Advancements in the performance of nanopore sensing and its implications towards the identification of biomarkers.

Abstract

Rapid and selective identification of biomarkers is a coveted need recapitulated by the ongoing pandemic that pierced the very fabric of human health and wellbeing. Moreover, there is a constant interest and demand for portable technologies that can meet the above-mentioned. Nanopores—a nanoscale aperture through an otherwise impermeable membrane—have demonstrated tremendous potential to this extent, which is further facilitated by their ability to detect and characterize a wide range of bio/synthetic molecules and particles (e.g., DNA, proteins, viruses). Fundamentally, they register analyte-specific information through resistive (or conductive) pulses as an analyte transverses the pore in response to an applied electric field. However, the presence of solid-state nanopores (SSNs) in the commercial space is still meager. Features such as stability, lifetime, resilience against clogging, pore noise, and signal magnitude are key for the progression of SSNs beyond a laboratory setup. The membrane and solution chemistry during fabrication was found to play an integral role where pores remained open for hours surpassing most recorded statistics concerning the five before-mentioned features. A wide range of solution chemistries (and breakdown conditions) was investigated to understand the chemistry of fabrication and its impact on the pore performance to push the existing boundaries of the technology. Afterward, to uncover finer molecular-scale details, especially those concerning smaller and fast-moving proteins, higher bandwidths (i.e., 10 MHz) were used. This enables the detection of events that last a few hundred nanoseconds which were previously impossible due to electronic limitations. With such advancements, the enormity of available data would enable the seamless coupling of machine learning for the identification of targets in complex mixtures such as serum. Thus, such developments would enable SSNs to perform well under real-world conditions and complex samples.