Nanopores exhibit a set of interesting transport properties that stem from interactions of the passing ions and molecules with the pore walls. Nanopores are used, for example, as ionic diodes and transistors, biosensors, and osmotic power generators. Using nanopores is however disadvantaged by their high resistance, small switching currents in nA range, low power generated, and signals that can be difficult to distinguish from the background. Here, we present a mesopore with ionic conductance reaching μS that rectifies ion current in salt concentrations as high as 1 M. The mesopore is conically shaped, and its region close to the narrow opening is filled with high molecular weight poly-l-lysine. To elucidate the underlying mechanism of ion current rectification (ICR), a continuum model based on a set of Poisson–Nernst–Planck and Stokes–Brinkman equations was adopted. The results revealed that embedding the polyelectrolyte in a conical pore leads to rectification of the effect of concentration polarization (CP) that is induced by the polyelectrolyte, and observed as voltage polarity-dependent modulations of ionic concentrations in the pore, and consequently ICR. Our work reveals the link between ICR and CP, significantly extending the knowledge of how charged polyelectrolytes modulate ion transport on nano- and mesoscales. The osmotic power application is also demonstrated with the developed polyelectrolyte-filled mesopores, which enable a power of up to ∼120 pW from one pore, which is much higher than the reported values using single nanoscale pores.

A nanopore decorated with crown ether and DNA is selective to potassium ions over sodium ions at concentrations up to 1 M.

Mechanical properties of cells are important features that are tightly regulated and are dictated by various pathologies. Deformability cytometry allows for the characterization of the mechanical properties at a rate of hundreds of cells per second, opening the way to differentiating cells via mechanotyping. A remaining challenge for detecting and classifying rare sub-populations is the creation of a combined experimental and analysis protocol that approaches the maximum potential classification accuracy for single cells. In order to find this maximum accuracy, we designed a microfluidic channel that subjects each cell to repeated deformations and relaxations and provides a comprehensive set of mechanotyping parameters. We track the shape dynamics of individual cells with high time resolution and apply sequence-based deep learning models for feature extraction. In order to create a dataset based solely on differing mechanical properties, a model system was created with treated and untreated HL60 cells. Treated cells were exposed to chemical agents that perturb either the actin or microtubule networks. Multiple recurrent and convolutional neural network architectures were trained using time sequences of cell shapes and were found to achieve high classification accuracy based on cytoskeletal properties alone. The best model classified two of the sub-populations of HL60 cells with an accuracy over 90%, significantly higher than the 75% we achieved with traditional methods. This increase in accuracy corresponds to a fivefold increase in potential enrichment of a sample for a target population. This work establishes the application of sequence-based deep learning models to dynamic deformability cytometry.

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