Optimal signal-to-noise ratio for silicon nanowire biochemical sensors.
Ontology highlight
ABSTRACT:
SUBMITTER: Rajan NK
PROVIDER: S-EPMC3144966 | biostudies-other | 2011 Jun
REPOSITORIES: biostudies-other
Similar Datasets
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.
| S-EPMC3017570 | biostudies-other
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.
| S-EPMC5666504 | biostudies-literature
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.
| S-EPMC3042585 | biostudies-literature
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.
| S-EPMC9370444 | biostudies-literature
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.
| S-EPMC2034356 | biostudies-literature
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.
| S-EPMC3039683 | biostudies-literature
Project description:PurposeWe compute the ultimate signal-to-noise ratio (uSNR) and G-factor (uGF) in a realistic head model from 0.5 to 21 Tesla.MethodsWe excite the head model and a uniform sphere with a large number of electric and magnetic dipoles placed at 3 cm from the object. The resulting electromagnetic fields are computed using an ultrafast volume integral solver, which are used as basis functions for the uSNR and uGF computations.ResultsOur generalized uSNR calculation shows good convergence in the sphere and the head and is in close agreement with the dyadic Green's function approach in the uniform sphere. In both models, the uSNR versus B0 trend was linear at shallow depths and supralinear at deeper locations. At equivalent positions, the rate of increase of the uSNR with B0 was greater in the sphere than in the head model. The uGFs were lower in the realistic head than in the sphere for acceleration in the anterior-posterior direction, but similar for the left-right direction.ConclusionThe uSNR and uGFs are computable in nonuniform body models and provide fundamental performance limits for human imaging with close-fitting MRI array coils. Magn Reson Med 78:1969-1980, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
| S-EPMC5532082 | biostudies-literature
Project description:The combined capabilities of both a nonplanar design and nonconventional carrier injection mechanisms are subject to recent scientific investigations to overcome the limitations of silicon metal oxide semiconductor field effect transistors. In this Letter, we present a multimode field effect transistors device using silicon nanowires that feature an axial n-type/intrinsic doping junction. A heterostructural device design is achieved by employing a self-aligned nickel-silicide source contact. The polymorph operation of the dual-gate device enabling the configuration of one p- and two n-type transistor modes is demonstrated. Not only the type but also the carrier injection mode can be altered by appropriate biasing of the two gate terminals or by inverting the drain bias. With a combined band-to-band and Schottky tunneling mechanism, in p-type mode a subthreshold swing as low as 143 mV/dec and an ON/OFF ratio of up to 10(4) is found. As the device operates in forward bias, a nonconventional tunneling transistor is realized, enabling an effective suppression of ambipolarity. Depending on the drain bias, two different n-type modes are distinguishable. The carrier injection is dominated by thermionic emission in forward bias with a maximum ON/OFF ratio of up to 10(7) whereas in reverse bias a Schottky tunneling mechanism dominates the carrier transport.
| S-EPMC4245716 | biostudies-literature
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 ?(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.
| S-EPMC4466709 | biostudies-other
Project description:Measuring the quality of three-dimensional (3D) reconstructed biological macromolecules by transmission electron microscopy is still an open problem. In this article, we extend the applicability of the spectral signal-to-noise ratio (SSNR) to the evaluation of 3D volumes reconstructed with any reconstruction algorithm. The basis of the method is to measure the consistency between the data and a corresponding set of reprojections computed for the reconstructed 3D map. The idiosyncrasies of the reconstruction algorithm are taken explicitly into account by performing a noise-only reconstruction. This results in the definition of a 3D SSNR which provides an objective indicator of the quality of the 3D reconstruction. Furthermore, the information to build the SSNR can be used to produce a volumetric SSNR (VSSNR). Our method overcomes the need to divide the data set in two. It also provides a direct measure of the performance of the reconstruction algorithm itself; this latter information is typically not available with the standard resolution methods which are primarily focused on reproducibility alone.
| S-EPMC1464087 | biostudies-literature