practices, we have obtained significant RSM models with good quality and stability for all three responses. It is worth mentioning that a few actual and predicted NO3/(NO3-+NO2-) removal capacity ratios are negative due to the negative NO3- removal capacities as shown in Figure 5e. However, these only occur at low initial [NO3-]0/([NO3-]0+[NO2-]0) concentration ratio of 0.2 and pH of 5.5. It is well-known that nitrite is unstable in acidic condition with respect to the disproportionation reaction (3HNO2 = H3O+ + NO3- + 2NO), which generates NO3-.23 In addition, the NO3- removal is expected to be suppressed due to the low [NO3-]0/([NO3-]0+[NO2-]0) concentration ratio. Therefore, more NO3- is generated than removed by IEX resin in this case, resulting in the negative NO3- removal capacities and NO3-/(NO3+NO2-) capacity ratios. By incorporating the actual data into the DOE modeling, the developed model is capable of predicting this real scenario.


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ACS Paragon Plus Environment

21


Figure 5 Summary on DOE RSM model for (NO3-+NO2-) removal capacity, (NO3-+NO2-)/SO42and NO3-/(NO3-+NO2-) removal ratio: a, c, e) actual vs. predicted; b, d, f) distribution of residuals. As shown in Figure 6, all three responses [(NO3-+NO2-) removal capacities, (NO3-+NO2-)/SO42and NO3-/(NO3-+NO2-) removal selectivity] are plotted in "Prediction Profile". By designating the desirability to simultaneously maximize (NO3-+NO2-) capacity and (NO3-+NO2-)/SO42- ratio (Importance 1:1), the model predicts the best performance to be 1.30 mmol/g and 7.40 at resin TEC = 1.1 meq/mL (Amberlite PWA-5), initial pH = 8.5, [NO3-]+[NO2-] = 2, [NO3-]/([NO3-]+

1

0.5

0.75

0

0.25

0.9

0.7

0.5

0.3

1.8

1

1.4

0.6

1.8

1

1.4

0.8 0.6

0.6

0.4

0.2

1.8

1

1.4

8.5 0.6

7.5

6.5

1

1.1 5.5

0.9

0.8

0.7

0 0.25 0.75 1

NO3/ (NO3+NO2) 0.868868 ±0.082622

(NO3+NO2)/ SO4 7.400612 ±0.902852

NO3+NO2 removal 1.300254 ±0.0846

[NO2-]) = 0.8, [SO42-] = 0.5, [Cl-] = 0.64 and [HCO3-] = 1.0, respectively.

Desirability 0.873152

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Figure 6 Prediction profiles for simultaneously maximizing (NO3-+NO2-) removal capacity and (NO3-+NO2-)/ SO42- removal ratio (importance 1:1). A series of optimal resin performances can be obtained from "Prediction Profile" by varying "Desirability" and "Importance" (See Table 5 for selected examples and Table S7 for a more comprehensive list). Although the detailed formulation recipes are very sensitive to the


22

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"Desirability" and "Importance", it is still clear that the maximal nitrates selective removals are achieved at high resin TEC (Amberlite PWA-5, TEC=1.1 meq/mL), high [NO3-]+[NO2-] and low [SO42-]. Meanwhile, the initial pH and [NO3-]/[NO3-] +[NO2-] ratio determine the relative selectivity towards (NO3-+NO2-), NO3- or NO2-. In general, high [NO3-]/[NO3-]+[NO2-] favors (NO3-+NO2-) and NO3-, and vice versa. The effect of initial pH is not so straightforward, except that high initial pH favors the selectivity towards nitrates versus sulfate. As for [Cl-] and [HCO3], they are fixed at ~ 1.0 (40 mM) in most cases, indicating that the models have weaker dependence on them. This is consistent with their insignificance in the model ("Prob>F" values in Table S6), and can be explained by the fact that Cl- is the counter-ion on the resins and the carbonate species are not stable at pH < 7. Table 5 Optimal conditions for optimal (NO3-+NO2-) removal capacities (mmol/g), (NO3-+ NO2-) /SO42- and NO3-/(NO3-+NO2-) removal selectivity predicted by "Prediction Profile". TEC (meq /mL)

Initial pH

[NO3] + [NO2]

[NO3]/ [NO3]+ [NO2]

Desirability

Importance

[SO4]

[Cl]

[HCO3]

Value

(NO3+NO2)

Maximize

4

(NO3+NO2)/SO4

Maximize

1

NO3/(NO3+NO2)

Maximize

1

0.81

(NO3+NO2)

Maximize

1

1.23

(NO3+NO2)/SO4

Maximize

4

NO3/(NO3+NO2)

Maximize

1

0.92

(NO3+NO2)

Maximize

1

1.22

(NO3+NO2)/SO4

Maximize

1

NO3/(NO3+NO2)

Maximize

4

(NO3+NO2)

Maximize

4

1.39 1.10

1.10

1.10

6.21

8.50

7.48

2.00

2.00

2.00

0.80

0.80

0.80

0.50

0.50

0.50

0.88

1.24

1.08

1.00

1.00

0.70

6.63

7.72

7.42 1.02

1.10

8.36

2.00

0.80

0.50

0.79

1.00

1.28


23


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(NO3+NO2)/SO4

Maximize

4

7.49

NO3/(NO3+NO2)

Maximize

1

0.90

(NO3+NO2)

Maximize

4

1.32

(NO3+NO2)/SO4

Maximize

1

NO3/(NO3+NO2)

Maximize

4

0.95

(NO3+NO2)

Maximize

1

1.14

(NO3+NO2)/SO4

Maximize

4

NO3/(NO3+NO2)

Maximize

4

1.08

(NO3+NO2)

Maximize

1

1.27

(NO3+NO2)/SO4

Maximize

1

NO3/(NO3+NO2)

Maximize

1

1.10

1.10

1.10

7.21

7.19

7.76

2.00

2.00

2.00

0.80

0.80

0.80

0.50

0.50

0.50

0.50

1.35

1.00

0.79

0.25

1.00

6.81

7.76

7.45 0.96

Case Study 2 High TDS Water Softening Although the detailed comparison on the high TDS water softening behavior between each resin requires the development of the DOE model, a simple quantitative analysis can be done first by comparing Strong Acid Cation (SAC) resins with Weak Acid Cation (WAC) resins at median level of Mg2+/Ca2+/Ba2+ initial concentration (250/250/100 ppm), where replicates are available (Figure 7). The statistical analysis indicates that in general, WAC resins are more effective for removing Mg2+ and Ca2+ than SAC resins resulting in statistically lower equilibrium concentrations and higher Mn2+ TECs. Considering that WAC resins have higher MHCs than SAC resins (Table 2), the difference in TECs can be even larger for dry resins. Although the SAC resins seem to give slightly better Ba2+ removal efficiency, the difference is insignificant from the statistical point of view. This insignificant difference in the Ba2+ removal could be explained by the higher affinity of cation exchange resin towards larger Ba2+ ion even at lower


24

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concentration so that Ba2+ can be effectively removed regardless of resin type. Overall, the experimental results at median level of Mg2+/Ca2+/Ba2+ suggest that WAC resins are preferred for the high TDS water softening.

Figure 7 Equilibrium concentration and Mn2+ TEC at center point: a-b) Mg2+, c-d) Ca2+, e-f) Ba2+.


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The superior performance of WAC resins than SAC resins for removing Mg2+ and Ca2+ cations might originate from their higher total TECs (Table 2). Hence, the average values and standard deviations of Mg2+, Ca2+ and Ba2+ TECs are calculated from all the values at median Mg2+/Ca2+/Ba2+ initial level (250/250/100 ppm) and varied pH (5.5/7.0/8.5) and Na+ level (1500/5000/15000 ppm), which are plotted against resin TECs in Figure 8. Here, the standard deviation originates from the dependence of TECs on pH and Na+ level. Thus, the larger standard deviation indicates the stronger dependence of Mn2+ TEC on pH and Na+ level. The linear relation between Mg2+ capacity and resin TECs is pretty good (R2~0.90), indicating that Mg2+ is mainly removed by Mg2+─Na+/H+ exchange. The different Mg2+ TECs between DOWEX MAC 3 and Purolite C104 (both resin TEC = 3.8 eq/L) suggests that other factors such as matrix and porosity might also contribute to the resin performance. As for Ca2+ cation, Amberlite IRC748 gives disproportionally higher capacity, leading to the lower linear relation (R2~0.67). This can be attributed to the chelating effect between Ca2+ and iminodiacetic acid (stabilization constant logβ~10.69 estimated for Ca[EDTA]4-; EDTA is dimer of iminodiacetic acid) in addition to the Ca2+─Na+/H+ exchange. As for Ba2+ removal, the fitting result shows it is independent of resin TECs consistent with the previous analysis.


26

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0.9

Mg TEC y = 0.1548x - 0.0042

0.8

Mn2+ TEC (meq/wet g resin)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Ca TEC

0.7

Ba TEC

0.6

R² = 0.8985 y = 0.0765x + 0.1543 R² = 0.6689 y = 0.0023x + 0.0506 R² = 0.1048

0.5 0.4 0.3 0.2 0.1 0.0 1

2

3

4

5

TEC (eq/L) Figure 8 The dependence of divalent cation removal on resin TEC. Besides the above analysis, it is also of great interests to develop a master RSM model for studying the dependence of resin performance on their structures. Figure 9 summarizes the resulting master RSM model qualities using the resin type as a categorical factor in addition to other DOE factors. Significant models ("p-value" 0.3). The relatively poor fitting result of [Ba2+]t is consistent with the above analysis that shows Ba2+ removal is independent of resin type. In addition, the random distributions of residuals in "Residual vs. Predicted" plots imply that all the important effects are included in the current model. Overall, the master RSM model is significant with good quality and balance.


27


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Figure 9 Summary on master RSM model for Mn2+ equilibrium concentration and TEC: a-d) Mg2+, e-h) Ca2+, i-l) Ba2+.


29


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The optimal high TDS water softening performance can be predicted using the "Prediction Profile" developed in the master RSM model. By designating the desirability to simultaneously minimize [Mg2+]t, [Ca2+]t, [Ba2+]t (Importance=1:1:1) at [Mg2+]0=[Ca2+]0=250 ppm and [Ba2+]0=100 ppm, Purolite C104 is predicted to give the best performance at pH=10 and [Na+]0=1500 ppm (Figure 10). Various optimal conditions can be obtained by defining different "Desirability"/"Importance" and solution matrix.


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Figure 10 Prediction profile for simultaneously a) minimizing [Mg2+]t, [Ca2+]t, [Ba2+]t and b) maximizing Mg2+, Ca2+ and Ba2+ TECs at [Mg2+]0=[Ca2+]0=250 ppm, [Ba2+]0=100 ppm (Importance=1:1:1) As shown above, "Prediction Profile" is a valuable tool to display how the model predicts the optimal conditions for the best high TDS water softening performance at designated conditions. A series of optimal resin performances for removing single Mg2+, Ca2+, Ba2+ are obtained and tabulated in Table 6 (selected examples at high Na+ level of 15000 ppm) and Table S9 (more comprehensive list at varied Na+ level) based on the following conditions: a) Only one [Mn2+]t is minimized or one Mn2+ TEC is maximized at one time. b) For the studied [Mn2+]t and Mn2+ TEC, the corresponding [Mn2+]0 is fixed at 250/100 ppm or 500/200 ppm while other [Mn2+]0 are allowed to change freely. c) [Na+]0 is fixed at 1500, 5000 and 15000 ppm.


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Given the large number of potential combinations of "Desirability" and the associated "Importance" as well as the varied matrix conditions, the above conditions produce the simplified cases in that only one divalent cation is optimized at one time. Several conclusions can be drawn from Table 6 and S9 which are summarized as follows: a) pH = 10 gives the best high TDS water softening results in all cases. b) Mg2+ removal: Purolite C104 gives the best performance regardless of [Na+]0 and [Mg2+]0. c) Ca2+ removal: DOWEX MAC 3 gives the best performance at the median [Ca2+]0 and the low to median [Na+]0; while Purolite C104 gives the best performance at the high [Ca2+]0 and the low to median [Na+]0. As for the high [Na+]0, Amberlite IRC748 gives the best performance regardless of [Ca2+]0. d) Ba2+ removal: DOWEX MAC 3 gives the best performance at the median [Ba2+]0 and the low to median [Na+]0; while Amberlite IRC 748 gives the best performance at the median [Ba2+]0 and the high [Na+]0. As for the high [Ba2+]0, Purolite C104 and Lewatit Monoplus S108H gives the best performance at high to median and low [Na+]0, respectively. Table 6 Optimal conditions for maximal removal of single divalent cations Resin

Min.

Ini. pH

[Na+]0

[Mg2+]0

[Ca2+]0

[Ba2+]0

[Mg2+]t

8

45.8

10.0

15000

250

19.8

3.4

[Mg2+]t

8

162.3

10.0

15000

500

12.9

4.9

[Ca2+]t

9

16.1

10.0

15000

18.3

250

6.6

[Ca2+]t

9

129.1

10.0

15000

8.9

500

11.3

[Ba2+]t

9

9.1

10.0

15000

13.7

55.5

100

[Ba2+]t

8

57.1

10.0

15000

11.8

208.9

200


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Resin

Max.

Ini. pH

[Na+]0

[Mg2+]0

[Ca2+]0

[Ba2+]0

Mg2+ TEC

8

0.84

10.0

15000

250

7.6

11.5

Mg2+ TEC

8

1.38

10.0

15000

500

16.0

8.1

Ca2+ TEC

9

0.59

10.0

15000

12.7

250

7.9

Ca2+ TEC

9

0.93

10.0

15000

5.5

500

7.2

Ba2+ TEC

9

0.07

10.0

15000

9.6

30.7

100

Ba2+ TEC

8

0.10

10.0

15000

6.1

239.4

200

In Table 6: a) Column 2 Resin: 8=Purolite C104; 9=Amberlite IRC748. b) Column 3: minimized [Mn2+]t in the unit of ppm and maximized Mn2+ TEC values in the unit of meq/wet g. c) Column 5 to 8: [Na+]0 and [Mn2+]0 in bold italic font means the initial level is fixed in the prediction; "Prediction Profile" can also be used to predict the optimal conditions for removing multiple divalent cations. Table 7 highlights the optimal resin performance for removing Mg2+ and Ca2+ simultaneously at high Na+ level of 15000ppm, based on the following conditions (More comprehensive list at varied Na+ level is in Table S10). a) Importance = 1: 1 for removing Mg2+ and Ca2+. b) For the studied [Mn2+]t and Mn2+ TEC, the corresponding [Mg2+]0/[Ca2+]0 is fixed at 400/100 ppm, 250/250 ppm and 100/400 ppm while [Ba2+]0 is allowed to change freely. c) [Na+]0 is fixed at 1500, 5000 and 15000 ppm. Given the assigned values of "Importance", the above predictions are based on the assumption that it is of equal importance for removing Mg2+ and Ca2+. Other values of "Importance" could be used which prioritize the removal and give potentially different optimal conditions. Several conclusions can be drawn from Table 7 and S10 as follow.


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a) pH = 10 and very low [Ba2+]0 (< 5 ppm) gives the best performance in all cases. b) In all the cases regardless of [Na+]0, [Mg2+]0, [Ca2+]0 and [Ba2+]0 levels, Purolite C104 gives the best performance except for DOWEX MAC 3 at [Na+]0=1500 ppm, [Mg2+]0/[Ca2+]0= 100/400 ppm . Table 7 Optimal conditions for maximal removal of Mg2+, Ca2+ with Importance=1:1

Resin

Min. [Mg2+]t

Min. [Ca2+]t

Ini. pH

[Na+]0

[Mg2+]0

[Ca2+]0

[Ba2+]0

8

122.8

8.3

10.0

15000

400

100

2.3

8

67.9

65.3

10.0

15000

250

250

3.5

8

11.9

116.2

10.0

15000

100

400

3.0

Resin

Max. Mg2+ TEC

Max. Ca2+ TEC

Ini. pH

[Na+]0

[Mg2+]0

[Ca2+]0

[Ba2+]0

8

1.14

0.23

10.0

15000

400

100

6.4

8

0.75

0.46

10.0

15000

250

250

3.9

8

0.36

0.71

10.0

15000

100

400

2.0

In Table 7: a) Column 1 Resin: 8=Purolite C104. b) Column 2 and 3: minimized [Mn2+]t in the unit of ppm and maximized Mn2+ TEC in the unit of meq/wet g; c) Column 5 to 8: [Na+]0 and [Mn2+]0 in bold italic font means the initial level is fixed in the prediction In summary, WAC resins give better performance than SAC resins for removing Mg2+ and Ca2+, consistent with the simple statistical analysis in Figure 7. The difference between WAC and SAC might be subtle in term of Ba2+ removal. Overall, Purolite C104 gives best performance, especially when removing Mg2+ and Ca2+ simultaneously. Interestingly, Amberlite IRC748 gives the best single Ca2+ removal at high Na+ level, in contrast to its lowest resin TEC. This might be


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attributed to the additional chelating effect between Ca2+ and iminodiacetic acid which is less sensitive to the Na+ background. Model Visualization via Tableau Software Although "Prediction Profile" provides valuable information on the optimal conditions at each designated matrix compositions as well as the "Desirability" with the associated "Importance", it is a point-by-point protocol that is less efficient to study the general trend of resin performance. Moreover, only the optimal performance of one resin will be given as the output, making the tool insufficient for resin comparison. Here, the Tableau software is superior in that it can incorporate a large quantity of experimental data and predict values as well as the associated error bars into one file and then plot them versus different factors. Most importantly, it gives users the freedom to choose any combination of those factors when comparing the resin performance using a simple point-and-click graphic user interface (GUI). The visualization of high TDS water softening RSM model was done using Tableau. Figure 11 shows the layout of the Tableau interface. It includes multiple worksheets for visualizing the dependence of resin performance on pH and Na+ level. It allows users to independently select the types of resins and levels of divalent cations using the right top column of “Resin Type & Resin Selector” and bottom row of “Level Selector”, respectively. “Zoom In” and “Zoom Out” functions are available to enlarge the area of interests. The predicted values and errors are shown in the format of Figure 11c. Multiple data points can be exported into “Excel” file for sharing with customers.


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c)

(Ba2+)0 (ppm): 100 (Ca2+)0 (ppm): 250 (Mg2+)0 (ppm): 250 Ini# pH: 9 Data Type: Predicted (Na+)0 (ppm): 5000 Resin: Amberlite IRC748 (Ba2+) t (ppm): 13.16 (Ca2+)t (ppm): 49.56 (Mg2+)t (ppm): 19.25

(Ba2+)0 (ppm): 100 (Ca2+)0 (ppm): 250 (Mg2+)0 (ppm): 250 Ini# pH: 9 Data Type: Predicted (Na+)0 (ppm): 5000 Resin: Amberlite IRC748 Ba2+ TEC (meq/wet g): 0.04169 Ca2+ TEC (meq/wet g): 0.3922 Mg2+ TEC (meq/wet g): 0.3086

Figure 11 Tableau layout: dependence of Mn2+ removal on a) pH, b) Na+ level; c) the format of predicted values. For example, Figure 11 illustrates the pH and Na+ dependences of DOWEX MAC 3, Amberlite IRC748 and DOWEX MARATHON C resin performances. Clearly, DOWEX MARATHON C gives the best performance for Ba2+ removal resulting in the lowest [Ba2+]t and highest Ba2+ TEC regardless of Na+ and pH levels. In the case of Ca2+ removal, DOWEX MAC 3 and Amberlite IRC 748 give the lowest [Ca2+]t and highest Ca2+ TEC at low and high [Na+]0, respectively. Finally, the best Mg2+ removal can be achieved using DOWEX MAC 3 though its superior performance to Amberlite IRC748 and DOWEX MARATHON C diminishes with increasing [Na+]0 and decreasing pH. CONCLUSIONS A HTR workflow in combination with DOE methodology has been developed for the rapid evaluation of IEX resin performance in complex water environment. The developed HTR workflow was successfully applied for studying the selective nitrate removal and high TDS


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water softening. Significant statistical models were developed for predicting the resin performance, from which the best resin candidates can be identified. Finally, the DOE model results can be illustrated using Tableau for better visualization and enhanced interaction with customers. Overall, the developed HTR IEX resin characterization workflow is expected to significantly accelerate the pace of screening IEX resins against new applications and better defining the scope and limitations of Dow’s current IEX resins in the field where the "water" matrix differs from individual environments. Supporting Information: Table S1 to S10 and Figure S1 to S6 are available free of charge via the Internet at http://pubs.acs.org. AUTHOR INFORMATION Corresponding Author Tel: +1 (989) 636-4097; Fax: +1 (989) 636-1102; Email: [email protected]. Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Funding Sources This work was supported by Growth Acceleration Funding from The Dow Chemical Company. Notes The authors declare no competing financial interests. ACKNOWLEDGMENT


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The authors would like to acknowledge Towhid Hasan, Craig Wintland and Qiang Li, Kalyani Martinelango for ICP-AES and IC analyses, respectively. REFERENCES 1. Maier, W.; Stöwe, K.; Sieg, S. Combinatorial and High-Throughput Materials Science. Angew. Chem. Int. Ed. 2007, 46, 6016-6067. 2. Iden, R.; Schrof, W.; Hadeler, J.; Lehmann, S. Combinatorial Materials Research in the Polymer Industry: Speed versus Flexibility. Macromol. Rapid Commun. 2003, 24, 63-72. 3. Potyrailo, R.; Olson, D.; Medford, G.; Brennan, M. Development of Combinatorial Chemistry Methods for Coatings: High-Throughput Optimization of Curing Parameters of Coatings Libraries. Anal. Chem. 2002, 74, 5676-5680. 4. Kelley, B.; Switzer, M.; Bastek, P.; Kramarczyk, J.; Molnar, K.; Yu, T.; Coffman, J. Highthroughput Screening of Chromatographic Separations: IV. Ion Exchange. Biotechnol. Bioeng. 2008, 100, 950-963. 5. Peil , K.; Neithamer, D.; Patrick, D.; Wilson, B.; Tucker, C. Applications of High Throughput Research at The Dow Chemical Company. Macromol. Rapid Commun. 2003, 25, 119-126. 6. Bishop, M.; Cesaretti, R.; Cohen, J.; Dermody, D.; Deshmukh, S.; Graf, I.; Grzesiak, A.; Harris, K.; Kuo, T.; Mecca, J.; Mohler, C.; Singh, A.; Seasholtz. M; Timpe, S.; Zhang, H.; Zalusky, A. High Throughput Research Capabilities for Formulated Materials. Dow Chemical Central Report Index (CRI) Report 2009, 2009001554. 7. Cao, K.; Liu, Y.; Tucker, C.; Baumann, M.; Grit, G.; Lasko, S. High-Throughput Method to Predict Pressure of Ceramic Pastes. ACS Comb. Sci. 2013, 25, 198-204. 8. Fisher, R. The Design of Experiments. 7th Edition, Edinburgh: Oliver and Boyd, 1960.


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20. Hays, M.; Bloxom, S.; Stapelkamp, W.; Gregory, J.; Harris, K.; Boomgaard, T.; Sweeney, J. Design and Validation of High Throughput Research pH and Conductivity Robot ‒ Part 2: pH Validation. Dow Chemical Central Report Index (CRI) Report 2011, 2011010714. 21. Rokushika, S.; Kihara, K.; Subosa, P.; Leng, W. Ion Chromatography of Nitrite, Bromide and Nitrate Ions in Brine Samples using a Chloride-Form Anion-Exchange Resin Column. J. Chromatogr. 1990, 514, 355-361. 22. Atkinson, A.; Donev, A.; Tobias, R. Optimum Experimental Designs, with SAS. Oxford University Press, 2007. 23. Greenwood, N; Earnshaw, A. Chemistry of the Elements. 2nd Edition, ButterworthHeinemann, 1997.


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Development of a High-Throughput Ion-Exchange ... - ACS Publications

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