Understanding disorder in monolayer graphene devices with gate-defined superlattices.

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.

Cellulose-Based Laser-Induced Graphene Devices for Electrochemical Monitoring of Bacterial Phenazine Production and Viability.

As an easily disposable substrate with a microporous texture, paper is a well-suited, generic substrate to build analytical devices for studying bacteria. Using a multi-pass lasing process, cellulose-based laser-induced graphene (cLIG) with a sheet resistance of 43.7 ± 2.3 Ωsq-1 is developed and utilized in the fabrication of low-cost and environmentally-friendly paper sensor arrays. Two case studies with Pseudomonas aeruginosa and Escherichia coli demonstrate the practicality of the cLIG sensors for the electrochemical analysis of bacteria. The first study measures the time-dependent profile of phenazines released from both planktonic (up to 60 h) and on-chip-grown (up to 22 h) Pseudomonas aeruginosa cultures. While similarities do exist, marked differences in phenazine production are seen with cells grown directly on cLIG compared to the planktonic culture. Moreover, in planktonic cultures, pyocyanin levels increase early on and plateau around 20 h, while optical density measurements increase monotonically over the duration of testing. The second study monitors the viability and metabolic activity of Escherichia coli using a resazurin-based electrochemical assay. These results demonstrate the utility of cLIG paper sensors as an inexpensive and versatile platform for monitoring bacteria and could enable new opportunities in high-throughput antibiotic susceptibility testing, ecological studies, and biofilm studies.

Systematic Study of Various Functionalization Steps for Ultrasensitive Detection of SARS-CoV-2 with Direct Laser-Functionalized Au-LIG Electrochemical Sensors.

The 2019 coronavirus (COVID-19) pandemic impaired global health, disrupted society, and slowed the economy. Early detection of the infection using highly sensitive diagnostics is crucial in preventing the disease's spread. In this paper, we demonstrate electrochemical sensors based on laser induced graphene (LIG) functionalized directly with gold (Au) nanostructures for the detection of SARS-CoV-2 with an outstanding limit of detection (LOD) of ∼1.2 ag·mL-1. To achieve the optimum performance, we explored various functionalization parameters to elucidate their impact on the LOD, sensitivity, and linearity. Specifically, we investigated the effect of (i) gold precursor concentration, (ii) cross-linker chemistry, (iii) cross-linker and antibody incubation conditions, and (iv) antigen-sensor interaction (diffusion-dominated incubation vs pipette-mixing), as there is a lack of a systematic study of these parameters. Our benchmarking analysis highlights the critical role of the antigen-sensor interaction and cross-linker chemistry. We showed that pipette-mixing enhances sensitivity and LOD by more than 1.6- and 5.5-fold, respectively, and also enables multimodal readout compared to diffusion-dominated incubation. Moreover, the PBA/Sulfo-NHS: EDC cross-linker improves the sensitivity and LOD compared to PBASE. The sensors demonstrate excellent selectivity against other viruses, including HCoV-229E, HCoV-OC43, HCoV-NL63, and influenza H5N1. Beyond the ability to detect antigen fragments, our sensors enable the detection of antigen-coated virion mimics (which are a better representative of the real infection) down to an ultralow concentration of ∼5 particles·mL-1.

Mechanistic Insight into Intestinal α-Synuclein Aggregation in Parkinson's Disease Using a Laser-Printed Electrochemical Sensor.

Aggregated deposits of the protein α-synuclein and depleting levels of dopamine in the brain correlate with Parkinson's disease development. Treatments often focus on replenishing dopamine in the brain; however, the brain might not be the only site requiring attention. Aggregates of α-synuclein appear to accumulate in the gut years prior to the onset of any motor symptoms. Enteroendocrine cells (specialized gut epithelial cells) may be the source of intestinal α-synuclein, as they natively express this protein. Enteroendocrine cells are constantly exposed to gut bacteria and their metabolites because they border the gut lumen. These cells also express the dopamine metabolic pathway and form synapses with vagal neurons, which innervate the gut and brain. Through this connection, Parkinson's disease pathology may originate in the gut and spread to the brain over time. Effective therapeutics to prevent this disease progression are lacking due to a limited understanding of the mechanisms by which α-synuclein aggregation occurs in the gut. We previously proposed a gut bacterial metabolic pathway responsible for the initiation of α-synuclein aggregation that is dependent on the oxidation of dopamine. Here, we develop a new tool, a laser-induced graphene-based electrochemical sensor chip, to track α-synuclein aggregation and dopamine level over time. Using these sensor chips, we evaluated diet-derived catechols dihydrocaffeic acid and caffeic acid as potential inhibitors of α-synuclein aggregation. Our results suggest that these molecules inhibit dopamine oxidation. We also found that these dietary catechols inhibit α-synuclein aggregation in STC-1 enteroendocrine cells. These findings are critical next steps to reveal new avenues for targeted therapeutics to treat Parkinson's disease, specifically in the context of functional foods that may be used to reshape the gut environment.

Recent advances in graphene-based electroanalytical devices for healthcare applications.

Graphene, with its outstanding mechanical, electrical, and biocompatible properties, stands out as an emerging nanomaterial for healthcare applications, especially in building electroanalytical biodevices. With the rising prevalence of chronic diseases and infectious diseases, such as the COVID-19 pandemic, the demand for point-of-care testing and remote patient monitoring has never been greater. Owing to their portability, ease of manufacturing, scalability, and rapid and sensitive response, electroanalytical devices excel in these settings for improved healthcare accessibility, especially in resource-limited settings. The development of different synthesis methods yielding large-scale graphene and its derivatives with controllable properties, compatible with device manufacturing - from lithography to various printing methods - and tunable electrical, chemical, and electrochemical properties make it an attractive candidate for electroanalytical devices. This review article sheds light on how graphene-based devices can be transformative in addressing pressing healthcare needs, ranging from the fundamental understanding of biology in in vivo and ex vivo studies to early disease detection and management using in vitro assays and wearable devices. In particular, the article provides a special focus on (i) synthesis and functionalization techniques, emphasizing their suitability for scalable integration into devices, (ii) various transduction methods to design diverse electroanalytical device architectures, (iii) a myriad of applications using devices based on graphene, its derivatives, and hybrids with other nanomaterials, and (iv) emerging technologies at the intersection of device engineering and advanced data analytics. Finally, some of the major hurdles that graphene biodevices face for translation into clinical applications are discussed.

A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva.

Multiplexed detection of biomolecules is of great value in various fields, from disease diagnosis to food safety and environmental monitoring. However, accurate and multiplexed analyte detection is challenging to achieve in mixtures using a single device/material. In this paper, we demonstrate a machine learning (ML)-powered multimodal analytical device based on a single sensing material made of electrodeposited molybdenum polysulfide (eMoSx) on laser induced graphene (LIG) for multiplexed detection of tyrosine (TYR) and uric acid (UA) in sweat and saliva. Electrodeposition of MoSx shows an increased electrochemically active surface area (ECSA) and heterogeneous electron transfer rate constant, k0. Features are extracted from the electrochemical data in order to train ML models to predict the analyte concentration in the sample (both singly spiked and mixed samples). Different ML architectures are explored to optimize the sensing performance. The optimized ML-based multimodal analytical system offers a limit of detection (LOD) that is two orders of magnitude better than conventional approaches which rely on single peak analysis. A flexible and wearable sensor patch is also fabricated and validated on-body, achieving detection of UA and TYR in sweat over a wide concentration range. While the performance of the developed approach is demonstrated for detecting TYR and UA using eMoSx-LIG sensors, it is a general analytical methodology and can be extended to a variety of electrochemical sensors to enable accurate, reliable, and multiplexed sensing.

Electrochemical Sensors Based on MoSx -Functionalized Laser-Induced Graphene for Real-Time Monitoring of Phenazines Produced by Pseudomonas aeruginosa.

Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen causing infections in blood and implanted devices. Traditional identification methods take more than 24 h to produce results. Molecular biology methods expedite detection, but require an advanced skill set. To address these challenges, this work demonstrates functionalization of laser-induced graphene (LIG) for developing flexible electrochemical sensors for P. aeruginosa based on phenazines. Electrodeposition as a facile approach is used to functionalize LIG with molybdenum polysulfide (MoSx ). The sensor's limit of detection (LOD), sensitivity, and specificity are determined in broth, agar, and wound simulating medium (WSM). Control experiments with Escherichia coli, which does not produce phenazines, demonstrate specificity of sensors for P. aeruginosa. The LOD for pyocyanin (PYO) and phenazine-1-carboxylic acid (PCA) is 0.19 × 10-6  and 1.2 × 10-6  m, respectively. Furthermore, the highly stable sensors enable real-time monitoring of P. aeruginosa biofilms over several days. Comparing square wave voltammetry data over time shows time-dependent generation of phenazines. In particular, two configurations-"Normal" and "Flipped"-are studied, showing that the phenazines time dynamics vary depending on how cells interact with sensors. The reported results demonstrate the potential of the developed sensors for integration with wound dressings for early diagnosis of P. aeruginosa infection.