“Diabetes is a major problem worldwide,” said Chuchu Chen, the study’s first author, highlighting the transformative potential of 3D printing in healthcare. The newly developed one-step 3D printing process utilizes a single-atom catalyst and enzymatic reactions to enhance signal detection of low-level biomarkers.

Unlike blood, sweat provides a non-invasive method for health monitoring. Uric acid levels in sweat can indicate risks for gout, kidney disease, and heart disease, while glucose and lactate levels help monitor diabetes and exercise intensity.

Traditionally, plant phenotyping—the science of accurately recording plant characteristics—has relied on time-consuming, manual measurements. Today, these processes are increasingly automated, supported by advanced sensor technologies and machine learning. These technologies record parameters such as size, fruit quality, leaf shape, and growth rates. Automated systems can often gather complex information about a plant that is difficult for humans to determine on a large scale.

A key aspect of this sensor-based breeding is the availability of precise reference materials. The sensors require data on a “standard plant” that includes all relevant characteristics, including three-dimensional properties such as leaf angle. A physical model offers clear advantages over purely digital or two-dimensional representations. For example, it can be used as a reference and internal control instance in a greenhouse or test field under real plants.