NAGase sensing in 3% milk: FET-based specific and label-free sensing in ultra-small samples of high ionic strength and high concentration of non-specific proteins.

Biosensing with biological field-effect transistors (bioFETs) is a promising technology toward specific, label-free, and multiplexed sensing in ultra-small samples. The current study employs the field-effect meta-nano-channel biosensor (MNC biosensor) for the detection of the enzyme N-acetyl-beta-D-glucosaminidase (NAGase), a biomarker for milk cow infections. The measurements are performed in a 0.5 µL drops of 3% commercial milk spiked with NAGase concentrations in the range of 30.3 aM-3.03 µM (Note that there is no background NAGase concentration in commercial milk). Specific and label-free sensing of NAGase is demonstrated with a limit-of-detection of 30.3 aM, a dynamic range of 11 orders of magnitude and with excellent linearity and sensitivity. Additional two important research outcomes are reported. First, the ionic strength of the examined milk is ∼120 mM which implies a bulk Debye screening length <1 nm. Conventionally, a 1 nm Debye length excludes the possibility of sensing with a recognition layer composed of surface bound anti-NAGase antibodies with a size of ∼10 nm. This apparent contradiction is removed considering the ample literature reporting antibody adsorption in a predominantly surface tilted configuration (side-on, flat-on, etc.). Secondly, milk contains a non-specific background protein concentration of 33 mg/ml, in addition to considerable amounts of micron-size heterogeneous fat structures. The reported sensing was performed without the customarily exercised surface blocking and without washing of the non-specific signal. This suggests that the role of non-specific adsorption to the BioFET sensing signal needs to be further evaluated. Control measurements are reported.

A review on accelerated development of skin-like MXene electrodes: from experimental to machine learning.

Foreshadowing future needs has catapulted the progress of skin-like electronic devices for human-machine interactions. These devices possess human skin-like properties such as stretchability, self-healability, transparency, biocompatibility, and wearability. This review highlights the recent progress in a promising material, MXenes, to realize soft, deformable, skin-like electrodes. Various structural designs, fabrication strategies, and rational guidelines adopted to realize MXene-based skin-like electrodes are outlined. We explicitly discussed machine learning-based material informatics to understand and predict the properties of MXenes. Finally, an outlook on the existing challenges and the future roadmap to realize soft skin-like MXene electrodes to facilitate technological advances in the next-generation human-machine interactions has been described.