Rorschach inkblots have had a striking impact on the worlds of art and science because of the remarkable variety of associations with recognizable and namable objects they induce. Originally adopted as a projective psychological tool to probe mental health, psychologists and artists have more recently interpreted the variety of induced images simply as a signature of the observers’ creativity. Here we analyze the relationship between the spatial scaling parameters of the inkblot patterns and the number of induced associations, and suggest that the perceived images are induced by the fractal characteristics of the blot edges. We discuss how this relationship explains the frequent observation of images in natural scenery.
Disorder increasingly affects performance as electronic devices are reduced in size. The ionized dopants used to populate a device with electrons are particularly problematic, leading to unpredictable changes in the behavior of devices such as quantum dots each time they are cooled for use. We show that a quantum dot can be used as a highly sensitive probe of changes in disorder potential and that, by removing the ionized dopants and populating the dot electrostatically, its electronic properties become reproducible with high fidelity after thermal cycling to room temperature. Our work demonstrates that the disorder potential has a significant, perhaps even dominant, influence on the electron dynamics, with important implications for "ballistic" transport in quantum dots.
We investigate the degree to which neurons are fractal, the origin of this fractality, and its impact on functionality. By analyzing three-dimensional images of rat neurons, we show the way their dendrites fork and weave through space is unexpectedly important for generating fractal-like behavior well-described by an ‘effective’ fractal dimension D. This discovery motivated us to create distorted neuron models by modifying the dendritic patterns, so generating neurons across wide ranges of D extending beyond their natural values. By charting the D-dependent variations in inter-neuron connectivity along with the associated costs, we propose that their D values reflect a network cooperation that optimizes these constraints. We discuss the implications for healthy and pathological neurons, and for connecting neurons to medical implants. Our automated approach also facilitates insights relating form and function, applicable to individual neurons and their networks, providing a crucial tool for addressing massive data collection projects (e.g. connectomes).
The continuous record of monomer and polymer concentrations, C m and C p , and cumulative weight-average mass, M w , furnished by automatic continuous online monitoring of polymerization reactions (ACOMP) has been harnessed to provide feedback to control reactor monomer flow in order to follow a target trajectory M w,t (t) during linear chain growth free radical polymerization. This was achieved without a detailed kinetic model. Two proportionality parameters to pilot the controller, α and p, result from (i) reaction rate = αC m and (ii) M w,inst = pC m , where M w,inst is instantaneous M w . Using Ansatz values for α and p, the controller periodically recomputes these, based on the ACOMP data stream, in order to follow M w,t (t). A histogram of concentration vs M w,inst estimates the molecular weight distribution width. Invoking an instantaneous distribution provides polydispersities. Results are compared to GPC analysis on end products. The concept of "isomorphic reaction pair" is introduced: two reactions that follow the same trajectory under different reaction variables, e.g., varying T at constant [initiator] and varying [initiator] at T = constant. The controller can be used, as is, for high solids reactions, and extended to copolymerization, including for possible control of composition gradients in controlled radical polymerization.
The prospect of replacing damaged body parts with artificial implants is being transformed from science fiction to science fact through the increasing application of electronics to interface with human neurons in the limbs, the brain, and the retina. We propose bio-inspired electronics which adopt the fractal geometry of the neurons they interface with. Our focus is on retinal implants, although performance improvements will be generic to many neuronal types. The key component is a multifunctional electrode; light passes through this electrode into a photodiode which charges the electrode. Its electric field then stimulates the neurons. A fractal electrode might increase both light transmission and neuron proximity compared to conventional Euclidean electrodes. These advantages are negated if the fractal’s field is less effective at stimulating neurons. We present simulations demonstrating how an interplay of fractal properties generates enhanced stimulation; the electrode voltage necessary to stimulate all neighboring neurons is over 50% less for fractal than Euclidean electrodes. This smaller voltage can be achieved by a single diode compared to three diodes required for the Euclidean electrode’s higher voltage. This will allow patients, for the first time, to see with the visual acuity necessary for navigating rooms and streets.
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