MRC Laboratory of Molecular Biology
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Controlling Neuromorphic Behavior of Organic Electrochemical Transistors by Blending Mixed and Ion Conductors
No full textWe examine the neuromorphic behavior of organic electrochemical transistors (OECTs) based on polymer blends of a mixed conductor (PEDOT:PSS) and an ion conductor (PSSNa). We show that the addition of ionic conductor into PEDOT:PSS decreases information retention time in paired-pulse depression (PPD) experiments and speeds up response in dynamic filtering experiments. Detailed studies of transient properties demonstrate that the relevant timescales for neuromorphic response are determined by ion injection in and extraction out of the film. These results bring to the forefront OECTs as devices with neuromorphic properties that are readily engineered
Elastohydrodynamic lubrication
No full text© 2020 by the authors. The development of EHL theory from its tentative beginnings is outlined, with an account of how Ertel explained its relation to Hertz contact theory. The problems caused by the failure of the early numerical analysts to understand that the film thickness depends on only two variables are emphasised, and answers of the form H = F(P, S) given. Early methods of measuring the film thickness are described, but these became archaic with the development of optical EHL. The behaviour of surface roughness as it passes through the high pressure region and suffers elastic deformation is described, and the implication for the traditional L-ratio noted. In contrast, the understanding of traction is far from satisfactory. The oil in the high pressure region must become non-Newtonian: the early explanation that the viscosity reduction is the effect of temperature proved inadequate. There must be some form of shear thinning (perhaps according to the Eyring theory), but also a limiting shear stress under which the lubricant shears as an elastic solid. It seems that detailed, and difficult, measurements of the high pressure, high shear-rate behaviour of individual oils are needed before traction curves can be predicted
Roadmap on multiscale materials modeling
No full text© 2020 The Author(s). Published by IOP Publishing Ltd. Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware
Roadels: Discrete information objects for production planning and control of road construction
No full textRoad construction is fundamentally different to building construction, in terms of its products, the types of work and operations, and the resources used. One of the key differences from the point of view of planning and control of production flow is that roads are composed of geometrically continuous courses rather than discrete 'products', making work packaging difficult. Clearly defined work packages that comprise work performed on distinct products are central to lean construction methods of planning and control, such as the Last Planner System. We therefore propose a product schema which models road sections with distinct road course segments that are dynamically defined aggregations of 'roadels'. A roadel is a fine-grained vertical triangular prism object that can be dynamically aggregated with other roadels to represent a road course segment associated with a work package, a planned task, or an as-built section. The schema represents the continuous nature of road construction, and its discrete entities enable representation and computations of as-made work using the raw data obtained from machine-mounted sensors and land surveys. We describe the information schema and illustrate its use for computation and analysis of lean production flow metrics. The schema has been tested using case study data from a 25,000 m2 parking lot project, which was modeled with more than 560,000 roadels with as-made status data collected from 33 working days over a period of two months
Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging
No full textKnowing the size of building stock is perhaps the most basic determinant in assessing energy use in buildings. However, official statistics on urban residential stock for many countries are piecemeal at best. Previous studies estimating stock size and energy use make various debateable methodological assumptions and only produce deterministic results. This paper presents a Bayesian approach to characterise stock turnover dynamics and estimate stock size uncertainties, applied here to China. Firstly, a probabilistic dynamic building stock turnover model is developed to describe the building aging and demolition process, governed by a hazard function specified by a parametric survival model. Secondly, using five candidate parametric survival models, the building stock turnover model is simulated through Markov Chain Monte Carlo to obtain posterior distributions of model-specific parameters, estimate marginal likelihood, and make predictions of stock size. Thirdly, Bayesian Model Averaging is applied to create a model ensemble that combines model-specific posterior predictive distributions of the recent historical stock evolution pathway in proportion to posterior model probabilities. Finally, the Bayesian Model Averaging model ensemble is extended to forecast future trajectories of residential stock development through 2100. The modelling results suggest that the total stock in China will peak around 2065, at between 42.4 and 50.1 billion m2. This Bayesian modelling framework produces probability distributions of annual total stock, age-specific substocks, annual new buildings and annual demolition rates. This can support future analysis of policy trade-offs across embodied-versus-operational energy consumption, in the context of sector-wide decarbonisation
Predicting bridge elements deterioration, using Collaborative Gaussian Process Regression
No full textRoadway and railway bridges are not only integral, but also vulnerable parts of terrestrial transport networks. Structural failures of bridges may lead to disastrous consequences on users and society at large. Bridge predictive deterioration models are extremely important for effective maintenance decision-making. However, the lack of enough inspection data between maintenance activities of a bridge complicates the development of accurate predictive models. Presented herein is a Gaussian Process Regression (GPR) based collaborative model for predicting the condition of bridge elements with limited available inspection data per bridge. This model has been applied in 137 bridge decks, showing that collaborative prognosis has the potential to predict the condition of different types of bridge elements, composing different types of bridges
A temperature-dependent multilayer model for direct current carrying HTS coated-conductors under perpendicular AC magnetic fields
No full textWhen a type II superconductor carrying a direct current is subjected to a perpendicular oscillating magnetic field, a direct current (DC) voltage will appear. This voltage can either result from dynamic resistance effect or from flux flow effect, or both. The temperature variation in the superconductor plays an important role in the nature of the voltage, and there has been little study of this so far. This paper presents and experimentally verifies a 2D temperature-dependent multilayer model of the second generation (2G) high temperature superconducting (HTS) coated conductors (CC), which is based on H-formulation and a general heat transfer equation. The model has coupled the electromagnetic and thermal physics, and it can simulate the behavior of 2G HTS coated conductors in various working conditions where the temperature rise has a significant impact. Representative electromagnetic phenomena such as the dynamic resistance effect and the flux flow effect, and thermal behavior like quench and recovery have been simulated. This thermal-coupled model is a powerful tool to study the thermal-electromagnetic behaviors of 2G HTS coated conductors in different working conditions, especially when the impact of temperature rise is important. This multilayer model is also very useful in analyzing the impact of different layers in the 2G HTS CCs, especially the metal stabilizer layers. It has been proven to be a very powerful tool to help understand more complicated characteristics in the CCs which could not be accurately measured or simulated by previous numerical models. The work is indicative and very useful in designing ac magnetic field controlled persistent current switches and flux pumps, in terms of increasing the off-state resistance, analyzing different sources of losses, minimizing detrimental losses, and enhancing the safety and stability
Efficient Bayesian Exploration for Soft Morphology-Action Co-optimization
No full textMorphology been shown to be a fundamental aspect of tactile sensing in soft robotics, one that can aid, and indeed enable, complex discrimination tasks. For a robot to change its sensor morphology as well as control appropriately, the parametric search over morphology and control parameters is usually slow and unsuited for real-world applications. We develop a framework based on Bayesian Exploration, to allow a robot to co-optimize both changes in tactile sensing morphology and robot action control, to aid in complex tactile object discrimination tasks. We test the framework by performing object discrimination on a set of eight objects, varying three different physical properties: geometry, surface texture, and stiffness. We integrate a capacitive tactile sensor into a flat end-effector and create three soft silicon-based filters with varying morphological properties. We incorporate the end-effector onto a robotic arm and perform repetitive, parameterized touch experiments, on each object. We show morphing is indeed necessary to dissociate amongst different object properties with the sensor at hand. Moreover, we show the proposed framework can consistently achieve optimal morphology-action configurations in approximately half the time than systematic search over parameters. This work marks a step towards the creation of robots capable of using morphology and action control to actively aid in discrimination tasks
High temperature characterization of a CMOS based infra-red source using thermal-incandescence microscopy
No full textThis paper presents the high temperature thermal characterization of a Micro-Electro-Mechanical Systems (MEMS) infra-red (IR) thermal source, using non-contact optical approaches, based on IR and thermo-incandescence microscopy. The IR thermal source was fabricated using a CMOS based processing technology and consists of a miniature micro-heater, fabricated using tungsten metallization. The performance and reliability of the IR source is highly dependent on its operating temperature. For short-wave (1.4 µm−2.5 µm) infra-red emission, the operating temperature is in excess of 800 °C. Work will be presented in this paper in which spot temperature measurements (>700 °C) were made on the IR source using thermal-incandescence microscopy. Thermal-optical calibration was achieved by utilizing the known melting point (MP) of different metal micro-particles. Optical measurements were compared to those obtained using an electrical approach. The thermal measurements suggest good temperature uniformity across the micro-heater of the IR source
Tailoring PEDOT properties for applications in bioelectronics
No full text© 2020 Elsevier B.V. Resulting from its wide range of beneficial properties, the conjugated conducting polymer poly(3,4‐ethylenedioxythiophene) (PEDOT) is a promising material in a number of emerging applications. These material properties, particularly promising in the field of bioelectronics, include its well‐known high‐degree of mechanical flexibility, stability, and high conductivity. However, perhaps the most advantageous property is its ease of fabrication: namely, low‐cost and straight‐forward deposition processes. PEDOT processing is generally carried out at low temperatures with simple deposition techniques, allowing for significant customization of the material properties through, as highlighted in this review, both process parameter variation and the addition of numerous additives. Here we aim to review the role of PEDOT in addressing an assortment of mechanical and electronic requirements as a function of the conditions used to cast or polymerize the films, and the addition of additives such as surfactants and secondary dopants. Contemporary bioelectronic research examples investigating and utilizing the effects of these modifications will be highlighted