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Micro Open Parallel Plate Separator ( m OPPS) : Performance and Applications

Micro Open Parallel Plate Separator ( m OPPS) : Performance and Applications. Blanca H. Lapizco-Encinas Department of Chemical and Materials Engineering University of Cincinnati. Outline. Introduction. Objectives. Comparison of separators with rectangular and circular cross sections.

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Micro Open Parallel Plate Separator ( m OPPS) : Performance and Applications

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  1. Micro Open Parallel Plate Separator (mOPPS): Performance and Applications Blanca H. Lapizco-Encinas Department of Chemical and Materials Engineering University of Cincinnati

  2. Outline • Introduction. • Objectives. • Comparison of separators with rectangular and circular cross sections. • Modeling mOPPS and equation for reduced plate height. • Concentration of trace species by displacement • Prediction of equilibrium isotherms for protein-salt systems. • Conclusions.

  3. Liquid Chromatography • Liquid Chromatography (LC) is one of the most important techniques used for separating a chemical mixture into its components. • Traditionally, LC was used for analytical applications. LC is now being used in preparative modes. • Preparative Liquid chromatography is used for scaling up separations of fragile substances. • Preparative modes of chromatography are becoming essential for pharmaceutical, biological and environmental applications.

  4. data acquisition solvent column sample detector pump waste Liquid ChromatographyBench scale system • A powerful separation technique….

  5. Inlet nipple Outlet nipple Outlet nipple Outlet nipple Inlet through Outlet through Outlet reservoir Inlet reservoir Top glass wafer Silicon Channel Channel Channel Electrode Signal in/out Signal in/out Signal in/out Bottom glass wafer Interconnecting metal line Bonding pad External bonding pad Bonding wire Micro OPPS: Integration of Column and Detector H.T. Henderson, N. deGouvea-Pinto, Liquid Chromatograph on a Chip. US Patent 6,258,263B1, July 10, 2001.

  6. Top view cross-section Bottom of the microchannel Channel. Wall [111] [111] [111] Electrode Detector cell Advantages of OPPS

  7. Cl- Br- SO4- - 1.5 1.4 1.3 1.2 1.1 Current (mA) 300 305 310 315 320 325 330 335 340 345 350 Time (s) Results Obtained with the Proof-of-ConceptmOPPS

  8. width diameter depth length length OPPS OTS Which Geometry is Better for Micro Separators?

  9. Objectives The goal of this research was to develop a mathematical model to describe the mOPPS, and through simulations achieve the following specific objectives: • To define the optimal geometry as a function of the separation characteristics. • To develop an equation for the reduced plate height of a mOPPS. • To investigate the device capabilities for concentrating trace species by displacement. • To study the potential of the mOPPS for predicting isotherm data for protein-salt systems.

  10. HETP = 20 cm HETP = 10 cm Plate 1 Plate 2 Plate 3 Plate 4 Plate 5 Plate 6 Plate 7 Plate 8 Plate 9 Plate 10 Plate 1 Plate 2 Plate 3 Plate 4 Plate 5 1 m. HETP • The first goal was to identify the parameters that influence the performance of the mOPPS. • The performance of the mOPPS was evaluated by using the height equivalent to a theoretical plate.

  11. ( ) ( ) æ ö æ ö æ ö ¶ ¶ + - - ¶ ¶ ¶ 2 2 2 2 2 2 2 2 2 C C b d x b z d C C C ç ÷ ç ÷ ç ÷ - = + + 3 v D ç ÷ ç ÷ ç ÷ ( ) ( ) avg ¶ ¶ - + - ¶ ¶ ¶ 2 2 2 2 2 2 2 2 2 t y b d x b z d x y z è ø è ø è ø x b z d Vavg y Numerical Model for OPPS • A 3-dimensional model was developed

  12. ( ) ( ) æ ö + 2 2 2 b d - 2 2 2 x - z d 1 b ç ÷ = - = ( ) v 6 v K x , z K ( ) ( ) ç ÷ v avg y 2 2 v 2 - + - 2 2 2 2 b d x b z d è ø t = £ C £ £ U £ £ Z £ 0 0 1 0 1 0 1 , , C(X,Y,Z)=0 Mathematical Model • Velocity profile (Spangler, 1998): • Initial condition

  13. ¶ C ¶ C * q = - = Condition 4: At X=0, 0 Condition 1: At X=1 D ¶ C ¶ x ¶ t = = X 0 x b ¶ C ¶ C = - Condition 5: 0 At Z=0, Condition 2: At Z=1 D = 0 ¶ Z ¶ Z = 1 Z = 0 Z ¶ C = Condition 3: Condition 6: At Y=1 At Y=0, 0 ¶ U = 1 U Z )= C C(X,0, feed < t £ t 0 feed C(X,0,Z)= 0 t > t feed Mathematical Model Boundary conditions

  14. ( ) ( ) æ ö - - 2 2 X 1 Z 1 1 ç ÷ j m æ ö 1 R S R æ ö R + n t t ç ÷ ç ÷ y y - d = + d + d - d 2 2 2 n n x ç ÷ 1 C 1 z C C 3 ç ÷ + n n 1 x j , k , m y z j , k , m y 0 j , k , m ç ÷ ( ) ( ) 2 b 3 3 3 3 è ø 3 è ø - + - 2 2 X 1 Z 1 ç ÷ j m è ø 2 d ( ) ( ) æ ö - - 2 2 X 1 Z 1 1 2 ç ÷ 1 j m 1 æ ö 2 R S R + æ ö R n t + + n n t ç ÷ ç ÷ x y - d = + d + d - d 3 2 2 2 y ç ÷ 1 C 1 z C C 3 3 ç ÷ + n 1 + n 1 y j , k , m x z j , k , m y 0 j , k , m ç ÷ 3 ( ) ( ) 2 b 3 3 3 3 è ø 3 è ø - + - 2 2 X 1 Z 1 ç ÷ j m è ø 2 d ( ) ( ) æ ö - - 2 2 X 1 Z 1 ç ÷ j m 2 2 æ ö R S 2 R æ ö R + + n + n n 1 ç ÷ ç ÷ x y - d = + d + d - d 2 2 2 z ç ÷ 1 C 1 y C C 3 3 t ç ÷ t z j , k , m x y j , k , m y 0 j , k , m ç ÷ ( ) ( ) 2 b 3 3 3 3 è ø è ø + n 1 - + - + 2 2 X 1 Z 1 n 1 3 ç ÷ j m è ø 2 d Numerical Procedure • Finite differences method. • Peaceman-Rachford scheme- Alternating Dimension Implicit (ADI).

  15. v L 2 2 v b v d avg avg avg = Pe q = q = X Z D D L D L 2 q a æ ö d H b = = = a Z ç ÷ = h q b è b ø d X eq Simulation Parameters • The following dimensionless parameters were used to characterize the performance of the mOPPS.

  16. v ( ) r avg = - + 2 C 6 R 16 R 11 h OTS D 48 ( ) - + 2 2 35 R 84 R 51 v 2 b avg = h OPPS d D 105 eq Existing Models for mOPPS and mOTS • Giddings et al. developed two models for predicting the reduced plate height of mOTS and mOPPS.

  17. = q * a C i i i Feed volume = 4% of the column volume ai=0.00792 cm q* Vavg 0.2 cm/s 10-1000 mm C Cfeed= 1000 mmol/cm3 10 – 100 mm 3 cm Simulations • Simulations for the mOPPS and mOTS were performed using the parameters shown below. • The solute was assumed to be KBr ion exchanging on a PEI activated surface.

  18. 14 v ( ) r 12 avg = - + 2 C 6 R 16 R 11 h 10 OTS D 48 OTS 8 h 6 4 2 OTS simulator Giddings et al. 0 0 20 40 60 80 100 OTS radius rC (mm) Simulations for mOTS • The first simulations were performed by using a program for the mOTS in order to compare the simulation results with an existing mOTS model (Giddings et al., 1983).

  19. 14 a =1 12 10 OPPS 8 h 6 4 ( ) - + 2 2 35 R 84 R 51 v 2 b OPPS simulator 2 avg = h Giddings et al. 0 OPPS d D 105 0.0 0.5 1.0 1.5 2.0 2.5 eq qX x 102 Simulations for mOPPS • Simulations for the mOPPS were performed, and the simulation results were compared with Giddings model for mOPPS

  20. 14 a=1 a=25 12 a=100 a=400 10 2 q æ ö d = = a ç ÷ Z OPPS q 8 è b ø X h 6 4 2 0 0.0 0.5 1.0 1.5 2.0 2.5 qX x 102 Simulations for mOPPS • Simulations for the mOPPS were performed changing the depth to width ratio (a)

  21. At the END of the channel length At one tenth of the channel length Why is the Depth to Width Ratio Important? • Concentration gradients develop along the microchannel width and depth(10 mm wide channel)

  22. At one tenth of the channel length At the END of the channel length Why is the Depth to Width Ratio Important? • Concentration gradients develop along the microchannel width and depth(50 mm wide channel)

  23. æ ö 0 . 27 4 . 81 Pe ç ÷ = + q 0 . 35 0 . 92 h 3 . 37 Pe b ç ÷ OPPS X a è ø 14 a=1 a=100 12 Our model 10 8 OPPS h 6 ( ) 4 - + 2 2 35 R 84 R 51 v 2 b avg = 2 h OPPS d D 105 0 0.0 0.5 1.0 1.5 2.0 2.5 eq qX x 102 Our mOPPS Model • After performing a wide range of simulations, an empirical equation for reduced plate height was developed

  24. Conclusions: HETP Equation • It was proven that reduced plate height models for mOPPS can not be developed by analogy with mOTS since concentration gradients along the depth influence the chromatographic characteristics. • A reduced plate height equation must include the following parameters: mOPPS geometry, flow and adsorption characteristics. • By using the predictions of the mOPPS simulator, an empirical equation has been developed for predicting plate height in mOPPS. Lapizco-Encinas, B.H., and Pinto, N.G., Performance Characteristics of Novel Open Parallel Plate Separator, Separation Science and Technology, Vol. 37, No. 12, 2745-2762, 2002.

  25. Concentration of Trace Species by Displacement • Why concentrate trace species? • In drug development it is necessary to concentrate samples of pharmaceuticals in order to continue with the experiments.

  26. Product train Feed Displacer sample development What is Displacement Chromatography?

  27. Displacement Chromatography • The ability to separate and concentrate a sample makes displacement chromatography particularly attractive for the enrichment of trace species. • Displacement chromatography has been used extensively in analytical applications. • Displacement chromatography has an enormous potential for preparative applications since high product-throughput can be obtained by displacement. Chen, T.W., N.G. Pinto and L. Van Brocklin, Rapid Method for DeterminingMulticomponent Langmuir Parameters for Displacement Chromatography, of Chromatogr., 484, 167 (1989). Jen, S.C.D. and N.G. Pinto, Use of Sodium Salt of Poly(vinylsufonic acid)as a Low Molecular Weight Displacer for Protein Separations by Ion-Exchange Displacement Chromatography, J. of Chromatogr., 519, 87 (1990). Jen, S.C.D. and N.G. Pinto, Theory of Optimization of Ideal Displacement Chromatography of Binary Mixtures, J. Chromatogr., 590, 3 (1992). Jen, S.C.D. and N.G. Pinto, Influence of Displacer Properties on the Displacement Chromatography of Proteins:A Theoretical Study, Reactive Polymers, 19, 145 (1993).

  28. Why Combine Displacement Chromatography with the mOPPS? • Preparation on conventional bench-scale systems is impractical in cases where the sample amount is limited or expensive. • Preparative separations of trace species are often performed using microbore columns: • detection sensitivity solvent consumption. • column capacity pressure-drop.

  29. Objectives • To investigate the capability of the mOPPS for concentrating trace species. • To make a comparison with mOTS, and quantify the performance of the microdevices by using the throughput and pressure drop • To perform a parametric study with the objective of maximizing throughput.

  30. Vm K C a C = = i i i i i q * i nc nc å å + + 1 K C 1 K C j j j j = = j 1 j 1 m = S TH + T T FT D Equations • The Langmuir isotherm model was used for simulating the non-linear behavior: • Throughput was calculated as follows:

  31. 18 18 trace 4 trace 4 OPPS, Pe = 7.5 x 10 OPPS, Pe = 1.5 x 10 16 16 displacer displacer a a = 1 = 1 14 14 12 12 1 1 10 10 C/CF C/CF 8 8 6 6 4 4 2 2 0 1.5 1.7 1.9 2.1 2.3 2.5 1.5 1.7 1.9 2.1 2.3 2.5 Time/T 0 Time/T 0 18 4 18 OTS, Pe = 1.5 x 10 trace 4 OTC, Pe = 7.5 x 10 16 16 displacer 14 14 12 12 1 10 1 10 C/CF C/CF 8 8 6 6 trace 4 4 displacer 2 2 0 0 2.4 2.6 2.8 3.0 3.2 3.4 2.4 2.6 2.8 3.0 3.2 3.4 Time/T Time/T 0 0 Comparing mOPPS and mOTS, Different Pe

  32. 8 a OPPS, a= 1 a OPPS, a= 4 7 a OPPS, a= 9 a OPPS, a= 25 6 OTS 5 / TH 4 OPPS 3 TH 2 1 Purity = 99% 0 0 1 2 3 4 5 6 7 8 9 - 4 Pe x 10 Comparing mOPPS and mOTS, Different a Channel width or diameter = 50 mm

  33. 30 a Purity = 99% OPPS, a= 1 m a = = 10 m b r OPPS, a= 4 C a OPPS, a= 9 25 a OPPS, a= 25 20 OTS / TH 15 OPPS TH 10 5 0 0 1 2 3 4 5 6 7 8 9 - 4 Pe x 10 Comparing mOPPS and mOTS, Different a Channel width or diameter = 20 mm

  34. 20 a OPPS, a= 1 18 a OPPS, a= 4 a OPPS, a= 9 16 a OPPS, a= 25 14 OTC 12 / TH 10 OPPS 8 TH 6 4 2 Purity = 99% 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 2 D P x 10 (psia) Comparing mOPPS and mOTS, Different a, Same DP Channel width or diameter = 50 mm

  35. Conclusions: Concentration of Trace Species • Simulations demonstrated that the rectangular geometry of the mOPPS offers advantages over the circular geometry of the mOTS for concentrating trace species. • It was proven that the mOPPS has an enormous potential for concentrating trace species. • Product throughput increases with high depth-to-width ratios. Lapizco-Encinas, B.H., and Pinto, N.G., A Comparison of Preparative Characteristics of mOPPS and Microbore Columns for Concentration of Trace Species by Displacement Chromatography, in press, Journal of Chromatography A, 2002.

  36. channel channel Conclusions: Concentration of Trace Species • Massively parallel mOPPS offers significant advantages for high throughput. Silicon wafers can be stacked to create a larger microchannel. • A single 5 cm wafer can have 1000 microchannels. • By stacking up wafers we increase the cross-section area which increases the production tremendously. 1 mm 5 stacked wafers make each channel five times larger.

  37. + = q* C Product A Byproduct B Design of separation process Isotherm data + = Adsorption Isotherms • Adsorption Isotherm expresses the equilibrium between the amount of solute in the mobile phase and the solute adsorbed in the stationary phase. • The shape of the adsorption isotherm can determine the chromatographic behavior of the solute.

  38. Prediction of Isotherms • Isotherm data are essential for scaling-up chromatographic processes. • The need for accurate equilibrium isotherm data is critical in preparative chromatography. • Traditionally, isotherms are obtained by batch methods, which are time consuming as they require significant amount of chemicals. • Dynamic methods based on chromatography can be used for the prediction of isotherm data with the advantages of higher accuracy and speed.

  39. Why Use the mOPPS for Isotherm Prediction • Low sample consumption, an advantage of microsystems, is very significant when dealing with expensive substances such as: proteins, pharmaceuticals, antibodies, etc. • Microsystems allow fast response. • Isotherm data obtained from the mOPPS have the potential to be used for scaling up chromatographic operations.

  40. Objectives for Isotherm Prediction • To predict equilibrium data for protein-salt systems by using the H-Root Method (HRM) and the numerical model of the mOPPS. • To check the validity of the assumptions made in HRM. • To explore the capabilities of the mOPPS as a tool for isotherm prediction.

  41. HTT 18 16 14 12 1 10 C/CF 8 6 4 2 0 1.5 1.7 1.9 2.1 2.3 2.5 Time/T 0 HRM H-Root Method • HRM was derived from the H-Transformation Theory (HTT) of chromatography (Helfferich and Klein, 1970). • HTT was developed to predict the chromatographic response. • HRM mainly consists of performing a back-calculation of HTT Operating conditions Langmuir parameters

  42. Vm Km C a C = = i i i i i q * i nc nc å å + + 1 Km C 1 Km C j j j j = = j 1 j 1 HRM • HRM is restricted to compounds obeying the Langmuir isotherm model. • HRM consists of two main parts: linear elution experiments used to calculate the linear isotherm coefficient ai, and nonlinear frontal experiments to calculate the competitive interference parameter Kmi.

  43. - - T T T T = = , , 0 0 B R i i K k i i T T 0 0 TR2 TB3 TB2 TB1 TR3 Concentration TR1 Concentration Time Time HRM Calculations • Linear elution experiments: obtain retention time • Nonlinear frontal experiments: obtain breakthrough time linear capacity factor frontal capacity factor

  44. æ ö T = ç ÷ a R , i - 1 b ç ÷ i T è ø 0 ( ) £ £ - 1 j n 1 æ ö æ ö ç ÷ ç ÷ ç ÷ n Km CF n Km CF å ç ÷ å - = i i 1 0 - = i i 1 0 ç ÷ ç ÷ K k k ç + ÷ = 1 j - 1 i - i 1 = i 1 i 1 ç ÷ ç ÷ K k K è ø è ø + j j 1 n HRM Equations • Linear elution experiments: calculation of ai • Nonlinear frontal experiments: calculation of Kmi

  45. - æ ö real predicted = ç ÷ % deviation 100 % è real ø Simulations • Simulations were carried out for 3 protein-salt systems: • Conalbumin-NaCl CON-NaCl • b-Lactoglobulin-NaCl LAC-NaCl • Myglobin-NaCl MYG-NaCl

  46. 8% i a 7% 6% 5% deviation prediction of 4% 3% 2% CON LAC 1% a = 1 MYG Pe = 1500 NaCl NaCl 0% 0 2 4 6 8 10 12 14 16 q 4 x 10 X,NaCl Linear Elution ExperimentsResults Prediction of ai

  47. 100% CON i LAC Km MYG 50% NaCl 0% -50% deviation prediction of -100% -150% a = 1 Pe = 150 NaCl -200% 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 q 4 x 10 X,NaCl Nonlinear Frontal Experiments Results Prediction of Kmi

  48. Conclusions: Isotherm Prediction by Using HRM and mOPPS • It was found that operating and geometrical conditions have an effect on the accuracy of the isotherm predictions. • It was found that the HRM has an enormous potential for isotherm prediction under appropriate operating and geometrical conditions. • The application of the HRM is simple and it produces satisfactory results. Lapizco-Encinas, B.H., and Pinto, N.G., Characterization of Equilibrium Adsorption Behavior of Protein-Salt Systems Using Micro Separators and H-Root Method, to be submitted, Journal Separation Science, 2002.

  49. Concluding Remarks • By using the predictions of the mOPPS simulator, an empirical equation has been developed for predicting plate height in mOPPS. It was proven that geometry of the separator has an influence on the chromatographic performance. • It was found that that the mOPPS has an enormous potential for concentrating trace species since it offers higher throughputs than the traditional circular columns. • Isotherm data were predicted successfully by employing the mOPPS and the H-Root Method (HRM). The mOPPS offers the advantage of saving time and chemicals. The application of the HRM is simple and it produces satisfactory results.

  50. Questions and Comments

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