Rapid diagnostic testing at the site of the patient is essential when a fully equipped laboratory is not accessible. To maximize the impact of this approach, low‐cost, disposable tests that require minimal user‐interference and external equipment are desired. Fluid transport by capillary wicking removes the need for bulky ancillary equipment to actuate and control fluid flow. Nevertheless, current microfluidic paper‐based analytical devices based on this principle struggle with the implementation of multistep diagnostic protocols because of fabrication‐related issues. Here, 3D‐printed microfluidic devices are demonstrated in a proof‐of‐concept enzyme‐linked immunosorbent assay in which a multistep assay timeline is completed by precisely engineering capillary wetting within printed porous bodies. 3D printing provides a scalable route to low‐cost microfluidic devices and obviates the assembly of discrete components. The resulting rapid and seamless transition between digital data and physical objects allows for rapid design iterations, and opens up perspectives on distributed manufacturing.

No abstract

No abstract

No abstract

Additive manufacturing or 3D printing can be applied in the food sector to create food products with personalized properties such as shape, texture, and composition. In this article, we introduce a computer aided engineering (CAE) methodology to design 3D printed food products with tunable mechanical properties. The focus was on the Young modulus as a proxy of texture. Finite element modelling was used to establish the relationship between the Young modulus of 3D printed cookies with a honeycomb structure and their structure parameters. Wall thickness, cell size, and overall porosity were found to influence the Young modulus of the cookies and were, therefore, identified as tunable design parameters. Next, in experimental tests, it was observed that geometry deformations arose during and after 3D printing, affecting cookie structure and texture. The 3D printed cookie porosity was found to be lower than the designed one, strongly influencing the Young modulus. After identifying the changes in porosity through X-ray micro-computed tomography, a good match was observed between computational and experimental Young’s modulus values. These results showed that changes in the geometry have to be quantified and considered to obtain a reliable prediction of the Young modulus of the 3D printed cookies.