Publications
2021
- IEEE SSCLKangping Hu, Christopher E. Arcadia, and Jacob K. RosensteinIEEE Solid-State Circuits Letters, 2021
This paper presents a large-scale fully integrated multimodal sensor array for biological imaging. The 512×256 sensor array can perform spatially-resolved electrochemical impedance spectroscopy (EIS) with switching frequencies up to 100 MHz, acquire multi-color optical images, and sense pH using titanium nitride (TiN) ion sensitive field effect transistors (ISFETs). The chip features code-division multiplexed (CDM) readout of groups of pixels simultaneously, enabling extended integration times at a given frame rate. The system is implemented in 180 nm CMOS with 9.5 μm × 11.5 μm pixels. Its overall fill factor is 57%, including peripheral control and readout circuits, yielding a wide-field spatially-resolved multimodal biosensing platform for advanced cell culture applications.
- IEEE ISCASChristopher E. Arcadia, Kangping Hu, Slava Epstein, and 3 more authorsIEEE International Symposium on Circuits and Systems, 2021
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.
- Proc. R. Soc. AAmanda Dombroski, Kady Oakley, Christopher Arcadia, and 7 more authorsProceedings of the Royal Society A, 2021
Chemical mixtures can be leveraged to store large amounts of data in a highly compact form and have the potential for massive scalability owing to the use of large-scale molecular libraries. With the parallelism that comes from having many species available, chemical-based memory can also provide the physical substrate for computation with increased throughput. Here, we represent non-binary matrices in chemical solutions and perform multiple matrix multiplications and additions, in parallel, using chemical reactions. As a case study, we demonstrate image processing, in which small greyscale images are encoded in chemical mixtures and kernel-based convolutions are performed using phenol acetylation reactions. In these experiments, we use the measured concentrations of reaction products (phenyl acetates) to reconstruct the output image. In addition, we establish the chemical criteria required to realize chemical image processing and validate reaction-based multiplication. Most importantly, this work shows that fundamental arithmetic operations can be reliably carried out with chemical reactions. Our approach could serve as a basis for developing more advanced chemical computing architectures.
- Chem. Sci.Christopher E. Arcadia, Amanda Dombroski, Kady Oakley, and 7 more authorsChemical Science, 2021
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.
- Sci. Rep.Eamonn Kennedy, Joseph Geiser, Christopher E. Arcadia, and 4 more authorsScientific Reports, 2021
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.
2020
- Nat. Commun.Christopher E. Arcadia, Eamonn Kennedy, Joseph Geiser, and 12 more authorsNature Communications, 2020
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. Small non-polymeric molecules have tremendous structural diversity that can be used to represent information. Here the authors encode data in synthesized libraries of Ugi products.
- IEEE Trans. Nanobio.Jacob K. Rosenstein, Christopher Rose, Sherief Reda, and 11 more authorsIEEE Transactions on NanoBioscience, 2020
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.
- Brown U.Christopher E. ArcadiaBrown University, 2020Arcadia, Christopher E., "Advancing High-Throughput Molecular Information Systems" (2020). Electrical Sciences and Computer Engineering Theses and Dissertations. Brown Digital Repository. Brown University Library. https://repository.library.brown.edu/studio/item/bdr:1129443/
While information systems are often associated with microelectronic devices, every physical process can be interpreted as an exchange of information. With the slowing of Moore’s law, engineers and scientists are scrambling to come up with creative solutions that further push the boundaries of data storage and computing devices. To scale beyond the limits of silicon, perhaps we can exploit molecular systems, which are ubiquitous and yet comparatively unexplored for information processing. Genomic technologies have been advancing at an exponential pace, which has inspired visions of molecular data systems that complement traditional ones with new opportunities to store and process information at dramatically higher densities, using less energy, and potentially lasting for millennia. In this thesis, we demonstrate several distinct uses of chemical systems to acquire, represent, and operate on information. An effective molecular data platform will demand high-performance chemical detection, so we first present a novel technique for nanopore sensing. The instrument enables hundreds of solid-state nanopores to be fabricated and experimentally measured per day. Additionally, thanks to micron-scale fluid contacts, we achieve unprecedentedly low parasitic capacitances, which can support high speed electrochemical measurements of single molecules. Second, we propose a general framework for digital information storage in molecular mixtures. By utilizing multicomponent chemical reactions to rapidly produce thousands of unique small molecules, we demonstrate that this format is capable of holding significant amounts of data. By combining high resolution mass spectrometry with contemporary machine learning algorithms, we show that the original data can be recovered with excellent fidelity. Finally, we extend the concept of small-molecule memory with a method of classifying chemically-encoded data, by mapping a single-layer neural network onto a series of of liquid transfer and pooling operations. Because multiple compounds can co-exist in a single fluid volume, any operation performed on a mixture will apply to all its chemical constituents. With a sufficiently large chemical library, this arrangement can provide a highly efficient means of parallel data processing. Each of these demonstrations were made possible with a combination of sensitive instrumentation, precise liquid handling, robotic automation, and flexible chemistry. These interdisciplinary tools for high throughput experimentation will be critical for the further advancement of molecular information systems.
2019
- PLOS ONEEamonn Kennedy, Christopher E. Arcadia, Joseph Geiser, and 4 more authorsPLOS ONE, 2019
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.
2018
- IEEE ICRCChristopher E. Arcadia, Hokchhay Tann, Amanda Dombroski, and 7 more authorsIEEE International Conference on Rebooting Computing, 2018
In this work, we introduce a new type of linear classifier that is implemented in a chemical form. We propose a novel encoding technique which simultaneously represents multiple datasets in an array of microliter-scale chemical mixtures. Parallel computations on these datasets are performed as robotic liquid handling sequences, whose outputs are analyzed by highperformance liquid chromatography. As a proof of concept, we chemically encode several MNIST images of handwritten digits and demonstrate successful chemical-domain classification of the digits using volumetric perceptrons. We additionally quantify the performance of our method with a larger dataset of binary vectors and compare the experimental measurements against predicted results. Paired with appropriate chemical analysis tools, our approach can work on increasingly parallel datasets. We anticipate that related approaches will be scalable to multilayer neural networks and other more complex algorithms. Much like recent demonstrations of archival data storage in DNA, this work blurs the line between chemical and electrical information systems, and offers early insight into the computational efficiency and massive parallelism which may come with computing in chemical domains.
- Elec. ActaRukshan T. Perera, Christopher E. Arcadia, and Jacob K. RosensteinElectrochimica Acta, 2018
We present measurements of the nucleation, growth and evolution of single hydrogen nanobubbles containing zeptomoles of molecular hydrogen and likely originating from a single catalytic site. These bubbles are measured using nanoscale platinum and gold meniscus-contacted electrodes, whose picoliter volumes facilitate rapid supersaturation and evolution of bubbles. Observed periodic current transients were utilized to understand the nucleation, growth, coalescence, and evolution of single nanobubbles at different overpotentials and pHs. This provides a novel platform to understand factors governing bubble nucleation and evolution. These measurements offer exciting possibilities to connect experiments with computational models in important energy applications.
- BPSChristopher E. Arcadia, Rukshan T. Perera, and Jacob K. RosensteinBiophysical Journal, 2018
Solid-state nanopores have shown high promise as electrochemical sensors of single biomolecules. However, experiments with these devices can be challenging due to the limited number of pores that can be fabricated and tested per day. Recently, we introduced a new platform for automated in situ fabrication and testing of solid-state nanopores. The system utilizes dielectric breakdown to form a nanopore in a thin insulating membrane which is contacted by a micron-scale liquid meniscus. The confined liquid contact area results in a nanopore interface with less than 0.2pF capacitance, and allows for arrays of more than 100 nanopores to be sequentially fabricated and measured on a single membrane in one day. Taking advantage of the system’s high throughput, we have performed a series of systematic studies to explore the effects of different chemical conditions and electrical protocols on pore formation and performance. Within these datasets, we assess the sensing viability of each individual pore by monitoring the baseline current stability and I-V characteristics. Understanding the factors which lead to consistent and high-quality nanopores is of utmost importance, as the dielectric breakdown method has the potential to usher in the broad adoption of low-cost solid-state nanopore sensing technology.
- BPSYinghua Qiu, Christopher Arcadia, Mohammad Amin Alibakhshi, and 2 more authorsBiophysical Journal, 2018
Hafnium dioxide (HfO2) nanopores can serve as a perfect candidate fornanopore-based-DNA sequencing, due to their superior resilience andchemically resistant properties, as compared with silicon nitride of similargeometry. Another attractive feature of HfO2is the slower speed by whichDNA molecules pass through the pore, due to strong interactions betweenthe pore surface and nucleic acids. While traditional HfO2nanoporeshave been fabricated using a transmission electron microscope, (TEM), is-sues with TEM-based fabrication, including long fabrication time ande-beam induced crystallization, often compromise the pore stability. Inthis work, we explore the use of dielectric breakdown as a means to producesmall nanopores in freestanding HfO2membranes that are sub-5-nm thick.We have produced amorphous and crystallized HfO2membranes usingatomic layer deposition and high-temperature annealing following deposi-tion, respectively. We further investigate the ability of such nanopores todetect DNA. Successful implementation of this approach may lead to asolid-state platform for DNA sequencing and other high-resolution single-molecule detection applications.
- ACSRukshan Perera, Christopher Arcadia, and Jacob RosensteinACS National Meeting, 2018
2017
- ACS NanoChristopher E. Arcadia, Carlos C. Reyes, and Jacob K. RosensteinACS Nano, 2017
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.
- BPSChristopher E. Arcadia, Rukshan T. Perera, and Jacob K. RosensteinBiophysical Journal, 2017
While biological nanopore platforms have recently made tremendous strides in DNA sequencing, solid-state nanopore sensors have been primarily limited to laboratory demonstrations. In contrast with earlier fabrication methods, the dielectric breakdown method of creating solid-state nanopores in situ has been shown to be comparatively fast and low cost, while requiring only simple electrochemical tools. Here we introduce a new system which localizes the breakdown-induced pore formation process to the tip of a scanning micropipette. This platform enables automated sequential fabrication and testing of large arrays of solid-state nanopores with micron-scale resolution. We anticipate that our new platform will accelerate and overcome many of the experimental bottlenecks associated with solid-state nanopore sensing experiments.
2015
- BPSChristopher Arcadia and Jacob K. RosensteinBiophysical Journal, 2015
Solid-state nanopores are attractive single-molecule biosensors but commonly suffer from low fabrication yield, poor repeatability, and weak signal-to-noise ratios as compared to biological protein-derived pores. Recent demonstrations of nanopore fabrication by dielectric breakdown in high electric fields offer a promising alternative to established methods which rely on electron microscopes or ion beams. Breakdown-derived nanopores can be fabricated on-demand in standard electrolytes, without need for vacuum systems or harsh chemicals. However, the stochastic and nonlinear nature of electrical breakdown introduces new challenges for achieving consistent sensor quality. We will present progress towards more reliable in situ formation of small-diameter solid-state nanopores, using self-limiting protocols that improve yield and avoid the runaway growth of nanopores in high electric fields.