Interdigitated Electrodes as Impedance and Capacitance Biosensors a Review

1. Introduction

Many improvements in the sensitivity and selectivity of biosensors have been made in the concluding two decades [1,2,3,iv,five]. They are mainly due to the sensors (lab-on-a-chip) designed in micro-nanotechnology facilities. For biological applications [6], accuse transfer sensors [seven], impedance-based sensors [8,9,10], and capacitance-based sensors are ofttimes used. Impedance spectroscopy is a well-known and powerful technique for biological characterizations on both the macroscopic and microscopic scales [eleven]. The electrodes apply an electric field to the sample beingness tested and measure electrical signals. They too tin can provide information on the relative permittivity and electric conductivity of biosamples that correspond to intrinsic parameters [12]. Impedance-based sensors are divided into four chief categories: cell trap sensors, cytometric sensors, matrix sensors and interdigitated sensors. The sensors in the get-go 3 categories have the best sensitivities because only i cell is analyzed at a fourth dimension. Trap sensors often use micro-hole or microcavity [xiii] systems to isolate cells from each other. Cytometric sensors use microchannels to focus on 1 prison cell at a time [xiv], and cells are dynamically characterized during their passage into a measurement area situated within the channel. Matrix electrode systems use multiple electrodes to perform numerous measurements at a time [15]. Despite the fact that these three techniques accept amend sensitivities, they are also the nearly hard to implement. Thus, interdigitated (ITD) sensors [16], composed of just one layer of coplanar electrodes, remain competitive for low-volume and low-concentration biological samples. For example, they are perfectly suitable for jail cell surface cultures [17] and low-book biological sampling, equally in DNA analyses [18]. Beyond the elementary interface, the geometrical backdrop of the electrodes can have a meaning impact on the efficiency of the biosensor [xix,20]. They tin can be optimized a priori during the sensors' design step according to the targeted application and the nature of the cells to exist analyzed [14]. In this piece of work, we advise a method for optimizing the ITD sensor frequency band involving the metalization ratio.

Beginning, nosotros advise a consummate electrical equivalent model that takes into account all the sensor's parameters, such as the electrode length, gap, width, and electrical properties of the medium. Interface capacitive furnishings, also known equally the double-layer effects, are also modeled to assess the touch on the global impedance spectrum.

In the second part, analytical simulations are performed to analyze the furnishings of geometrical parameters on bioimpedance measurements. It is important to maintain a sufficiently wide bandwidth in order to be able to characterize a biosample over many decades. To achieve optimization, the touch of the metalization ratio α on the bandwidth was studied. This ratio is defined as α = Westward/(S + W), where Chiliad is the gap (thou) and W is the width (m) of the IDT sensor digits (meet Section 2.1 below).

The last section focuses on the experimental validation of the 2 previous sections. 3 sensor designs with unlike degrees of optimization corresponding to different values of the ratio α were made using a standard microfabrication process. Characterizations were performed in calibrated electrolytic solutions to validate our model. Finally, the capability of our optimized sensor in terms of characterizing a blood sample was tested.

2. Theoretical Considerations

2.i. Sensor Construction and Cell Factor

An IDT sensor is equanimous of two comb-like electrodes deposited on an insulated substrate. Each electrode's digits have a width W and a length L, and there is a gap Southward between digits [21,22], equally presented in Figure 1a. This specific electrode configuration produces an elliptic current displacement inside the sample, as shown in Figure 1b. Changes in W and S allow the electric penetration depth in the sample to exist adjusted. Ninety-5 percentage of the electrical excitation betoken ability is full-bodied within ii(S + W). This makes IDT sensors perfectly suitable for low-surface sample characterizations of, for instance, cell cultures or small-book samples. An electrical equivalent model for an IDT sensor loaded with an electrolyte is presented in Figure 1c. The electrolyte is generally used as a simple reference medium for impedance-based sensor characterization.

Co-ordinate to Olthuis et al. [23], R_sol and C_sol , the resistance and capacitance of the ionic solution, can exist linked via Equation (one) to electrolyte conductivity and permittivity using a cell factor Thou_cell . This cistron can be calculated equally a function of IDT geometries using Equation (2).

$$R_{sol}=\frac{K_{cell}}{{\sigma }_{\mathrm{southward}o\mathrm{fifty}}};C_{\mathrm{due\; south}ol}=\frac{{\epsilon }_0{\epsilon }_{r,so\mathrm{50}}}{{\mathrm{Grand}}_{c\mathrm{due\; east}\mathrm{fifty}\mathrm{50}}},$$

(1)

$${\mathrm{Yard}}_{c\mathrm{due\; east}ll}=\frac{\mathrm{two}}{\mathrm{50}\left(N-1\right)}\frac{K\left(k\right)}{\mathrm{One\; thousand}\left(\sqrt{\mathrm{i}-k²}\right)},$$

(two)

where σ_sol is the electrolyte electrical conductivity (S·m^−one), ε_sol is the electrolyte relative permittivity, N is the number of digits, Fifty is the digit length (m), W is the digit width (g), S is the space betwixt two digits (m), and K_cell is the jail cell factor (thou⁻¹). The office Thousand(k) is the incomplete integral of the outset module k, and is calculated with Equation (three). The metalization ratio is defined by α = W/(S + W).

$$K\left(k\right)=\underset{0}{\overset{1}{{\displaystyle \int }}}\frac{1}{\sqrt{\left(1-t²\right)\left(\mathrm{one}-m²t²\right)}}dtwith\mathrm{yard}=\cos \left(\frac{\pi }{2}\frac{W}{\mathrm{Southward}+W}\right).$$

(3)

where σ_sol is the electrolyte conductivity (S·m⁻¹), ε_sol is the electrolyte relative permittivity, N is the number of digits, L is the digit length (m), W is the digit width (one thousand), S is the space between two digits (g), and Grand_{jail cell} is the cell factor (chiliad⁻¹). The function Chiliad(m) is the incomplete integral of the beginning module grand, and is calculated with Equation (three). This function is used to formulate the elliptic electric field distribution, as described above. The metalization ratio is defined by α = W/(S + W).

2.2. Double Layer Impedance

The double-layer impedance represents the interface furnishings that occur when the polarized electrodes are in contact with an electrolyte. The double layer corresponds to two parallel layers of charge on a thin section (nanometric scale) of the electrode surface. These effects deed as a barrier for low- frequency measurements and need to be taken into account in global modeling. More often than not, it is necessary to limit these furnishings to increase the bandwidth of interest. A typical model for interface effects is composed of 3 elements making upwards an equivalent excursion, every bit presented in Effigy 2. The charge transfer resistance R_CT represents the resistive outcome, and the double-layer capacitance C_DL represents the capacitive effect. The Warburg impedance Z_W with a constant phase (−π/ii) is used to model the non-linear decrease in impedance induced past the diffusion of ionic species. These elements tin can be very difficult to model for circuitous electrolytes, such as claret plasma. Moreover, when the frequency is high plenty, the decrease in C_DL , the impedance short-circuit R_CT and Z_{due west} , and the interface impedance get negligible compared to the impedance of the sample. Hence, double-layer impedance is by and large just modeled by C_DL when only the sample impedance is studied. This capacitance effect is proportional to the electrode surface and captured equally the surface capacitance C ₀, with typical values ranging from 10 to l µF/cmⁱⁱ. For interdigitated sensors, global induced capacitance C_interface can exist calculated with the unitary digit capacitances C_int,p and C_{int,northward} using Equations (4)–(six).

$$C_{int,p}=C_{i\mathrm{northward}t,n}=LW{C}_0,$$

(4)

$$C_{interface}=\frac{N}{4}L\mathrm{Westward}{C}_0,$$

(5)

$$Z_{i\mathrm{due\; north}t\mathrm{due\; east}rface}=\frac{1}{j\omega C_{int\mathrm{east}rface}},$$

(6)

where C_int,p and C_{int,northward} are the capacitances at each electrode digits (F), C_interface is the global capacitance at the sensor (F), Z_interface is the induced impedance in series with the sample impedance (Ω), and C ₀ is the surface capacitance at the interface electrode/electrolyte (F/grand⁻¹).

two.iii. Sample Impedance

In a linear, homogeneous, and isotropic samples, measured impedance Z_samp (Ω) and admittance Y_samp (S) depend on the sample's intrinsic properties (electrical conductivity σ_samp and permittivity ε_samp ) and the sensor cistron K_{jail cell} , every bit seen in Equation (seven) [24]. Z_samp is the sample's impedance and does not take into business relationship other effects, such as double-layer capacitance.

$$Z_{\mathrm{southward}a\mathrm{yard}p}=\frac{{\mathrm{Thousand}}_{c\mathrm{eastward}l\mathrm{fifty}}}{{\sigma }_{sagp}+j\omega {\epsilon }_0{\epsilon }_{r,samp}}\to Y_{\mathrm{southward}agp}=\frac{{\sigma }_{sagp}+j\omega {\epsilon }_0{\epsilon }_{r,sakp}}{{\mathrm{Yard}}_{ce\mathrm{50}\mathrm{fifty}}}\to \left\{\begin{array}{c}{G}_{\mathrm{due\; south}amp}=\frac{{\sigma }_{sakp}}{G_{c\mathrm{eastward}l\mathrm{fifty}}}\\ C_{\mathrm{south}amp}=\frac{{\epsilon }_0{\epsilon }_{r,sa\mathrm{yard}p}}{K_{cell}}\end{array}\right..$$

(seven)

For a simple sample, such as that of an electrolyte, σ_samp and ε_{r, samp} tin can exist considered constants. For more complex biological samples, such as claret, these values depend on frequency and are referred to as circuitous conductivity σ_samp (ω) and circuitous relative permittivity ε_{r, samp} (ω), respectively. Figure three shows the typical values for the circuitous conductivity and relative permittivity of claret [12]. The two levels of relative permittivity represent the furnishings of jail cell membrane capacitance and water permittivity, respectively. The two levels of electrical conductivity represent the effects of extracellular medium conductivity and the combination of extracellular/cytoplasm conductivity, respectively. The passage from the beginning to second level is known as the β dispersion and occurs when the impedance of the capacitive cell membrane becomes negligible compared to the cytoplasmic impedance. The 2d increasing of conductivity is chosen the γ dispersion and appears at microwave frequencies (GHz). Information technology corresponds to water molecular excitation and is non studied here. The claret conductivity diagram shows the necessity of performing the conductivity extraction correctly before and after β dispersion when characterizing a biological sample. In the case of a blood sample, this dispersion begins at approximately 250 kHz.

After adding the interface effect in series with the samples, the global measured impedance becomes

$$\left\{\begin{array}{c}{G}_{Tot}\left(\omega \right)=\frac{{\omega }^{\mathrm{ii}}{G}_{\mathrm{south}amp}{C}_{i\mathrm{north}t\mathrm{due\; east}rface}^{\mathrm{two}}}{{\mathrm{Grand}}_{samp}^{\mathrm{ii}}+{\omega }^{\mathrm{two}}{\left(C_{\mathrm{south}amp}+C_{interfac\mathrm{eastward}}\right)}^{\mathrm{ii}}}\\ C_{Tot}\left(\omega \right)=\frac{C_{i\mathrm{due\; north}t\mathrm{eastward}rface}{K}_{\mathrm{southward}a\mathrm{thousand}p}^2+{\omega }^2{C}_{samp}{C}_{int\mathrm{eastward}rface}\left(C_{samp}+C_{i\mathrm{north}terface}\right)}{{\mathrm{Thou}}_{samp}^{\mathrm{ii}}+{\omega }^{\mathrm{two}}{\left(C_{\mathrm{south}a\mathrm{thousand}p}+C_{int\mathrm{eastward}rfac\mathrm{east}}\right)}^{\mathrm{two}}}\end{array}\right..$$

(viii)

$$\underset{\omega \to 0}{\lim }{C}_{Tot}=C_{int\mathrm{eastward}rface}$$

(9)

Yard_Tot and C_Tot represent to global measured conductance and capacitance induced by the sample and interface impedances, respectively. At depression frequencies, the global capacitance tends to C_interface , as presented in Equation (nine). When the frequency increased, Z_interface became negligible compared to Z_samp , and total impedance can be considered equal to Z_samp . The typical curve profiles for a biological sample and an electrolytic sample, showing the double-layer capacitance effect, are shown in Effigy four. Two plateaus are clearly visible and represent to claret conductivity. If the lower cutoff frequency is besides loftier, the first plateau can exist completely hidden past double-layer impedance and interfere with the measurement. The low cutoff frequency is defined in Equation (ten) below via an analogy with depression and loftier passive first-order filters using the conductivity of the medium.

$$\begin{gathered} f_{low}=\frac{1}{\mathrm{two}\pi R_{samp}{C}_{interface}} \\ \mathrm{westward}henf=f_{\mathrm{fifty}o\mathrm{due\; west}},|Y_{samp}|=\frac{G_{\mathrm{southward}amp}}{\sqrt{2}}and|Z_{\mathrm{due\; south}a\mathrm{grand}p}|=\sqrt{2}{R}_{sa\mathrm{chiliad}p} \end{gathered}$$

(10)

Since Z_interface is not purely capacitive, the first relation in Equation (10) may bear witness difficult to use. Hence, the second equation is usually preferred. In this piece of work, this second equation was used to decide f_depression from the measured impedance spectrum. R_samp and G_samp can be extracted from the real function of the impedance or admittance when a plateau is visible in the spectrum (predominance of resistive/conductive furnishings). These parameters are extracted from the real function of the impedance measured in the middle of each plateau. Furthermore, σ_samp can exist calculated using the extracted R_samp or G_samp and Equation (7).

3. Sensor Optimization

As mentioned in a higher place, the interface impedance acts equally a barrier at depression frequencies. Optimizing the frequency band consists of reducing the f_low value. Since f_depression depends on both the sample properties and sensor geometry, it is possible to optimize the frequency band by optimizing the sensor design. Equation (10) was developed by using Equations (1), (two) and (5) to cheque which geometric parameter was the most suitable one to study. Hence, Equation (11) is obtained. As σ_sol and C ₀ depend on sample properties, decreasing f_low is equivalent to decrease the term given in expression (12). It can be seen that this term depends on N, Due west, and Southward. In improver, the term (N − 1)/N will be negligible for big Due north.

$$f_{\mathrm{50}ow}=\frac{1}{\pi }\frac{{\sigma }_{samp}}{C_0}\frac{\left(N-\mathrm{one}\right)}{N}\frac{1}{W}\frac{K\left(\sqrt{\mathrm{one}-k²}\right)}{\mathrm{Thousand}\left(\mathrm{thou}\right)}$$

(11)

$$\frac{\left(N-1\right)}{\mathrm{Due\; north}}\frac{\mathrm{i}}{W}\frac{\mathrm{Yard}\left(\sqrt{1-\mathrm{1000}²}\right)}{K\left(\mathrm{chiliad}\right)}$$

(12)

Only W and S seem to accept a significant impact on f_depression through the Grand_cell and "1/W" terms. Information technology is relevant to written report the optimization of α. Clearly, f_low could be reduced by increasing W, but doing so implies an increase in sensor size as well as the measurement book: the smaller the cell constant (big electrodes), the lower the cutting-off frequency, but the faster the measurement volume will increase. Here, nosotros are interested in optimizing the sensor for a given volume/measurement surface on a microscopic scale. (W + Southward) and N were set up to abiding values to maintain the same book for the investigation. Analytical simulations were performed by varying the α ratio from 0.1 to 1 and holding the other parameters constant. Fifty was set to two mm, and (W + S) was prepare to fifty µm. Finally, σ_samp was prepare to 0.6 Due south/g (conductivity of blood at depression frequency), and C ₀ was ready to 40 µF/cm². Three values of N (20, 40 and 50) were tested. The results are shown in Figure 5. These results demonstrate that the value of N does not accept a significant touch on on the cutoff frequency. In contrast, the value of f_low mainly depends on α and is a minimum for α = 0.6. These results prove that it is possible to decrease the low cutoff frequency past a cistron up to approximately two.5 by optimizing the metalization ratio α.

four. Sensor Realization

4.i. Sensor Manufacturing

To prove our assumptions, 3 sensors were realized in a clean room using standard microfabrication techniques. Platinum electrodes were structured via sputtering degradation and optical lithography on a glass substrate. Wells were created by using negative SU_8 resin with a thickness of approximately 400 µm. Sensor i is illustrated in Figure half-dozen. All sensors presented the same periodicity, electrode number and electrode length. Dissimilar α ratios were tested to prove our assumptions. The sensor geometries are listed in Table ane. All sensors presented the same pitch (W + South) and the same surface of 2 × 2 mm² for ease of comparison. The pitch was set to 50 µm to provide a penetration depth of only several cell sizes (from 6 µm to 15 µm for cherry claret cells and white blood cells, respectively). The smallest W and S values were of the same size order as blood cells. These dimensions allowed increased sensitivity past performing characterizations of a few jail cell layers in depth.

four.2. Reference Measurements

To verify both the validity of our model and the integrity of the sensors, measurements were performed for three different calibrated solutions of 84 µS/cm, 100 µS/cm, and 1413 µS/cm. All measurements were performed by depositing a 2 µL drop into the well with a micropipette. Examples of the resulting impedance diagrams are shown in Figure seven for Sensor 1. All results are reported in Table ii. The K_cell cistron was calculated for R_sol on the curves' plateaus using Equation (1). The calculated values of K_cell were in accordance with the simulated ones. Sensor 3 had a higher fault in K_cell determination compared to the others, which can exist explained by the fact this sensor has the smallest useful gap size (S = 10 µm). Using optical lithography, the resolution is approximately 0.five µm for each edge, or i µm for a digit, representing a possible error of 10% for a 10 µm digit, which is close to the G_cell mistake for Sensor 3. Note that Sensor 1, with α = 0.6, presents the smallest cutoff frequencies, as predicted in the "Sensor optimization" department. The capacitance consequence observed in the college frequencies can be attributed to the experimental setup limitations, including the capacitance induced by the interfacing Printed Excursion Lath (PCB), the connections, and the measurement devices.

5. Claret Characterization

five.1. Experimental Setup

Figure 8 represents a general view of the instrumentation setup developed in this work for the electrical characterization of biological media. This instrumentation setup consists of the post-obit elements:

(a)

Biofluid samples placed directly on the sensor (Figure ix).

(b)

A microscope to observe the position of the volume of liquid.

(c)

A thermometer to measure the ambient temperature.

(d)

A micropipette (Socorex Micropipette Acura 825)

(e)

HF2TA current amplifier (manufactured by Zurich Instrument),

(f)

HF2IS impedance spectroscope for the frequency range from 0.7 μHz to l MHz.

(g)

A figurer for observing and processing measurement data using LabVIEW^® application.

This application allows us to enter the measurement parameters described above and save the values of the impedance spectra equally text files.

The optical microscope allows usa to correctly identify the drop of the biofluid on the sensor. The interface card containing the sensors (Figure 9) is connected to the HF2IS impedance analyzer and to the HF2TA current amplifier via short SMA cables in order to reduce the length of the measurement loop equally much as possible. The impedance spectroscope is continued via a USB cablevision to a computer to command it and recollect the data.

v.two. Sample Grooming

Blood sampling was performed nether medical supervision (University Infirmary, CHRU Nancy, French republic) using the same donor. The samples were placed into three 6 mL tubes with heparin. Measurements were taken within x min of sampling.

To ensure skillful repeatability for the measurements of all sensors, each sample was deposited on each sensor using the post-obit procedure:

• The tube was shaken slightly for 1 min before sampling with a micropipette.

• A 2 µL sample was obtained with an adjustable micropipette and deposited into the well (Figure 9). This volume was called to ensure that all the sensor cavities were full. As described in Section two.one, simply the commencement 100 µm of thickness of the sample in contact with the IDT was actually due to the small penetration depth of the IDT sensors, ensuring that measurements will not be impaired by circular-shaped aerosol, the sample surface in contact with the air or cavity walls, or the sample rising above the cavity.

• The impedance spectrum conquering started x s subsequently sample deposition. The impedance measurement was and so performed within several seconds at one Five sinus aamplitude.

• The room temperature was maintained at 25 ± ane °C during the measurement campaign.

5.3. Results

Measurements were performed using the experimental setup described in Department 5.1 and the sampling process described in a higher place for each sensor. The results are shown in Effigy 10 in the course of Bode diagrams for the modules and phases. f_low represents the measured low-frequency cut-off using the method proposed in Section 2.3. Equally in the measurements of the calibrated solution, Sensor one has the smallest depression-frequency cutting-off and the widest bandwidth. Unlike the two other sensors, two plateaus are visible from and subsequently the cutoff frequency for sensor 1. This upshot proves that only sensor 1 is able to characterize the complete spectrum for claret impedance. For the other sensors, the double-layer impedance remains non-negligible until the cutoff frequency and interferes with sample impedance measurement.

Low-frequency blood conductivity was calculated using the R_sol values measured on the plateaus only afterward the f_low values and Equation (1). The results extracted from the impedance spectrum are summarized in Table 3. The values 0.69, 0.89, and 0.43 S·k⁻ⁱ were obtained for sensors 1, two, and 3, respectively. These conductivities are of the same order every bit the 0.7 South/m value obtained by Gabriel [12]. The divergence betwixt sensor 1 and the other two sensors can exist explained by its lower amnesty to the interface effect that disturbs the R_sol measurement and electrical conductivity extraction. For sensor i, f_low is slightly lower than at the showtime of the β dispersion band studied in the theoretical section (215 kHz, down from 250 kHz) and allows the first plateau of conductivity to be measured. For the other sensors, the measurements were performed away from this plateau and are incorrect.

vi. Conclusions

An analytical model for an IDT sensor was proposed. Using this model, the upshot of geometric parameters on global impedance was studied in club to propose an optimized sensor for blood characterization. Information technology appears that the number of electrodes N and digit length L do not contribute significantly to improving sensor bandwidth. To the contrary, the metalization ratio α, which depends simply on the digit width W and gap South, is a relevant parameter for optimizing the sensor. The belittling simulations showed that the best optimization is obtained for α = 0.6. This α value permits the low cut-off frequency to be reduced by a gene of up to 2.5. To validate our theoretical results, measurements were performed on three unlike sensors using the same agile surface area (two mm × 2 mm) but dissimilar ratios α. The results obtained with the calibrated solutions proved the validity of our model and the possibility improving sensor bandwidth. Finally, blood sample characterizations were performed using the sensors. The optimized sensor was able to characterize the blood sample and extract its intrinsic property (electric conductivity), achieving good cyclopedia with the reference blood conductivity provided in the literature.

Author Contributions

Conceptualization—T.-T.N., J.C., D.G. and Thou.Northward.; methodology—T.-T.N., J.C., D.K. and M.N.; validation—T.-T.North., J.C. and M.N.; data curation—T.-T.North., J.C.; writing, original draft preparation—J.C.; writing, review and editing—J.C., D.Yard. and M.N.; project administration—G.N., D.Thousand.; funding acquisition—J.C., D.Thousand. and Yard.Northward. All authors have read and agreed to the published version of the manuscript.

Funding

This inquiry was done with the support of the "Région Grand Est—France" and the European Regional Development Fund (FEDER). It was also supported by the eighty′ Prime number program of the Conseil National de la Recherche Scientifique (CNRS).

Acknowledgments

The authors are grateful to the MINALOR Skill Center of the Institut Jean Lamour (IJL) at the Université de Lorraine for the technical support.

Conflicts of Interest

The authors declare no disharmonize of interest.

References

1. Vikas, A.; Pundir, C.S. Biosensors: Future belittling tools. Sens. Transducers J. 2007, 76, 935–936. [Google Scholar]
2. Yoon, J.; Shin, One thousand.; Lee, T.; Choi, J.-W. Highly sensitive biosensors based on biomolecules and functional nanomaterials depending on the types of nanomaterials: A perspective review. Materials 2020, thirteen, 299. [Google Scholar] [CrossRef] [PubMed]
3. Turner, A.P. Biosensors: Sense and sensibility. Chem. Soc. Rev. 2013, 21, 3184–3196. [Google Scholar] [CrossRef] [PubMed]
4. Sang, South.; Zhang, W.; Zhao, Y. Review on the Design Fine art of Biosensors. State of the Fine art in Biosensors—Full general Aspects; Rinken, T., Ed.; IntechOpen: London, Britain, 2013; Available online: https://www.intechopen.com/books/state-of-the-art-in-biosensors-general-aspects/review-on-the-design-fine art-of-biosensors (accessed on 15 December 2020). [CrossRef]
5. Ahmed, S.; Shaikh, N.; Pathak, N.; Sonawane, A.; Pandey, V.; Maratkar, Due south. An overview of sensitivity and selectivity of biosensors for ecology applications. In Tools, Techniques and Protocols for Monitoring Environmental Contaminants; Elsevier: Amsterdam, The Netherlands, 2019; Chapter 3; pp. 53–73. [Google Scholar]
6. Chuanrui, C.; Yue, K.; Peining, C.; Huisheng, P. Recent advances of tissue-interfaced chemical biosensors. J. Mater. Chem. B 2020, 8, 3371–3381. [Google Scholar]
7. RIgreja, R.; Dias, C.J. Belittling evaluation of the interdigital electrodes capacitance for a multi-layered structure. Sens. Actuators A Phys. 2004, 112, 291–301. [Google Scholar] [CrossRef]
8. Moraes, M.L.; Maki, R.1000.; Paulovich, F.5.; Rodrigues Filho, U.P.; De Oliveira MC, F.; Riul, A.; De Souza, North.C.; Ferreira, M.; Gomes, H.L.; Oliveira, O.N. Strategies to optimize biosensors based on impedance spectroscopy to find phytic acid using layer-by-layer films. Anal. Chem. 2010, 82, 3239–3246. [Google Scholar] [CrossRef]
9. Radhakrishnan, R.; Suni, I.; Bever, C.S.; Hammock, B.D. Impedance biosensors: Applications to sustainability and remaining technical challenges. ACS Sustain. Chem. Eng. 2014, 2, 1649–1655. [Google Scholar] [CrossRef]
10. Song, S.; Xu, H.; Fan, C. Potential diagnostic applications of biosensors: Current and future directions. Int. J. Nanomed. 2006, ane, 433–440. [Google Scholar] [CrossRef]
11. Katz, E.; Willner, I. Probing biomolecular interactions at conductive and semiconductive surfaces by impedance spectroscopy: Routes to impedimetric immunosensors, Deoxyribonucleic acid-sensors, and enzyme biosensors. Electroanalysis 2003, 15, 913–947. [Google Scholar] [CrossRef]
12. Gabriel, C.; Gabriel, Due south.; Corthout, Eastward. The dielectric properties of biological tissues: I. Literature survey. Phys. Med. Biol. 1996, 41, 2231–2249. [Google Scholar] [CrossRef]
13. Park, Y.; Cha, J.J.; Seo, S.; Yun, J.; Woo Kim, H.; Park, C.; Lee, J.H. Ex vivo characterization of age-associated impedance changes of single vascular endothelial cells using micro electric impedance spectroscopy with a cell trap. Biomicrofluidics 2016, 10, 014114. [Google Scholar] [CrossRef] [PubMed]
14. Claudel, J.; Alves De Araujo, A.L.; Nadi, M.; Kourtiche, D. Lab-on-a-scrap device for yeast cell characterization in depression-conductivity media combining cytometry and bio-impedance. Sensors 2019, 19, 3366. [Google Scholar] [CrossRef] [PubMed]
15. Alves de Araujo, A.Fifty.; Claudel, J.; Kourtiche, D.; Nadi, K. Use of an insulation layer on the connection tracks of a biosensor with coplanar electrodes to increase the normalized impedance variation. Biosensors 2019, 9, 108. [Google Scholar] [CrossRef]
16. Ertl, P.; Heer, R. Interdigitated impedance sensors for analysis of biological cells in microfluidic biochips. EI Elektrotechnik Inf. 2009, 126, 47–50. [Google Scholar] [CrossRef]
17. Posseckardt, J.; Schirmer, C.; Kick, A.; Rebatschek, Chiliad.; Lamz, T.; Mertig, M. Monitoring of saccharomyces cerevisiae viability by non-Faradaic impedance spectroscopy using interdigitated screen-printed platinum electrodes. Sens. Actuators B Chem. 2018, 255, 3417–3424. [Google Scholar] [CrossRef]
18. Wang, L.; Veselinovic, M.; Yang, Fifty.; Geiss, B.J.; Peachy, D.S.; Chen, T. A sensitive Dna capacitive biosensor using interdigitated electrodes. Biosens. Bioelectron. 2017, 87, 646–653. [Google Scholar] [CrossRef]
19. Ibrahim, M.; Claudel, J.; Kourtiche, D.; Nadi, M. Geometric parameters optimization of planar interdigitated electrodes for bioimpedance spectroscopy. J. Electr. Bioimpedance 2013, iv, 13–22. [Google Scholar] [CrossRef]
20. Ngo, T.T.; Bourjilat, A.; Claudel, J.; Kourtiche, D.; Nadi, G. Design and realization of a planar interdigital microsensor for biological medium characterization. In Adjacent Generation Sensors and Systems; Springer: Cham, Switzerland, 2016; pp. 23–54. [Google Scholar]
21. Starzyk, F. Parametrisation of interdigit comb capacitor for dielectric impedance spectroscopy. Arch. Mater. Sci. Eng. 2008, 34, 31–34. [Google Scholar]
22. Kidner, Due north.J.; Homrighaus, Z.J.; Mason, T.O.; Garboczi, E.J. Modeling interdigital electrode structures for the dielectric characterization of electroceramic thin films. Thin Solid Film. 2006, 496, 539–545. [Google Scholar] [CrossRef]
23. Olthuis, Westward.; Streekstra, W.; Bergveld, P. Theoretical and experimental determination of jail cell constants of planar-interdigitated electrolyte electrical conductivity sensors. Sens. Actuators B Chem. 1995, 25, 252–256. [Google Scholar] [CrossRef]
24. Steendijk, P.; Mur, Grand.; Van Der Velde, E.T.; Baan, J. The iv-electrode resistivity technique in anisotropic media: Theoretical analysis and application on myocardial tissue in vivo. IEEE Trans. Bio Med. Eng. 1993, twoscore, 1138–1148. [Google Scholar] [CrossRef] [PubMed]

Figure ane. (a) Geometric parameters of interdigitated sensors; (b) Electric current displacement between electrodes; (c) Electric model of an interdigitated sensor and sample (an ionic solution).

Effigy 1. (a) Geometric parameters of interdigitated sensors; (b) Electric current displacement between electrodes; (c) Electrical model of an interdigitated sensor and sample (an ionic solution).

Effigy 2. Electric equivalent circuit for the double-layer impedance of one electrode.

Figure 2. Electrical equivalent circuit for the double-layer impedance of one electrode.

Figure 3. Typical complex conductivity and relative permittivity values for a blood sample.

Effigy 3. Typical complex conductivity and relative permittivity values for a claret sample.

Figure 4. Typical bend profiles for biological and electrolyte samples.

Figure 4. Typical curve profiles for biological and electrolyte samples.

Figure 5. Depression cutoff frequency as a function of α.

Figure five. Low cutoff frequency as a office of α.

Figure vi. (a) Photograph of Sensors 1 with SU_8 well and blood sample, and (b) optical microscope image of different Sensor one digits.

Effigy six. (a) Photograph of Sensors one with SU_8 well and blood sample, and (b) optical microscope epitome of dissimilar Sensor one digits.

Effigy seven. Bode diagrams of the impedances for three unlike calibrated solutions measured with Sensor i.

Figure 7. Bode diagrams of the impedances for iii different calibrated solutions measured with Sensor 1.

Figure viii. Bio impedance measurement setup.

Figure 8. Bio impedance measurement setup.

Figure 9. Interdigital electrode sensors connected to the PCB excursion.

Figure nine. Interdigital electrode sensors connected to the PCB excursion.

Figure x. Bode diagrams for blood impedance characterizations (a) in modules, and (b) in phases.

Effigy 10. Bode diagrams for blood impedance characterizations (a) in modules, and (b) in phases.

Table one. Sensor geometries.

Table 1. Sensor geometries.

Sensor Number	N	Fifty (µm)	W (µm)	S (µm)	Theoretical Grandjail cell (k−ane)	α Ratio
one	40	2000	30	twenty	21.8	0.6
2	xl	2000	15	35	35.viii	0.3
3	40	2000	40	10	xv.ane	0.8

Table two. Sensor results for the calibrated solutions.

Tabular array 2. Sensor results for the calibrated solutions.

Sensor Number	Theoretical Kjail cell (m−1)	α Ratio		84 µS/cm		100 µS/cm			1413 µS/cm
			fc,50 (kHz)	Yardcell	Yardcell Error (%)	fc,L (kHz)	Mjail cell	Mjail cell Error (%)	fc,Fifty (kHz)	Kcell	Kcell Fault (%)
one	21.8	0.6	75.6	22.3	ii.3	85	22.01	0.96	433	22.65	3.ix
2	35.8	0.3	170	35.49	0.87	486	34.99	2.26	1960	35.55	0.vii
3	xv.ane	0.8	135	16.83	eleven.48	305	16.08	6.49	2400	17	12.57

Table iii. Sensor results for the calibrated solution.

Tabular array three. Sensor results for the calibrated solution.

Sensor Number	Due north	L (µm)	W (µm)	S (µm)	fdepression (kHz)	Measured Kcell (m−i)	α	Rsol (Ω)	σb lood (Due south/m)
one	xl	2000	thirty	20	215	22.32	0.6	32.35	0.69
2	40	2000	15	35	358	35.834	0.3	40.26	0.89
3	40	2000	40	10	614	16.64	0.viii	38.70	0.43

Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© 2020 past the authors. Licensee MDPI, Basel, Switzerland. This commodity is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC Past) license (http://creativecommons.org/licenses/by/iv.0/).

keenatimenswo1936.blogspot.com

Source: https://www.mdpi.com/2079-6374/10/12/208/htm

Share this post