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In developing solid-state nanopore sensors for single molecule detection, comprehensive evaluation of the nanopore quality is important. Existing studies typically rely on comparing the noise root mean square or power spectrum density values. Nanopores exhibiting lower noise values are generally considered superior. This evaluation is valid when the single molecule signal remains consistent. However, the signal can vary, as it is strongly related to the solid-state nanopore size, which is hard to control during fabrication consistently. This work emphasized the need to report the baseline current for evaluating solid-state nanopore sensors. The baseline current offers insight into several experimental conditions, particularly the nanopore size. Our experiments show that a nanopore sensor with more noise is not necessarily worse when considering the signal-to-noise ratio (SNR), particularly when the pore size is smaller. Our findings suggest that relying only on noise comparisons can lead to inaccurate evaluations of solid-state nanopore sensors, considering the inherent variability in fabrication and testing setups among labs and measurements. We propose that future studies should include reporting baseline current and sensing conditions. Additionally, using SNR as a primary evaluation tool for nanopore sensors could provide a more comprehensive understanding of their performance.

 

ProMagBot introduces scalable electromagnetic control of magnetic beads. The device is a handheld, battery-powered, and field-deployable sample preparation device that can extract viral RNA from plasma samples in under 20 minutes.

 

Abstract While the electrical models of the membrane-based solid-state nanopores have been well established, silicon-based pyramidal nanopores cannot apply these models due to two distinctive features. One is its 35.3° half cone angle, which brings additional resistance to the moving ions inside the nanopore. The other is its rectangular entrance, which makes calculating the access conductance challenging. Here, we proposed and validated an effective transport model (ETM) for silicon-based pyramidal nanopores by introducing effective conductivity. The impact of half cone angle can be described equivalently using a reduced diffusion coefficient (effective diffusion coefficient). Because the decrease of diffusion coefficient results in a smaller conductivity, effective conductivity is used for the calculation of bulk conductance in ETM. In the classical model, intrinsic conductivity is used. We used the top-down fabrication method for generating the pyramidal silicon nanopores to test the proposed model. Compared with the large error (≥25% in most cases) when using the classical model, the error of ETM in predicting conductance is less than 15%. We also found that the ETM is applicable when the ratio of excess ion concentration and bulk ion concentration is smaller than 0.2. At last, it is proved that ETM can estimate the tip size of pyramidal silicon nanopore. We believe the ETM would provide an improved method for evaluating the pyramidal silicon nanopores.