4.6 Conclusions
5.1.2 Flexible G-FETs for Electrophysiological Studies of Retina
Figure 5.1a depicts an optical image of a flexible G-FET device constructed on a poly- dimethylsiloxane (PDMS) based microfluidic platform. The flexible G-FETs on a 7 µm thick perforated polyimide substrate (Figure 5.1b) were fabricated using the same method as previously described for flexible graphene electrodes (see more details in Chapter 3, while the assembly with the PDMS-based platform has been demonstrated in Chapter 4.
An optical image of a typical G-FET is shown in Figure 5.1c A G-FET consists of a piece of graphene as the channel material connecting two metal pads (drain and source electrodes), which are coated with a SU8 passivation layer to open an average 80×80 µm2 graphene window that allows for the graphene to directly contact the electrolyte and respond to elec- trochemical changes.
A third gold electrode in contact with Ames is used as an electrolyte gate. Figure 5.1d is the corresponding gate-dependent drain curve obtained when the gate voltage is swept across the Dirac point. As the gate voltage increases, the conductance of the G-FET ini- tially decreases, then increases as the applied gate voltage passes the Dirac point voltage
with a value of ∼0.15 V (VGDirac). This ambipolar behavior illustrates the transition of graphene from p-type doping to n-type doping. The number density of electrons in the graphene channel depends on the capacitance between the graphene and electrolyte gate (e.g. conducting media in contact with a Ag/Ag/Cl or gold electrode). This capacitance is composed of the electrostatic capacitance due to the electrical double-layer and the quan- tum capacitance of graphene (CQ), which depends on the density of states of the graphene.
The transconductance of the transistor is proportional to this capacitance. Since the ca- pacitance between a graphene and an electrolyte gate is much higher than that of a typical back-gated device, an electrolyte-gated graphene transistor can achieve a transconductance of up to several hundred Siemens, while a back-gated device typically has a transconduc- tance of around tens of Siemens. This high transconductance allows for a wide range of modulation to the graphene channel. The gate modulation range for an electrolyte-gated device can be achieved within several volts, while for a back-gated device it typically re- quires tens of volts. Higher transconductance can also be achieved by using high dielectric constant materials.
One of the issues that can arise with electrolyte-gated transistors is hysteretic conduc- tance. When gate voltage is swept in one direction, gate-dependent transport curves can be obtained. However, when the gate voltage is swept in the opposite direction, the result- ing gate-dependent curve is shifted and exhibits hysteresis. In the case of electrolyte-gated transistors, this hysteresis effect is due to the potential in the electrolyte media not being updated immediately. As a result, the graphene is always modulated by the previous state of the gate voltage. To minimize the hysteresis effect, a lower gate sweeping rate is often required.
When integrated with scanning photocurrent microscopy, G-FETs offer a novel sensing platform that can detect neuronal activities at a much higher spatial resolution than MEAs.
For this measurement, a diffraction-limited 785 nm laser beam was used to scan from one metal contact to the other in order to obtain a mapping of the photocurrent across the entire
device region as a function of position. The laser spot is around 1 µm by using a 40X objective. The generated photocurrent can be collected by an external preamplifier when the laser beam is deflected by a nanometer-resolution scan mirror to sweep over the entire graphene ribbon. The corresponding reflection image is simultaneously recorded by a sil- icon detector. Figure 5.1e and 5.1f show the photocurrent and reflection image of a single G-FET obtained with a 785 nm laser beam, respectively. Due to the high optical trans- missivity of monolayer graphene, the graphene ribbon is almost invisible in the reflection mapping, while the gold metal electrodes are clearly visible. However, the photocurrent image allows for visualization of the shape and position of the graphene ribbon. The over- all photocurrent for the graphene ribbon is relatively small due to its low light absorption, but a pronounced photocurrent is observed at the graphene-electrode junctions. This can be explained by the band structure of graphene. As the measurement is performed at zero gate voltage, the graphene is naturally p-type doped. At the graphene-gold electrode junc- tion regions, back-to-back Schottky-like barriers are formed. Since the electric field is proportional to local potential gradients, pronounced photocurrent occurs at these junction regions, while almost no photocurrent is observed in the central region of the channel.
As is well known, traditional MEA devices rely on the number of electrodes fabricated on a single platform to detect signals. However, due to limitations in fabrication tech- niques, the size and density of probes are currently limited to micron-scale resolution. In contrast, our platform, which incorporates both SPCM and G-FETs, allows us to detect the electrical activity of the retina through the measurement of photocurrent. By utilizing a 40X objective, the size of the laser beam spot focused on the graphene plane can reach submicron levels. Therefore, the detection resolution of this platform is determined by the laser beam size, which is significantly reduced compared to traditional patterned gold or graphene probes. Furthermore, due to the planar structure of graphene, our integrated SPCM enables us to move the detection location by manipulating the position of the laser through the scanning mirror or stage, rather than switching the detecting channel as is the
case with fixed electrodes in MEAs. By utilizing these properties, we can combine scan- ning photocurrent microscopy with a diffraction-limited laser spot and a G-FET device to detect local conductance induced by charged molecules with a high spatial resolution at the sub-micron scale.
Figure 5.1: (a) Optical image of a flexible and transparent G-FET device with PDMS based on a microfluidic platform. The device is mounted on a copper holder. (b) The zoom-in image of the center G-FET array sensor fabricated on a perforated polyimide substrate.
The graphene flake was patterned into a ribbon shape between drain and source electrode with etched holes around to enhance the physical contact between graphene and detected tissue. The scaler bar is 300 µm. (c) The zoom-in image of a typical G-FET shows the transparent graphene in between and the passivation layer on the gold electrode region.
The scaler bar is 20 µm. (d) Gate-dependent electrical measurement of G-FET. With the gate voltage increasing, the graphene changes from p-type doping to n-typing doping. (e) The scanning photocurrent image and (f) Reflection image, respectively, under a 785 nm diffraction-limited laser beam.
Here we used a G-FET to detect the extracellular electrical activity of the retina. The G-FET device was mounted on a PDMS-based microfluidic platform and the retina was transferred onto the sensing region. The experimental operation including retina transfer and microfluidic platform manipulation follows the same procedure as the previous de- scription in chapter 3 and chapter 4. To observe the state of the retina, we carefully transfer
it into a well with the RGC layer in direct contact with the sensing region. Figures 5.2a and 5.2b show typical fluorescent images of a healthy and unhealthy retina under an in- verted microscope, with excitation wavelengths of 475 nm and emission wavelengths of 534 nm, respectively. In a healthy retina, we can observe clear optic nerve head and axon bundles, as well as a large number of RGCs, which is important for ensuring that most of the electrodes can detect spontaneous signals. However, when the retina is not in good condition, it may be difficult to see a clear optic nerve head or a significant number of RGCs. It is worth noting that the retina tissue is very delicate and the dissection process can easily introduce cuts or holes in the tissue, as shown in Figure 5.2b. Therefore, it is crucial to handle the retina with care to avoid damaging it. After obtaining a healthy retina, negative pressure is applied to the suction channels in the microfluidic circuit. As shown in Figure 5.2c, the RGC layer and the electrode layer are initially not in the same focal plane. However, when negative pressure is applied to the retina tissue through the perforated polyimide layer in the central region, the retina tissue is gently pulled toward the electrodes. The fluorescent image in Figure 5.2c demonstrates that the electrodes and RGC layer almost reach the same focal plane, resulting in a very small distance between the retina and electrodes and ensuring good contact. This gentle and effective method for aligning the retina and electrodes without damaging the delicate tissue helps to maximize the number of electrodes that can detect signals and improve the overall performance of the system. It is crucial to carefully control the size, ratio, and distance of the holes to the corresponding electrodes to ensure good contact between the retina and electrodes. The pressure applied to the vacuum channel must also be well-controlled, as too small a nega- tive pressure may not achieve good contact, and too large a negative pressure may damage the delicate retina tissue.
In Figure 5.3a, a typical photocurrent mapping of a G-FET in the AMEs media is shown. As discussed in the previous paragraph, the physical distance between the retina tis- sue and the G-FET can be controlled by applying negative pressure to the negative vacuum
Figure 5.2: (a) Typical fluorescent images of a healthy and (b) unhealthy retina. The healthy retina can show a clear optic nerve head (black spot) and axon bundles (white lines), as well as a large number of RGCs (white dots). (c) The evolution of the retina tissue to the plane of the electrodes before and after the application of negative pressure to the fluidic channel, demonstrates that the perforated polyimide substrate device can help to tune the physical contact between retina tissue and the electrodes sensor. The scale bars are 400µm.
channel. Figure 5.3b shows the photocurrent mapping after the negative pressure system is activated for 5 minutes. Several changes are observed in the photocurrent image, with all the photocurrent mappings at the same intensity color bar. Firstly, the photocurrent at the graphene/gold electrode region is decreased. Secondly, the polarity of the corresponding photocurrent at the graphene/electrode junction region has reversed, as highlighted by the black dotted box. Thirdly, more pronounced photocurrent patterns were observed in the center region of the graphene ribbon. After the retina sits for a while and the photocurrent reaches a stable state (Figure 5.3c), all the changes regarding the photocurrent signal are further enhanced.
As discussed in the first chapter, the local potential gradient is proportional to the pho- tocurrent. Therefore, by numerically integrating the photocurrent profile along the black dotted line in Figure 5.3a, 5.3b, 5.3c, the corresponding band diagram of the device can be obtained. Figure 5.3d, 5.3e, 5.3f depict the band structure of a graphene transistor at dif- ferent retina states, with the black dotted line representing the Fermi level of graphene. The band structure refers to the arrangement of energy levels within the graphene ribbon that determines its electrical conductivity. As the measurement is performed at the zero-gate voltage, the graphene is a naturally p-type doped without the retina on top of it, which can also be further verified by the corresponding gate-dependent measurement in Figure 5.3h.
However, once the retina is transferred onto the graphene and negative pressure is applied to the system, the main body of the graphene ribbon eventually becomes n-type as the band edge moves below the Fermi level. This phenomenon occurs because the retina on top of the graphene functions as a global gate, modulating the carrier concentration and thereby changing the doping level of the graphene ribbon. The change in doping level is reflected in the gate-dependent measurement, as seen in Figure 5.3h and Figure 5.3i, where the Dirac point shifts from 0.15 V to -0.06 V after the transfer of the retina.
Figure 5.3g depicts the fluorescent image of the corresponding G-FET, showing mul- tiple axon bundles crossing the G-FET and several RGCs distributed at different positions
and depth levels. Upon examining the details of the photocurrent mapping and the corre- sponding fluorescent image, we can conclude that only the soma and axons can generate action potentials. However, numerous photocurrent signals occurred in the region without an apparent retina structure on top of it. What could be the cause of these photocurrent signals beside the action potentials generated by the RGCs? One possible explanation is that the cell membrane with a weak charge can contribute charges to the graphene and be detected by the photocurrent, which detects local charge changes. Additionally, the mouse strain used in our measurements is Thy1-YFP, and only the RGCs are expressed under green fluorescence. Therefore, the photocurrent signal detected may also originate from other types of cells in the retina, such as muller cells. These cells support and pro- tect neurons in the retina, maintain the correct balance of ions and neurotransmitters in the extracellular space, and buffer changes in pH while regulating glucose uptake and re- lease [139]. Muller cells have been shown to generate electrical signals in response to various stimuli, including light, neurotransmitters, and changes in extracellular potassium concentration [140; 141; 142].
5.2 Interactions between Graphene and Cell membrane