MRC Laboratory of Molecular Biology
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Carbon cluster formation and mobility degradation in 4H-SiC MOSFETs
No full textThe performance of SiC MOSFETs is limited by many defects at the SiC/SiO2 interface. However, there are no fully consistent atomic models of these defects or how their large densities arise. We show how the high heat of formation of SiO2 causes a selective oxidation of Si in SiC, leaving carbon clusters in SiO2. We consider chemical potentials across the interface from both the thermochemical and oxidation kinetics viewpoint. SiO2 native defects give states too far in energy from the SiC band edges, while defects in bulk SiC have too high a formation energy. Only carbon clusters have a low enough formation energy to give sufficient defect densities to account for the mobility loss, and experimental evidence for them is discussed
Guided Assembly and Patterning of Intrinsically Fluorescent Amyloid Fibers with Long-Range Order
No full textFibrillar amyloids exhibit a fascinating range of mechanical, optical, and electronic properties originating from their characteristic β-sheet-rich structure. Harnessing these functionalities in practical applications has so far been hampered by a limited ability to control the amyloid self-assembly process at the macroscopic scale. Here, we use core-shell electrospinning with microconfinement to assemble amyloid-hybrid fibers, consisting of densely aggregated fibrillar amyloids stabilized by a polymer shell. Up to centimeter-long hybrid fibers with micrometer diameter can be arranged into aligned and ordered arrays and deposited onto substrates or produced as free-standing networks. Properties that are characteristic of amyloids, including their high elastic moduli and intrinsic fluorescence signature, are retained in the hybrid fiber cores, and we show that they fully persist through the macroscopic fiber patterns. Our findings suggest that microlevel confinement is key for the guided assembly of amyloids from monomeric proteins
Assessing the influence of pore structure formation on heavy metal immobilization through image-based CFD
No full textPorous media are widely adopted as immobilization sorbents in environmental engineering. The microscale difference in pore structure formation causes significant deflection in a vast landscape. Computational fluid dynamics (CFD) offers a comparative approach to evaluate the individual influence from pore structure formation with strictly controlled surface and volume properties. This paper presents a comprehensive comparison between the performance of cylindrical media and spherical-media in heavy metal immobilization. Digital testing was performed to measure the surface area, specific surface area, density and porosity. Image-based input technique was developed to reconstruct the cylindrical media. It was found that although the surface area, specific surface area and porosity were the same, the spherical media still had an accelerated immobilization rate. Results further showed that the spherical media in floatation arrangement had an immobilization rate of 16% higher than the cylindrical media with the same surface properties. Non-floatation arrangement of the spherical media caused a reduction in immobilization capacity up to 32.8% lower than the cylindrical media. The cylindrical media demonstrated an advantage of being structurally stable under high porosity, the latter of which resulted in an increased immobilization capacity compared with the spherical-media. The results suggest that the cylindrical bio-microstructure is desirable for heavy metal immobilization in a non-flotational environment. The computational approach provides a digital solution to evaluate the immobilization in 3D architected media. The proposed testing methods are feasible for both experimentally obtained images and structures from algorithm-generation
Using Redundant and Disjoint Time-Variant Soft Robotic Sensors for Accurate Static State Estimation
No full textSoft robotic sensors have been limited in their applications due to their highly nonlinear time variant behavior. Current studies are either looking into techniques to improve the mechano-electrical properties of these sensors or into modelling algorithms that account for the history of each sensor. Here, we present a method for combining multi-material soft strain sensors to obtain equivalent higher quality sensors; better than each of the individual strain sensors. The core idea behind this work is to use a combination of redundant and disjoint strain sensors to compensate for the time-variant hidden states of a soft-bodied system, to finally obtain the true strain state in a static manner using a learning-based approach. We provide methods to develop these variable sensors and metrics to estimate their dissimilarity and efficacy of each sensor combinations, which can double down as a benchmarking tool for soft robotic sensors. The proposed approach is experimentally validated on a pneumatic actuator with embedded soft strain sensors. Our results show that static data from a combination of nonlinear time variant strain sensors is sufficient to accurately estimate the strain state of a system
Eddy resolving strategies in turbomachinery and peripheral components
No full textThe successful application of eddy resolving simulations to most areas of a modern gas turbine aeroengine is considered. A coherent modeling framework is presented to address coupling challenges. A flow classification is also given. The extensive results presented are shown to be promising but many challenges remain. In the short term, the use of eddy resolving simulations should see greater use in Reynolds-averaged Navier-Stokes (RANS) and lower-order model calibration/development-this is starting to happen already. Ideally, in the near future, RANS, large eddy simulation (LES), and test should work in harmony. It is advocated that currently, certain costly engineering design problems can be avoided or understood using scale resolving simulations
Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria
No full textGlobally, ambient air pollution claims ~9 million lives yearly, prompting researchers to investigate changes in air quality. Of special interest is the impact of COVID-19 lockdown. Many studies reported substantial improvements in air quality during lockdowns compared with pre-lockdown or as compared with baseline values. Since the lockdown period coincided with the onset of the rainy season in some tropical countries such as Nigeria, it is unclear if such improvements can be fully attributed to the lockdown. We investigate whether significant changes in air quality in Nigeria occurred primarily due to statewide COVID-19 lockdown. We applied a neural network approach to derive monthly average ground-level fine aerosol optical depth (AODf) across Nigeria from year 2001–2020, using the Multi-angle Implementation of Atmospheric Correction (MAIAC) AODs from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, AERONET aerosol optical properties, meteorological and spatial parameters. During the year 2020, we found a 21% or 26% decline in average AODf level across Nigeria during lockdown (April) as compared to pre-lockdown (March), or during the easing phase-1 (May) as compared to lockdown, respectively. Throughout the 20-year period, AODf levels were highest in January and lowest in May or June, but not April. Comparison of AODf levels between 2020 and 2019 shows a small decline (1%) in pollution level in April of 2020 compare to 2019. Using a linear time-lag model to compare changes in AODf levels for similar months from 2002 to 2020, we found no significant difference (Levene's test and ANCOVA; α = 0.05) in the pollution levels by year, which indicates that the lockdown did not significantly improve air quality in Nigeria. Impact analysis using multiple linear regression revealed that favorable meteorological conditions due to seasonal change in temperature, relative humidity, planetary boundary layer height, wind speed and rainfall improved air quality during the lockdown
Three-dimensional, multifunctional neural interfaces for cortical spheroids and engineered assembloids
No full textThree-dimensional (3D), submillimeter-scale constructs of neural cells, known as cortical spheroids, are of rapidly growing importance in biological research because these systems reproduce complex features of the brain in vitro. Despite their great potential for studies of neurodevelopment and neurological disease modeling, 3D living objects cannot be studied easily using conventional approaches to neuromodulation, sensing, and manipulation. Here, we introduce classes of microfabricated 3D frameworks as compliant, multifunctional neural interfaces to spheroids and to assembloids. Electrical, optical, chemical, and thermal interfaces to cortical spheroids demonstrate some of the capabilities. Complex architectures and high-resolution features highlight the design versatility. Detailed studies of the spreading of coordinated bursting events across the surface of an isolated cortical spheroid and of the cascade of processes associated with formation and regrowth of bridging tissues across a pair of such spheroids represent two of the many opportunities in basic neuroscience research enabled by these platforms
Microelectrode Arrays for Simultaneous Electrophysiology and Advanced Optical Microscopy
No full textAdvanced optical imaging techniques address important biological questions in neuroscience, where structures such as synapses are below the resolution limit of a conventional microscope. At the same time, microelectrode arrays (MEAs) are indispensable in understanding the language of neurons. Here, the authors show transparent MEAs capable of recording action potentials from neurons and compatible with advanced microscopy. The electrodes are made of the conducting polymer poly(3,4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) and are patterned by optical lithography, ensuring scalable fabrication with good control over device parameters. A thickness of 380 nm ensures low enough impedance and >75% transparency throughout the visible part of the spectrum making them suitable for artefact-free recording in the presence of laser illumination. Using primary neuronal cells, the arrays record single units from multiple nearby sources with a signal-to-noise ratio of 7.7 (17.7 dB). Additionally, it is possible to perform calcium (Ca2+) imaging, a measure of neuronal activity, using the novel transparent electrodes. Different biomarkers are imaged through the electrodes using conventional and super-resolution microscopy (SRM), showing no qualitative differences compared to glass substrates. These transparent MEAs pave the way for harnessing the synergy between the superior temporal resolution of electrophysiology and the selectivity and high spatial resolution of optical imaging
Catalyst‐mediated enhancement of carbon nanotube textiles by laser irradiation: Nanoparticle sweating and bundle alignment
No full textThe photonic post‐processing of suspended carbon nanotube (CNT) ribbons made by floating catalyst chemical vapor deposition (FC‐CVD) results in selective sorting of the carbon nano-tubes present. Defective, thermally non‐conductive or unconnected CNTs are burned away, in some cases leaving behind a highly crystalline (as indicated by the Raman G:D ratio), highly conductive network. However, the improvement in crystallinity does not always occur but is dependent on sample composition. Here, we report on fundamental features, which are observed for all samples. Pulse irradiation (not only by laser but also white light camera flashes, as well as thermal processes such as Joule heating) lead to (1) the sweating‐out of catalyst nanoparticles resulting in molten catalyst beads of up to several hundreds of nanometres in diameter on the textile surface and (2) a significant improvement in CNT bundle alignment. The behavior of the catalyst beads is material dependent. Here, we show the underlying mechanisms of the photonic post‐treatment by modelling the macro‐ and microstructural changes of the CNT network and show that it is mainly the amount of residual catalyst which determines how much energy these materials can withstand before their complete decomposition
Measurement and simulation of sooting characteristics by an ATJ-SKA biojet fuel and blends with Jet A-1 fuel in laminar non-premixed flames
No full textWe investigate the sooting propensity of an Alcohol-to-Jet-Synthetic Kerosene with Aromatics (ATJ-SKA) biojet fuel. The soot volume fraction and primary particle size in the pre-vaporised diffusion flames using ATJ-SKA biojet and blends with Jet A-1 at atmospheric conditions were measured experimentally and compared to numerical simulations. The measurements were conducted using extinction calibrated laser induced incandescence (LII). The soot volume fractions measured using the ATJ-SKA fuel do not show significant differences relative to measurements with Jet A-1. A comparison of the chemical composition of the fuels suggests that the Degree of Unsaturation (DoU) may not determine the sooting propensity of biojet fuels. The SEM analysis shows that diffusion flames using neat Jet A-1 produce finer soot particles and larger number density compared to the biojet and biojet surrogate. The soot model employs a semi-detailed chemical kinetic mechanism and a physical model which integrates the population balance equation governing the soot particle size distribution with an in-house reactive flow solver for multicomponent ideal gases. The model predicts the maximum soot volume fraction (SVFm) in the neat biojet case and the blended cases with Jet A-1 fuels within an error margin of 13% of the measured values. However, the predicted soot volume fraction distribution patterns differ from the measured one and the possible causes are discussed