18 — PNS

Pore Number Sequencing

What It Is

PNS is Hardin Labs' proprietary DNA and RNA sequencing technology, inspired by nanopore-based sequencing principles but uniquely enhanced by the integration of Silixon-PDC nanopore arrays, galinstan signal amplification electrodes, and a Rodin-geometry signal encoding protocol. In standard nanopore sequencing, a DNA strand is ratcheted through a biological protein pore under electrophoretic drive while an ionic current is measured; the current is disrupted in a characteristic pattern by each passing nucleotide base, allowing the sequence to be read in real time. PNS replaces the biological protein pore with a Silixon-PDC solid-state nanopore, replaces the ionic electrolyte current with a galinstan direct-current signal, and maps the resulting blockade signatures using a Pore Number encoding scheme based on Rodin vortex mathematics — assigning each of the four DNA bases a unique number code within the base-9 Rodin numeral system to allow lossless compression of raw sequencing data during acquisition.

Nanopore Array Design

The PNS flow cell is a Silixon-PDC membrane, 20 nm thick, spanning a 5 mm × 5 mm window in a Silixon-PCB carrier chip. The membrane contains 1,024 × 1,024 pores at 500 nm pitch — approximately 1 million simultaneous read channels per chip — each pore measuring 2.5 nm in diameter and coated with a galinstan-wetting surface treatment that draws galinstan into the pore neck, creating a liquid-metal ionic bridge. The sensitivity of this galinstan bridge to the passage of charged DNA molecules is approximately 200× greater than the ionic salt solution used in conventional nanopore devices, allowing individual nucleotide discrimination at strand velocities up to 10× faster than existing technology. Read lengths are limited only by DNA fragment length, with ultra-long reads of hundreds of kilobases achievable using high-molecular-weight DNA preparation protocols.

Data Processing

The 1 million simultaneous current traces from the PNS chip are digitized at 1 GHz per channel and streamed to the Silixon-GPU's signal processing pipeline. Basecalling uses a convolutional neural network trained on galinstan-specific current signatures to convert raw squiggle data to nucleotide sequences in real time, outputting a fully annotated genomic sequence within minutes of sample loading. The Rodin Pore Number encoding format compresses the raw squiggle traces to 1/9th of their uncompressed size before transmission to the GNDS storage array, enabling the entire human genome to be sequenced, processed, and stored within a single operating session.