Real-time, specific, and label-free transistor-based sensing of organophosphates in liquid.

Organophosphates (OP), commonly used in agriculture and as chemical warfare agents, pose significant environmental risks, necessitating real-time, low-cost OP detection methods. In particular, liquid-phase OP sensing with minimal sample volumes is crucial. While several methods allow rapid detection of low concentrations of OP vapors, they are effective only in the short term, while vapors are still being produced. Many OP compounds are semi-volatile, leading to the contamination of water, soil, and surfaces, posing a risk of secondary, long-term exposure. Detecting this contamination requires methods that can be directly applied to droplets of the affected medium. Currently, no method provides the desired combination of ultra-sensitivity, quantitative detection, rapid response, and low-cost for detecting OPs in liquid samples. This study aims to demonstrate quantitative, low-cost, real-time, specific, and label-free OP sensing in ultra-small samples using a transistor-based approach. The current work employs the 2-(4-Aminophenyl)-1,1,1,3,3,3-hexafluoro-2-propanol (aminophenyl-HFIP) functionalized meta-nano-channel field-effect chemical sensor (MNChem sensor) to monitor the organophosphate, diethyl cyanophosphonate (DCNP), in liquid samples. The silicon component of the MNChem is fabricated using a complementary metal-oxide semiconductor (CMOS) process, and the amine-based chemical functionalization of the sensing area is performed post-fabrication. The MNChem sensor provides electrostatic control over the source-drain current (IDS), allowing an optimized channel configuration that efficiently transduces localized OP recognition events into significant IDS variations. Sensing is performed using 0.5 µL buffer solution to simulate a miniature field-deployable sensor for on-site liquid analysis. We report the sensing of DCNP with a limit-of-detection of 100 fg/mL, a dynamic range of 9 orders of magnitude, and excellent linearity (≥0.97) and sensitivity. Control measurements confirm the specificity and reliability of the sensor's response, validating its applicability. This study introduces a novel method for OP detection in contaminated droplets using a low-cost disposable transistor technology.

Addressing the challenge of solution gating in biosensors based on field-effect transistors.

Transistor-based biosensing (BioFET) is a long-enduring vision for next generation medical diagnostics. The study addresses a challenge associated with the BioFET solution gating. The standard BioFET sensing measurement involves sweeping of the solution gate (Vsol) with a concurrent measurement of the source-drain current (IDS). This IDS-Vsol sweep poses a great challenge, as Vsol does not only determine IDS, but also determines the pH levels, ion concentrations, and electric fields at the sensing area double layer accommodating the biomolecules. Therefore, inevitably, an IDS-Vsol sweep implies that the sensing area double layer is not in an electrochemical equilibrium, but rather in a continuous transient state as electrochemical potential gradients induce transient ion currents continuously affecting double layer hosting the biomolecules and the biological interactions. This challenge calls for a BioFET design which permits IDS sweeping from an off-state to an on-state while keeping Vsol constant and the double layer sensing area in electrochemical equilibrium. The study explores a BioFET design addressing this challenge by decoupling the solution potential from IDS gating. Specific and label-free sensing of ferritin in 0.5 µL drops of 1:100 diluted plasma is pursued. We show an excellent sensing performance once the solution potential and IDS gating are decoupled, with a limit-of-detection of 10 fg/ml, a dynamic range of 10 orders of magnitude in ferritin concentration and excellent linearity and sensitivity. In contrast, a poor sensing performance is recorded for the conventional Vsol sweep performed in parallel to the above. Extensive control measurements quantifying the non-specific signals are reported.

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.