Nanotechnology has been around for several decades and been adopted by many

researchers in biological science and medicine. It has transformed and changed the way many

researchers perform experiments. This includes ability to visualize molecules, protein at smaller

scale with high precision and cost-effective. In addition, nanofabrication,

microelectromechanical systems (MEMS) and surface analysis techniques derived from

nanotechnology enable innovative studies and allow researcher to design novel experiment that

have not been done before. In this thesis, we focus on the application of nanotechnological

techniques and instruments on biological research including gas sensing, biosensing and

developmental study of pluripotent stem cells towards cardiomyocytes.

The invention of the internet began the age of information as well as exponentially increased the number of complex systems in our world. As the age of information comes to an end, so does the persevering trend known as Moore's Law. This means that the number of circuit elements on an integrated chip will no longer double every two years, nor will the processing speed of computers. Personal computers utilize the Von Neumann architecture which separates storage from processing. This separation causes information transfer lags as a computer processes information much faster than it can be fetched from information storage. Thus, to circumvent both the limitations on elemental packing, and areal density a movement into neuromorphic hardware has occurred. Neuromorphic chips seek to emulate brain-like processing of information through low-power, highly parallel, densely interconnected, and closely packed individual elements which have a non-intuitive entangled relationship. This work explored the potential of atomic switch networks (ASNs) for reservoir, natural, and unconventional computing, provides evidence for ASNs as complex adaptive systems operating in and around the edge of chaos, presents a new material for use in ASNs, and evaluates spoken digit recognition using reservoir computing. A second project herein explores the maturation of human pluripotent stem cell-derived cardiomyocytes for use in studying heart disease, which is the leading cause of death in the world. Maturation of these cells is significant to the field. Via a chemically defined differentiation regimen with a monolayer cell culture technique on top of a multi-electrode array for real-time measurements of electrophysiological properties, in vivo development was reproduced. Both systems described above required data science analysis of time-series multi-electrode array information.

Human embryonic stem cell derived cardiomyocytes (hESC-CMs) can be potential used to in a stem cell-based regenerative therapy to cure heart related disease. The maturity of HESC-CMs still remains fetal-type even though many differentiation protocols have been discovered. In the first part of the thesis, I will focus on electrical physiological characterization of the maturation process of a confluent layer of hESC-CMs. With the high throughput of monitoring microelectrode array (MEA) system, we observed the development of hESC-CMs over weeks and analyze the electrophysiological features during maturation in great detail. In the second part of the thesis, we further investigate the external factors that can affect maturation process of stem cell derived cardiomyocytes in vitro such as electrical stimulation. In the third part of the thesis, the mechanical properties of cancer cells are explored. We discussed the role of mechanical factor in cancer cells. This could add a new dimensional approach in characterize and enhance the maturation of hESC-CMs in future research. In the fourth part of the thesis, we present nanoscale characterization of action and actin binding proteins using Atomic Force Mircoscope (AFM).

The past decade has seen a sharp rise in the development and manufacture of different hardware frameworks to meet the ever-rising computational demands of the machine learning software community. Conventional computing architectures require massive server farms and consume large quantities of energy to perform these tasks. Consequently, this necessitates that end users must connect wirelessly to powerful servers capable of performing complex machine learning tasks. The aforementioned shortcomings have sparked a pursuit for the development of energy efficient hardware capable of successfully performing complex computational tasks offline.Self-organized nanowire arrays of memristive materials, known as atomic switch networks, are the collection of billions of individual memristive elements randomly intertwined as an interconnected network of electrically active junctions. The resulting morphology of the network has a number of attractive neuromorphic properties and emergent phenomena which yield an intrinsic capacity to perform complex computational tasks on a physical substrate. Under an external stimulus these networks exhibit a dynamic, non-equilibrium modulation of conductance across the entire network. The resultant non-linear dynamics are capable of being utilized as both logic and memory components operating in parallel through a technique called in-materio computing. This form of computing enables a physical substrate to be utilized as a dynamic reservoir capable of transforming a simple external stimulus into higher dimensional non-linear outputs. The output layer is then mapped onto a desired computational task through a technique called reservoir computing (RC). Silver selenide (Ag2Se) and silver iodide-based (AgI) nanowire networks were characterized and implicated as efficient memristive materials for RC applications. Both materials were successfully employed within an RC framework for waveform regression, spoken digit recognition and handwritten digit classification tasks. Conventional techniques for nanoscale manufacturing have also begun to hit their limit of resolution, garnering interest in developing new techniques capable of manufacturing materials with molecular and/or atomic precision. Atomically precise manufacturing (APM) aims to implement scanning probe microscopy techniques in conjunction with tailor made molecular tools as a powerful system capable of realizing atomic scale 3D printing. The preliminary state of APM is explored using molecular tools for the abstraction/donation of individual atoms.

Atomic force microscopy (AFM) is a class of high-resolution scanning probe microscopy (SPM) for non-conducting materials. Of these non-conducting materials, biomolecules such as cells, organelles, proteins, and nucleic acids are of interest as we seek to describe laws of nature and life. However, it is not trivial to resolve structures or mechanics of biomolecules due to their small sizes and soft constructions. AFM enables one to obtain high-resolution images of biological structures in their native state both in air and in fluids as AFM is considered as a non-destructive technique. In addition to these advantages, AFM is providing the mechanical information of the sample surface. For these reasons, since the development of AFM, an expansion and re-assessment has led to significant discoveries and scientific enlightenment in biology. In this dissertation, I focus on the application of AFM to biological systems, which will add new scores in the field of both microscopy as well as a higher understanding of specific biological systems. I have explored the mechanical properties of mammalian cells and bacterial cells. I have examined mammalian cell stiffness in relation to cancer metastasis and the changes in extracellular biofilm adhesions from bacterial cells. I have resolved the high-resolution structure of exosomes which are 50-120 nm sized vesicles secreted from mammalian cells. With an accurate adjustment of the force applied to the sample, I have been able to observe and verify nanofilaments attached to exosomes. I also have discovered that the surface and the size distributions of exosomes are different depending on their purification methods. Lastly, I have obtained high-resolution images of an actin binding protein, INF2, which showed nanostructured self-assemblies with or without f-actin.

The emergent dynamical behaviors of biological neuronal networks and other natural, complex systems point towards new computing paradigms which can overcome limitations of digital computers. This work catalogues the development and characterization of an electronic circuit purpose built to exhibit emergent behaviors intended for use in neuromorphic computation. These circuits, atomic switch networks (ASNs), are fabricated through a self-assembly process that yields a highly interconnected network of silver nanowires with embedded inorganic synapses known as atomic switches. When stimulated with external bias voltage, ASNs are shown to possess the synaptic and memory properties of individual atomic switches, as well as network-specific behavior consisting of distributed, system wide switching events. These emergent behaviors exhibit striking similarity to those observed in many natural systems, including biological neural networks. Experiment and numerical simulations have provided proof of principle that ASNs are complex systems whose emergent behaviors may be used in implementations of neuromorphic computing paradigms such as reservoir computing. Furthermore, they demonstrate the utility of ASNs as a uniquely scalable physical platform useful for exploring complexity, neuroscience, and engineering.

Presented in this dissertation are designs and characterizations of nanodevices with applications in medical science, information technology, and material design. A broad perspective is taken to facilitate unconventional problem solving and creative thinking in pursuit of producing disruptive technology. At its core, the devices presented here rely on quantum mechanical effects and nanoscale dynamics including the piezoelectric effect, ballistic transport, redox state transitions, and quantum confinement, to name a few. The focus is less on chemical synthesis and more on physical models to manipulate material properties for practical use. In addition, high dimensional materials and complex systems are keenly investigated due to their rich dynamics and indeterminacy. Specifically, Ag2S atomic switches and conductive polymers are used in information technology while facile medical devices are developed utilizing nanomaterials for their sensitive but well characterized dynamics. Here, nanotechnology is at forefront of research where the transition from the nanoscale to the mesoscale induces the creative solutions.

Recent advances towards compute-in-memory technology using topological atomic switches allowed for the predictive modeling of traffic flow and fault predicting. The presence of intrinsic nonlinear dynamics in volatile atomic switches enabled for the construction of nonlinear circuits as envisioned for volatile thermodynamic computing. A hardware implemented cognitive computing device using an atomic switch network (ASN) as the processing and memory element is capable of accomplishing tasks such as chaotic time series prediction using < 1 milliWatts of energy per prediction. Tunable volatility in the active layer within a ~10 nm junction switch is paramount and a technique to adaptively control the dynamics through iterative voltammetric control loops is presented.

In complex systems, control and understanding become intertwined. Following Ilya Prigogine, we define complex systems as having control parameters which mediate transitions between distinct modes of dynamical behavior. From this perspective, determining the nature of control parameters and demonstrating the associated dynamical phase transitions are practically equivalent and fundamental to engaging with complexity.

In the first part of this work, a control parameter is determined for a non-equilibrium electrochemical system by studying a transition in the morphology of structures produced by an electroless deposition reaction. Specifically, changing the size of copper posts used as the substrate for growing metallic silver structures by the reduction of Ag+ from solution under diffusion-limited reaction conditions causes a dynamical phase transition in the crystal growth process. For Cu posts with edge lengths on the order of one micron, local forces promoting anisotropic growth predominate, and the reaction produces interconnected networks of Ag nanowires. As the post size is increased above 10 microns, the local interfacial growth reaction dynamics couple with the macroscopic diffusion field, leading to spatially propagating instabilities in the electrochemical potential which induce periodic branching during crystal growth, producing dendritic deposits. This result is interesting both as an example of control and understanding in a complex system, and as a useful combination of top-down lithography with bottom-up electrochemical self-assembly.

The second part of this work focuses on the technological development of devices fabricated using this non-equilibrium electrochemical process, towards a goal of integrating a complex network as a dynamic functional component in a neuromorphic computing device. Self-assembled networks of silver nanowires were reacted with sulfur to produce interfacial "atomic switches": silver-silver sulfide junctions, which exhibit complex dynamics (e.g. both short- and long-term changes in conductivity) in response to applied voltage signals. Characterization of these atomic switch networks (ASNs) brought out interesting parallels to biological neural networks, including power-law scaling in the statistics of electrical signal propagation and dynamic self-organization of differentiated subnetworks. A reservoir computing (RC) strategy was employed to utilize measurements of electrical signals dynamically generated in ASNs to perform time-series memory and manipulation tasks including a parity test and arbitrary waveform generation. These results represent the useful integration of a complex network into a dynamic physical RC device.

Human embryonic and induced pluripotent stem cell-derived cardiomyocytes (hESC-CM and hiPSC-CM, respectively) have held considerable scientific interest for their potential applications in the fields of drug screening, disease modeling, and tissue engineering. One of the most significant roadblocks currently hindering the use of hESC-CMs and hiPSC-CMs concerns their inability to achieve cellular phenotypic maturity. This roadblock is referred to as the “maturation block” problem: cardiomyocytes derived from stem cells are known to experience limitations in phenotypic expression, in which the cells demonstrate characteristics analogous to late fetal stage cells, rather than adult cells. If hESC-CM and hiPSC-CM cultures are to achieve their full potential in pharmacology and regenerative medicine applications, the maturation block problem must be resolved.

This dissertation sought to improve upon the current understanding of the mechanisms involved in stem cell-derived cardiomyocyte maturation. Here, analysis of microelectrode array (MEA) recordings of hESC-CM and hiPSC-CM cultures revealed that the pacemaker region often moves (translocates) across the MEA. The variable length of the quiescent period between translocation events was found to obey a power law probability distribution. Such distributions are a characteristic of critical systems, or systems that demonstrate complex spatiotemporal dynamics and emergent properties. The power law exponent obtained for pacemaker translocation quiescent periods (α = -1.58) closely mirrors the power law exponent observed in several critical systems (α = -1.5), indicating that critical dynamics may play a crucial role in the development of a stable pacemaker region in the cardiomyocyte culture.

The computational tools developed for cardiomyocyte power law analysis were expanded to investigate a variety of cardiomyocyte properties, including local activation time and conduction velocity, as well as spatial relationships between the pacemaker region and cardiomyocyte electrical properties. This led to the development of Cardio PyMEA, a free and open source, graphical user interface-based program that was written in Python for the analysis of MEA cardiomyocyte data. Cardio PyMEA was made available on Github for any interested individual to use for MEA-based cardiomyocyte analysis and could serve as an evolving platform for such analyses in the future.