Interference reflection microscopy (IRM) is a powerful, label-free technique to visualize the surface structure of biospecimens. However, stray light outside a focal plane obscures the surface fine structures beyond the diffraction limit (dxy ≈ 200 nm). Here, we developed an advanced interferometry approach to visualize the surface fine structure of complex biospecimens, ranging from protein assemblies to single cells. Compared to 2-D, our unique 3-D structure illumination introduced to IRM enabled successful visualization of fine structures and the dynamics of protein crystal growth under lateral (dx-y ≈ 110 nm) and axial (dx-z ≤ 5 nm) resolutions and dynamical adhesion of microtubule fiber networks with lateral resolution (dx-y ≈ 120 nm), 10 times greater than unstructured IRM (dx-y ≈ 1000 nm). Simultaneous reflection/fluorescence imaging provides new physical fingerprints for studying complex biospecimens and biological processes such as myogenic differentiation and highlights the potential use of advanced interferometry to study key nanostructures of complex biospecimens.

Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.

Supplemental Tables, Figures, and Methods

Polyaniline-based atomic switches are material building blocks whose nanoscale structure and resultant neuromorphic character provide a new physical substrate for the development next-generation, nanoarchitectonic-enabled computing systems. Metal ion-doped devices consisting of a Ag/metal ion doped polyaniline/Pt sandwich structure were fabricated using an in situ wet process. The devices exhibited repeatable resistive switching between high (ON) and low (OFF) conductance states in both Ag+ and Cu2+ ion-doped devices. The threshold voltage for switching was>0.8 V and average ON/OFF conductance ratios (30 cycles for 3 samples) were 13 and 16 for Ag+ and Cu2+ devices, respectively. The ON state duration was determined by the decay to an OFF state after pulsed voltages of differing amplitude and frequency. The switching behaviour is analagous to short-term (STM) and long-term (LTM) memories of biological synapses. Memristive behaviour and evidence of quantized conductance were also observed and interpreted in terms of metal filament formation bridging the metal doped polymer layer. The successful realization of these properties within physical material systems indicate polyaniline frameworks as suitable neuromorphic substrates for in materia computing.

To explore a proof-of-concept for atomically precise manufacturing (APM) using scanning probe microscopy (SPM), first principle theoretical calculations of atom-by-atom transfer from the apex of an SPM tip to an individual radical on a surface-bound organic molecule have been performed. Atom transfer is achieved by spatially controlled motion of a gold terminated tip to the radical. Two molecular tools for SPM-based APM have been designed and investigated, each comprising an adamantane core, a radical end group, and trithiol linkers to enable strong chemisorption on the Au(111) surface: ethynyl-adamantane-trithiol and adamantyl-trithiol. We demonstrate the details of controlled Au atom abstraction during tip approach toward and retraction from the radical species. Upon approach of the tip, the apical Au atom undergoes a transfer toward the carbon radical at a clearly defined threshold separation. This atomic displacement is accompanied by a net energy gain of the system in the range −0.5 to −1.5 eV, depending on the radical structure. In the case of a triangular pyramidal apex model, two tip configurations are possible after the tip atom displacement: (1) an Au atom is abstracted from the tip and bound to the C radical, not bound to the tip base anymore, and (2) apical tip atoms rearrange to form a continuous neck between the tip and radical. In the second case, subsequent tip retraction leads to the same final configuration as the first, with the abstracted Au atom bound to radical carbon atom of the molecular tool. For the less reactive adamantyl-trithiol radical molecular tool, Au atom transfer is less energetically favored, but this has the advantage of avoiding other apex gold atoms from rearrangement.

This NCE Focus Issue is motivated by the intriguingly neuromorphic properties of many-body systems self-assembled from nanoscale elementary components. The rationale behind this is that biological neural networks, including in particular their nanoscale synapses, are formed by bottom-up self-assembly, rather than top-down design. Self-assembled nanosystems inherit a disordered network structure and the nonlinear interactions between the networked elements can give rise to emergent properties, as espoused by the legendary Nobel laureate Phillip W. Anderson in his famous article “More is Different” (Science 177, 393, 1972).

Food safety science is an important field due to its practical applications in maintaining public safety and confidence in consumer goods. A significant component of food safety science is the detection and regulation of heavy metals in food. Heavy metals such as mercury (Hg) are of particular concern because of their potential to damage the nervous system, gastrointestinal tract, and other organ systems in humans and other organisms. The stringent standards and practices for the analysis of Hg in fish, as implemented by institutions such as the U.S. Food and Drug Administration (FDA), require both skilled analytical chemists and sensitive quantitative techniques, e.g., inductively coupled plasma mass spectrometry (ICP-MS). These needs inspired the development of an upper-division undergraduate analytical chemistry experiment that is designed to teach students how to quantify mercury in commercial fish products via ICP-MS analysis. In this hands-on laboratory exercise, students were taught how to use a standard reference material (SRM) for method validation and to understand how different matrices can affect the accuracy of the analysis. Students also learned how to optimize ICP-MS instrument parameters such as the kinetic energy discrimination (KED) voltage. Students worked in small groups and across lab sections to analyze their data and to identify the best parameter set for their experimental conditions. This lab exercise provides a rigorous, practical, and challenging experience for aspiring analytical chemists and can be readily adapted to the needs and interests of any institution with access to an ICP-MS instrument.

Power laws are of interest to several scientific disciplines because they can provide important information about the underlying dynamics (e.g. scale invariance and self-similarity) of a given system. Because power laws are of increasing interest to the cardiac sciences as potential indicators of cardiac dysfunction, it is essential that rigorous, standardized analytical methods are employed in the evaluation of power laws. This study compares the methods currently used in the fields of condensed matter physics, geoscience, neuroscience, and cardiology in order to provide a robust analytical framework for evaluating power laws in stem cell-derived cardiomyocyte cultures. One potential power law-obeying phenomenon observed in these cultures is pacemaker translocations, or the spatial and temporal instability of the pacemaker region, in a 2D cell culture. Power law analysis of translocation data was performed using increasingly rigorous methods in order to illustrate how differences in analytical robustness can result in misleading power law interpretations. Non-robust methods concluded that pacemaker translocations adhere to a power law while robust methods convincingly demonstrated that they obey a doubly truncated power law. The results of this study highlight the importance of employing comprehensive methods during power law analysis of cardiomyocyte cultures.

Open source analytical software for the analysis of electrophysiological cardiomyocyte data offers a variety of new functionalities to complement closed-source, proprietary tools. Here, we present the Cardio PyMEA application, a free, modifiable, and open source program for the analysis of microelectrode array (MEA) data obtained from cardiomyocyte cultures. Major software capabilities include: beat detection; pacemaker origin estimation; beat amplitude and interval; local activation time, upstroke velocity, and conduction velocity; analysis of cardiomyocyte property-distance relationships; and robust power law analysis of pacemaker spatiotemporal instability. Cardio PyMEA was written entirely in Python 3 to provide an accessible, integrated workflow that possesses a user-friendly graphical user interface (GUI) written in PyQt5 to allow for performant, cross-platform utilization. This application makes use of object-oriented programming (OOP) principles to facilitate the relatively straightforward incorporation of custom functionalities, e.g. power law analysis, that suit the needs of the user. Cardio PyMEA is available as an open source application under the terms of the GNU General Public License (GPL). The source code for Cardio PyMEA can be downloaded from Github at the following repository: https://github.com/csdunhamUC/cardio_pymea.

Disruptive technology in computational devices is required as the universal computing machines approach quantum mechanical limits. Integration of state-of-the-art memristive devices provides optimal scaling of current technologies beyond this limit through the adoption of neuromorphic models. Universal computing machines pioneered by Alan Turing are strictly based on top-down intelligent design. Neuromorphic models instead engage in bottom-up programmability by emulating mammalian brain design and characteristics. Here we show the design, characterization, and implementation of a massively parallel memristor neuromorphic network based on metal chalcogenide atomic switch network (ASN) systems with key characteristics such as short- and long-term potentiation, power-law dynamics, and scale-free topology.

AbstractExtracellular vesicles (EVs) are a unique, heterogeneous class of biological nanoparticles secreted by most cells. As potential a class of novel diagnostics and therapeutics, the physio‐chemical characterization as well as the biomolecular composition of EVs are widely investigated. However, there is emerging evidence suggesting that biomechanical analysis of lipid‐bilayer membrane‐bound single EVs may provide key insights into their biological structure, biomarker functions, and potential therapeutic functions. In this review, we focus on the unique biomechanical properties of single EVs such as elasticity, stiffness, and deformability. We compare common indentation models used in atomic force microscopy (AFM)‐based biomechanical analysis of EVs, as well as the benefits and drawbacks of each model encompassing the heterogeneous EV sub‐populations—mainly the small EVs (or exosomes). Next, we discuss high‐throughput approaches to determine the biomechanical landscape of EVs that may overcome some of the challenges associated with the accurate determination of particle sizes and particle‐by‐particle indentations. Finally, we highlight exciting new opportunities for EV biomechanical fingerprinting emanating from machine learning capabilities. In particular, we propose multi‐parametric AFM structure‐mechanical analysis to further advance label‐free, orthogonal biophysical understanding of EVs beyond biomolecular or particle size analysis, with significant implications for research and clinical use.

Breast cancer cells secrete abundant nanometer-sized vesicles. Small extracellular vesicle (or sEV) cargos are known to have similar biomolecular signatures to their secreting parental breast cancer cells. However, whether malignant transformation modulates the physical and biomechanical profiles of secreted nanosized sEVs (40-120 nm) has not been established. Here, using multiparametric atomic force microscopy imaging, we directly compared the structure-mechanical properties (including topographic height, Young's modulus, and adhesion) of breast cancer cell-derived sEVs and secreting cells. Our findings reveal that sEVs show reduced Young's modulus concomitant with a decrease in cell stiffness as cells progress from nontumor to noninvasive to invasive breast cancer phenotypes across different probing forces, isolation techniques, and particle sizes. Further, single sEV structure-mechanical analysis of actual patient plasma samples showed alterations in biomechanical properties of sEVs in breast cancer patients compared to sEVs from benign healthy controls. Our study demonstrates that precise biomechanical fingerprinting of single nanoscale sEVs provides an attractive label-free, cell-free, and orthogonal approach to detect changes in parental cells, such as during malignant transformation.

We show that an isotropic dipolar particle in the vicinity of a substrate made of nonreciprocal plasmonic materials can experience a lateral thermal-fluctuations-induced force and torque when the particle's temperature differs from that of the slab and the environment. We connect the existence of the lateral force to the asymmetric dispersion of nonreciprocal surface polaritons and the existence of the lateral torque to the spin-momentum locking of such surface waves. Using the formalism of fluctuational electrodynamics, we show that the features of lateral force and torque should be experimentally observable using a substrate of doped indium antimonide (InSb) placed in an external magnetic field, and for a variety of dielectric particles. Interestingly, we also find that the directions of the lateral force and the torque depend on the constituent materials of the particles, which suggests a sorting mechanism based on nonequilibrium fluctuational electrodynamics.

Inductively coupled plasma-mass spectrometry (ICP-MS) is a powerful analytical technique that can quantify elements of interest at parts-per-trillion concentrations. In this laboratory class, students performed ICP-MS analysis to quantify mercury concentration of standard reference material (SRM) 1947 (Lake Michigan fish tissue) and canned tuna from a local supermarket. These two samples were digested in two different matrices (HNO3/ H2O2 or HNO3/HCl/H2O2) and then analyzed using no-gas mode or helium mode with two different kinetic energy discrimination voltages (2V or 4V). The inclusion of HCl in the matrix produced more accurate results and stabilized mercury over the 8-day period after the digestion. Based on their analysis, the students were asked to draw their own conclusions about what they perceived to be the most accurate representation of the true mercury concentration of the tuna samples. This laboratory class provides students with a wide range of scientific concepts to explore such as method verification with SRM, kinetic energy discrimination, matrix effect, and trace metal stability over time.

Numerous studies suggest critical dynamics may play a role in information processing and task performance in biological systems. However, studying critical dynamics in these systems can be challenging due to many confounding biological variables that limit access to the physical processes underpinning critical dynamics. Here we offer a perspective on the use of abiotic, neuromorphic nanowire networks as a means to investigate critical dynamics in complex adaptive systems. Neuromorphic nanowire networks are composed of metallic nanowires and possess metal-insulator-metal junctions. These networks self-assemble into a highly interconnected, variable-density structure and exhibit nonlinear electrical switching properties and information processing capabilities. We highlight key dynamical characteristics observed in neuromorphic nanowire networks, including persistent fluctuations in conductivity with power law distributions, hysteresis, chaotic attractor dynamics, and avalanche criticality. We posit that neuromorphic nanowire networks can function effectively as tunable abiotic physical systems for studying critical dynamics and leveraging criticality for computation.

The Art|Sci Collective invited the POM conference’s audience and colleagues, to jump collectively with us from one space of possibility–where quantum mechanics asserts, we ‘don't know’ and ‘can't know’, to the next —in which experimental techniques such as time-resolved microscopy, ultrafast spectroscopy, single molecule spectroscopy, or even single particle imaging, enable us the precision of observing and measuring infinitesimal dynamics at very small length and time scales. What does quantum biology offer us as multiplicities and alternative realities when considering the attempt to subvert and confront absolute order, stability, and control in the socio-political sphere? We offer a randomly guided immersion in a sequence of live and pre-recorded video performances and video-poems, speculating on quantum effects in living systems, using DIY microscopy, data visualization, generative 3D modelling and animation, machine learning, and other media art techniques.

Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.