3D-printed microstructure electrodes coated with MXene enable rapid, low-cost and sensitive diagnosis of subclinical mastitis.
Subclinical mastitis, a disease of livestock, has detrimental effects on dairy farms worldwide, causing billions of dollars in losses each year globally. Unlike clinical mastitis, which can be detected by symptoms such as swollen udder and clearly abnormal milk, cases of subclinical mastitis are not easily detected.
“Subclinical mastitis costs dairy farmers millions each year because it often goes undetected until serious damage has already occurred,” says Dr. Azhar Ali, an assistant professor in the College of Animal Sciences at Virginia Tech. The cows look healthy, and their milk looks good, but the underlying infection slowly affects the quality of the milk and the health of the animals. Traditional laboratory tests such as the California mastitis test take a long time: significant damage may have already been done by the time cases are confirmed.
Dr. Ali and his colleagues are tackling this challenge. “Our technology turns the milk itself into a diagnostic sample in real time, allowing farmers to assess udder health directly on the farm within minutes rather than waiting days for lab results,” he says.
They have developed a coin-sized device called 2.5D MiSENSE (Micro-Architectural Sensing Electrode). This innovative sensor uses a cost-effective stereolithography-printed microstructure, coated with a proprietary biomarker. The biomarker (antibody) can identify trace amounts of N-acetyl-β-D-glucosaminidase (NAG – an enzymatic indicator of mastitis) in raw milk samples within minutes.
This sensitivity allows NAG to be captured at concentrations that indicate the very early stages of subclinical mastitis, enabling intervention before the disease progresses.
“What is exciting is that we have achieved high-performance biosensing without the need for expensive clean rooms,” says Matin Atay Katchoy, a doctoral student at Virginia Tech and co-author of the study. “By combining 3D-printed microstructured electrodes, MXene nanomaterials, and machine learning, we have created a low-cost platform that provides laboratory-level sensitivity in real-world conditions.”
The device achieves its high sensitivity through microscopic engineering. Its surface is designed with a landscape of small hills and pyramidal features, each just 80 micrometers across. The surfaces feature pine-line structures that fall between 2D and 3D geometries, creating a unique “2.5D” structure. Controlled surface relief in the vertical dimension increases the active sensing area and signal transmission. The ridge pattern also directs molecular motion toward the sensing interface due to spherical diffusion, enabling faster detection.
The sensor microstructures are coated with MXenes, which act as oxygen-free electrocatalysts and supporting materials to immobilize the biomarker.
Due to the complex composition of raw milk and the small amount of NAG, the sensor needs to identify the NAG signal pattern against dozens of background noise. For this purpose, machine learning algorithms are used to enhance the accuracy of the sensor. This allows the device to reliably distinguish between healthy and infected cows, even using untreated milk samples.
The research team is now trying to improve the long-term durability of the sensor’s nanomaterial layers and develop portable signal readers suitable for farm conditions. Looking to the future, large-scale field trials across diverse dairy herds, integration with automated milking systems for continuous monitoring, and expansion to detect multiple health biomarkers simultaneously will make this device a full-fledged commercial product.
Reference: M. Atai Kashwi, b. Corll, and M. A. Ali, “2.5D printed microstructures with material-specific functionalizations for tunable biosensingAdvanced Materials Technologies (2026), https://doi.org/10.1002/admt.202501783.
Featured image: “Dairy Cattle Farming” by National Rural Knowledge Exchange via Flickr, CCP 2.0
