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Abstract In fighting against infectious diseases such as COVID‐19, simple‐to‐use, sensitive, scalable, and rapid diagnostics are crucial for early disease diagnosis. In this regard, electrochemical biosensors are particularly attractive in developing point‐of‐need diagnostics. Importantly, by being compatible with nano‐ and microfabrication methods, they are amenable to miniaturization, which reduces background noise and the required sample volume. However, miniaturization also reduces the signal level, making it challenging to detect low virus counts. In this work, microfabricated electrochemical sensors with a dual signal amplification scheme based on evaporation‐enhanced redox cycling (E2RC) in a generator–collector configuration are developed. A scalable, nanolithography‐free fabrication method is proposed to achieve a controllable sub‐micrometer gap between three dimensional (3D) interdigitated microelectrodes by combining photolithography with template‐driven electrodeposition. Using the optimized electrodes, the sensors achieve rapid detection with a limit of quantification of ≈1.2 × 10³particles mL⁻¹through continuous measurement in evaporating droplets containing SARS‐CoV‐2 virion mimics. Investigating particle charge and size reveals the role of electrophoretic enrichment in the overall response. The sensor performance is also validated using heat‐inactivated SARS‐CoV‐2 virions, with selective response to SARS‐CoV‐2 against HCoV‐299E, SARS‐CoV S1, and MERS‐CoV S1 (captured using antibody‐functionalized magnetic nanoparticles). The proposed sensing method is sensitive, rapid, scalable, and can be extended to broader applications, including detection of bacteria, extracellular vesicles, and other viruses. 

Abstract Engineering superlattices (SLs)—which are spatially periodic potential landscapes for electrons—is an emerging approach for the realization of exotic properties, including superconductivity and correlated insulators, in two-dimensional materials. While moiré SL engineering has been a popular approach, nanopatterning is an attractive alternative offering control over the pattern and wavelength of the SL. However, the disorder arising in the system due to imperfect nanopatterning is seldom studied. Here, by creating a square lattice of nanoholes in the SiO₂dielectric layer using nanolithography, we study the SL potential and the disorder formed in hBN-graphene-hBN heterostructures. Specifically, we observe that while electrical transport shows distinct SL satellite peaks, the disorder of the device is significantly higher than graphene devices without any SL. We use finite-element simulations combined with a resistor network model to calculate the effects of this disorder on the transport properties of graphene. We consider three types of disorder: nanohole size variations, adjacent nanohole mergers, and nanohole vacancies. Comparing our experimental results with the model, we find that the disorder primarily originates from nanohole size variations rather than nanohole mergers in square SLs. We further confirm the validity of our model by comparing the results with quantum transport simulations. Our findings highlight the applicability of our simple framework to predict and engineer disorder in patterned SLs, specifically correlating variations in the resultant SL patterns to the observed disorder. Our combined experimental and theoretical results could serve as a valuable guide for optimizing nanofabrication processes to engineer disorder in nanopatterned SLs. 

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. 

Recent advances in graphene-based electroanalytical biodevices: different methods for graphene synthesis, functionalization, device fabrication, and transduction mechanisms are discussed for various healthcare applications. 

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. ReferencesA. 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).