Multidimensional separation using magnetic particles

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The goal of the DFG priority program 2045 is to identify separation techniques that are able to classify simultaneously with respect to several particle properties and thus can be described as multidimensional. For this purpose, the applicant investigates the application of magnetic seeded filtration (MSF) as a selective and multidimensional separation method. In MSF, magnetic auxiliary particles are first added to a target suspension and a selective agglomeration is induced between these magnetic particles and the non-magnetic particles that are to be classified. In a second step, the agglomerates formed in this way can be separated by simple magnetic separation. During the first funding period, it was shown that the process is dependent on both the surface properties (separation characteristic 1) and the particle size (separation characteristic 2) and is therefore suitable for multidimensional classification. It was possible to identify and quantify the influences of various process-relevant parameters. In addition, parallel model calculations of the process provided further, experimentally non-accessible information. Furthermore, the commissioning of a new experimental apparatus allows a defined adjustment of important process parameters in future investigations.For the second funding period, extensive parameter studies for multidimensional classification are planned on the experimental side. Here, the analytical techniques will first be extended and the suitability of further surface properties, e.g. hydrophobicity, as separation characteristics will be tested. In addition, the agglomerate breakage after the separation step is regarded as a core objective of the second funding period, which allows the recovery of the used magnetic auxiliary particles and is decisive for a resource-saving implementation of the process. On the theoretical side, the model calculations, which already provided valuable insights in the first funding period, are to be transferred and extended to the multidimensional classification case.