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While classical electrochemical impedance spectroscopy (EIS) focuses on measurements from a single working electrode, dense active microelectrode arrays offer opportunities for new modes of sensing. Here we present experimental results with an integrated sensor array for electrochemical imaging. The system uses a 100 x 100 custom CMOS electrode array with 10 micron pixels, which measures impedance at frequencies up to 100 MHz. The sensor chip is uniquely designed to take advantage of the electrostatic coupling between groups of nearby pixels to re-shape the local electric field. Multiple bias voltages and clock phases create new types of signal diversity that will enable enhanced sensing modes for computational imaging and impedance tomography. 

This paper presents a 100 x 100 super-resolution integrated sensor array for microscale electrochemical impedance spectroscopy (EIS) imaging. The system is implemented in 180 nm CMOS with 10 x 10 micron pixels. Rather than treating each electrode independently, the sensor is designed to measure the mutual capacitance between programmable sets of pixels. Multiple spatially-resolved measurements can then be computationally combined to produce super-resolution impedance images. Experimental measurements of sub-cellular permittivity distributions within single algae cells demonstrate the potential of this new approach. 

Abstract 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. 

Microorganisms account for most of the biodiversity on earth. Yet while there are increasingly powerful tools for studying microbial genetic diversity, there are fewer tools for studying microorganisms in their natural environments. In this paper, we present recent advances in CMOS electrochemical imaging arrays for detecting and classifying microorganisms. These microscale sensing platforms can provide non-optical measurements of cell geometries, behaviors, and metabolic markers. We review integrated electronic sensors appropriate for monitoring microbial growth, and present measurements of single-celled algae using a CMOS sensor array with thousands of active pixels. Integrated electrochemical imaging can contribute to improved medical diagnostics and environmental monitoring, as well as discoveries of new microbial populations. 

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. 

Abstract 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.