Description
f (x) columns for faster and more reliable mechanistic modeling results To determine column parameters required for the model, you need to perform column characterization experiments. This takes time and requires expert knowledge to ensure that accurate parameter values are produced. With f (x) columns, your parameter values are instantly available, accurate, and include: Parameters included in the Results of Analysis for the following f (x) columns Ion exchange and multimodal chromatography Hydrophobic interaction chromatography Bed height (cm) Yes Yes Total porosity Yes Yes Interstitial porosity Yes Yes Ionic capacity (μmol/mL), column specific and in resin backbone Yes N/A Bed volume (mL) Yes Yes Bead radius (µm) Yes Yes Total specific adsorber surface area (m ^-1 ) Yes Yes Specific adsorber surface area (m ^-1 ) Yes Yes Ligand density (µmol/m²) Yes N/A Ligand pK _a Yes N/A Axial dispersion (mm ^2 /s) Yes Yes f (x) columns give you confidence about the column parameters used for your model (including colloidal particle adsorption (CPA) model ), and free up your time to focus on generating molecule-specific data. They are a good complement to GoSilico™ Chromatography Modeling Software . A suitable column format for your process development work Tricorn™ columns are high-quality, prepacked columns for robust and reproducible process development and validation. Because of the narrow 10 mm inner diameter combined with 20 cm or 10 cm bed height typically used in biomanufacturing, these columns are especially suited to scale-down studies. About mechanistic modeling in chromatography Mechanistic models use computer simulations to decrease the number of experiments needed during process development, and increase the design space investigated. These simulations are based on known physiochemical phenomena involved in chromatography and offer you the following advantages: Cost reduction and time saving — more data obtained from fewer experiments, which accelerates your process development. Improved robustness and efficiency to fulfill regulatory demands on quality by design. Learn more about mechanistic modeling .
