A portable, microfluidic blood plasma separation device is presented featuring a constriction-expansion design, which produces 100.0% purity for undiluted blood at 9% yield. This level of purity represents an improvement of at least 1 order of magnitude with increased yield compared to that achieved previously using passive separation. The system features high flow rates, 5-30 μL/min plasma collection, with minimal clogging and biofouling. The simple, portable blood plasma separation design is hand-driven and can easily be incorporated with microfluidic or laboratory scale diagnostic assays. The separation system was applied to a paper-based diagnostic test for malaria that produced an amplified color change in the presence of Plasmodium falciparum histidine-rich protein 2 at a concentration well below clinical relevancy for undiluted whole blood.

Symbolic regression methods simultaneously determine the model functional form and the regression parameter values by generating expression trees. Symbolic regression can capture the complexity of real‐world phenomena but the use of deterministic optimization for symbolic regression has been limited due to the complexity of the search space of existing formulations. We present a novel deterministic mixed‐integer nonlinear programming formulation for symbolic regression that incorporates derivative constraints through auxiliary expression trees. By applying the chain rule to mathematical operations, binary expression trees are capable of representing the calculation of first and second derivatives. We apply this formulation to illustrative examples using derivative information to show increased model discrimination capability. In addition, we perform a case study of a thermodynamic equation of state to gain insight on valid functional forms with thermodynamics‐based constraints on the first and second derivatives.