Development of Bioelectronic Devices Using Bionanohybrid Materials for Biocomputation System

Abstract

Bioelectronic devices have been researched widely because of their potential applications, such as information storage devices, biosensors, diagnosis systems, organism-mimicking processing system cell chips, and neural-mimicking systems. Introducing biomolecules including proteins, DNA, and RNA on silicon-based substrates has shown the powerful potential for granting various functional properties to chips, including specific functional electronic properties. Until now, to extend and improve their properties and performance, organic and inorganic materials such as graphene and gold nanoparticles have been combined with biomolecules. In particular, bionanohybrid materials that are composed of biomolecules and other materials have been researched because they can perform core roles of information storage and signal processing in bioelectronic devices using the unique properties derived from biomolecules. This review discusses bioelectronic devices related to computation systems such as biomemory, biologic gates, and bioprocessors based on bionanohybrid materials with a selective overview of recent research. This review contains a new direction for the development of bioelectronic devices to develop biocomputation systems using biomolecules in the future.

Keywords: bioelectronic devices; biologic gate; biomemory; bionanohybrid material; bioprocessor; nanoparticles; nucleic acid; protein.

Figure 1

Bioelectronic devices based on bionanohybrid materials to develop biomemory, biologic gates, and bioprocessors for biocomputation systems.

Figure 2

Multilevel biomemory device. (A) Schematic image demonstrating a multilevel biomemory device using metal ions states to control two different kinds of metalloprotein. (B) Cyclic voltammogram of a multilevel biomemory device composed of recombinant azurin and cytochrome c that shows two apparently distinguished reduction potential peaks and two oxidation potential peaks. (C) Memory performance of a multilevel biomemory device including writing, reading, and erasing steps by applying the potential values of reduction and oxidation potential peak values and the OCP values of metalloproteins. (Reproduced with permission from [43], published by John Wiley and Sons, 2010).

Figure 3

Electrochemical signal enhanced biomemory device. (A) Schematic image of the biomemory device composed of Azu and gold nanoparticles (GNP). (B) Redox potential peak values for optimizing the GNP diameter. (C) Cyclic voltammogram of Azu–GNP and Azu. (D) memory performance of Azu–GNP and Azu. (Reproduced with permission from [17], published by John Wiley and Sons, 2011).

Figure 4

Resistive biomemory device. (A) Schematic image of a resistive biomemory device composed of pRNA-3WJ and quantum dot (QD) on a gold substrate, (B) I–V curves of bare Au, pRNA-3WJ, QD and pRNA-3WJ, and QD. (C) Resistive switching function and stability test for a resistive biomemory device composed of MoS₂ and DNA on a gold substrate with apparently distinguished resistance states and long-term stability for 10 days. (Reproduced with permission from [50], published by the American Chemical Society, 2015, and reproduced with permission from [52], published by Elsevier, 2019).

Figure 5

Biologic gates. (A) Schematic image of a DNA-based biologic gate based on metal ions inserted inside mismatched DNA pairs and differential pulse voltammetry (DPV) results of this device by controlling output signals through Ag⁺ and Hg²⁺ ions inserted inside mismatched DNA pairs. (B) Schematic image of a protein/DNA-based biologic gate through the signal transduction of a protein-based biologic gate to a DNA-based biologic gate for the final outputted fluorescence signal (Reproduced with permission from [64], published by John Wiley and Sons, 2013 and reproduced with permission from [65], published by John Wiley and Sons, 2016).

Figure 6

Analog decision mimicking bioelectronic device. (A) Schematic image and theory of this bioelectronic device through the analogously processed output signals by two different external factors inputted (negative input and positive input) by electrochemical investigation. (B) Bionanohybrid material used for this device composed of metalloprotein used as signal generator, organic chemical linkers as signal controller, and inorganic materials used for signal modulation. (C) The plotted results of analog decision-making based on the analysis of electrochemical signal by defined external factors showed the decision variation of 12 persons based on the defined threshold values. (Reproduced with permission from [59], the figures follow the terms of use under a Creative Commons Attribution 4.0 International License.).

Figure 7

Bioprocessors. (A) Schematic image and electrophoresis results of DNA-based bioprocessor composed of six stages, including the first stage as problem encoder, the second stage as DNA solution bay for converted DNA preparation, the third as mixing controller for mixing and ligase of appropriate DNA sequences to make the template of DNA duplexes, the fourth as solution purifier for isolation of optimal DNA template from impurities such as the incompletely hybridized oligonucleotides or enzymes, the fifth as PCR amplifier for amplification of optimal DNA template which is the optimal route, and the sixth as gel electrophoresis to acquire the final electrophoresis data for solving optimal-route-planning problems, (B) Schematic image of bioprocessor based on bionanohybrid materials to demonstrate the specific processing functions including the electrochemical signal reinforcement, regulation, and amplification. (Reproduced with permission from [82], published by American Chemical Society, 2015, and reproduced with permission from [31], published by John Wiley and Sons, 2013).