Inkjet printed graphene (IPG) has recently shown tremendous promise in reducing the cost and complexity of graphene circuit fabrication. Herein we demonstrate, for the first time, the fabrication of an ion selective electrode (ISE) with IPG. A thermal annealing process in a nitrogen ambient environment converts the IPG into a highly conductive electrode (sheet resistance changes from 52.8 ± 7.4 MΩ/□ for unannealed graphene to 172.7 ± 33.3 Ω/□ for graphene annealed at 950 °C). Raman spectroscopy and field emission scanning electron microscopy (FESEM) analysis reveals that the printed graphene flakes begin to smooth at an annealing temperature of 500 °C and then become more porous and more electrically conductive when annealed at temperatures of 650 °C and above. The resultant thermally annealed, IPG electrodes are converted into potassium ISEs via functionalization with a poly(vinyl chloride) (PVC) membrane and valinomycin ionophore. The developed potassium ISE displays a wide linear sensing range (0.01−100 mM), a low detection limit (7 μM), minimal drift (8.6 × 10 −6 V/s), and a negligible interference during electrochemical potassium sensing against the backdrop of interfering ions [i.e., sodium (Na), magnesium (Mg), and calcium (Ca)] and artificial eccrine perspiration. Thus, the IPG ISE shows potential for potassium detection in a wide variety of human fluids including plasma, serum, and sweat.
Solution-phase printing of exfoliated graphene flakes is emerging as a low-cost means to create flexible electronics for numerous applications. The electrical conductivity and electrochemical reactivity of printed graphene has been shown to improve with post-print processing methods such as thermal, photonic, and laser annealing. However, to date no reports have shown the manipulation of surface wettability via post-print processing of printed graphene. Herein, we demonstrate how the energy density of a direct-pulsed laser writing (DPLW) technique can be varied to tune the hydrophobicity and electrical conductivity of the inkjet-printed graphene (IPG). Experimental results demonstrate that the DPLW process can convert the IPG surface from one that is initially hydrophilic (contact angle ∼47.7°) and electrically resistive (sheet resistance ∼21 MΩ □) to one that is superhydrophobic (CA ∼157.2°) and electrically conductive (sheet resistance ∼1.1 kΩ □). Molecular dynamic (MD) simulations reveal that both the nanoscale graphene flake orientation and surface chemistry of the IPG after DPLW processing induce these changes in surface wettability. Moreover, DPLW can be performed with IPG printed on thermally and chemically sensitive substrates such as flexible paper and polymers. Hence, the developed, flexible IPG electrodes treated with DPLW could be useful for a wide range of applications such as self-cleaning, wearable, or washable electronics.
This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information–nucleic acids, proteins, and lipids—from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.
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