Optimal signal-to-noise ratio for silicon nanowire biochemical sensors.
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ABSTRACT: The signal-to-noise ratio (SNR) for silicon nanowire field-effect transistors operated in an electrolyte environment is an essential figure-of-merit to characterize and compare the detection limit of such devices when used in an exposed channel configuration as biochemical sensors. We employ low frequency noise measurements to determine the regime for optimal SNR. We find that SNR is not significantly affected by the electrolyte concentration, composition, or pH, leading us to conclude that the major contributions to the SNR come from the intrinsic device quality. The results presented here show that SNR is maximized at the peak transconductance.
Project description:Si nanowire transistors are ideal for the sensitive detection of atmospheric species due to their enhanced sensitivity to changes in the electrostatic potential at the channel surface. In this study, we present unique ambipolar Si junctionless nanowire transistors (Si-JNTs) that incorporate both n- and p-type conduction within a single device. These transistors enable scalable detection of nitrogen dioxide (NO2), a critical atmospheric oxidative pollutant, across a broad concentration range, from high levels (25-50 ppm) to low levels (250 ppb-2 ppm). Acting as an electron acceptor, NO2 generates holes and functions as a pseudodopant for Si-JNTs, altering the conductance and other device parameters. Consequently, ambipolar Si-JNTs exhibit a dual response at room temperature, reacting on both p- and n-conduction channels when exposed to gaseous NO2, thereby offering a larger parameter space compared to a unipolar device. Key characteristics of the Si-JNTs, including on-current (Ion), threshold voltage (Vth) and mobility (μ), were observed to dynamically change on both the p- and n-channels when exposed to NO2. The p-conduction channel showed superior performance across all parameters when compared to the device's n-channel. For example, within the NO2 concentration range of 250 ppb to 2 ppm, the p-channel achieved a responsivity of 37%, significantly surpassing the n-channel's 12.5%. Additionally, the simultaneous evolution of multiple parameters in this dual response space enhances the selectivity of Si-JNTs toward NO2 and improves their ability to distinguish between different pollutant gases, such as NO2, ammonia, sulfur dioxide and methane.
Project description:The 1∕f noise of silicon nanowire biochemical field effect transistors is fully characterized from weak to strong inversion in the temperature range 100-300 K. At 300 K, our devices follow the correlated Δn-Δμ model. As the temperature is lowered, the correlated mobility fluctuations become insignificant and the low frequency noise is best modeled by the Δn-model. For some devices, evidence of random telegraph signals is observed at low temperatures, indicating that fewer traps are active and that the 1∕f noise due to number fluctuations is further resolved to fewer fluctuators, resulting in a Lorentzian spectrum.
Project description:This paper reports our findings on how to prepare a graphene oxide-based gas sensor for sensing fast pulses of volatile organic compounds with a better signal-to-noise ratio. We use rapid acetone pulses of varying concentrations to test the sensors. First, we compare the effect of graphene oxide deposition method (dielectrophoresis versus solvent evaporation) on the sensor's response. We find that dielectrophoresis yields films with uniform coverage and better sensor response. Second, we examine the effect of chemical reduction. Contrary to prior reports, we find that graphene oxide reduction leads to a reduction in sensor response and current noise, thus keeping the signal-to-noise ratio the same. We found that if we sonicated the sensor in acetone, we created a sensor with a few flakes of reduced graphene oxide. Such sensors provided a higher signal-to-noise ratio that could be correlated to the vapor concentration of acetone with better repeatability. Modeling shows that the sensor's response is due to one-site Langmuir adsorption or an overall single exponent process. Further, the desorption of acetone as deduced from the sensor recovery signal follows a single exponent process. Thus, we show a simple way to improve the signal-to-noise ratio in reduced graphene oxide sensors.
Project description:Molecular sensors based on intramolecular Förster resonance energy transfer (FRET) have become versatile tools to monitor regulatory molecules in living tissue. However, their use is often compromised by low signal strength and excessive noise. We analyzed signal/noise (SNR) aspects of spectral FRET analysis methods, with the following conclusions: The most commonly used method (measurement of the emission ratio after a single short wavelength excitation) is optimal in terms of signal/noise, if only relative changes of this uncalibrated ratio are of interest. In the case that quantitative data on FRET efficiencies are required, these can be calculated from the emission ratio and some calibration parameters, but at reduced SNR. Lux-FRET, a recently described method for spectral analysis of FRET data, allows one to do so in three different ways, each based on a ratio of two out of three measured fluorescence signals (the donor and acceptor signal during a short-wavelength excitation and the acceptor signal during long wavelength excitation). Lux-FRET also allows for calculation of the total abundance of donor and acceptor fluorophores. The SNR for all these quantities is lower than that of the plain emission ratio due to unfavorable error propagation. However, if ligand concentration is calculated either from lux-FRET values or else, after its calibration, from the emission ratio, SNR for both analysis modes is very similar. Likewise, SNR values are similar, if the noise of these quantities is related to the expected dynamic range. We demonstrate these relationships based on data from an Epac-based cAMP sensor and discuss how the SNR changes with the FRET efficiency and the number of photons collected.
Project description:A highly sensitive silicon nanowire (SiNW)-based sensor device was developed using electron beam lithography integrated with complementary metal oxide semiconductor (CMOS) technology. The top-down fabrication approach enables the rapid fabrication of device miniaturization with uniform and strictly controlled geometric and surface properties. This study demonstrates that SiNW devices are well-aligned with different widths and numbers for pH sensing. The device consists of a single nanowire with 60 nm width, exhibiting an ideal pH responsivity (18.26 × 106 Ω/pH), with a good linear relation between the electrical response and a pH level range of 4-10. The optimized SiNW device is employed to detect specific single-stranded deoxyribonucleic acid (ssDNA) molecules. To use the sensing area, the sensor surface was chemically modified using (3-aminopropyl) triethoxysilane and glutaraldehyde, yielding covalently linked nanowire ssDNA adducts. Detection of hybridized DNA works by detecting the changes in the electrical current of the ssDNA-functionalized SiNW sensor, interacting with the targeted ssDNA in a label-free way. The developed biosensor shows selectivity for the complementary target ssDNA with linear detection ranging from 1.0 × 10-12 M to 1.0 × 10-7 M and an attained detection limit of 4.131 × 10-13 M. This indicates that the use of SiNW devices is a promising approach for the applications of ion detection and biomolecules sensing and could serve as a novel biosensor for future biomedical diagnosis.
Project description:The signal-to-noise ratio (SNR), a commonly used measure of fidelity in physical systems, is defined as the ratio of the squared amplitude or variance of a signal relative to the variance of the noise. This definition is not appropriate for neural systems in which spiking activity is more accurately represented as point processes. We show that the SNR estimates a ratio of expected prediction errors and extend the standard definition to one appropriate for single neurons by representing neural spiking activity using point process generalized linear models (PP-GLM). We estimate the prediction errors using the residual deviances from the PP-GLM fits. Because the deviance is an approximate χ(2) random variable, we compute a bias-corrected SNR estimate appropriate for single-neuron analysis and use the bootstrap to assess its uncertainty. In the analyses of four systems neuroscience experiments, we show that the SNRs are -10 dB to -3 dB for guinea pig auditory cortex neurons, -18 dB to -7 dB for rat thalamic neurons, -28 dB to -14 dB for monkey hippocampal neurons, and -29 dB to -20 dB for human subthalamic neurons. The new SNR definition makes explicit in the measure commonly used for physical systems the often-quoted observation that single neurons have low SNRs. The neuron's spiking history is frequently a more informative covariate for predicting spiking propensity than the applied stimulus. Our new SNR definition extends to any GLM system in which the factors modulating the response can be expressed as separate components of a likelihood function.
Project description:We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the "cross-talk" phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive.
Project description:Sensitivity in BOLD fMRI is characterized by the signal to noise ratio (SNR) of the time-series (tSNR), which contains fluctuations from thermal and physiological noise sources. Alteration of an acquisition parameter can affect the tSNR differently depending on the relative magnitude of the physiological and thermal noise, therefore knowledge of this ratio is essential for optimizing fMRI acquisitions. In this study, we compare image and time-series SNR from array coils at 3T with and without parallel imaging (GRAPPA) as a function of image resolution and acceleration. We use the "absolute unit" SNR method of Kellman and McVeigh to calculate the image SNR (SNR(0)) in a way that renders it comparable to tSNR, allowing determination of the thermal to physiological noise ratio, and the pseudo-multiple replica method to quantify the image noise alterations due to the GRAPPA reconstruction. The Kruger and Glover noise model, in which the physiological noise standard deviation is proportional to signal strength, was found to hold for the accelerated and non-accelerated array coil data. Thermal noise dominated the EPI time-series for medium to large voxel sizes for single-channel and 12-channel head coil configurations, but physiological noise dominated the 32-channel array acquisition even at 1 mm × 1mm × 3 mm resolution. At higher acceleration factors, image SNR is reduced and the time-series becomes increasingly thermal noise dominant. However, the tSNR reduction is smaller than the reduction in image SNR due to the presence of physiological noise.
Project description:Rolling circle amplification (RCA) for generation of distinct fluorescent signals in situ relies upon the self-collapsing properties of single-stranded DNA in commonly used RCA-based methods. By introducing a cross-hybridizing DNA oligonucleotide during rolling circle amplification, we demonstrate that the fluorophore-labeled RCA products (RCPs) become smaller. The reduced size of RCPs increases the local concentration of fluorophores and as a result, the signal intensity increases together with the signal-to-noise ratio. Furthermore, we have found that RCPs sometimes tend to disintegrate and may be recorded as several RCPs, a trait that is prevented with our cross-hybridizing DNA oligonucleotide. These effects generated by compaction of RCPs improve accuracy of visual as well as automated in situ analysis for RCA based methods, such as proximity ligation assays (PLA) and padlock probes.