Biomimetic Carbon-Fiber Systems Engineering: A Modular Design Strategy to Generate Biofunctional Composites from Graphene and Carbon Nanofibers

electrical conductivity. It is additionally advantageous if such materials resembled the structural and biochemical features of the natural extracellular environment. Here we show a novel modular design strategy to engineer biomimetic carbon-fiber based scaffolds. Highly porous ceramic zinc oxide (ZnO) microstructures serve as 3D sacrificial templates and are infiltrated with carbon nanotube (CNT) or graphene dispersions. Once the CNTs and graphene uniformly coat the ZnO template, the ZnO is either removed by hydrolysis or converted into carbon by chemical vapor deposition (CVD). The resulting 3D carbon scaffolds are both hierarchically ordered and free-standing. The properties of the micro-fibrous scaffolds were tailored with a high porosity (up to 93 %), high Young’s modulus (~0.027 to ~22 MPa), and an electrical conductivity of (~0.1 to ~330 S/m), as well as different surface compositions. Cell viability and fibroblast proliferation rate and protein adsorption rate assays have shown that the generated scaffolds are biocompatible and have a high protein adsorption capacity (up to 77.32 ±6.95 mg/cm3), so that they not only are able to resemble the ECM structurally, but also biochemically. The scaffolds also allow for the successful growth and adhesion of fibroblast cells showing that we provide a novel, highly scalable modular design strategy to generate biocompatible carbon-fiber systems that mimic the extracellular matrix with the additional feature of conductivity.RA gratefully acknowledges partial project funding by the Deutsche Forschungsgemeinschaft under contract FOR1616. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. GrapheneCore2 785219. CS is supported by the European Research Council (ERC StG 336104 CELLINSPIRED, ERC PoC 768740 CHANNELMAT), by the German Research Foundation (RTG 2154, SFB 1261 project B7). MT acknowledges support from the German Academic Exchange Service (DAAD) through a research grant for doctoral candidates (91526555-57048249). We acknowledge funding from EPSRC grants EP/P02534X/1, ERC grant 319277 (Hetero2D) the Royal Academy of Engineering Enterprise Scheme, the Trinity College, Cambridge, and the Isaac Newton Trust

Microarchitected Compliant Scaffolds of Pyrolytic Carbon for 3D Muscle Cell Growth

The integration of additive manufacturing technologies with the pyrolysis of polymeric precursors enables the design-controlled fabrication of architected 3D pyrolytic carbon (PyC) structures with complex architectural details. Despite great promise, their use in cellular interaction remains unexplored. This study pioneers the utilization of microarchitected 3D PyC structures as biocompatible scaffolds for the colonization of muscle cells in a 3D environment. PyC scaffolds are fabricated using micro-stereolithography, followed by pyrolysis. Furthermore, an innovative design strategy using revolute joints is employed to obtain novel, compliant structures of architected PyC. The pyrolysis process results in a pyrolysis temperature- and design-geometry-dependent shrinkage of up to 73%, enabling the geometrical features of microarchitected compatible with skeletal muscle cells. The stiffness of architected PyC varies with the pyrolysis temperature, with the highest value of 29.57 ± 0.78 GPa for 900 °C. The PyC scaffolds exhibit excellent biocompatibility and yield 3D cell colonization while culturing skeletal muscle C2C12 cells. They further induce good actin fiber alignment along the compliant PyC construction. However, no conclusive myogenic differentiation is observed here. Nevertheless, these results are highly promising for architected PyC scaffolds as multifunctional tissue implants and encourage more investigations in employing compliant architected PyC structures for high-performance tissue engineering applications

Core Probability Model : Efficient macroscopic sensitivity modelling: Conceptual design

Transport and Plannin

Improving probabilistic traffic modelling through advanced sampling

In traffic models certain presumptions are often made to simplify the complex systems that rule the world of traffic flow. This is necessary as not every variable can be considered. Furthermore, it is commonplace that equilibrium states are sought that give a good average, or rather deterministic, representation of the dynamics of traffic. Such an approach makes presumptions of traffic demand and supply for a (non-existent) average situation. However there must be a realisation that traffic is hardly ever ‘average’ [1]. It is especially in the terms ‘average’ and ‘deterministic’ that a realisation must exist that these terms are composed of extensively varying situations. By considering real stochasticity in these processes, a more complete picture of the traffic system is gaine

De ontwerpweggebruiker. Kenmerken van de weggebruiker en relatie met verkeersmanagement

Er is een continue behoefte aan transport, maar de capaciteit van het wegennet is beperkt e

Core Probability Model : Efficient macroscopic sensitivity modelling: Conceptual design

Transport and Plannin

Probability in traffic: A challenge for modelling

In the past decade an increase in research regarding stochasticity and probability in traffic modelling has occurred. The realisation has grown that simple presumptions and basic stochastic elements are insufficient to give accurate modelling results in many cases. This paper puts forward a strong argument for the further development and application of probabilistic models and argues that a realisation must arise of the detrimental effects of blindly applying non-probabilistic models to traffic where probability is rife. This is performed by the demonstration that deterministic and simple stochastic models will, in many cases, produce substantially biased results where variability is present in traffic. Prior to this demonstration, recent developments in probabilistic modelling are discussed. While the case for probabilistic modelling is strong in theory, the application of such modelling approaches is only possible with sufficiently developed models. However there are still certain challenges to be addressed in probabilistic modelling before a widespread implementation is likely. Remaining challenges for probabilistic approaches are therefore discussed and it is shown that computational efficiency, correlations between variables, and data gathering and processing all remain difficulties that have yet to be fully overcome.Transport and PlanningCivil Engineering and Geoscience

Improving traffic management through consideration of uncertainty and stochastics in traffic flow

In a bid to cost-effectively tackle congestion, traffic management is often seen as a key option to utilise road capacity. Prior to the application of traffic management measures, a-priori analysis allows the effectiveness of measures to be judged and where necessary adapted. However, current approaches do this without considering the effects of stochastic uncertainty and fluctuations in traffic flow. These stochastic effects have been shown to substantially influence the evaluation of traffic management measures. In this contribution, a methodological framework is proposed and demonstrated in a multi-part case study, applying approaches that explicitly consider stochastic variations and applications for traffic management. The results of the case study demonstrate the effectiveness of the models and highlight the necessity to consider uncertainty and fluctuations when a-priori evaluating traffic management measures.Transport and PlanningTransport and Plannin

Strategy-Based Driving Behaviour on Freeways: Findings of Test-Drive and On-Line Survey Study

Freeways form an important part of the road network. Drivers’ behavior can be split in longitudinal (acceleration and deceleration) and lateral behavior (lane changing). The combination of these two behaviors on freeways play a key role in traffic operations. This paper tries to describe the driving behavior, emphasizing the relation between the lateral and longitudinal behavior. To this end, an experimental study based on a test-drive and on-line questionnaire has been carried out. For the test-drive, 34 participants drove a vehicle equipped with monitoring systems. Based on the test drives a survey was developed regarding driving behavior in specific situations. This survey was answered by 1258 drivers who were questioned using videos for specific and relevant situations. The results show that most people choose a speed first, and stick to that. Fewer people adapt a strategy of having a desired speed which might change when they are in a different lane to overtake, or a strategy to choose a desired lane, and tries to adapt speed. A small fraction of the respondents mentioned that they have neither a desired speed or a desired lane. Most people (80%) use the right lane if possible, and 80% will not overtake at the right. 70% may have a courtesy behavior when needed. The outcome of this study has shed some light on the naturalistic driving behavior on freeways under different situations. The findings of this work can be implemented in traffic simulation programs, which are able to delay with this scale of traffic behavior. Repeating this survey in international context will reveal differences between drivers in various countries