POLYDIGIT2

Projekt finansowany przez Narodowe Centrum Nauki w ramach konkursu „Sonata Bis – 11”.

Polimery o programowalnych poprzez sekwencję właściwościach jako materiały nowej generacji do archiwizacji danych.

Nr projektu: UMO-2021/42/E/ST4/00010
Wartość projektu: 3.101.140,00 PLN
Wartość dofinansowania: 3.101.140,00 PLN
Okres realizacji projektu: 03.10.2022 – 02.10.2027

Kierownik projektu: dr inż. Róża Szweda

Since ancient times, people have had to store information in order to pass gained knowledge to the next generations. The methodology for storing the information significantly evolved over centuries. However, even though the existing technology is advanced, it cannot keep up with growing numbers of bits. Nowadays, the amount of generated data is greater than the storage capacities of conventional media such as hard discs or flash memory devices and the tendency is rapidly growing. The drawback of commonly used hard drives is their limited stability and huge energy consumption. Nature is using nucleic acids as the main information carriers. It was demonstrated that synthetic DNA can be used to carry a binary code that can store text or computer processor instructions using a binary system, i.e., 0 and 1 notations represented by two chosen nucleotides. Sequence-defined polymers (SDP) offer a stable, resource- and energy-efficient and sustainable data storage solution, an advantageous alternative to DNA. Properties of synthetic polymers can be finely tuned and adjusted to demands. Polymer characteristics can be modulated by the choice of building blocks from a broad library of synthetic monomers. This way we can increase stability and extend lifetime or simplify the readout of information. Moreover, the extended alphabet of synthetic monomers enables to reach for higher information density. In turn, the data storage capacity using synthetic polymers can be amplified by a spatial organization of digital polymers using non-covalent synthesis, e.g., Layer-by-Layer deposition of polyelectrolytes. These types of methods eliminate limitations associated with polymer synthesis. The macromolecules can be sequentially organized in the small area, providing high data storage capacity materials. Comparing to common hard drives, write/read data speed for macromolecules is much slower. Polymers will not supplant the use of hard disks, but they can minimize the problem of data storage and be used as long-term archiving media. However, the key demand is the efficiency and speed of reading the information. The project aims to develop sequence-defined polymers, designed for reading information encoded in the monomer sequence based on fluorescence properties. The monomers bearing fluorescent dyes will be synthesized and used for writing information in macromolecule chains using iterative synthesis. It will be crucial to select appropriate monomer structures that will yield polymers characterized by sequence-dependent fluorescent properties. The obtained polymer characteristics will be used for the training of neural networks. With the help of artificial intelligence tools, it will be possible to evaluate fluorescence as a reading method for revealing information encoded in macromolecules. In this project, it is hypothesized that fluorescence spectroscopy represents an advantageous technique for the readout of digital polymers. Benefits of using fluorescence as a polymer sequencing technique include that it provides fast, cost-efficient, non-destructive measurements, there is no need for sample recovery, it is very sensitive, miniaturized & suitable for high-throughput parallel analysis. Despite the great potential of fluorescence as the sequencing method, the main challenge is to develop polymers that generate a sequence dependent fluorescent signal, which will be the main scientific challenge of this project. Therefore, the monomers are designed to ensure sensitivity to the distance between fluorophore units that reflects in energy transfer and sensitivity to the environment. The expected results of this project will be coming from new valuable fundamental knowledge on the sequenceproperty relationship of fluorescent polymers. The project will fill the knowledge gap on the sequence-properties relationship of multifluorophore functionalized macromolecules. Studies of discrete systems will enable systematic comparison of different sequences that will enable a deeper understanding of occurring phenomena. Moreover, the AI tools will enable to reach for findings inaccessible using conventional methods. Hence, the gained knowledge will advance polymer chemistry (synthesis of new structures) and functional materials discipline (fabrication of new materials for data storage) and will enable evaluation of fluorescence as a new polymer sequencing technique.