Computational study of the band broadening in two-dimensional etched packed bed columns for on-chip high-performance liquid chromatography

Gzil, Piotr; Smet, Jan De; Vervoort, Nico; Verelst, Harry; Baron, Gino V.; Desmet, Gert

doi:10.1016/j.chroma.2003.12.038

Cited by 35 publications

(28 citation statements)

References 16 publications

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“…The former is, however, in many cases easily definable (in the present case it is the diameter d p of the micro-pillars), whereas the geometry of the through-pore space is often too complex to establish a correct definition for their characteristic mass transfer size. This already holds for the relatively simple case of perfectly ordered pillar arrays (see [53,57] for our unsuccessful attempts to find a correct characteristic size for the through-pore space in ordered pillar arrays).…”

Section: Appendix Amentioning

confidence: 68%

Modelling the relation between the species retention factor and the C‐term band broadening in pressure‐driven and electrically driven flows through perfectly ordered 2‐D chromatographic media

Wilde

Detobel

Billen

et al. 2009

J of Separation Science

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The band broadening that can be expected in perfectly ordered cylindrical pillar arrays has been calculated for a wide range of intra-particle diffusion coefficients (D sz ) and zone retention factors (0ok 00 o10) using an in-house-developed computational fluid dynamicsalgorithm. Both pressure-driven and electrically driven (PD and ED) flows are considered. In the case of a large intra-pillar diffusion coefficient (D sz /D mol 5 0.5), the minimal reduced plate height varies between h 5 0.8 (low k 00 ) and h 5 1.2 (high k 00 ), while the C-term range of the Van Deemter curve depends only slightly on k 00 . In the case of a small intra-pillar diffusion coefficient (D sz /D mol 5 0.1), h is nearly independent of k 00 and approximately equals h min 5 1.1, while the C-term band broadening decreases strongly with increasing k 00 . The difference between ED and PD flows in perfectly ordered porous pillar arrays is much smaller than in packed beds, generally only 3% difference around the minimum of the plate-height curve and maximally 10% at high-reduced velocities. The generated data sets were also ideal to verify the accuracy of k 00 and D sz dependency of the general plate height model of chromatography. Determining the values of the geometrical parameters appearing in the model through curve fitting, the average fitting error can be reduced to a value of about 1%.

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Section: Appendix Amentioning

confidence: 68%

Modelling the relation between the species retention factor and the C‐term band broadening in pressure‐driven and electrically driven flows through perfectly ordered 2‐D chromatographic media

Wilde

Detobel

Billen

et al. 2009

J of Separation Science

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show abstract

“…This plays a crucial role in determining the separation efficiency of a chromatography column and there have been several studies which have modeled both intra column band spreading [7][8][9] and the effects that dispersion in tubing and other instruments have upon a chromatographic profile. [10][11][12] In the latter case, these effects become more pronounced at smaller scales, owing to the increased contribution made by extra column equipment toward the total system volume.…”

Section: Impact Of Band Broadening On Elution Profilesmentioning

confidence: 99%

Ultra scale‐down approach to correct dispersive and retentive effects in small‐scale columns when predicting larger scale elution profiles

Hutchinson

Chhatre

Baldascini

et al. 2009

Biotechnology Progress

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Ultra scale-down approaches represent valuable methods for chromatography development work in the biopharmaceutical sector, but for them to be of value, scale-down mimics must predict large-scale process performance accurately. For example, one application of a scale-down model involves using it to predict large-scale elution profiles correctly with respect to the size of a product peak and its position in a chromatogram relative to contaminants. Predicting large-scale profiles from data generated by small laboratory columns is complicated, however, by differences in dispersion and retention volumes between the two scales of operation. Correcting for these effects would improve the accuracy of the scale-down models when predicting outputs such as eluate volumes at larger scale and thus enable the efficient design and operation of subsequent steps. This paper describes a novel ultra scale-down approach which uses empirical correlations derived from conductivity changes during operation of laboratory and pilot columns to correct chromatographic profiles for the differences in dispersion and retention. The methodology was tested by using 1 mL column data to predict elution profiles of a chimeric monoclonal antibody obtained from Protein A chromatography columns at 3 mL laboratory- and 18.3 L pilot-scale. The predictions were then verified experimentally. Results showed that the empirical corrections enabled accurate estimations of the characteristics of larger-scale elution profiles. These data then provide the justification to adjust small-scale conditions to achieve an eluate volume and product concentration which is consistent with that obtained at large-scale and which can then be used for subsequent ultra scale-down operations.

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“…Neglecting the serial connection effect can lead to large errors (>100%) in ordered cylinder arrays. Table 4 shows how CFD could be helpful in investigating the effect of column design and auxiliaries on chromatographic performance 70–73…”

Section: Chromatographymentioning

confidence: 99%

Review of Computational fluid dynamics applications in biotechnology processes

Sharma

Malhotra

Rathore

2011

Biotechnology Progress

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Computational fluid dynamics (CFD) is well established as a tool of choice for solving problems that involve one or more of the following phenomena: flow of fluids, heat transfer,mass transfer, and chemical reaction. Unit operations that are commonly utilized in biotechnology processes are often complex and as such would greatly benefit from application of CFD. The thirst for deeper process and product understanding that has arisen out of initiatives such as quality by design provides further impetus toward usefulness of CFD for problems that may otherwise require extensive experimentation. Not surprisingly, there has been increasing interest in applying CFD toward a variety of applications in biotechnology processing in the last decade. In this article, we will review applications in the major unit operations involved with processing of biotechnology products. These include fermentation,centrifugation, chromatography, ultrafiltration, microfiltration, and freeze drying. We feel that the future applications of CFD in biotechnology processing will focus on establishing CFD as a tool of choice for providing process understanding that can be then used to guide more efficient and effective experimentation. This article puts special emphasis on the work done in the last 10 years.

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Computational study of the band broadening in two-dimensional etched packed bed columns for on-chip high-performance liquid chromatography

Cited by 35 publications

References 16 publications

Modelling the relation between the species retention factor and the C‐term band broadening in pressure‐driven and electrically driven flows through perfectly ordered 2‐D chromatographic media

Modelling the relation between the species retention factor and the C‐term band broadening in pressure‐driven and electrically driven flows through perfectly ordered 2‐D chromatographic media

Ultra scale‐down approach to correct dispersive and retentive effects in small‐scale columns when predicting larger scale elution profiles

Review of Computational fluid dynamics applications in biotechnology processes

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