Nanomechanoelectrical approach to highly sensitive and specific label-free DNA detection

Significance

Miniaturized, high-precision DNA analysis holds significant potential for advancing biotechnology development and enabling applications in diagnostics, healthcare, and drug discovery. DNA detection using all-electronic devices offers a promising pathway to unlock this potential. However, existing all-electronic methods are prone to limited specificity and detection limit due to interference from nonspecific electrostatic and electrochemical interactions induced by prevalent charged species in solutions. To address this challenge, we drive nanostructural DNA strands, tethered to a graphene transistor, to oscillate in an alternating electric field. We find that the resulting transistor-current spectral characteristics are resistant to the interference interactions, leading to ultrahigh specificity and a detection limit improved by two orders of magnitude compared to existing methods.

Abstract

Electronic detection of DNA oligomers offers the promise of rapid, miniaturized DNA analysis across various biotechnological applications. However, known all-electrical methods, which solely rely on measuring electrical signals in transducers during probe–target DNA hybridization, are prone to nonspecific electrostatic and electrochemical interactions, subsequently limiting their specificity and detection limit. Here, we demonstrate a nanomechanoelectrical approach that delivers ultra-robust specificity and a 100-fold improvement in detection limit. We drive nanostructural DNA strands tethered to a graphene transistor to oscillate in an alternating electric field and show that the transistor-current spectra are characteristic and indicative of DNA hybridization. We find that the inherent difference in pliability between unpaired and paired DNA strands leads to the spectral characteristics with minimal influence from nonspecific electrostatic and electrochemical interactions, resulting in high selectivity and sensitivity. Our results highlight the potential of high-performance DNA analysis based on miniaturized all-electronic settings.

Label-free detection of DNA oligomers using miniaturized electronic systems offers the promise of advancing biotechnology development and enabling a spectrum of downstream biomedical applications in diagnostics, healthcare, and drug discovery (1–3). Since DNA hybridization affinity depends exponentially on DNA length (4), short DNA oligomers are difficult to be quantified at low concentrations (e.g., a ~nM detection limit for 20-nt DNA oligomers) (5, 6). Transistor devices based on nanomaterials (7), including two-dimensional materials such as graphene, and functionalized by probe DNA oligonucleotides which bind specifically to electron-rich, aromatic target DNA oligomers with complementary sequences, solely convert DNA hybridization occurrence to electric-current response through electrostatic gating (8) and charge transfer (9–11) effects. In comparison with other methods, these devices offer outstanding precision (12, 13), reliability (14), and scalability (6), presenting a promising all-electrical pathway to label-free DNA analysis. However, the specificity and the limit of detection for typical transistor-based methods (15–17) remain unsatisfactory. This is due to the interrogated electrical signal’s vulnerability to interference from electrostatic and electrochemical interactions induced by nonspecific mobile charged molecules, including ions and unbound DNA oligomers, which are prevalent in sample solution and can also be physically/chemically adsorbed onto the transducer interface (18, 19). In an alternative method, transistor DNA sensors are washed and air-dried after target-probe hybridization and measured in dry state (6), which improves specificity but compromises the signal-to-noise ratio, resulting in a modest detection limit (e.g., ~1.0 nM for 22-mer target DNA) as well.

Results

We develop a nanomechanoelectrical approach to label-free quantification of DNA oligomers, achieving ultrahigh selectivity and a detection limit improved by two orders of magnitude compared to typical all-electrical methods. As shown in Fig. 1 A–C and SI Appendix, Fig. S1, we measure the electric current of a transistor of graphene functionalized by DNA nanostructures (20–22), where each nanostructure comprises a rigid tetrahedral double-stranded DNA (dsDNA) base tethering a 15-mer single-stranded DNA (ssDNA) probe oligonucleotide capable of hybridizing with a complementary target DNA oligomer and oscillating—in either an unhybridized single-stranded or a hybridized double-stranded configuration—in an external alternating electric field normal to the graphene basal plane. Utilizing graphene as the transistor channel material enables high-quality functionalization of the channel by the DNA nanostructures through π−π interactions (23), high-performance DNA detection with outstanding precision and extremely low electrical noise (24), and nanoscale uniformity of the applied normal electric field.

Fig. 1.

Nanomechanoelectrical fingerprint of DNA hybridization. (A) Oscillation of DNA tethered to a tetrahedral DNA nanostructure on graphene substrate in an alternating electric field ( $E$ ). The 15-mer probe/target DNA strands are in purple/red. (B) Optical microscopic image of the graphene field-effect transistor. The area of graphene is indicated by the red box. (The scale bar is 100 μm.) (C) The measurement system that incorporates the transistor device and the electronic interface board. (D) Relative-current spectra in response to different concentrations of target DNA and control DNA with a random sequence. The Top Inset schematic illustrates the oscillations for ssDNA (Left) and dsDNA (Right) in the ranges of frequency that are indicative (<200 kHz) and nonindicative (>200 kHz), respectively, to DNA hybridization. The Bottom Left Inset energy band diagram illustrates the shift of the Fermi level of a graphene field-effect transistor upon the competing coeffect of electrochemical charge transfer and electrostatic gating. The dash lines are for eye-guiding.

The graphene transistors are manufactured by using nanofabrication techniques. The DNA nanostructures are immobilized on the graphene substrate through cross-linking and uniformly distributed with a density of ~174 μm⁻² (SI Appendix, Fig. S2), providing an optimal average distance of ~48 nm between neighboring DNA nanostructures, which yields high sensitivity in DNA detection yet minimal risk of collisions between the oscillatory DNA oligonucleotides. The alternating electric field is generated through a liquid gate at a sinuous voltage bias with an amplitude of 190 mV, and the frequency is swept from 0.1 to 2,000 kHz. The gate-voltage range (−190 mV to 190 mV) entirely falls in the hole-doped regime of the graphene I-V_g curves (SI Appendix, Fig. S3), and the source-drain current variation is monotonic as the gate voltage increases/decreases. The electric current in graphene is measured at an aperture time of 166.67 ms which covers numerous DNA oscillation cycles (typically in the range from 17 to 3 × 10⁵). Consequently, the measured current is a time-averaged response and minimally affected by the initial conditions of DNA oscillation. The graphene-transistor current spectra at various target-DNA concentrations are shown in SI Appendix, Fig. S4.

Nanomechanoelectrical Fingerprint of DNA Hybridization.

Fig. 1D shows the relative-current spectra, which are generated by subtracting the baseline current spectrum for buffer solution without target DNA from the current spectra obtained in response to different concentrations of target DNA. The relative-current spectra exhibit two distinct effects: 1) a systematic reduction in relative current across the entire frequency range as the target concentration increases and 2) pronounced attenuation and strengthening at frequencies around 14 kHz and 62 kHz, respectively.

Upon the hybridization of target DNA oligomers with probe DNA oligonucleotides on the graphene channel, the graphene electric current is modulated by negative charges in the nucleotides of the target DNA oligomers through competing electrostatic gating (8) and charge-transfer (9–11) effects (see Fig. 1 D, Bottom Inset). Effect 1) reflects that, broadly, the current response is primarily determined by the charge-transfer effect over the gating effect. This finding is consistent with the short electrostatic interaction range (0.79 nm), the Debye screening length, for the buffer solution used in our experiment with a 150-mM ionic strength. It also aligns with the negative shift observed in the I-V_g characteristics of the device upon probe–target hybridization (SI Appendix, Fig. S5), which has been made apparent by previous experiments based on liquid-gated graphene-transistor DNA sensors (13, 25).

Effect 2), encompassing spectral characteristics, is a high-performance fingerprint for DNA hybridization. Within a frequency range of 0.1 to 200 kHz, the magnitudes of the attenuation and strengthening are significant and depend systematically on target concentration. In contrast, the spectra for the random, 5′-end mismatched, and center mismatched sequences (see SI Appendix, Table S1, for the sequences) exhibit minimal frequency dependence throughout the entire frequency range, as shown in Fig. 1D and SI Appendix, Fig. S6. Apart from the frequency-dependent target spectral characteristics, note that between 200 and 2,000 kHz, the target relative current displays minimal frequency dependence and remains constant within this frequency range.

High-Specificity, High-Sensitivity DNA Quantification.

To demonstrate the use of Effect 2) for DNA quantification, we study the relationship between target concentration and the peak-to-valley response (Δ), which is defined as the difference between the minimum attenuation relative current and the maximum strengthening relative current (SI Appendix, Fig. S7). Fig. 2A shows that the Δ -concentration data can be well fit by the Sips model that describes the occupancy of target DNA oligomers at graphene surface functionalized by probe oligomers (26, 27):

$$\mathrm{\Delta }=A\frac{{\left(c/K_D\right)}^a}{1+{\left(c/K_D\right)}^a}+B,$$ [1]

Fig. 2.

Response curve for the nanomechanoelectrical approach in comparison with the all-electrical approaches. (A) Peak-to-valley relative-current response (Δ) vs. target/control concentration. (B) Relative current averaged over the frequency range from 1,500 to 2,000 kHz vs. target/control concentration. (C) Dirac voltage shift as a function of target/control concentration. The diamond (gray), hexagonal (cyan), and pentagon (blue) data points in A–C correspond to responses for random, center mismatched, and 5′-end mismatched sequences, respectively. The error bars in A–C are determined based on the uncertainties in extracting the corresponding quantities and, in A and B, the sizes of the error bars are smaller than the size of the data points. (D) Ratios of control DNA responses to that of target DNA at the same concentration (100 nM) for different methods. The error bars are determined by error propagation.

where $c$ is target concentration, $A$ the maximum response with all binding sites occupied, $B$ a baseline shift, $K_D$ the equilibrium dissociation constant, and $a$ a parameter that represents the Gaussian distribution of DNA binding energies (6, 28). The best fit to the data yields fitting parameters $A$ = 0.27 ± 0.01 μA, $B$ = −0.15 ± 0.01 μA, $K_D$ = 8.09 ± 2.23 nM, and $a$ = 0.49 ± 0.12. A finite value of $B$ has been made apparent in previous research in DNA mechano-optical transduction (29) and can be attributed to constitutive activity and/or amplification effects in a transduction that involves the integration of different sensory modes (30). The best-fit value of $K_D$ well agrees with the 2-nM value obtained using conventional surface plasmon–enhanced diffraction techniques for target DNA of a similar length (5). The value of $a$ implies a heterogeneous adsorption isotherm with a distribution of binding energies (6, 28) rather than a single-value DNA–DNA binding energy which would yield $a$ = 1. The DNA analytical detection limit, shown in Fig. 2A and SI Appendix, Table S2, is ~10 pM, representing a sensitivity enhancement of over 100-fold compared to the dry-state method developed in prior research (6). Moreover, the peak-to-valley responses for the controls, shown in Fig. 2A and SI Appendix, Fig. S6, are considerably smaller than those of the target, highlighting the ultrahigh specificity of our approach.

For comparison, we also study two frequency-independent quantities that depend primarily on electrical transduction, as opposed to mechanoelectrical transduction, in response to DNA hybridization: One is the average of relative current from 1,500 to 2,000 kHz, which is found in the frequency-independent regime of the spectra shown in Fig. 1D and reflects Effect 1), and the other is the shift of Dirac voltage extracted from the I-V_g curves in SI Appendix, Fig. S5. For both approaches, the response vs. target concentration, as shown in Fig. 2 B and C, displays excellent agreement with the Sip’s model, with $K_D$ values comparable to that of the nanomechanoelectrical approach (see SI Appendix, Table S2, for the best-fitting parameters). However, compared to these two all-electrical approaches, the nanomechanoelectrical approach provides the highest selectivity (Fig. 2D) and a ~100× enhancement in detection limit (SI Appendix, Table S2). The limitations in specificity and sensitivity for the all-electrical approaches are due to nonspecific interactions, such as the physiochemical absorption of the control DNA oligomers onto the graphene channel, the passivation layer, and/or the liquid gate, which affect the electrical signal of the graphene channel through gating and charge-transfer effects.

It is intriguing to note in Fig. 2D that, for all the employed mechanoelectrical/all-electrical approaches, the response to the control sequence with a base mismatch at the 5′ end is more pronounced, albeit slightly, compared to the control sequence with a mismatch at the center which, in turn, is more pronounced than that to the random control sequence. This trend is in line with the dependence of DNA binding affinity on the position of base mismatch, as elucidated by experiments based on oligonucleotide microarrays (31, 32) and positional-dependent-nearest-neighbor simulation (32).

Principle of the Mechanoelectrical Transduction.

The mechanoelectrical transduction of DNA hybridization in our approach involves two processes: the electrically driven oscillation of DNA strands and the induction of frequency-dependent current variation in graphene by the electron-rich nucleotides in the oscillatory DNA strands. The DNA oscillation can be modeled as a charged mass experiencing an alternating electrical force, as well as a damping force due to water viscosity, as shown in Fig. 3 A, Inset. Respectively, ssDNA and dsDNA strands can form coil-like and rod-like configurations (33) due to their differences in flexibility, with ssDNA being flexible/semiflexible (34) and dsDNA being rigid (35). The difference between the ssDNA and the dsDNA in terms of the damping effect on their oscillations can be described by a geometric factor, $g$ , which depends on the pliability of the ssDNA/dsDNA strand (1.24 nm for the ssDNA strand and 4.94 nm for the dsDNA strand, as detailed in SI Appendix, Supporting Information Text 1).

Fig. 3.

Simulations for DNA oscillation and current response. (A) The distance from a ssDNA oligomer mass center to the graphene substrate as a function of time. The time span of the plot covers ~0.3% of the measurement aperture time (166.67 ms). The inset shows the model: the oscillation of a charged mass moving in an alternating electric field ( $\mathit{E}$ ) with a damping effect (damping coefficient $c$ ) generated by viscous force in the solution. Here, x_c is the distance of the mass center to the substrate. (B) Percentage of time that the ssDNA/dsDNA is at the position closest to the graphene substrate as a function of liquid-gate bias frequency. (C) Simulated relative current as a function of frequency at $k$ = 0.3. Note the correspondences of the attenuation and strengthening frequencies with the cut-off frequencies in Fig. 3B and the stabilization of relative current at ~200 kHz. The dash lines in B and C are for eye-guiding.

The simulation result of the motion indicates a clear dependence of oscillation amplitude on frequency. The Fig. 3A shows that an increase in frequency leads to an increased average distance between the ssDNA oligomer and the graphene substrate. The motion of the dsDNA oligomer exhibits a similar trend, yet with a frequency lag (SI Appendix, Fig. S8), due to the dsDNA oligomer’s lower pliability, and, consequently, a geometric factor ( $g$ ) 4× than the corresponding ssDNA oligomer. This is well reflected in the spectrum of the percentage of time a DNA strand spends closest to the graphene substrate (Fig. 3B), which shows a redshift from ssDNA to dsDNA. Thus, the oscillation amplitude of a dsDNA oligomer is considerably smaller than that of the corresponding ssDNA oligomer for a given frequency in the range of 0.1 to 200 kHz, as shown by the Top Inset schematic of Fig. 1D. At frequencies >200 kHz, both the ssDNA and dsDNA oligomers, repelled by the negative electric potential of the graphene substrate, are upright (due to the negative source-drain voltage) with a distance of ~9.6 nm from their mass center to the substrate (Fig. 1A) and oscillate within a small range <0.3 nm in vertical distance (SI Appendix, Fig. S9).

The oscillation of DNA strands induces current variation in graphene through the competing modulation effects of electrostatic gating and electrochemical charge transfer (11) (Fig. 1 D, Inset). Previous investigations of DNA sensors based on graphene transistors consider only one of the two effects (8–11). However, simulations that consider the individual effects of either charge transfer or gating, as shown in SI Appendix, Fig. S10, clearly show that the emergence of the attenuation and strengthening peaks arises from the competitive interplay between these two effects. Therefore, we analyze the system by incorporating both effects into a unified model (see SI Appendix, Supporting Information Text 2, for details), which includes only one, dimensionless parameter, $k$ , ranging between 0 and 1, to characterize the charge-transfer efficiency for net charges in the ssDNA/dsDNA strand on the charge–substrate distance. The charge-transfer effect is addressed empirically because, even though it has been extensively reported, the underlying mechanisms of this effect, specifically between DNA and graphene, remain largely elusive. This presents an ongoing scientific challenge that necessitates further theoretical and computational investigations.

The simulated relative current spectra (Fig. 3C and SI Appendix, Fig. S11) well display attenuation at ~14.2 kHz and strengthening at ~57.0 kHz, perfectly aligning with the peak positions observed in the measured spectra (~14 kHz for attenuation and ~62 kHz for strengthening) as shown in Fig. 1D. The characteristic attenuation and strengthening positions agree with the cut-off frequencies in the time-ratio spectrum (Fig. 3B) which is entirely determined by the kinetic simulation based on Eq. S1 in SI Appendix, Supporting Information Text 1. Furthermore, by varying the value for $k$ , the only adjustable, interaction-related parameter in our simulation, it is evident that the attenuation and strengthening frequencies are $k$ -independent (SI Appendix, Fig. S11). These findings indicate that the attenuation and strengthening frequencies are primarily determined by the intrinsic disparity in pliability between unhybridized and hybridized DNA strands. These disparities manifest as the fourfold difference in $g$ between the ssDNA oligomer and the dsDNA oligomer, ultimately leading to the key robust specificity and sensitivity of the mechanoelectrical DNA-hybridization transduction approach within the pliability-indicative frequency range (0.1 to 200 kHz). In contrast, within the 200 to 2,000 kHz frequency range, the simulated relative current spectra exhibit minimal frequency dependence, similar to the measured spectra within the same frequency range, due to the limited range of oscillation variation (<0.3 nm) for both the ssDNA and dsDNA oligomers, which is significantly smaller compared to the characteristic length (1 to 10 nm) for the long-range charge transport/transfer in DNA (36, 37).

The simulation result (SI Appendix, Fig. S11) also shows that while the attenuation and strengthening frequencies are $k$-independent, the optimal value of $k$ for achieving agreement in magnitude between the simulated and measured relative current is frequency dependent. Specifically, at frequencies >200 kHz, the experimental data match the simulation result when $k$ is approximately 0.2, while at frequencies <20 kHz, the agreement occurs when $k$ falls within the range of 0.3 to 0.4. This observation is consistent with the inverse dependence of charge transfer efficiency on average DNA–graphene distance and, thereby, oscillation frequency.

Discussion

In this study, we interrogate the hybridization state of DNA strands that are tethered to a graphene transistor and oscillating in an alternating electric field, as opposed to existing all-electrical approaches that are based on nonoscillatory DNA strands. Our results demonstrate that the graphene-transistor current spectral characteristics are indicative of DNA hybridization, remaining minimally affected by nonspecific interactions and exhibiting robust specificity and a ~100× improved detection limit compared to all-electrical approaches. We analyze the system by incorporating both electrostatic gating and electrochemical charge-transfer effects into a unified model, contrasting with existing investigations of graphene transistor–based DNA sensors which typically consider only one of these effects (8–11). Our analysis shows that the nanomechanoelectrical transduction approach is enabled by the intrinsic difference in pliability between unpaired and paired DNA strands, highlighting its key robust specificity and sensitivity. Building on the findings and the methodologies developed in this study, future research points toward creating high-performance miniaturized mechanoelectrical nucleic-acid sensors for applications that involve complex biosolutions and nonspecific interferences, including rapid diagnosis, prognosis, and treatment-response monitoring. In addition, the unified modeling approach could facilitate future investigation of the charge-transfer effect between graphene and DNA, an issue that remains an open question.

Materials and Methods

Graphene Growth and Transfer.

Monolayer graphene is synthesized via CVD on copper substrate. Copper foil (Alfa Aesar, 46986) is treated with acetic acid for 10 min, loaded into a quartz tube (22 mm in I.D.; 4 feet in length), and annealed for 30 min at 1,060 °C in hydrogen (99.999%; flow rate = 200 sccm) and argon (99.999%; flow rate = 500 sccm) to remove oxide residues. Monolayer graphene is deposited on the copper substrate in the mixed gas of hydrogen (99.999%; flow rate = 9 sccm), argon (99.999%; flow rate = 500 sccm), and precursor methane (99.99%; flow rate = 0.5 sccm) at 1,035 °C for 20 min.

To transfer the CVD graphene on copper substrate, a 400-nm layer of poly(methyl methacrylate) (PMMA), 950 A4, Kayaku Advanced Materials, Inc., is spin-coated on the CVD graphene. The PMMA–graphene–copper film is soaked in copper etchant (Alfa Aesar, 44583) until copper is fully etched. The remaining PMMA–graphene film is thoroughly cleaned by three deionized (DI) water baths and transferred onto a 1-mm-thick fused silica (University wafer 1943) chip with 70-nm-thick Cr/Au contact electrodes which are prefabricated using photolithographic techniques, oxygen plasma etching, and e-beam evaporation.

Device Fabrication.

The PMMA–graphene film on the fused-silica chip is left to dry overnight, soaked in acetone for 1 h to remove PMMA, thoroughly rinsed by isopropyl alcohol (IPA), and dried by nitrogen. Graphene channels are then defined using photolithography based on a bilayer photoresist of PMGI SF 2S (Kayaku Advanced Materials, Inc.) and S1813 (Kayaku Advanced Materials, Inc.). Graphene outside of the defined channel areas is removed by oxygen plasma etching for 1 min (40 W, 4-mTorr oxygen pressure). Photoresist residue on the chip is removed by soaking the chip in remover PG (Kayaku Advanced Materials, Inc.) at 60 °C for 30 min, followed by being rinsed by IPA and then dried by nitrogen. After that, photolithography based on three layers of the photoresist of PMGI SF 2S, LOR 5A (Kayaku Advanced Materials, Inc.), and S1813 is used to define a 150 × 150 µm sensing area on each of the graphene channel areas. A 100-nm Si₃N₄ is sputtered to passivate metal contacts and the edges of the graphene channels. Finally, the chip is soaked in remover PG, rinsed with IPA, and dried with nitrogen air.

Tetrahedral DNA Nanostructure Synthesis and Immobilization.

Equimolar quantities (1 µM) of DNA oligomers with four sequences (A17-5T-Probe, NH₂-B17, NH₂-C17, and NH₂-D17 in SI Appendix, Table S1) are mixed in physiological buffer solution (150 mM, pH = 8.0). Using a thermal cycler (Eppendorf Mastercycler EP gradient), the solution is heated to 95 °C, kept at this temperature for 10 min, and cooled to 4 °C immediately so that the DNA oligomers can form double-stranded nanostructures with tetrahedral bases.

A graphene field-effect transistor device is immersed in 1-mM 1-Pyrenebutyric acid N-hydroxysuccinimide ester (PBASE, Sigma-Aldrich 457078) in dimethylformamide (DMF, Thermo Scientific 327175000) solution for 20 h at room temperature to allow the pyrenyl groups of the PBASE molecules to bind to the graphene basal plane via π−π stacking interaction. Then, the device is washed thoroughly with DMF, IPA, and DI water and dried with nitrogen air. A polypropylene well is placed around the graphene transistor and bound in position. Then 200 µL of the preprepared tetrahedral DNA-nanostructure solution (1 µM) is added in the well and incubated for 12 h at room temperature in a humid atmosphere. The amine group carried at each bottom vertex of the tetrahedral DNA nanostructure binds to the PBASE through nucleophilic substitution of N-hydroxysuccinimide. Then, the chip is incubated in 10-mM ethanolamine in PBS buffer (200 µL) for 30 min to deactivate the unreactive groups. Finally, the DNA nanostructure–functionalized graphene is thoroughly washed by physiological buffer solution to remove the noncovalently bounded DNA oligomers on the graphene surface.

Electrical Measurement.

The device is connected to an electronic interface board by gold-wire bonding. A Pt wire (0.25 mm in diameter) is used as liquid gate. A polyolefin film is used to cover the mouth of the well to suppress evaporation. A sinuous ac voltage (190 mV peak) is generated by a Tektronix TSG4102 signal generator through the liquid gate. The liquid-gate voltage frequency is swept from 0.1 to 2,000 kHz by 1,732 steps with 300-ms interval period. A Keithley 2450 source meter is used to apply a −50-mV dc source-drain voltage to the graphene field-effect transistor. A Keysight 2987B electrometer in ammeter mode (100-pA resolution, 166.67-ms integration time) is used to quantify the drain-source current of the device.

The graphene electrode of the device is exposed to 500 µL 1× physiological buffer solution (Fisher BioReagents BP661-50; pH = 8.0, ionic strength = 150 mM) in the well for baseline measurement. Target/control DNA (SI Appendix, Table S1) in 1× physiological buffer solution is loaded into the well and the corresponding response is measured. Since the DNA hybridization and the response of liquid-gated graphene field-effect transistors are sensitive to pH, the pH values of the solutions are precisely controlled at 8.0 throughout the experiment. Measurements are conducted under strictly controlled ambient temperature conditions (25.0 ± 0.2 °C) and initiated only after an adequate time has elapsed following the loading of the target/control DNA to ensure complete DNA hybridization.

Supplementary Material

Appendix 01 (PDF)

Data, Materials, and Software Availability

All study data are included in the article and/or SI Appendix.