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a b s t r a c tA direct model, using the explicit geometry of stacked products in boxes, was developed and used to study the local and average airflow through stacks of horticultural products. The discrete element method was employed to generate a random stacking of spherical products in the box. A computational fluid dynamics model was then applied to study explicitly the airflow through the air gaps in the box and in the voids between the stacks of different random fillings. The flow resistance was affected by the confinement ratio, product size, porosity, box vent hole ratio, and much less by the random filling. The predicted pressure drop over stacks agreed with experimental correlations for porous media. Air velocity profiles inside the boxes compared well to measurements. The methodology was used to obtain more accurate pressure drop correlation for stacks of vented boxes that can now be used in large scale simulations of cool rooms.

An electrokinetic driven microfluidic lab-on-a-chip was developed for glucose quantification using double-enzyme assay. The enzymatic glucose assay involves the two-step oxidation of glucose, which was catalyzed by hexokinase and glucose-6-phosphate dehydrogenase, with the concomitant reduction of NADP + to NADPH. A fluorescence microscopy setup was used to monitor the different processes ͑fluid flow and enzymatic reaction͒ in the microfluidic chip. A two-dimensional finite element model was applied to understand the different aspects of design and to improve the performance of the device without extensive prototyping. To our knowledge this is the first work to exploit numerical simulation for understanding a multisubstrate double-enzyme on-chip assay. The assay is very complex to implement in electrokinetically driven continuous system due to the involvement of many species, which has different transport velocity. With the help of numerical simulation, the design parameters, flow rate, enzyme concentration, and reactor length, were optimized. The results from the simulation were in close agreement with the experimental results. A linear relation exists for glucose concentrations from 0.01 to 0.10 g l −1 . The reaction time and the amount of enzymes required were drastically reduced compared to off-chip microplate analysis.

Although several applications of electrowetting on dielectric digital lab-on-a-chips are reported in literature, there is still a lack of knowledge about the influence of operational and design parameters on the performance of an analytical assay. This paper investigates how droplet size variability, introduced by droplet dispensing and splitting, influences the assay performance with respect to repeatability and accuracy and presents a novel method to reduce this variability. Both a theoretical and experimental approach were followed. Monte Carlo simulations were applied to study the cumulative effect of the variability caused by different droplet manipulations on the final assay performance. It is shown that a highly controllable droplet generation and manipulation is achieved with respect to droplet size variability through an accurate control of actuation voltage, activation time, relaxation time, and electrode size. As a case study, it is illustrated that through optimization of these parameters a complete on-chip calibration curve is obtained for a D-glucose assay with an average CV-value of 2%. These new insights aim to bring the digital lab-on-a-chip technology closer to researchers in the field of diagnostics offering them a valuable and accessible alternative to standard analysis platforms.

A model-based methodology was developed to optimize microfluidic chips for the simultaneous enzymatic quantification of sucrose, D-glucose and D-fructose in a single microfluidic channel with an integrated optical detection system. The assays were based on measuring the change in concentration of the reaction product NADH, which is stoichiometrically related to the concentration of those components via cascade of specific enzymatic reactions. A reduced order mathematical model that combines species transport, enzyme reaction, and electrokinetic bulk flow was developed to describe the operation of the microfluidic device. Using this model, the device was optimized to minimize sensor response time and maximize signal output by manipulating the process conditions such as sample and reagent volume and flow rate. According to this simulation study, all sugars were quantified within 2.5 min in the optimized microchip. A parallel implementation of the assays can further improve the throughput. In addition, the amount of consumed reagents was drastically reduced compared to microplate format assays. The methodology is generic and can easily be adapted to other enzymatic microfluidic chips.Keywords Lab-on-a-chip Á Reduced order model Á CFD Á Food Á Michaelis-Menten Á Microfluidics List of symbolsA c cross-sectional area of the channel (m 2 ) h height of microchannel (lm) w width of microchannel (lm) C i concentration of species i (mol m -3 ) D i diffusion coefficient of the ith species (m 2 s -1 ) I electric current (A) k cat rate constant (s -1 ) r rate of reaction (mol m -3 s -1 ) R electrical resistance (X) K M Michaelis-Menten constant (mol m -3 ) K S dissociation constant (mol m -3 ) L length (m) M S molecular weight of molecule (g/mol) V molar volume at its boiling point (m 3 /kg mol) t time (s) u eo electroosmotic slip velocity (m s -1 ) u ep electrophoretic velocity (m s -1 ) Q flow rate (m 3 s -1 ) / applied potential (V) e 0 permittivity of vacuum (C 2 N -1 m -2) l eo electroosmotic mobility (m 2 V -1 s -1 ) q density of the fluid (kg m -3 ) g dynamic viscosity of fluid (kg m -1 s -1 ) r electrical conductivity of fluid (S m -1 ) f zeta potential (V) k D thickness of electric double layer (nm) e r dielectric constant of the medium l ep, i electrophoretic mobility of the ith species (m 2 V -1 s -1 ) M number of junctions N number of branches Abbreviations EDL