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Abstract The diagnosis of milk fever or hypocalcemia in lactating cows has a significant economic impact on the dairy industry. It is challenging to identify asymptomatic subclinical hypocalcemia (SCH) in transition dairy cows. Monitoring subclinical hypocalcemia in milk samples can expedite treatment and improve the health, productivity, and welfare of dairy cows. In this study, an attomolar-sensitive sensor is developed using extrusion-based 3D-printed sensing structures to detect the ratio of ionized calcium to phosphate levels in milk samples. The unique geometries of the lateral structure of 3D-printed sensors, along with the wrinkled surfaces, provide a limit of detection down to the attomole (138 am) concentration of the target analyte. The calcium-to-phosphate ratio in milk samples not only provides early disease indications but also enables on-site testing. This highly selective test is validated using real milk and blood samples, and the results are compared with those of commercial meters. This fast response (~10 s) low-cost sensor opens a promising tool for the farm-side diagnostic of dairy cows that can promote best practice management of dairy cows.

Abstract On‐site rapid multi‐ion sensing accelerates early identification of environmental pollution, water quality, and disease biomarkers in both livestock and humans. This study introduces a pocket‐sized 3D‐printed sensor, manufactured using additive manufacturing, specifically designed for detecting iron (Fe2+), nitrate (NO3–), calcium (Ca2+), and phosphate (HPO42−). A unique feature of this device is its utilization of a universal ion‐to‐electron transducing layer made from highly redox‐active poly‐octylthiophene (POT), enabling an all‐solid‐state electrode tailored to each ion of interest. Manufactured with an extrusion‐based 3D printer, the device features a periodic pattern of lateral layers (width = 80 µm), including surface wrinkles. The superhydrophobic nature of the POT prevents the accumulation of nonspecific ions at the interface between the gold and POT layers, ensuring exceptional sensor selectivity. Lithography‐free, 3D‐printed sensors achieve sensitivity down to 1 ppm of target ions in under a minute due to their 3D‐wrinkled surface geometry. Integrated seamlessly with a microfluidic system for sample temperature stabilization, the printed sensor resides within a robust, pocket‐sized 3D‐printed device. This innovation integrates with milking parlors for real‐time calcium detection, addressing diagnostic challenges in on‐site livestock health monitoring, and has the capability to monitor water quality, soil nutrients, and human diseases.

Abstract Sensing of viral antigens has become a critical tool in combating infectious diseases. Current sensing techniques have a tradeoff between sensitivity and time of detection; with 10–30 min of detection time at a relatively low sensitivity and 6–12 h of detection at a high (picomolar) sensitivity. In this research, uniquely nanoengineered interfaces are demonstrated on 3D electrodes that enable the detection of spike antigens of SARS‐CoV‐2 and their variants in seconds at femtomolar concentrations with excellent specificity, thus, overcoming this tradeoff. The 3D electrodes, manufactured using a high‐resolution aerosol jet 3D nanoprinter, consist of a microelectrode array of sintered gold nanoparticles coated with graphene and antibodies specific to severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) spike antigens. An impedance‐based sensing modality is employed to sense several pseudoviruses of SARS‐CoV‐2 variants of concern (VOCs). This device is sensitive to most of the pseudoviruses of SARS‐CoV‐2 VOCs. A high sensitivity of 100 fm, along with a low limit‐of‐detection of 9.2 fm within a test range of 0.1–1000 pm, and a detection time of 43 s are shown. This work illustrates that effective nano‐bioengineering of interfaces can be used to create an ultrafast and ultrasensitive healthcare diagnostic tool for combating emerging infections.

To promote sustainability, this review explores: 1) the utilization of affordable high‐performance sensors that can enhance food safety and quality, plant growth, and disease management and 2) the Internet of Things (IoT)‐supported sensors to empower farmers, stakeholders, and agro‐food industries via rapid testing and predictive analysis based on sensing generated informatics using artificial intelligence (AI). The performance of such sensors is noticeable, but this technology is still not suitable to be used in real applications owing to the lack of focus, scalability, well‐coordinated research, and regulations. To cover this gap, this review carefully and critically analyzes state‐of‐the‐art sensing technologies developed for food quality assurance (i.e., contaminants, toxins, and packaging testing) and plant growth monitoring (i.e., phenotyping, stresses, volatile organic components, nutrient levels, hormones, and pathogens) along with the possible challenges. The following has been proposed for future research: 1) promoting the optimized combination of green sensing units supported by IoT to perform testing at the location, considering the remote and urban areas as a key focus, and 2) analyzing generated informatics via AI should also be a focus for risk assessment understanding and optimizing necessary safety majors. Finally, the perspectives of IoT‐enabled sensors, along with their real‐world limitations, are discussed.

Here, the authors introduce a biosensing platform with multi-length-scale electrode architecture consisting of 3D printed silver micropillars decorated with graphene flakes. They demonstrate that this breaks a barrier to the biomolecule limit-of-detection, enabling detection down to femtomolar level.

A liquid biopsy combined with an exosome-chip (EC) is an important detection tool for early cancer diagnosis. Exosomes have a crucial function in the exchange of information between cells and are present in biological fluids. ECs are miniaturized microfluidic devices designed to isolate, capture, and analyze exosomes for analysis of patient samples. Such devices offer on-chip detection, high-throughput analysis, and multiplex measurements. Further, these chips can integrate with electrochemical and optical detectors, and mass spectrometry enabling comprehensive studies of diseases. This review will cover the outlook on chip-based diagnostics for liquid biopsy, detection, and isolation of exosomes to support cancer diagnostics.

Biosensors hold great potential for revolutionizing personalized medicine and environmental monitoring. Their construction is the key factor which depends on either manufacturing techniques or robust sensing materials to improve efficacy of the device. Functional graphene is an attractive choice for transducing material due to its various advantages in interfacing with biorecognition elements. Graphene and its derivatives such as graphene oxide (GO) are thus being used extensively for biosensors for monitoring of diseases. In addition, graphene can be patterned to a variety of structures and is incorporated into biosensor devices such as microfluidic devices and electrochemical and plasmonic sensors. Among biosensing materials, GO is gaining much attention due to its easy synthesis process and patternable features, high functionality, and high electron transfer properties with a large surface area leading to sensitive point-of-use applications. Considering demand and recent challenges, this perspective review is an attempt to describe state-of-the-art biosensors based on functional graphene. Special emphasis is given to elucidating the mechanism of sensing while discussing different applications. Further, we describe the future prospects of functional GO-based biosensors for health care and environmental monitoring with a focus on additive manufacturing such as 3D printing.