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  1. Abstract

    Nitric oxide (NO) plays an important role in cardiovascular function, immune response, and intercellular signaling. However, due to its short lifetime, real-time detection of NO is challenging. Herein, an electrochemical sensor based on fibronectin-modified, solution-processed graphene ink for NO detection is developed using a facile fabrication method involving spin-coating and hot-plate annealing. The sensor is first electrochemically characterized with a NO donor, spermine NONOate, exhibiting a dynamic range of 10–1000μM. The fibronectin-functionalized graphene supports the attachment and growth of MDA-MB-231 breast cancer cells, as confirmed by optical microscopy. Extracellular NO production is stimulated using the amino acid L-arginine. NO production results in morphological changes to the adhered cells, which are reversible upon the addition of the NO synthase antagonist Nω-nitro-L-arginine methyl ester. The production of NO is also confirmed using real-time amperometric measurements with the fibronectin-functionalized graphene sensors. While this work focuses on NO detection, this potentially scalable platform could be extended to other cell types with envisioned applications including the high-throughput evaluation of therapeutics and biocompatible coatings.

     
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  2. Neurotransmitters are small molecules involved in neuronal signaling and can also serve as stress biomarkers.1Their abnormal levels have been also proposed to be indicative of several neurological diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington disease, among others. Hence, measuring their levels is highly important for early diagnosis, therapy, and disease prognosis. In this work, we investigate facile functionalization methods to tune and enhance sensitivity of printed graphene sensors to neurotransmitters. Sensors based on direct laser scribing and screen-printed graphene ink are studied. These printing methods offer ease of prototyping and scalable fabrication at low cost.

    The effect of functionalization of laser induced graphene (LIG) by electrodeposition and solution-based deposition of TMDs (molybdenum disulfide2and tungsten disulfide) and metal nanoparticles is studied. For different processing methods, electrochemical characteristics (such as electrochemically active surface area: ECSA and heterogenous electron transfer rate: k0) are extracted and correlated to surface chemistry and defect density obtained respectively using X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy. These functionalization methods are observed to directly impact the sensitivity and limit of detection (LOD) of the graphene sensors for the studied neurotransmitters. For example, as compared to bare LIG, it is observed that electrodeposition of MoS2on LIG improves ECSA by 3 times and k0by 1.5 times.3Electrodeposition of MoS2also significantly reduces LOD of serotonin and dopamine in saliva, enabling detection of their physiologically relevant concentrations (in pM-nM range). In addition, chemical treatment of LIG sensors is carried out in the form of acetic acid treatment. Acetic acid treatment has been shown previously to improve C-C bonds improving the conductivity of LIG sensors.4In our work, in particular, acetic acid treatment leads to larger improvement of LOD of norepinephrine compared to MoS2electrodeposition.

    In addition, we investigate the effect of plasma treatment to tune the sensor response by modifying the defect density and chemistry. For example, we find that oxygen plasma treatment of screen-printed graphene ink greatly improves LOD of norepinephrine up to three orders of magnitude, which may be attributed to the increased defects and oxygen functional groups on the surface as evident by XPS measurements. Defects are known to play a key role in enhancing the sensitivity of 2D materials to surface interactions, and have been explored in tuning/enhancing the sensor sensitivity.5Building on our previous work,3we apply a custom machine learning-based data processing method to further improve that sensitivity and LOD, and also to automatically benchmark different molecule-material pairs.

    Future work includes expanding the plasma chemistry and conditions, studying the effect of precursor mixture in laser-induced solution-based functionalization, and understanding the interplay between molecule-material system. Work is also underway to improve the machine learning model by using nonlinear learning models such as neural networks to improve the sensor sensitivity, selectivity, and robustness.

    References

    A. J. Steckl, P. Ray, (2018), doi:10.1021/acssensors.8b00726.

    Y. Lei, D. Butler, M. C. Lucking, F. Zhang, T. Xia, K. Fujisawa, T. Granzier-Nakajima, R. Cruz-Silva, M. Endo, H. Terrones, M. Terrones, A. Ebrahimi,Sci. Adv.6, 4250–4257 (2020).

    V. Kammarchedu, D. Butler, A. Ebrahimi,Anal. Chim. Acta.1232, 340447 (2022).

    H. Yoon, J. Nah, H. Kim, S. Ko, M. Sharifuzzaman, S. C. Barman, X. Xuan, J. Kim, J. Y. Park,Sensors Actuators B Chem.311, 127866 (2020).

    T. Wu, A. Alharbi, R. Kiani, D. Shahrjerdi,Adv. Mater.31, 1–12 (2019).

     
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    Free, publicly-accessible full text available August 28, 2024