Repurposing Waste Chemicals for Sustainable and Durable Molecular Data Storage.

Molecular data storage offers the intriguing possibility of higher theoretical density and longer lifetimes than today's electronic memory devices. Some demonstrations have used deoxyribonucleic acid (DNA), but bottlenecks in nucleic acid synthesis continue to make DNA data storage orders of magnitude more expensive than electronic storage media. Additionally, despite its potential for long-term storage, DNA faces durability challenges from environmental degradation. In this work, we demonstrate nongenomic molecular data storage using molecular libraries redirected from chemical waste streams. This approach requires no synthetic effort and can be implemented by using molecules that have a minimal associated cost. While the technique is agnostic about the exact molecular content of its inputs, we confirmed that some sources contained poly fluoroalkyl substances (PFAS), which persist for long periods in the natural environment and could offer extremely durable information storage as well as environmental benefits. These demonstrations provide a perspective on some of the valuable possibilities for nongenomic molecular information systems.

Digital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling.

Acid-base reactions are ubiquitous, easy to prepare, and execute without sophisticated equipment. Acids and bases are also inherently complementary and naturally map to a universal representation of "0" and "1." Here, we propose how to leverage acids, bases, and their reactions to encode binary information and perform information processing based upon the majority and negation operations. These operations form a functionally complete set that we use to implement more complex computations such as digital circuits and neural networks. We present the building blocks needed to build complete digital circuits using acids and bases for dual-rail encoding data values as complementary pairs, including a set of primitive logic functions that are widely applicable to molecular computation. We demonstrate how to implement neural network classifiers and some classes of digital circuits with acid-base reactions orchestrated by a robotic fluid handling device. We validate the neural network experimentally on a number of images with different formats, resulting in a perfect match to the in-silico classifier. Additionally, the simulation of our acid-base classifier matches the results of the in-silico classifier with approximately 99% similarity.

Microscale 3-D Capacitance Tomography with a CMOS Sensor Array.

Electrical capacitance tomography (ECT) is a non-optical imaging technique in which a map of the interior permittivity of a volume is estimated by making capacitance measurements at its boundary and solving an inverse problem. While previous ECT demonstrations have often been at centimeter scales, ECT is not limited to macroscopic systems. In this paper, we demonstrate ECT imaging of polymer microspheres and bacterial biofilms using a CMOS microelectrode array, achieving spatial resolution of 10 microns. Additionally, we propose a deep learning architecture and an improved multi-objective training scheme for reconstructing out-of-plane permittivity maps from the sensor measurements. Experimental results show that the proposed approach is able to resolve microscopic 3-D structures, achieving 91.5% prediction accuracy on the microsphere dataset and 82.7% on the biofilm dataset, including an average of 4.6% improvement over baseline computational methods.

Super-Resolution Electrochemical Impedance Imaging With a 512 × 256 CMOS Sensor Array.

Super-resolution imaging is a family of techniques in which multiple lower-resolution images can be merged to produce a single image at higher resolution. While super-resolution is often applied to optical systems, it can also be used with other imaging modalities. Here we demonstrate a 512 × 256 CMOS sensor array for micro-scale super-resolution electrochemical impedance spectroscopy (SR-EIS) imaging. The system is implemented in standard 180 nm CMOS technology with a 10 µm × 10 µm pixel size. The sensor array is designed to measure the mutual capacitance between programmable sets of pixel pairs. Multiple spatially-resolved impedance images can then be computationally combined to generate a super-resolution impedance image. We use finite-element electrostatic simulations to support the proposed measurement approach and discuss straightforward algorithms for super-resolution image reconstruction. We present experimental measurements of sub-cellular permittivity distribution within single green algae cells, showing the sensor's capability to produce microscale impedance images with sub-pixel resolution.

Programmable Electrochemical Stimulation on a Large-Scale CMOS Microelectrode Array.

In this paper we present spatio-temporally controlled electrochemical stimulation of aqueous samples using an integrated CMOS microelectrode array with 131,072 pixels. We demonstrate programmable gold electrodeposition in arbitrary spatial patterns, controllable electrolysis to produce microscale hydrogen bubbles, and spatially targeted electrochemical pH modulation. Dense spatially-addressable electrochemical stimulation is important for a wide range of bioelectronics applications.

Secret messaging with endogenous chemistry.

Data encoded in molecules offers opportunities for secret messaging and extreme information density. Here, we explore how the same chemical and physical dimensions used to encode molecular information can expose molecular messages to detection and manipulation. To address these vulnerabilities, we write data using an object's pre-existing surface chemistry in ways that are indistinguishable from the original substrate. While it is simple to embed chemical information onto common objects (covers) using routine steganographic permutation, chemically embedded covers are found to be resistant to detection by sophisticated analytical tools. Using Turbo codes for efficient digital error correction, we demonstrate recovery of secret keys hidden in the pre-existing chemistry of American one dollar bills. These demonstrations highlight ways to improve security in other molecular domains, and show how the chemical fingerprints of common objects can be harnessed for data storage and communication.

Leveraging autocatalytic reactions for chemical domain image classification.

Autocatalysis is fundamental to many biological processes, and kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use an autocatalytic reaction, the copper-catalyzed azide-alkyne cycloaddition, to perform digital image recognition tasks. Images are encoded in the concentration of a catalyst across an array of liquid samples, and the classification is performed with a sequence of automated fluid transfers. The outputs of the operations are monitored using UV-vis spectroscopy. The growing interest in molecular information storage suggests that methods for computing in chemistry will become increasingly important for querying and manipulating molecular memory.

Principles of Information Storage in Small-Molecule Mixtures.

Molecular data systems have the potential to store information at dramatically higher density than existing electronic media. Some of the first experimental demonstrations of this idea have used DNA, but nature also uses a wide diversity of smaller non-polymeric molecules to preserve, process, and transmit information. In this paper, we present a general framework for quantifying chemical memory, which is not limited to polymers and extends to mixtures of molecules of all types. We show that the theoretical limit for molecular information is two orders of magnitude denser by mass than DNA, although this comes with different practical constraints on total capacity. We experimentally demonstrate kilobyte-scale information storage in mixtures of small synthetic molecules, and we consider some of the new perspectives that will be necessary to harness the information capacity available from the vast non-genomic chemical space.

Multicomponent molecular memory.

Multicomponent reactions enable the synthesis of large molecular libraries from relatively few inputs. This scalability has led to the broad adoption of these reactions by the pharmaceutical industry. Here, we employ the four-component Ugi reaction to demonstrate that multicomponent reactions can provide a basis for large-scale molecular data storage. Using this combinatorial chemistry we encode more than 1.8 million bits of art historical images, including a Cubist drawing by Picasso. Digital data is written using robotically synthesized libraries of Ugi products, and the files are read back using mass spectrometry. We combine sparse mixture mapping with supervised learning to achieve bit error rates as low as 0.11% for single reads, without library purification. In addition to improved scaling of non-biological molecular data storage, these demonstrations offer an information-centric perspective on the high-throughput synthesis and screening of small-molecule libraries.

Encoding information in synthetic metabolomes.

Biomolecular information systems offer exciting potential advantages and opportunities to complement conventional semiconductor technologies. Much attention has been paid to information-encoding polymers, but small molecules also play important roles in biochemical information systems. Downstream from DNA, the metabolome is an information-rich molecular system with diverse chemical dimensions which could be harnessed for information storage and processing. As a proof of principle of small-molecule postgenomic data storage, here we demonstrate a workflow for representing abstract data in synthetic mixtures of metabolites. Our approach leverages robotic liquid handling for writing digital information into chemical mixtures, and mass spectrometry for extracting the data. We present several kilobyte-scale image datasets stored in synthetic metabolomes, which can be decoded with accuracy exceeding 99% using multi-mass logistic regression. Cumulatively, >100,000 bits of digital image data was written into metabolomes. These early demonstrations provide insight into some of the benefits and limitations of small-molecule chemical information systems.

Quasi-reference electrodes in confined electrochemical cells can result in in situ production of metallic nanoparticles.

Nanoscale working electrodes and miniaturized electroanalytical devices are valuable platforms to probe molecular phenomena and perform chemical analyses. However, the inherent close distance of metallic electrodes integrated into a small volume of electrolyte can complicate classical electroanalytical techniques. In this study, we use a scanning nanopipette contact probe as a model miniaturized electrochemical cell to demonstrate measurable side effects of the reaction occurring at a quasi-reference electrode. We provide evidence for in situ generation of nanoparticles in the absence of any electroactive species and we critically analyze the origin, nucleation, dissolution and dynamic behavior of these nanoparticles as they appear at the working electrode. It is crucial to recognize the implications of using quasi-reference electrodes in confined electrochemical cells, in order to accurately interpret the results of nanoscale electrochemical experiments.

CMOS-Integrated Low-Noise Junction Field-Effect Transistors for Bioelectronic Applications.

In this work, we present a CMOS-integrated low-noise junction field-effect transistor (JFET) developed in a standard 0.18 pm CMOS process. These JFETs reduce input-referred flicker noise power by more than a factor of 10 when compared to equally sized n-channel MOS devices by eliminating oxide interfaces in contact with the channel. We show that this improvement in device performance translates into a factor-of-10 reduction in the input-referred noise of integrated CMOS operational amplifiers when JFET devices are used at the input, significant for many applications in bioelectronics.

In Situ Nanopore Fabrication and Single-Molecule Sensing with Microscale Liquid Contacts.

In this article, we introduce a flexible technique for high-throughput solid-state nanopore analysis of single biomolecules. By confining the electrolyte to a micron-scale liquid meniscus at the tip of a glass micropipette, we enable automation and reuse of a single solid-state membrane chip for measurements with hundreds of distinct nanopores per day. In addition to overcoming important experimental bottlenecks, the microscale liquid contact dramatically reduces device capacitance, which is a key limiting factor to the speed and fidelity of solid-state nanopore sensor recordings.

A 155-dB Dynamic Range Current Measurement Front End for Electrochemical Biosensing.

An integrated current measurement system with ultra wide dynamic range is presented and fabricated in a 180-nm CMOS technology. Its dual-mode design provides concurrent voltage and frequency outputs, without requiring an external clock source. An integrator-differentiator core provides a voltage output with a noise floor of 11.6 fA/ [Formula: see text] and a -3 dB cutoff frequency of 1.4 MHz. It is merged with an asynchronous current-to-frequency converter, which generates an output frequency linearly proportional to the input current. Together, the voltage and frequency outputs yield a current measurement range of 155 dB, spanning from 204 fA (100 Hz) or 1.25 pA (10 kHz) to 11.6 µA. The proposed architecture's low noise, wide bandwidth, and wide dynamic range make it ideal for measurements of highly nonlinear electrochemical and electrophysiological systems.

Measurement of DNA Translocation Dynamics in a Solid-State Nanopore at 100 ns Temporal Resolution.

Despite the potential for nanopores to be a platform for high-bandwidth study of single-molecule systems, ionic current measurements through nanopores have been limited in their temporal resolution by noise arising from poorly optimized measurement electronics and large parasitic capacitances in the nanopore membranes. Here, we present a complementary metal-oxide-semiconductor (CMOS) nanopore (CNP) amplifier capable of low noise recordings at an unprecedented 10 MHz bandwidth. When integrated with state-of-the-art solid-state nanopores in silicon nitride membranes, we achieve an SNR of greater than 10 for ssDNA translocations at a measurement bandwidth of 5 MHz, which represents the fastest ion current recordings through nanopores reported to date. We observe transient features in ssDNA translocation events that are as short as 200 ns, which are hidden even at bandwidths as high as 1 MHz. These features offer further insights into the translocation kinetics of molecules entering and exiting the pore. This platform highlights the advantages of high-bandwidth translocation measurements made possible by integrating nanopores and custom-designed electronics.

Hybrid integrated biological-solid-state system powered with adenosine triphosphate.

There is enormous potential in combining the capabilities of the biological and the solid state to create hybrid engineered systems. While there have been recent efforts to harness power from naturally occurring potentials in living systems in plants and animals to power complementary metal-oxide-semiconductor integrated circuits, here we report the first successful effort to isolate the energetics of an electrogenic ion pump in an engineered in vitro environment to power such an artificial system. An integrated circuit is powered by adenosine triphosphate through the action of Na(+)/K(+) adenosine triphosphatases in an integrated in vitro lipid bilayer membrane. The ion pumps (active in the membrane at numbers exceeding 2 × 10(6) mm(-2)) are able to sustain a short-circuit current of 32.6 pA mm(-2) and an open-circuit voltage of 78 mV, providing for a maximum power transfer of 1.27 pW mm(-2) from a single bilayer. Two series-stacked bilayers provide a voltage sufficient to operate an integrated circuit with a conversion efficiency of chemical to electrical energy of 14.9%.

Statistical inference for nanopore sequencing with a biased random walk model.

Nanopore sequencing promises long read-lengths and single-molecule resolution, but the stochastic motion of the DNA molecule inside the pore is, as of this writing, a barrier to high accuracy reads. We develop a method of statistical inference that explicitly accounts for this error, and demonstrate that high accuracy (>99%) sequence inference is feasible even under highly diffusive motion by using a hidden Markov model to jointly analyze multiple stochastic reads. Using this model, we place bounds on achievable inference accuracy under a range of experimental parameters.

Single-molecule bioelectronics.

Experimental techniques that interface single biomolecules directly with microelectronic systems are increasingly being used in a wide range of powerful applications, from fundamental studies of biomolecules to ultra-sensitive assays. In this study, we review several technologies that can perform electronic measurements of single molecules in solution: ion channels, nanopore sensors, carbon nanotube field-effect transistors, electron tunneling gaps, and redox cycling. We discuss the shared features among these techniques that enable them to resolve individual molecules, and discuss their limitations. Recordings from each of these methods all rely on similar electronic instrumentation, and we discuss the relevant circuit implementations and potential for scaling these single-molecule bioelectronic interfaces to high-throughput arrayed sensing platforms.

Integrated circuit-based electrochemical sensor for spatially resolved detection of redox-active metabolites in biofilms.

Despite advances in monitoring spatiotemporal expression patterns of genes and proteins with fluorescent probes, direct detection of metabolites and small molecules remains challenging. A technique for spatially resolved detection of small molecules would benefit the study of redox-active metabolites that are produced by microbial biofilms and can affect their development. Here we present an integrated circuit-based electrochemical sensing platform featuring an array of working electrodes and parallel potentiostat channels. 'Images' over a 3.25 × 0.9 mm(2) area can be captured with a diffusion-limited spatial resolution of 750 µm. We demonstrate that square wave voltammetry can be used to detect, identify and quantify (for concentrations as low as 2.6 µM) four distinct redox-active metabolites called phenazines. We characterize phenazine production in both wild-type and mutant Pseudomonas aeruginosa PA14 colony biofilms, and find correlations with fluorescent reporter imaging of phenazine biosynthetic gene expression.