Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Zhaoling Huang
 -- 5514 2024-01-17 04:46:54 |
2 layout & references
Sirius Huang
 Meta information modification 5514 2024-01-18 01:42:57 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Alahi, M.E.E.; Rizu, M.I.; Tina, F.W.; Huang, Z.; Nag, A.; Afsarimanesh, N. Graphene-Based Implantable Electrodes for Neural Recording/Stimulation. Encyclopedia. Available online: https://encyclopedia.pub/entry/53932 (accessed on 26 December 2024).
Alahi MEE, Rizu MI, Tina FW, Huang Z, Nag A, Afsarimanesh N. Graphene-Based Implantable Electrodes for Neural Recording/Stimulation. Encyclopedia. Available at: https://encyclopedia.pub/entry/53932. Accessed December 26, 2024.
Alahi, Md Eshrat E., Mubdiul Islam Rizu, Fahmida Wazed Tina, Zhaoling Huang, Anindya Nag, Nasrin Afsarimanesh. "Graphene-Based Implantable Electrodes for Neural Recording/Stimulation" Encyclopedia, https://encyclopedia.pub/entry/53932 (accessed December 26, 2024).
Alahi, M.E.E., Rizu, M.I., Tina, F.W., Huang, Z., Nag, A., & Afsarimanesh, N. (2024, January 17). Graphene-Based Implantable Electrodes for Neural Recording/Stimulation. In Encyclopedia. https://encyclopedia.pub/entry/53932
Alahi, Md Eshrat E., et al. "Graphene-Based Implantable Electrodes for Neural Recording/Stimulation." Encyclopedia. Web. 17 January, 2024.
Graphene-Based Implantable Electrodes for Neural Recording/Stimulation
Edit
This entry is adapted from the peer-reviewed paper 10.3390/s23249911

Implantable electrodes represent a groundbreaking advancement in nervous system research, providing a pivotal tool for recording and stimulating human neural activity. This capability is integral for unraveling the intricacies of the nervous system’s functionality and for devising innovative treatments for various neurological disorders. Implantable electrodes offer distinct advantages compared to conventional recording and stimulating neural activity methods. Crucially, the development of implantable electrodes necessitates key attributes: flexibility, stability, and high resolution. Graphene emerges as a highly promising material for fabricating such electrodes due to its exceptional properties. It boasts remarkable flexibility, ensuring seamless integration with the complex and contoured surfaces of neural tissues. 

graphene implantable electrode neural recording/stimulation GFET recording

1. Introduction

The implantable microelectrode array has been instrumental in comprehending regular neural processes, studying neurological behaviors, and facilitating bidirectional communication between electronic devices and the central nervous system (CNS) [1]. Conventional clinical procedures involve the insertion of electrodes into the brain, often employing sharp silicon-based variants for critical applications. Unfortunately, this method presents notable drawbacks, including potential inflammation and damage from the implantation process. Minimally invasive techniques have been explored to mitigate these concerns, focusing on implementing flexible microelectrode arrays. Notably, graphene has emerged as a key contributor to recent advancements in this field. A flexible microelectrode array can record the neural signal and stimulate the neurons to understand the neuronal behavior of the cortical circuits. Despite technological advancement and innovation, the implantable microelectrode array has significant challenges and limitations [2][3][4].
The current era of science and technology has witnessed the development of highly efficient sensing devices for various intrinsic human body signals. Among these, the design and advancement of high-quality neural recording and stimulation systems have been particularly impactful [5][6][7]. These signals are pivotal in comprehending the fundamental processes within the neural and neurological domains. In clinical practices, microelectromechanical (MEMS) silicon-based electrodes have been employed for intracranial applications [7][8][9]. However, this approach has drawbacks, including side effects like inflammation and implantation damage. Consequently, there has been a paradigm shift toward considering flexible sensing prototypes and electrodes as a cornerstone for the past two decades [10][11]. These electrodes have been fabricated using various materials and techniques, resulting in electrodes with optimized electromechanical parameters for specific applications. One popular category among these flexible electrodes, particularly in biomedical contexts, is flexible microelectrode arrays due to their non-invasive nature [12][13][14]. Scientists have made significant progress in enhancing electrode performance, addressing key aspects like long-term signal stability, detection sensitivity, multifunctionalization, and in vivo biocompatibility [3][15][16][17][18][19]. Conventional brain electrodes have historically leaned toward the use of noble metals such as Ag [20], Au [21], and Pt [22] for their stability and ease of production. Their limited electron transfer capabilities have constrained neural signal detection sensitivity. As a result, alternative nanomaterials have been explored for electrode development, emphasizing specific characteristics such as non-invasiveness, robust interfacing, high charge transfer capacity, and low output impedance [23]. While conventional electrodes made from pure metals or alloys are adequate to a certain extent, modifications involving compounds like iridium oxide, titanium nitride, PEDOT, and carbon nanotubes have been pursued to enhance charge injection capacity for improved simulation capabilities [24]. However, the poor adhesion of these modified electrodes has been a significant challenge, leading to delamination during real-time neural recordings [16].
Therefore, these electrodes have been formed using various nanomaterials to form enhanced electrodes with high sensitivity and selectivity. Some of the common materials used to create flexible electrodes are carbon-based elements like carbon nanotubes (CNTs) [25][26][27][28], graphene [29][30][31][32], graphite [33][34][35], and other metallic elements like copper [36][37][38], platinum [39][40][41], gold [42][43][44], and silver [45][46][47]. These elements effectively form excellent flexible electrodes due to their enhanced electrical, mechanical, and thermal characteristics. Enhanced graphene is an emergent two-dimensional nanomaterial boasting remarkable traits including high transparency (497.3%), low electronic resistivity (1.00 × 10−8 Ω·m), and exceptional electron mobility (105 cm2·V−1·s−1) [48][49][50][51][52][53][54][55][56]. It forms a stable electrode–neural tissue interface, closely integrating with brain tissue, thus holding immense promise as a next-generation neuronal electrode [48][49][50][51][52]. The electrical signal transfer is much more sensitive thanks to graphene’s exceptional electrical and optical properties. Due to this, optical imaging and optogenetic stimulation techniques can be used directly on brain tissue, enhancing the spatial and temporal resolution of neural activity detection [53][54]. Moreover, graphene-related electrodes’ tensile flexibility has the potential to facilitate wound healing, diminish scar damage, and reduce tissue inflammation, making them highly viable for long-term neural signaling activity monitoring [55][56]. Researchers have extensively utilized graphene and other materials to create sensors with exceptional physiochemical properties, making it a crucial element in developing prototypes for neuro-interfacing applications. The two-dimensional (2D) hexagonal carbon lattices are linked by covalent solid in-plane σ-σ bonds, supplemented by extra π-π bonds within the electronic orbitals aligned along the vertical plane of the graphene [57]. This leads to electrons’ delocalization, which is vital for electrochemical sensing applications. Due to the zero bandgap between the valence and conduction bands of graphene, a solid ambipolar electric field effect is observed with a high charge carrier mobility of ≈10,000 cm2·V−1·s−1 at room temperature [58]. In addition, the white light absorbance, thermal stability, mechanical strength, and specific surface area are 2.3%, 3000 ≈ 5000 W·m−1·K−1, ≈1 TPa, and 2630 m2·g−1, respectively [59]. These superior characteristics of graphene are an ideal candidate for applying strain [60][61][62], electrochemical [63][64][65][66], and electrical [67][68][69] applications. In the realm of electrochemical and electrical applications of graphene for signal detection within the body, establishing direct contact and exposing neurons to graphene-based electrodes proves imperative. This ensures a close adhesion between cell membranes and the interfacing electrodes. This adhesive behavior leads to detecting small signals, in order of microvolts, during the extracellular recordings and tissue stimulation processes [70]. The remarkable electrical conductivity and minimal noise characteristics of graphene present a promising avenue for upscaling the structural dimensions of the electrodes, ranging from single-cell measurements to macro-sized ones, by improving the SNR. These properties also reduce the electrical impedance and improve the charge injection capacity (CIC). The CIC refers to the maximum amount of electric charge that can be delivered or extracted by an electrode interface without causing irreversible chemical reactions or damage to the electrode or surrounding tissue. In the context of neural stimulation or recording, CIC is an important parameter to consider for electrodes used in implantable devices. For neural stimulation, the electrode must deliver a controlled charge to activate neurons without causing harm. On the other hand, in neural recording, the electrode should be capable of detecting minute electrical signals generated by neural activity. The high mechanical and chemical stability is excellent for using graphene to develop flexible sensors, especially for soft biological tissue interfaces.

2. Neural Tissue Interface Enhancement with Graphene

The intricate interplay of neural cells within brain tissue generates essential neuroelectric signals through electrophysiological activity. This process involves the passage of potassium and sodium ions via ion channels, thereby influencing the extracellular potential [39]. Neural electrodes have revolutionized our ability to record extracellular potential changes, offering critical insights into neural activity and underlying pathological mechanisms. These electrodes facilitate the non-invasive examination of neural activity in vitro, providing valuable data for in vivo studies without invasive procedures on living organisms.
The implantable electrode interface links brain–computer interface (BCI) devices and neurons within the central nervous system. This interface is instrumental in advancing our understanding of various neurological processes and restoring function in disorders like epilepsy, paralysis, Alzheimer’s disease, and motor dysfunction due to limb loss. Both implantable electrodes and BCI devices play an integral role as neural interfaces, enabling neural stimulation and recording while maintaining high signal quality and minimizing noise arising from individual neurons—commonly referred to as action potentials [39]. Neural signals are categorized into electroencephalography (EEG) [55], electrocorticogram (ECoG) [56][57], local field potentials (LFPs) [58], and action potentials (APs) [59] based on the location of the recording sites.
The choice of implantable electrode positioning significantly affects the quality of neural signals captured using various neural recording technologies. Electroencephalography (EEG) is a non-invasive technique widely employed for observing sleep patterns and understanding brain activity, particularly for conditions like seizure treatment [60][61][62][63]. However, EEG signals can be susceptible to interference from local field potentials (LFPs) and have limitations in capturing signals from specific brain regions due to low transfer rates, typically ranging from 5 to 25 bits per second (bps) [64][65]. Moreover, the densely packed nature of brain tissues, coupled with intervening layers like skin and the skull, obstructs EEG signals, compromising their spatiotemporal resolution [3].
Electrocorticography (ECoG), on the other hand, offers advantages for minimally invasive neural recording purposes, surpassing EEG’s limitations. ECoG significantly reduces noise levels and enables the high-frequency and accurate recording of neural signals. When placed on the cortex, implantable ECoG electrodes mitigate interference from neighboring tissues, resulting in higher quality signals [66]. Nonetheless, ECoG has difficulties capturing individual neural signals from neurons and superficial regions. To address the need for precise, unique neural signal recording across specific cortex areas, implantable local field potentials (LFPs) are utilized, primarily from deeper brain regions. LFPs allow the extraction of local neural activities from precise sites, including action potentials (APs) and membrane potential fluctuations, thus providing valuable insights into specific brain areas [67].
The selection of an appropriate neural signal recording method hinges on the application’s focal point, electrode design, material attributes, and the chosen implantation site. Recent trends highlight substantial strides in implantable electrode technologies, particularly in surpassing the capabilities of EEG and ECoG by focusing on single-neuron activities. Implantable electrodes, coupled with neural devices, hold promise in controlling epileptogenic regions and addressing Parkinson’s disease through targeted neural stimulation, offering more sophisticated neural interfacing capabilities than EEG and ECoG devices [66][67]. As research endeavors progress, innovations in implantable device technologies encompass enhanced spatial resolution, augmented recording sites, and multifunctionality tailored to various neural activities. Integrating simulations, materials science, mechanical design, and electronic engineering accelerates chronic in vivo neural recordings and stimulations facilitated by implantable electrodes alongside BCI devices [70][71][72][73][74][75][76]. A central objective entails the development of fully miniaturized, biointegrated, flexible, and wireless BCI platforms, ensuring mechanical compatibility and seamless integration with neural tissues [77][78].
Establishing stable graphene–neuron interfaces holds significant implications. Innovative techniques, like a graphene liquid-gate transistor packaging method, help alleviate bending stress, enhancing interface stability [59]. However, challenges surrounding graphene transfer between substrates are addressed through ultraviolet ozone treatment, effectively reducing contact noise and improving electrical performance [60]. These findings underscore graphene’s promise in establishing stable interfaces with neurons, thus augmenting implantable electrodes’ capabilities for precisely recording neural signals.

3. Potentiality of Graphene for Implantable Electrodes

Traditional neural electrodes primarily employ metal-based materials, a common choice in biomedical applications. However, there is a growing interest in exploring alternative materials for enhanced performance and biocompatibility [38][79][80][81]. However, they often grapple with high impedance, negatively impacting their recording sensitivity. Moreover, when in contact with tissue, these electrodes can lead to uneven electron transfer, potentially causing discomfort and tissue damage at the interface with brain tissue. Despite efforts to integrate materials like silicon and flexible polymers into electrode production, impedance continues to pose a considerable hurdle [82]. Graphene’s exceptional biocompatibility and electrical properties make it ideal for interfacing with neural tissues. Graphene’s capacity to precisely record neuro-electrophysiological activity with high temporal resolution and stable regulation presents unprecedented advantages for microengineering applications. Notably, graphene’s distinct edge over conventional metal electrodes lies in its transferability onto transparent substrates, thereby enabling the creation of transparent neural electrode arrays. Moreover, it is essential to highlight that doped graphene exhibits higher transmittance than ITO and ultrathin metals. This characteristic eliminates artifacts when high-intensity light is directly applied to an electrode site, making doped graphene particularly advantageous. Such enhanced transmittance facilitates the successful implementation of various optical techniques, including optogenetic stimulation, optical coherence tomography (OCT), and fluorescence imaging, beneath the graphene electrode sites [83]. Table 1 presents a comparative analysis of flexible electrode materials, highlighting the distinct advantages of graphene over other counterparts. Graphene, with its exceptional electrical conductivity of 243.5 ± 15.9 kΩ (~200 µm diameter) and extreme flexibility (~1 TPa), emerges as a superior choice. Its high transparency and excellent biocompatibility further enhance its desirability for various applications. In contrast, other materials like PEDOT, PT, PPY, PANI, carbon nanofiber (CNF), glassy carbon, and diamond exhibit varying levels of conductivity, flexibility, transparency, and biocompatibility, with graphene consistently demonstrating superior performance in multiple key aspects.
Table 1. Comparative properties of flexible electrode materials.
Electrode Material Electrical Property Young’s Modulus Transparency Biocompatibility Reference
PEDOT High conductivity: 1200 S·cm−1 Flexible: 2.6 ± 1.4 GPa Limited Excellent [84][85]
PT Moderate conductivity: 10–100 S·cm−1 Stiff: 3 GPa Limited Moderate [86]
PPY Variable conductivity: 40–200 S·cm−1 Moderate flexibility: 430–800 MPa Limited Good [87][88]
PANI Low conductivity: 5 S·cm−1 Stiff: 2–4 GPa Limited Moderate [89]
Graphene Excellent conductivity: 243.5 ± 15.9 kΩ (~200 µm diameter) Extremely flexible: ~1 TPa High Excellent [83][90]
Carbon nanofiber (CNF) Moderate conductivity: ~1 MΩ (2 cm length, 25.7 × 16.6 µm2) Variable stiffness: 6–207 GPa Limited Good [91][92]
Glassy carbon Good conductivity: 11.0 ± 5.4 kΩ (300 µm diameter) Stiff: 20 GPa Limited Good [93]
Diamond Moderate conductivity: ~207.9 kΩ (0.0079 mm2) Very stiff: ~103 GPa High Excellent [94]
A significant breakthrough was achieved in 2009 by Kim, who led the way in producing graphene films and was instrumental in advancing the chemical vapor deposition method on thin nickel layers [95]. These films demonstrated meager resistance (below 280 ohms per square) and optical transparency exceeding 80% [95]. Furthermore, graphene films on SiO2 substrates exhibited impressive electron mobility of 3700 cm2 V−1 s−1 [95]. These attributes substantially enhanced the detection of neuronal signals. This innovative approach not only facilitated electrophysiological signal recording but also upheld the quality of imaging data. Electrodes with increased sensitivity for electrophysiological recording are essential for diagnosing neurological illnesses. Traditional opaque brain electrode arrays frequently have poor spatial resolution and sensitivity, which causes problems like image artifacts and data loss. As a result, graphene, with its exceptional properties, including transparency, flexibility, and controlled electrical conductivity, emerges as a highly promising material for developing advanced brain electrodes.

4. Characteristics of Graphene-Based Implantable Electrodes

Implantable electrodes made from graphene represent a revolutionary leap forward in the field of neural engineering. These electrodes exhibit exceptional properties that make them highly attractive for various applications within the field and are crafted from a single layer of carbon atoms meticulously arranged in a two-dimensional honeycomb lattice. The subsequent sections will delve into some of the significant features of these electrodes.

4.1. Flexible Electrodes

The insertion of rigid-surfaced electrodes, including materials like gold, platinum, iridium, stainless steel, and tungsten, for clinical and biological research purposes can inadvertently damage neurons. This phenomenon occurs as these hard electrodes are implanted, leading to the destruction of surrounding neural tissue. Additionally, widely used silicon-based implantable electrodes, exemplified by the Utah electrode array [96] or Michigan electrode, have become staples in neuroscience research. However, their inherent rigidity poses limitations for long-term neural recording and stimulation due to the potential for tissue damage and reduced compatibility. As a result, alternative approaches and materials are sought to address these drawbacks and enable safer and more effective long-term applications in neuroscientific investigations.
In this context, graphene emerges as a promising candidate. With its distinctive hexagonal honeycomb structure of carbon atoms, graphene exhibits remarkable electrical, mechanical, and chemical characteristics. Notably, its inherent flexibility allows for seamless integration with surrounding tissues, mitigating the potential harm to nerve tissues during implantation. This unique feature positions graphene as a viable replacement for conventional materials like silicon and metal in the development of innovative neural interfaces [97][98]. The porous graphene electrode, in particular, stands out for its expansive low impedance, specific surface area, and robust charge injection capacity, collectively elevating the standard of cortical recording and stimulation quality [99].
Kuzum and colleagues played a pivotal role in pioneering the fabrication of three-dimensional porous graphene foam. This innovative material was crafted through a direct etching process on a polyimide substrate, utilizing laser pyrolysis as the key technique [100]. They incorporated Cr/Au metal leads and contact pads into the structure. As an encapsulation layer, they employed negative photoresist SU-8. This procedure resulted in a flexible graphene neural electrode array renowned for its heightened porosity and surface irregularities. Remarkably, the impedance of this array was nearly a hundredfold lower than that of gold electrodes with comparable dimensions. Additionally, a chemical doping process involving nitric acid treatment was employed to diminish impedance further, concurrently augmenting the charge injection limit (CIL) from 2 to 3.1 mC·cm−2. The heightened CIL bears substantial implications for electrode performance, signifying the electrode’s ability to deliver charge efficiently while ensuring safety thresholds are not exceeded for both the surrounding tissue and the electrodes themselves. The achieved CIL value demonstrates exceptional suitability for a broad spectrum of applications, outperforming materials such as IrxO, carbon nanotubes, PEDOT, Ta2O5, and titanium nitride. Following this success, the electrodes were carefully positioned on the surface of the rat sensory cortex to record sensory-evoked potentials. Notably, applying stimulation to the motor cortex using these electrodes led to a distinct flexion in both ankle and knee joints. Particularly in the fields of electrical microstimulation and the mapping of spatial–temporal cortical dynamics, this groundbreaking study provides a potent instrument.
Garrett and his team employed a thorough wet spinning technique to create fibers from graphene oxide. Following this, the fibers underwent annealing at 220 °C, forming liquid crystal graphene oxide (LCGO) fibers [101]. To provide insulation, a protective layer of Parylene C was applied. The fiber endings underwent precise laser ablation, yielding a neural electrode with significantly increased charge injection capacity. This process increased surface roughness and nanoporosity creation. The study team used this state-of-the-art electrode to stimulate ganglion cells in a detached rat retina in an in vitro setting. They also recorded a thorough whole-cell patch clamp simultaneously. The electrode’s surface was carefully coated with a water-soluble sucrose coating to create micron-scale needles. The cat’s visual brain was then implanted with this modified flexible electrode. The sucrose layer was then removed, making it easier to monitor brain activity. Creating a self-supporting, flexible shank that effortlessly combines with the electrode was a significant development in this work. With this invention, there was no longer a need for complex material interfaces or the time-consuming procedure of connecting a larger electrode to a smaller wire.
Graphene’s versatility extends to its integration with various materials for fabricating neural electrodes, harnessing the strengths of multiple substances. In a pioneering study conducted by Jang and colleagues, they introduced a neural probe with a recording site crafted from a composite material amalgamating poly(3,4-ethylenedioxythiophene) (PEDOT), gold (Au), and zinc oxide (ZnO) nanowires. Complementing this, a lead wire constructed from a combination of gold (Au) and graphene was also integrated into the probe [102]. The integration of graphene with various materials in neural electrode fabrication shows its remarkable versatility. Including ZnO nanowires with a PEDOT coating significantly increased the electrode’s surface area and charge storage capacity, reducing impedance. The Au–graphene lead exhibited remarkable flexibility and conductivity, showcasing the reinforcing effect of graphene on the electrode’s resilience to bending. This combination holds great potential for enhanced neural electrodes.

4.2. Transparent Electrodes

Transparent neural interfaces play a pivotal role in minimizing light-induced artifacts and ensuring conductivity for the precise measurement of electrical signals [103][104]. Achieving optimal electrochemical impedance between the electrode sensing site and tissue is crucial for the effective transmission of neural signals. Additionally, minimizing trace resistance between the sensing site and the percutaneous connector is essential for obtaining high-quality electrophysiological signals. When designing transparent electrode arrays, material selection becomes paramount. Carbon or polymer-based electrodes, while offering transparency, often exhibit unfavorable electrical properties compared to metal wiring in terms of trace resistance, especially when considering the same interconnect line width and length. In the case of carbon-based neural electrode arrays, conductive metals are frequently introduced to enhance the electron path intuitively, albeit at the expense of interconnect transparency. On the other hand, polymer electrode arrays can mitigate trace resistance by modifying the molecular structure through specialized doping or post-treatment, maintaining transparency throughout the device, including the conducting path [105][106].
Prioritizing both high transparency and conductivity is crucial when selecting materials for multimodal device design. Recently, graphene has emerged as a material of significant interest for transparent neural interfaces. Beyond its remarkable electrical conductivity, graphene boasts excellent transparency, attributed to its unique two-dimensional honeycomb structure. Graphene exhibits exceptional intrinsic optical transparency, surpassing 90%, across ultraviolet (UV) and infrared (IR) light spectrums [107][108]. This remarkable transparency is particularly significant for transparent neural interfaces, as it ensures high visible light transmittance, thereby minimizing optical blocking. In the context of transparent neural interfaces, maintaining high visible light transmittance is crucial for optimal performance. Moreover, the high optical transmittance of neural interface materials, extending to both UV and IR light, holds significant advantages for applications such as optogenetic stimulation and photo-induced imaging [109]. These properties enhance the versatility of transparent neural interfaces, enabling precise stimulation and imaging techniques.
Williams et al. [90] introduced a carbon-layered electrode array (CLEAR) employing four graphene layers on a Parylene C substrate, featuring 16 electrode locations with exceptional transparency surpassing 90% across UV to IR spectra. Adjusting optical power effectively mitigated artifacts caused by illumination. Positioned precisely in the cerebral cortex of a Thy1:ChR2 transgenic mouse, the CLEAR device optimally responded to 473 nm blue light, seamlessly capturing neuroelectrical signals. It enabled fluorescent imaging and optical coherence tomography of cortical blood vessels at the electrode site, owing to graphene’s broad light transmission spectrum. The transparent electrode facilitated the unobstructed visualization of underlying tissue, allowing for detailed imaging [83].
In a subsequent investigation, transparent microelectrode arrays fabricated from graphene were employed for micro-electrocorticography (μECoG) studies. This innovative approach enabled the concurrent application of neuroelectrical stimulation and the imaging of neural activity within the cortex of transgenic mice featuring the GCaMP6f indicator [110]. The exceptional light transmittance of graphene facilitated the visualization of neural activity, elicited by electrical stimulation using fluorescent calcium imaging. Remarkably, the graphene electrode exhibited an impressive charge injection limit (CIL) ranging from 116.07 to 174.10 μC·cm−2. Furthermore, it was observed that cathodic stimulation elicited a more robust neural response compared to anodic stimulation, affirming a more efficient charge transfer to the brain. The extraordinary light permeability of graphene enabled the observation of neural responses triggered by electrical stimulation via fluorescent calcium imaging.
In their investigation, Kuzum et al. used transparent, bendable graphene electrode arrays to record electrophysiology while simultaneously photographing optical signals [59]. The flexible polyimide substrate, p-type doped graphene site, and SU-8 encapsulation of the doped graphene electrode allowed it to exhibit exceptional properties like low impedance and high charge storage. This unique design allowed for optical artifact-free simultaneous calcium ion imaging and electrophysiological recordings of slices of hippocampus tissue. Furthermore, transparent graphene electrodes demonstrated proficiency in detecting high-frequency electrical activity. This aspect offered high spatial resolution, albeit with lower temporal resolution, providing a valuable complement to calcium imaging. The corrosion of the Ag electrode was notably inhibited by encasing it with graphene. After six months of immersion in a phosphate buffer, it was observed that the graphene-coated Au electrode had been effectively protected. The Raman spectrum post-immersion revealed distinct graphene peaks, confirming that transparent electrodes were created by graphene with minimal interference from noise. Furthermore, it was confirmed that graphene served as a layer that protected metal microelectrodes from corrosion, assuring their long-term stability.
An electrochemical bubbling technique for transferring graphene onto a 50 μm thick polyethene terephthalate substrate was introduced by Thunemann et al. [111]. The resulting graphene sheet underwent rigorous surface cleaning before being shaped into electrode locations to avoid crack development and the lingering presence of organic materials. Developing a transparent 16-channel SU-8 encapsulation-coated graphene microelectrode array was made possible. The impedance was less than 1.5 M at 1 kHz, and it could endure continuous bend (up to 20 times) at a radius of curvature of 5 mm, which falls well below the mouse cortex’s normal bending range, without experiencing any failures. Subsequently, the electrode was situated atop the mouse’s primary somatosensory cortex to facilitate two-photon imaging of interneurons and blood vessels, reaching depths up to 1200 μm. Notably, the adaptability of the electrode was showcased as it permitted the activation of both the local field potential (LFP) and calcium ions in the opposite cheek area with a single pulse. This encompassed the synchronized recording of ion transient signals, capturing LFP signals under optogenetic modulation, performing two-photon imaging of arteriole expansion, conducting simultaneous hemodynamic optical imaging, and registering neuroelectric activity under cheek stimulation.
In electroretinography (ERG) studies, an application has been found for transparent electrodes. An innovation by Duan et al. led to the development of flexible and transparent graphene contact lens electrodes (GRACEs) [112]. These electrodes demonstrated exceptional light transmittance across a wide spectral range, establishing a snug and seamless interface with the cornea. Importantly, no observable harm to the cornea was noted during conventional ERG recording. High-fidelity recordings of various ERG signals were enabled by this electrode design. In the domain of full-field ERG, corneal potential amplitudes recorded by GREACEs were found to exceed those obtained with commercial ERG-Jet electrodes. Furthermore, the adeptness of these electrodes in capturing multifocal ERG signals was attributed to the preservation of the eye’s refractive power by the conformal interface. Furthermore, a multilocation see-through graphene electrode array was utilized to differentiate spatially resolved ERG reactions. The research noted that the magnitude of the ERG signal was most prominent at the cornea’s midpoint, progressively diminishing in the temporal and nasal regions.
Duygu Kuzum et al. created the transparent, flexible graphene electrode array with simultaneous electrophysiology and optical neuroimaging in 2014 [59]. The previous gold pattern was transferred onto a polyimide substrate with CVD graphene produced on Cu. The graphene was then patterned using plasma etching. The entire electrode was insulated using SU-8, except for the graphene spots. Nitric acid was then used to dope the electrodes, lowering the sheet resistance of graphene and causing NO3− groups to adhere to the material’s surface, producing p-type doping. The doped G electrode’s phase angle in EIS spectroscopy was constant (−50°) over a large frequency range, indicating more complex charge transport than the Au electrode. The doped G electrode had a capacitive characteristic. Large interface capacitance in brain re-recording electrodes helps reduce the electrode noise caused by resistive charge transfer. Due to the doped G electrode’s low charge transfer resistance and high capacitance, electronic noise could be avoided. Due to the doped G electrode’s high charge storage capacity, the charge transfer amount necessary for neurostimulation electrodes may be increased. Dong-Wook Park et al. produced transparent graphene electrodes for optogenetic applications in another study published in 2014 by the same group. Transferred onto the Parylene C substrate, four-layer CVD growth graphene was then designed. Au had patterned the connection pads, initial parts of the tracks, and electrode sites to ensure a robust mechanical connection to the zero insertion force printed circuit board (PBC). However, Au had not patterned how these elements would meet the brain tissue. The doped, four-layer graphene electrode transmitted 90% of the signal with the lowest sheet resistance possible. Doped graphene has a higher transmittance than ITO and ultrathin metals because there are no artifacts when high-intensity light is directly applied to an electrode site, therefore optogenetic stimulation, optical coherence tomography (OCT), and fluorescence imaging can be successfully carried out beneath the graphene electrode sites [83].

4.3. Hybrid Graphene Electrode

A hybrid graphene electrode combines the remarkable properties of graphene with other materials to create a composite structure, often surpassing the performance of individual components. This concept is especially crucial in applications demanding specific electrical, mechanical, or chemical characteristics. The integration of nanomaterials with graphene electrodes represents a promising frontier in neural activity recording within the field of neurotechnology. Specifically, the application of nanotechnology in neuroscience has led to the development of nanoelectrodes, which possess critical dimensions on the nanometer scale. Analogous to their minute size, a paradigm shift in electrochemical response control is facilitated by these nanoelectrodes, encompassing individual nanoelectrodes, nanoelectrode arrays (NEAs), and nanoelectrode ensembles (NEEs). Unlike conventional electrodes with millimeter diameters, nanoscale electrodes facilitate rapid mass transport through radial diffusion, expediting electrochemical reactions by removing the limitations of mass transport.
Consequently, this progress harbors significant promise for a range of neural interface applications, notably in amplifying sensitivity, facilitating single-cell investigations, and catalyzing the creation of highly efficient customized biosensors. Nevertheless, hurdles persist, particularly concerning the increase in impedance and Johnson noise as electrode dimensions decrease. The development of nanoelectrodes stands at the cusp of a transformation with the rise in novel materials such as conductive polymers and hybrid organic–inorganic nanomaterials. They maintain mechanical robustness and electrical charge transfer capabilities even after miniaturization. This marks a pivotal shift from the classical metallic materials previously used in neural electrode fabrication. Researchers have turned to hybrid nanocomposites to enhance electrode performance without compromising structural integrity or operational lifespan [113][114][115][116]. These composites leverage two charge storage mechanisms concurrently. Hybrid electrodes are formed by embedding graphitic carbons within pseudocapacitive materials like conducting polymers and metal oxides [117][118][119]. These hybrids consistently outperform traditional electrodes, particularly in supercapacitor applications. Graphene and carbon nanotubes have emerged as leading candidates due to their atomically thin carbon structure, resulting in an extensive specific surface area, superior electrical conductivity, and impressive mechanical properties [120][121][122]. For instance, laser-scribed graphene has demonstrated a specific capacitance of approximately 202 F/g [123]. Meanwhile, bioinspired solvated graphene-based supercapacitors have exhibited an even higher capacitance of about 215 F/g. Moreover, chemically modified graphene has displayed a commendable capacitance of roughly 135 F/g [124]. Conducting polymers like polypyrrole (PPy) have undergone extensive study in pseudocapacitive materials. PPy exhibits superior redox performance, characterized by its cost-effectiveness, environmental stability, and suitability for large-scale processes. These properties significantly elevate overall performance when integrated into a hybrid graphene electrode, particularly in neural interfacing applications. This integrated approach enables more efficient and precise recording, stimulation, and interaction with neural tissue [125][126].
Kim, Gook Hwa et al. [127] have innovatively designed transparent electrodes employing a combination of graphene and vertically aligned carbon nanotubes (VACNT) for extracellular recording of spontaneous action potentials in primary cortex neurons of Sprague-Dawley rats. The graphene component fulfills a dual role: it establishes contact with the VACNTs and allows for the visual monitoring of cell viability. The hybrid electrodes display impressive performance, presenting significant peak-to-peak signal amplitudes (1600 μV) alongside minimal noise levels. This exceptional performance is credited to the close integration between the cells and the contoured carbon nanotubes (CNTs). Introducing transparent graphene vertically aligned carbon nanotube hybrid (TGVH) electrodes revolutionized the field by enabling optical cell monitoring alongside simultaneous extracellular signal recording. Recording spontaneous action potentials from cortical neurons through TGVH electrodes exhibited remarkably high signal amplitudes and signal-to-noise ratios (SNR). This heightened performance is a distinctive feature of carbon nanotubes (CNTs), owing to their porous network, surface properties promoting cell adhesion and proliferation, and impressive electrical conductivity. The vertically aligned carbon nanotubes (VACNTs) within the TGVH electrodes possess a protruded structure ideally suited for cellular interfacing.

4.4. Biocompatibility

Biocompatibility, an essential consideration for implantable devices, integrates biological, chemical, and physical properties. Key objectives involve avoiding toxic or immunologic reactions, preventing harm to enzymes, cells, or tissues, and minimizing compression-related issues in adjacent tissues, emphasizing the need for robust biocompatibility in implantable technologies [128]. This approach aims to seamlessly integrate biomedical devices with living systems, ensuring longevity, functionality, and safety in diverse medical applications. The biocompatibility of implanted recording electrodes depends on various factors, including electrode materials, device geometry, and ambient surroundings. Biocompatibility, in the material context, is defined as the “ability of a material to elicit an appropriate host response in a specific application” [129]. Ideal biomaterials for neural recording implants should demonstrate in vivo non-cytotoxicity, releasing minimal substances at low, non-toxic concentrations. The desired outcome includes minimal glial encapsulation and a mild foreign body reaction, avoiding necrosis or implant rejection [130][131].
Critical assessments of material and device biocompatibility involve various tests, such as cytotoxicity, acute/chronic systemic toxicity, sub-acute/sub-chronic toxicity, sensitization, irritation, genotoxicity, hemocompatibility, toxicokinetic studies, and immunotoxicology [132]. The International Organization for Standardization (ISO) [133] establishes thorough test and evaluation protocols, considering potential variations in a material’s response across diverse biological environments. This approach considers factors like body contact types, contact time, intended use environments (in vitro, ex vivo, or in vivo), and test methodologies, as outlined by Hanson et al. [134]. Rigorous evaluations are essential for ensuring the compatibility and safety of neural recording implants across various physiological contexts.
Electrodes constructed from graphene have garnered substantial attention in monitoring neural activity. A crucial consideration entails the comprehensive evaluation of graphene and its derived materials in relation to human well-being, encompassing factors such as compatibility with biological systems, potential harm, and any environmental risks, particularly in situations involving incorporation with human skin or implantation. While studies on graphene-based nanomaterials (GBNs) abound, a discernible gap exists in systematic research regarding their effects on human health and the environment [135]. Safety evaluations are paramount in novel material development [136]. In the research literature, “graphene” broadly encompasses various GBNs, including GO and rGO [137][138]. Given the absence of standardized descriptions, key parameters for classification include layer count, average lateral size, and the carbon-to-oxygen (C/O) ratio, especially when considering various synthesis methods [137][139].
The physicochemical properties, including dosage, purity, shape, layers, surface chemistry, lateral size, and thickness, play a significant role in determining the toxicity of GBNs. These elements impact biodistribution, transference to secondary organs, aggregation, deterioration, and elimination [135][138]. Following exposure to neural cells or biomolecules, GBN properties and biological behavior dynamically shift, potentially leading to degradation or biotransformation. Moreover, these characteristics may evolve in different biological milieus over time, emphasizing the significance of in situ assessments for prospective applications. The materials employed in implantable electrodes must exhibit exceptional biocompatibility and minimal toxicity in human interaction. Hence, the evaluation of graphene-based implant safety holds paramount importance.

References

  1. Gunasekera, B.; Saxena, T.; Bellamkonda, R.; Karumbaiah, L. Intracortical recording interfaces: Current challenges to chronic recording function. ACS Chem. Neurosci. 2015, 6, 68–83.
  2. Vitale, F.; Vercosa, D.G.; Rodriguez, A.V.; Pamulapati, S.S.; Seibt, F.; Lewis, E.; Yan, J.S.; Badhiwala, K.; Adnan, M.; Royer-Carfagni, G. Fluidic microactuation of flexible electrodes for neural recording. Nano Lett. 2018, 18, 326–335.
  3. Wang, M.; Mi, G.; Shi, D.; Bassous, N.; Hickey, D.; Webster, T.J. Nanotechnology and nanomaterials for improving neural interfaces. Adv. Funct. Mater. 2018, 28, 1700905.
  4. Tybrandt, K.; Khodagholy, D.; Dielacher, B.; Stauffer, F.; Renz, A.F.; Buzsáki, G.; Vörös, J. High-density stretchable electrode grids for chronic neural recording. Adv. Mater. 2018, 30, 1706520.
  5. Gosselin, B. Recent advances in neural recording microsystems. Sensors 2011, 11, 4572–4597.
  6. Kang, H.; Kim, J.-Y.; Choi, Y.-K.; Nam, Y. Feasibility study of extended-gate-type silicon nanowire field-effect transistors for neural recording. Sensors 2017, 17, 705.
  7. Deku, F.; Cohen, Y.; Joshi-Imre, A.; Kanneganti, A.; Gardner, T.J.; Cogan, S.F. Amorphous silicon carbide ultramicroelectrode arrays for neural stimulation and recording. J. Neural Eng. 2018, 15, 016007.
  8. Yang, L.; Lee, K.; Villagracia, J.; Masmanidis, S.C. Open source silicon microprobes for high throughput neural recording. J. Neural Eng. 2020, 17, 016036.
  9. Jun, J.J.; Steinmetz, N.A.; Siegle, J.H.; Denman, D.J.; Bauza, M.; Barbarits, B.; Lee, A.K.; Anastassiou, C.A.; Andrei, A.; Aydın, Ç. Fully integrated silicon probes for high-density recording of neural activity. Nature 2017, 551, 232–236.
  10. Nag, A.; Simorangkir, R.B.; Valentin, E.; Björninen, T.; Ukkonen, L.; Hashmi, R.M.; Mukhopadhyay, S.C. A transparent strain sensor based on PDMS-embedded conductive fabric for wearable sensing applications. IEEE Access 2018, 6, 71020–71027.
  11. Nag, A.; Mukhopadhyay, S.C.; Kosel, J. Wearable flexible sensors: A review. IEEE Sens. J. 2017, 17, 3949–3960.
  12. Sperry, Z.J.; Na, K.; Parizi, S.S.; Chiel, H.J.; Seymour, J.; Yoon, E.; Bruns, T.M. Flexible microelectrode array for interfacing with the surface of neural ganglia. J. Neural Eng. 2018, 15, 036027.
  13. Soscia, D.A.; Lam, D.; Tooker, A.C.; Enright, H.A.; Triplett, M.; Karande, P.; Peters, S.K.; Sales, A.P.; Wheeler, E.K.; Fischer, N.O. A flexible 3-dimensional microelectrode array for in vitro brain models. Lab A Chip 2020, 20, 901–911.
  14. Wang, J.; Zhao, Q.; Wang, Y.; Zeng, Q.; Wu, T.; Du, X. Shape-Programmable Electronics: Self-Unfolding Flexible Microelectrode Arrays Based on Shape Memory Polymers (Adv. Mater. Technol. 11/2019). Adv. Mater. Technol. 2019, 4, 1970063.
  15. Patel, S.R.; Lieber, C.M. Precision electronic medicine in the brain. Nat. Biotechnol. 2019, 37, 1007–1012.
  16. Hong, G.; Lieber, C.M. Novel electrode technologies for neural recordings. Nat. Rev. Neurosci. 2019, 20, 330–345.
  17. Xie, C.; Liu, J.; Fu, T.-M.; Dai, X.; Zhou, W.; Lieber, C.M. Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes. Nat. Mater. 2015, 14, 1286–1292.
  18. Acarón Ledesma, H.; Li, X.; Carvalho-de-Souza, J.L.; Wei, W.; Bezanilla, F.; Tian, B. An atlas of nano-enabled neural interfaces. Nat. Nanotechnol. 2019, 14, 645–657.
  19. Fabbro, A.; Scaini, D.; León, V.n.; Vázquez, E.; Cellot, G.; Privitera, G.; Lombardi, L.; Torrisi, F.; Tomarchio, F.; Bonaccorso, F. Graphene-based interfaces do not alter target nerve cells. ACS Nano 2016, 10, 615–623.
  20. Bond, H.; Ho, H.P. Solid maniature silver-silver chloride electrodes for chronic implantation. Electroencephalogr. Clin. Neurophysiol. 1970, 28, 206–208.
  21. Gross, G.W. Simultaneous single unit recording in vitro with a photoetched laser deinsulated gold multimicroelectrode surface. IEEE Trans. Biomed. Eng. 1979, 26, 273–279.
  22. EVERETT, J.W. Ovulation in rats from preoptic stimulation through platinum electrodes. Importance of duration and spread of stimulus. Endocrinology 1965, 76, 1195–1201.
  23. Huang, H.; Su, S.; Wu, N.; Wan, H.; Wan, S.; Bi, H.; Sun, L. Graphene-based sensors for human health monitoring. Front. Chem. 2019, 7, 399.
  24. Polikov, V.S.; Tresco, P.A.; Reichert, W.M. Response of brain tissue to chronically implanted neural electrodes. J. Neurosci. Methods 2005, 148, 1–18.
  25. Alahi, M.E.E.; Mukhopadhyay, S.C. Preparation and Characterization of the Selectivity Material of Nitrate Sensor. In Smart Nitrate Sensor; Springer: Berlin/Heidelberg, Germany, 2019; pp. 91–113.
  26. Hu, N.; Fukunaga, H.; Atobe, S.; Liu, Y.; Li, J. Piezoresistive strain sensors made from carbon nanotubes based polymer nanocomposites. Sensors 2011, 11, 10691–10723.
  27. Xiao, T.; Qian, C.; Yin, R.; Wang, K.; Gao, Y.; Xuan, F. 3D Printing of Flexible Strain Sensor Array Based on UV-Curable Multiwalled Carbon Nanotube/Elastomer Composite. Adv. Mater. Technol. 2020, 6, 2000745.
  28. Nag, A.; Alahi, M.; Eshrat, E.; Mukhopadhyay, S.C.; Liu, Z. Multi-Walled Carbon Nanotubes-Based Sensors for Strain Sensing Applications. Sensors 2021, 21, 1261.
  29. Nag, A.; Mitra, A.; Mukhopadhyay, S.C. Graphene and its sensor-based applications: A review. Sens. Actuators A Phys. 2018, 270, 177–194.
  30. Han, T.; Nag, A.; Simorangkir, R.B.; Afsarimanesh, N.; Liu, H.; Mukhopadhyay, S.C.; Xu, Y.; Zhadobov, M.; Sauleau, R. Multifunctional flexible sensor based on laser-induced graphene. Sensors 2019, 19, 3477.
  31. Jia, Y.; Zhou, L.; Shao, J. Electrochemical performance of flexible graphene-based fibers as electrodes for wearable supercapacitors. Synth. Met. 2018, 246, 108–114.
  32. Nag, A.; Alahi, M.E.E.; Mukhopadhyay, S.C. Recent progress in the fabrication of graphene fibers and their composites for applications of monitoring human activities. Appl. Mater. Today 2021, 22, 100953.
  33. Nag, A.; Feng, S.; Mukhopadhyay, S.; Kosel, J.; Inglis, D. 3D printed mould-based graphite/PDMS sensor for low-force applications. Sens. Actuators A Phys. 2018, 280, 525–534.
  34. Bessonov, A.; Kirikova, M.; Haque, S.; Gartseev, I.; Bailey, M.J. Highly reproducible printable graphite strain gauges for flexible devices. Sens. Actuators A Phys. 2014, 206, 75–80.
  35. Zhang, M.; Wang, C.; Wang, Q.; Jian, M.; Zhang, Y. Sheath–core graphite/silk fiber made by dry-meyer-rod-coating for wearable strain sensors. ACS Appl. Mater. Interfaces 2016, 8, 20894–20899.
  36. Xu, X.; Wang, R.; Nie, P.; Cheng, Y.; Lu, X.; Shi, L.; Sun, J. Copper nanowire-based aerogel with tunable pore structure and its application as flexible pressure sensor. ACS Appl. Mater. Interfaces 2017, 9, 14273–14280.
  37. Kim, G.-J.; Kim, T.; Pak, J. Development of Flexible Glucose Measurement Sensor Based on Copper Nanocubes Electroplated Laser Induced Graphene Electrode. Trans. Korean Inst. Electr. Eng. 2018, 67, 413–418.
  38. Zhang, Y.; Li, N.; Xiang, Y.; Wang, D.; Zhang, P.; Wang, Y.; Lu, S.; Xu, R.; Zhao, J. A flexible non-enzymatic glucose sensor based on copper nanoparticles anchored on laser-induced graphene. Carbon 2020, 156, 506–513.
  39. De Oliveira, G.C.M.; de Souza Carvalho, J.H.; Brazaca, L.C.; Vieira, N.C.S.; Janegitz, B.C. Flexible platinum electrodes as electrochemical sensor and immunosensor for Parkinson’s disease biomarkers. Biosens. Bioelectron. 2020, 152, 112016.
  40. Song, R.-M.; Li, Z.-H.; Wei, P.-J.; Zhao, X.-L.; Chen, C.; Zhu, Z.-G. Flexible hydrogen peroxide sensors based on platinum modified free-standing reduced graphene oxide paper. Appl. Sci. 2018, 8, 848.
  41. Basu, N.; Konduri, A.K.; Basu, P.K.; Keshavan, S.; Varma, M.M.; Bhat, N. Flexible, Label-Free DNA Sensor using Platinum oxide as the sensing element. IEEE Sens. J. 2017, 17, 6140–6147.
  42. Peng, Y.; Lin, D.; Gooding, J.J.; Xue, Y.; Dai, L. Flexible fiber-shaped non-enzymatic sensors with a graphene-metal heterostructure based on graphene fibres decorated with gold nanosheets. Carbon 2018, 136, 329–336.
  43. Han, T.; Nag, A.; Afsarimanesh, N.; Akhter, F.; Liu, H.; Sapra, S.; Mukhopadhyay, S.; Xu, Y. Gold/Polyimide-Based Resistive Strain Sensors. Electronics 2019, 8, 565.
  44. Zhu, B.; Ling, Y.; Yap, L.W.; Yang, M.; Lin, F.; Gong, S.; Wang, Y.; An, T.; Zhao, Y.; Cheng, W. Hierarchically structured vertical gold nanowire array-based wearable pressure sensors for wireless health monitoring. ACS Appl. Mater. Interfaces 2019, 11, 29014–29021.
  45. Maruthupandy, M.; Rajivgandhi, G.; Muneeswaran, T.; Vennila, T.; Quero, F.; Song, J.-M. Chitosan/silver nanocomposites for colorimetric detection of glucose molecules. Int. J. Biol. Macromol. 2019, 121, 822–828.
  46. Gao, Y.; Wu, Y.; Di, J. Colorimetric detection of glucose based on gold nanoparticles coupled with silver nanoparticles. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2017, 173, 207–212.
  47. Min, S.-H.; Lee, G.-Y.; Ahn, S.-H. Direct printing of highly sensitive, stretchable, and durable strain sensor based on silver nanoparticles/multi-walled carbon nanotubes composites. Compos. Part B Eng. 2019, 161, 395–401.
  48. Keefer, E.W.; Botterman, B.R.; Romero, M.I.; Rossi, A.F.; Gross, G.W. Carbon nanotube coating improves neuronal recordings. Nat. Nanotechnol. 2008, 3, 434–439.
  49. Chen, C.; Lin, C.; Chen, J.; Hsu, W.; Chang, Y.; Yeh, S.; Li, L.; Yao, D. A graphene-based microelectrode for recording neural signals. In Proceedings of the 2011 16th International Solid-State Sensors, Actuators and Microsystems Conference, Beijing, China, 5–9 June 2011; pp. 1883–1886.
  50. Suyatin, D.B.; Wallman, L.; Thelin, J.; Prinz, C.N.; Jörntell, H.; Samuelson, L.; Montelius, L.; Schouenborg, J. Nanowire-based electrode for acute in vivo neural recordings in the brain. PLoS ONE 2013, 8, e56673.
  51. Zhu, M.; Wang, H.; Li, S.; Liang, X.; Zhang, M.; Dai, X.; Zhang, Y. Flexible electrodes for in vivo and in vitro electrophysiological signal recording. Adv. Healthc. Mater. 2021, 10, 2100646.
  52. Yang, J.; Kwak, T.J.; Zhang, X.; McClain, R.; Chang, W.-J.; Gunasekaran, S. Digital pH test strips for in-field pH monitoring using iridium oxide-reduced graphene oxide hybrid thin films. ACS Sens. 2016, 1, 1235–1243.
  53. Garaj, S.; Hubbard, W.; Reina, A.; Kong, J.; Branton, D.; Golovchenko, J. Graphene as a subnanometre trans-electrode membrane. Nature 2010, 467, 190–193.
  54. Warner, J.H.; Rümmeli, M.H.; Ge, L.; Gemming, T.; Montanari, B.; Harrison, N.M.; Büchner, B.; Briggs, G.A.D. Structural transformations in graphene studied with high spatial and temporal resolution. Nat. Nanotechnol. 2009, 4, 500–504.
  55. Bitounis, D.; Ali-Boucetta, H.; Hong, B.H.; Min, D.H.; Kostarelos, K. Prospects and challenges of graphene in biomedical applications. Adv. Mater. 2013, 25, 2258–2268.
  56. Fabbri, R.; Saracino, E.; Treossi, E.; Zamboni, R.; Palermo, V.; Benfenati, V. Graphene glial-interfaces: Challenges and perspectives. Nanoscale 2021, 13, 4390–4407.
  57. Obraztsov, A.; Obraztsova, E.; Tyurnina, A.; Zolotukhin, A. Chemical vapor deposition of thin graphite films of nanometer thickness. Carbon 2007, 45, 2017–2021.
  58. Huang, X.; Zeng, Z.; Fan, Z.; Liu, J.; Zhang, H. Graphene-based electrodes. Adv. Mater. 2012, 24, 5979–6004.
  59. Kuzum, D.; Takano, H.; Shim, E.; Reed, J.C.; Juul, H.; Richardson, A.G.; De Vries, J.; Bink, H.; Dichter, M.A.; Lucas, T.H. Transparent and flexible low noise graphene electrodes for simultaneous electrophysiology and neuroimaging. Nat. Commun. 2014, 5, 5259.
  60. Yan, T.; Wang, Z.; Wang, Y.-Q.; Pan, Z.-J. Carbon/graphene composite nanofiber yarns for highly sensitive strain sensors. Mater. Des. 2018, 143, 214–223.
  61. Ramírez, J.; Rodriquez, D.; Urbina, A.D.; Cardenas, A.M.; Lipomi, D.J. Combining High Sensitivity and Dynamic Range: Wearable Thin-Film Composite Strain Sensors of Graphene, Ultrathin Palladium, and PEDOT: PSS. ACS Appl. Nano Mater. 2019, 2, 2222–2229.
  62. Baloda, S.; Ansari, Z.A.; Singh, S.; Gupta, N. Development and Analysis of Graphene Nanoplatelets (GNP) Based Flexible Strain Sensor for Health Monitoring Applications. IEEE Sens. J. 2020, 20, 13302–13309.
  63. Yoon, H.; Nah, J.; Kim, H.; Ko, S.; Sharifuzzaman, M.; Barman, S.C.; Xuan, X.; Kim, J.; Park, J.Y. A chemically modified laser-induced porous graphene based flexible and ultrasensitive electrochemical biosensor for sweat glucose detection. Sens. Actuators B Chem. 2020, 311, 127866.
  64. Yang, X.; Peng, H.; Xie, Q.; Zhou, Y.; Liu, Z. Clean and efficient transfer of CVD-grown graphene by electrochemical etching of metal substrate. J. Electroanal. Chem. 2013, 688, 243–248.
  65. Poo-arporn, Y.; Pakapongpan, S.; Chanlek, N.; Poo-arporn, R.P. The development of disposable electrochemical sensor based on Fe3O4-doped reduced graphene oxide modified magnetic screen-printed electrode for ractopamine determination in pork sample. Sens. Actuators B Chem. 2019, 284, 164–171.
  66. Alahi, M.E.E.; Nag, A.; Mukhopadhyay, S.C.; Burkitt, L. A temperature-compensated graphene sensor for nitrate monitoring in real-time application. Sens. Actuators A Phys. 2018, 269, 79–90.
  67. Heo, C.; Yoo, J.; Lee, S.; Jo, A.; Jung, S.; Yoo, H.; Lee, Y.H.; Suh, M. The control of neural cell-to-cell interactions through non-contact electrical field stimulation using graphene electrodes. Biomaterials 2011, 32, 19–27.
  68. Cohen-Karni, T.; Qing, Q.; Li, Q.; Fang, Y.; Lieber, C.M. Graphene and nanowire transistors for cellular interfaces and electrical recording. Nano Lett. 2010, 10, 1098–1102.
  69. Pati, M.K.; Pattojoshi, P.; Roy, G.S. Synthesis of graphene-based nanocomposite and investigations of its thermal and electrical properties. J. Nanotechnol. 2016, 2016, 5135420.
  70. Alahi, M.E.E.; Liu, Y.; Xu, Z.; Wang, H.; Wu, T.; Mukhopadhyay, S.C. Recent advancement of electrocorticography (ECoG) electrodes for chronic neural recording/stimulation. Mater. Today Commun. 2021, 29, 102853.
  71. Shin, S.R.; Li, Y.-C.; Jang, H.L.; Khoshakhlagh, P.; Akbari, M.; Nasajpour, A.; Zhang, Y.S.; Tamayol, A.; Khademhosseini, A. Graphene-based materials for tissue engineering. Adv. Drug Deliv. Rev. 2016, 105, 255–274.
  72. Kitko, K.E.; Zhang, Q. Graphene-based nanomaterials: From production to integration with modern tools in neuroscience. Front. Syst. Neurosci. 2019, 13, 26.
  73. Lu, Y.; Liu, X.; Kuzum, D. Graphene-based neurotechnologies for advanced neural interfaces. Curr. Opin. Biomed. Eng. 2018, 6, 138–147.
  74. Jang, H.; Park, Y.J.; Chen, X.; Das, T.; Kim, M.S.; Ahn, J.H. Graphene-based flexible and stretchable electronics. Adv. Mater. 2016, 28, 4184–4202.
  75. Kireev, D.; Offenhäusser, A. Graphene & two-dimensional devices for bioelectronics and neuroprosthetics. 2D Mater. 2018, 5, 042004.
  76. Hejazi, M.; Tong, W.; Ibbotson, M.R.; Prawer, S.; Garrett, D.J. Advances in carbon-based microfiber electrodes for neural interfacing. Front. Neurosci. 2021, 15, 658703.
  77. Ouyang, J.; Rao, S.; Liu, R.; Wang, L.; Chen, W.; Tao, W.; Kong, N. 2D materials-based nanomedicine: From discovery to applications. Adv. Drug Deliv. Rev. 2022, 185, 114268.
  78. Cellot, G.; Franceschi Biagioni, A.; Ballerini, L. Nanomedicine and graphene-based materials: Advanced technologies for potential treatments of diseases in the developing nervous system. Pediatr. Res. 2022, 92, 71–79.
  79. Dong, R.; Wang, L.; Hang, C.; Chen, Z.; Liu, X.; Zhong, L.; Qi, J.; Huang, Y.; Liu, S.; Wang, L. Printed stretchable liquid metal electrode arrays for in vivo neural recording. Small 2021, 17, 2006612.
  80. Zhang, M.; Yu, P.; Mao, L. Rational design of surface/interface chemistry for quantitative in vivo monitoring of brain chemistry. Acc. Chem. Res. 2012, 45, 533–543.
  81. Razzino, C.A.; Serafín, V.; Gamella, M.; Pedrero, M.; Montero-Calle, A.; Barderas, R.; Calero, M.; Lobo, A.O.; Yáñez-Sedeño, P.; Campuzano, S. An electrochemical immunosensor using gold nanoparticles-PAMAM-nanostructured screen-printed carbon electrodes for tau protein determination in plasma and brain tissues from Alzheimer patients. Biosens. Bioelectron. 2020, 163, 112238.
  82. Guan, S.; Wang, J.; Gu, X.; Zhao, Y.; Hou, R.; Fan, H.; Zou, L.; Gao, L.; Du, M.; Li, C. Elastocapillary self-assembled neurotassels for stable neural activity recordings. Sci. Adv. 2019, 5, eaav2842.
  83. Park, D.-W.; Schendel, A.A.; Mikael, S.; Brodnick, S.K.; Richner, T.J.; Ness, J.P.; Hayat, M.R.; Atry, F.; Frye, S.T.; Pashaie, R. Graphene-based carbon-layered electrode array technology for neural imaging and optogenetic applications. Nat. Commun. 2014, 5, 5258.
  84. Abidian, M.R.; Martin, D.C. Experimental and theoretical characterization of implantable neural microelectrodes modified with conducting polymer nanotubes. Biomaterials 2008, 29, 1273–1283.
  85. Groenendaal, L.; Jonas, F.; Freitag, D.; Pielartzik, H.; Reynolds, J.R. Poly(3,4-ethylenedioxythiophene) and its derivatives: Past, present, and future. Adv. Mater. 2000, 12, 481–494.
  86. Wang, X.-S.; Feng, X.-Q. Effects of thickness on mechanical properties of conducting polythiophene films. J. Mater. Sci. Lett. 2002, 21, 715–717.
  87. Guimard, N.K.; Gomez, N.; Schmidt, C.E. Conducting polymers in biomedical engineering. Prog. Polym. Sci. 2007, 32, 876–921.
  88. Sevil, B.; Zuhal, K. Synthesis and characterization of polypyrrole nanoparticles and their nanocomposites with poly (propylene). In Macromolecular Symposia; WILEY-VCH Verlag: Weinheim, Germany, 2010; pp. 59–64.
  89. Passeri, D.; Alippi, A.; Bettucci, A.; Rossi, M.; Tamburri, E.; Terranova, M. Indentation modulus and hardness of polyaniline thin films by atomic force microscopy. Synth. Met. 2011, 161, 7–12.
  90. Lee, S.K.; Kim, H.; Shim, B.S. Graphene: An emerging material for biological tissue engineering. Carbon Lett. 2013, 14, 63–75.
  91. Guo, Y.; Jiang, S.; Grena, B.J.; Kimbrough, I.F.; Thompson, E.G.; Fink, Y.; Sontheimer, H.; Yoshinobu, T.; Jia, X. Polymer composite with carbon nanofibers aligned during thermal drawing as a microelectrode for chronic neural interfaces. ACS Nano 2017, 11, 6574–6585.
  92. Lawrence, J.G.; Berhan, L.M.; Nadarajah, A. Elastic properties and morphology of individual carbon nanofibers. ACS Nano 2008, 2, 1230–1236.
  93. Vomero, M.; Castagnola, E.; Ciarpella, F.; Maggiolini, E.; Goshi, N.; Zucchini, E.; Carli, S.; Fadiga, L.; Kassegne, S.; Ricci, D. Highly stable glassy carbon interfaces for long-term neural stimulation and low-noise recording of brain activity. Sci. Rep. 2017, 7, 40332.
  94. Fan, B.; Rusinek, C.A.; Thompson, C.H.; Setien, M.; Guo, Y.; Rechenberg, R.; Gong, Y.; Weber, A.J.; Becker, M.F.; Purcell, E. Flexible, diamond-based microelectrodes fabricated using the diamond growth side for neural sensing. Microsyst. Nanoeng. 2020, 6, 42.
  95. Kim, K.S.; Zhao, Y.; Jang, H.; Lee, S.Y.; Kim, J.M.; Kim, K.S.; Ahn, J.-H.; Kim, P.; Choi, J.-Y.; Hong, B.H. Large-scale pattern growth of graphene films for stretchable transparent electrodes. Nature 2009, 457, 706–710.
  96. Maynard, E.M.; Nordhausen, C.T.; Normann, R.A. The Utah intracortical electrode array: A recording structure for potential brain-computer interfaces. Electroencephalogr. Clin. Neurophysiol. 1997, 102, 228–239.
  97. Lee, C.; Wei, X.; Kysar, J.W.; Hone, J. Measurement of the elastic properties and intrinsic strength of monolayer graphene. Science 2008, 321, 385–388.
  98. Bae, S.; Kim, H.; Lee, Y.; Xu, X.; Park, J.-S.; Zheng, Y.; Balakrishnan, J.; Lei, T.; Ri Kim, H.; Song, Y.I. Roll-to-roll production of 30-inch graphene films for transparent electrodes. Nat. Nanotechnol. 2010, 5, 574–578.
  99. Zhao, S.; Li, G.; Tong, C.; Chen, W.; Wang, P.; Dai, J.; Fu, X.; Xu, Z.; Liu, X.; Lu, L. Full activation pattern mapping by simultaneous deep brain stimulation and fMRI with graphene fiber electrodes. Nat. Commun. 2020, 11, 1788.
  100. Lu, Y.; Lyu, H.; Richardson, A.G.; Lucas, T.H.; Kuzum, D. Flexible neural electrode array based-on porous graphene for cortical microstimulation and sensing. Sci. Rep. 2016, 6, 33526.
  101. Apollo, N.V.; Maturana, M.I.; Tong, W.; Nayagam, D.A.; Shivdasani, M.N.; Foroughi, J.; Wallace, G.G.; Prawer, S.; Ibbotson, M.R.; Garrett, D.J. Soft, flexible freestanding neural stimulation and recording electrodes fabricated from reduced graphene oxide. Adv. Funct. Mater. 2015, 25, 3551–3559.
  102. Ryu, M.; Yang, J.H.; Ahn, Y.; Sim, M.; Lee, K.H.; Kim, K.; Lee, T.; Yoo, S.-J.; Kim, S.Y.; Moon, C. Enhancement of interface characteristics of neural probe based on graphene, ZnO nanowires, and conducting polymer PEDOT. ACS Appl. Mater. Interfaces 2017, 9, 10577–10586.
  103. Cho, Y.; Park, S.; Lee, J.; Yu, K.J. Emerging materials and technologies with applications in flexible neural implants: A comprehensive review of current issues with neural devices. Adv. Mater. 2021, 33, 2005786.
  104. Cardin, J.A.; Carlén, M.; Meletis, K.; Knoblich, U.; Zhang, F.; Deisseroth, K.; Tsai, L.-H.; Moore, C.I. Targeted optogenetic stimulation and recording of neurons in vivo using cell-type-specific expression of Channelrhodopsin-2. Nat. Protoc. 2010, 5, 247–254.
  105. Wellman, S.M.; Eles, J.R.; Ludwig, K.A.; Seymour, J.P.; Michelson, N.J.; McFadden, W.E.; Vazquez, A.L.; Kozai, T.D. A materials roadmap to functional neural interface design. Adv. Funct. Mater. 2018, 28, 1701269.
  106. Cho, I.H.; Shin, H.; Lee, H.J.; Cho, I.-J. Effects of fabrication process variation on impedance of neural probe microelectrodes. J. Electr. Eng. Technol. 2015, 10, 1138–1143.
  107. Park, D.-W.; Brodnick, S.K.; Ness, J.P.; Atry, F.; Krugner-Higby, L.; Sandberg, A.; Mikael, S.; Richner, T.J.; Novello, J.; Kim, H. Fabrication and utility of a transparent graphene neural electrode array for electrophysiology, in vivo imaging, and optogenetics. Nat. Protoc. 2016, 11, 2201–2222.
  108. Guan, S.; Wang, J.; Fang, Y. Transparent graphene bioelectronics as a new tool for multimodal neural interfaces. Nano Today 2019, 26, 13–15.
  109. Pang, S.; Hernandez, Y.; Feng, X.; Müllen, K. Graphene as transparent electrode material for organic electronics. Adv. Mater. 2011, 23, 2779–2795.
  110. Park, D.-W.; Ness, J.P.; Brodnick, S.K.; Esquibel, C.; Novello, J.; Atry, F.; Baek, D.-H.; Kim, H.; Bong, J.; Swanson, K.I. Electrical neural stimulation and simultaneous in vivo monitoring with transparent graphene electrode arrays implanted in GCaMP6f mice. ACS Nano 2018, 12, 148–157.
  111. Thunemann, M.; Lu, Y.; Liu, X.; Kılıç, K.; Desjardins, M.; Vandenberghe, M.; Sadegh, S.; Saisan, P.A.; Cheng, Q.; Weldy, K.L. Deep 2-photon imaging and artifact-free optogenetics through transparent graphene microelectrode arrays. Nat. Commun. 2018, 9, 2035.
  112. Yin, R.; Xu, Z.; Mei, M.; Chen, Z.; Wang, K.; Liu, Y.; Tang, T.; Priydarshi, M.K.; Meng, X.; Zhao, S. Soft transparent graphene contact lens electrodes for conformal full-cornea recording of electroretinogram. Nat. Commun. 2018, 9, 2334.
  113. Pushparaj, V.L.; Shaijumon, M.M.; Kumar, A.; Murugesan, S.; Ci, L.; Vajtai, R.; Linhardt, R.J.; Nalamasu, O.; Ajayan, P.M. Flexible energy storage devices based on nanocomposite paper. Proc. Natl. Acad. Sci. USA 2007, 104, 13574–13577.
  114. Zhang, J.; Zhao, X. On the configuration of supercapacitors for maximizing electrochemical performance. ChemSusChem 2012, 5, 818–841.
  115. Chen, T.; Dai, L. Flexible supercapacitors based on carbon nanomaterials. J. Mater. Chem. A 2014, 2, 10756–10775.
  116. Yu, D.; Dai, L. Self-assembled graphene/carbon nanotube hybrid films for supercapacitors. J. Phys. Chem. Lett. 2010, 1, 467–470.
  117. Yuan, L.; Lu, X.-H.; Xiao, X.; Zhai, T.; Dai, J.; Zhang, F.; Hu, B.; Wang, X.; Gong, L.; Chen, J. Flexible solid-state supercapacitors based on carbon nanoparticles/MnO2 nanorods hybrid structure. ACS Nano 2012, 6, 656–661.
  118. Yu, G.; Xie, X.; Pan, L.; Bao, Z.; Cui, Y. Hybrid nanostructured materials for high-performance electrochemical capacitors. Nano Energy 2013, 2, 213–234.
  119. Jiang, H.; Lee, P.S.; Li, C. 3D carbon based nanostructures for advanced supercapacitors. Energy Environ. Sci. 2013, 6, 41–53.
  120. Geim, A.K.; Novoselov, K.S. The rise of graphene. Nat. Mater. 2007, 6, 183–191.
  121. Zhang, Y.; Tan, Y.-W.; Stormer, H.L.; Kim, P. Experimental observation of the quantum Hall effect and Berry’s phase in graphene. Nature 2005, 438, 201–204.
  122. Salvetat, J.-P.; Bonard, J.-M.; Thomson, N.; Kulik, A.; Forro, L.; Benoit, W.; Zuppiroli, L. Mechanical properties of carbon nanotubes. Appl. Phys. A 1999, 69, 255–260.
  123. Yang, X.; Zhu, J.; Qiu, L.; Li, D. Bioinspired effective prevention of restacking in multilayered graphene films: Towards the next generation of high-performance supercapacitors. Adv. Mater. 2011, 23, 2833–2838.
  124. Stoller, M.D.; Park, S.; Zhu, Y.; An, J.; Ruoff, R.S. Graphene-based ultracapacitors. Nano Lett. 2008, 8, 3498–3502.
  125. Olsson, H.; Nyström, G.; Strømme, M.; Sjödin, M.; Nyholm, L. Cycling stability and self-protective properties of a paper-based polypyrrole energy storage device. Electrochem. Commun. 2011, 13, 869–871.
  126. An, K.H.; Jeon, K.K.; Heo, J.K.; Lim, S.C.; Bae, D.J.; Lee, Y.H. High-capacitance supercapacitor using a nanocomposite electrode of single-walled carbon nanotube and polypyrrole. J. Electrochem. Soc. 2002, 149, A1058.
  127. Kim, G.H.; Kim, N.Y.; Lee, Z.; Jung, S.D.; Lee, J.-O. A high-performance transparent graphene/vertically aligned carbon nanotube (VACNT) hybrid electrode for neural interfacing. RSC Adv. 2017, 7, 3273–3281.
  128. Marin, C.; Fernández, E. Biocompatibility of intracortical microelectrodes: Current status and future prospects. Front. Neuroeng. 2010, 3, 1212.
  129. Williams, D.F. Definitions in Biomaterials. In Proceedings of the Consensus Conference of the European Society for Biomaterials, Chester, UK, 3–5 March 1986.
  130. Navarro, X.; Krueger, T.B.; Lago, N.; Micera, S.; Stieglitz, T.; Dario, P. A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems. J. Peripher. Nerv. Syst. 2005, 10, 229–258.
  131. Márton, G.; Tóth, E.Z.; Wittner, L.; Fiáth, R.; Pinke, D.; Orbán, G.; Meszéna, D.; Pál, I.; Győri, E.L.; Bereczki, Z. The neural tissue around SU-8 implants: A quantitative in vivo biocompatibility study. Mater. Sci. Eng. C 2020, 112, 110870.
  132. Feron, K.; Lim, R.; Sherwood, C.; Keynes, A.; Brichta, A.; Dastoor, P.C. Organic bioelectronics: Materials and biocompatibility. Int. J. Mol. Sci. 2018, 19, 2382.
  133. Available online: https://www.iso.org/home.html (accessed on 11 December 2023).
  134. Ratner, B.D.; Hoffman, A.S.; Schoen, F.J.; Lemons, J.E. An introduction to materials in medicine. Biomater. Sci. 1996, 1, 484.
  135. Dasari Shareena, T.P.; McShan, D.; Dasmahapatra, A.K.; Tchounwou, P.B. A review on graphene-based nanomaterials in biomedical applications and risks in environment and health. Nano-Micro Lett. 2018, 10, 53.
  136. Park, M.V.; Bleeker, E.A.; Brand, W.; Cassee, F.R.; van Elk, M.; Gosens, I.; de Jong, W.H.; Meesters, J.A.; Peijnenburg, W.J.; Quik, J.T. Considerations for safe innovation: The case of graphene. ACS Nano 2017, 11, 9574–9593.
  137. Reina, G.; González-Domínguez, J.M.; Criado, A.; Vázquez, E.; Bianco, A.; Prato, M. Promises, facts and challenges for graphene in biomedical applications. Chem. Soc. Rev. 2017, 46, 4400–4416.
  138. Fadeel, B.; Bussy, C.; Merino, S.; Vázquez, E.; Flahaut, E.; Mouchet, F.; Evariste, L.; Gauthier, L.; Koivisto, A.J.; Vogel, U. Safety assessment of graphene-based materials: Focus on human health and the environment. ACS Nano 2018, 12, 10582–10620.
  139. Wick, P.; Louw-Gaume, A.E.; Kucki, M.; Krug, H.F.; Kostarelos, K.; Fadeel, B.; Dawson, K.A.; Salvati, A.; Vázquez, E.; Ballerini, L. Classification framework for graphene-based materials. Angew. Chem. Int. Ed. 2014, 53, 7714–7718.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Materials Science, Biomaterials
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Md Eshrat E. Alahi
,
Mubdiul Islam Rizu
,
Fahmida Wazed Tina
,
Zhaoling Huang
,
Anindya Nag
,
Nasrin Afsarimanesh
View Times: 367
Revisions: 2 times (View History)
Update Date: 18 Jan 2024
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback