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The gas sensor market is growing fast, driven by many socioeconomic and industrial factors. Mid-infrared (MIR) gas sensors offer excellent performance for an increasing number of sensing applications in healthcare, smart homes, and the automotive sector. Having access to low-cost, miniaturized, energy efficient light sources is of critical importance for the monolithic integration of MIR sensors. Here, we present an on-chip broadband thermal MIR source fabricated by combining a complementary metal oxide semiconductor (CMOS) micro-hotplate with a dielectric-encapsulated carbon nanotube (CNT) blackbody layer. The micro-hotplate was used during fabrication as a micro-reactor to facilitate high temperature (>700 ∘ C) growth of the CNT layer and also for post-growth thermal annealing. We demonstrate, for the first time, stable extended operation in air of devices with a dielectric-encapsulated CNT layer at heater temperatures above 600 ∘ C. The demonstrated devices exhibit almost unitary emissivity across the entire MIR spectrum, offering an ideal solution for low-cost, highly-integrated MIR spectroscopy for the Internet of Things.

Optical‐field driven electron tunneling in nanojunctions has made demonstrable progress toward the development of ultrafast charge transport devices at subfemtosecond time scales, and have evidenced great potential as a springboard technology for the next generation of on‐chip “lightwave electronics.” Here, the empirical findings on photocurrent the high nonlinearity in metal–insulator–metal (MIM) nanojunctions driven by ultrafast optical pulses in the strong optical‐field regime are reported. In the present MIM device, a 14th power‐law scaling is identified, never achieved before in any known solid‐state device. This work lays important technological foundations for the development of a new generation of ultracompact and ultrafast electronics devices that operate with suboptical‐cycle response times. The authors report on the high nonlinearity photocurrent in metal‐insulator‐metal nano‐junctions driven by ultrafast optical pulses in the strong optical‐field regime. A 14th power‐law scaling is first achieved in the solid‐state device. This work lays important technological foundations for the development of a new generation of ultracompact and ultrafast electronics devices that operate with sub‐optical‐cycle response times.

The nanocone-shaped carbon nanotubes field-emitter array (NCNA) is a near-ideal field-emitter array that combines the advantages of geometry and material. In contrast to previous methods of field-emitter array, laser ablation is a low-cost and clean method that does not require any photolithography or wet chemistry. However, nanocone shapes are hard to achieve through laser ablation due to the micrometer-scale focusing spot. Here, we develop an ultraviolet (UV) laser beam patterning technique that is capable of reliably realizing NCNA with a cone-tip radius of ≈300 nm, utilizing optimized beam focusing and unique carbon nanotube–light interaction properties. The patterned array provided smaller turn-on fields (reduced from 2.6 to 1.6 V/μm) in emitters and supported a higher (increased from 10 to 140 mA/cm2) and more stable emission than their unpatterned counterparts. The present technique may be widely applied in the fabrication of high-performance CNTs field-emitter arrays.

This thesis presents the development of chemical vapour deposited (CVD) graphene and multi-walled carbon nanotubes (MWCNTs) as enabling technologies for flexible transparent conductors offering enhanced functionality. The technologies developed could be employed as thin film field emission sources, optical sensors and substrate-free wideband optical polarisers. Detailed studies were performed on CVD Fe and Ni catalysed carbon nanotubes and nanofibres on indium tin oxide, aluminium and alumina diffusion barriers. Activations energies of 0.5 and 1.5 eV were extracted supporting surface diffusion limited catalysis forCNTs and CNFs. For the first time an activation energy of 2.4 eV has been determined for Cu-catalysed growth of CVD graphene. Graphene was shown to deviate significantly from the more traditional rate-limited surface diffusion and suggests carbon-atom-lattice integration limited catalysis. An aligned dry-transferred MWCNT thin film fabrication technique was developed using MWCNTs of varied lengths to control the optical transparency and conductivity. A process based on the hot-press lamination of bilayer CVD graphene (HPLG) was also developed. Transport studies revealed that these thin films behave, in a macroscopic sense, similar to traditional c-axis conductive graphite and deviate toward tunnel dominated conduction with increasing degrees of network disorder. Various MWCNT-based thin film field emitters were considered. Solution processing was shown to augment the surface work function of the MWCNTs resulting in reduced turn-on electric fields. Integrated zinc oxide nanowires were investigated and were shown to ballast the emission, thereby preventing tip burn out, and offered lower than expected turn-on fields due to the excitation of a hot electron population. To obviate nearest neighbour electrostatic shielding effects an electrochemical catalyst activation procedure was developed to directly deposit highly aligned sparse carbon nanofibres on stainless steel mesh. Highly-aligned free-standing MWCNT membranes were fabricated through a solid-state peeling technique. Membranes were spanned across large distances thereby offering an ideal platform to investigate the unambiguous photoresponse of MWCNTs by removing all extraneous substrate interfaces, charge traps and nanotube-electrode Shottky barriers as well as using pure, chemically untreated material. Oxygen physisorbtion was repeatedly implicated through in-situ lasing and in-situ heated EDX measurements, FT-IR and low temperature transport and transfer measurements. A MWCNT membrane absorptive polariser was fabricated. Polarisers showed wideband operation from 400 nm to 1.1 μm and offered operation over greater spectral windows than commercially available polymer and glass-support dichroic films. Ab-initio simulations showed excellent agreement with the measured polarisation attributing the effect to long-axis selective absorption.

Magnetic bearings now exist in a variety of industrial applications. However, there still exist concerns over the fault tolerance and control integrity of rotor/magnetic bearing systems in general. Unless control systems can be developed that have the ability to maintain safe operation when the system is in an abnormal, degraded or faulty state then many, otherwise viable, magnetic bearing applications will remain unfulfilled.In this thesis, a variety of potential fault conditions are considered for flexible rotor systems. These conditions are classified into two main groups. The first comprises those that are external to the control system, for which tolerance can be achieved through appropriate controller design. It is shown that these faults can be modelled as system disturbances in the framework of robust controller design, and that optimal (H and H2) controllers can be designed to cope with such faults. The performance of such controllers under the designed fault conditions is assessed and compared with more standard control implementations.The second group of faults is considered internal to the control system, for which tolerance cannot be achieved through standard control design methods. A method is proposed for the detection and identification of such fault conditions using a single layer feed-forward neural network, running in real time. As a example it is demonstrated that the neural network can be trained to identify faults affecting the system position transducer measurements, and that the output from the neural network can be used as a decision tool for reconfiguring control. In this way satisfactory control of the system can be maintained during failure of a controller input. The method requires no knowledge of the system dynamics or system disturbances, and the network can be trained on-line.

To understand the evolution of the biosphere, we need to know how much oxygen was present in Earth's atmosphere during most of the past 2.5 billion years. However, there are few proxies sensitive enough to quantify O 2 at the low levels present until slightly less than 1 billion years ago. Lu et al. measured iodine/calcium ratios in marine carbonates, which are a proxy for dissolved oxygen concentrations in the upper ocean. They found that a major, but temporary, rise in atmospheric O 2 occurred at around 400 million years ago and that O 2 levels underwent a step change to near-modern values around 200 million years ago. Science , this issue p. 174 The I/Ca ratio in marine carbonates tracks atmospheric oxygen levels for the past 2.5 billion years. Rising oceanic and atmospheric oxygen levels through time have been crucial to enhanced habitability of surface Earth environments. Few redox proxies can track secular variations in dissolved oxygen concentrations around threshold levels for metazoan survival in the upper ocean. We present an extensive compilation of iodine-to-calcium ratios (I/Ca) in marine carbonates. Our record supports a major rise in the partial pressure of oxygen in the atmosphere at ~400 million years (Ma) ago and reveals a step change in the oxygenation of the upper ocean to relatively sustainable near-modern conditions at ~200 Ma ago. An Earth system model demonstrates that a shift in organic matter remineralization to greater depths, which may have been due to increasing size and biomineralization of eukaryotic plankton, likely drove the I/Ca signals at ~200 Ma ago.

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 ‘radiologically normal for age’ head MRI examinations from two large UK hospitals for model training and testing (age range = 18–95 years), and demonstrate fast (< 5 s), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy ‘excessive for age’. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.

The matrix (M) protein of vesicular stomatitis virus (VSV) has a complex role in infection and immune evasion, particularly with respect to suppression of Type I interferon (IFN). Viral strains bearing the wild-type (wt) M protein are able to suppress Type I IFN responses. We recently reported that the 22–25 strain of VSV encodes a wt M protein, however its sister plaque isolate, strain 22–20, carries a M[MD52G] mutation that perturbs the ability of the M protein to block NFκB, but not M-mediated inhibition of host transcription. Therefore, although NFκB is activated in 22–20 infected murine L929 cells infected, no IFN mRNA or protein is produced. To investigate the impact of the M[D52G] mutation on immune evasion by VSV, we used transcriptomic data from L929 cells infected with wt, 22–25, or 22–20 to define parameters in a family of executable logical models with the aim of discovering direct targets of viruses encoding a wt or mutant M protein. After several generations of pruning or fixing hypothetical regulatory interactions, we identified specific predicted targets of each strain. We predict that wt and 22–25 VSV both have direct inhibitory actions on key elements of the NFκB signaling pathway, while 22–20 fails to inhibit this pathway.

Manufacturing is an imperfect process that requires frequent checks and verifications to ensure products are being produced properly. In many cases, such as visual inspection, these checks can be automated to a certain degree. Incorporating advanced inspection techniques (i.e., via deep learning) into real-world inspection pipelines requires different mechanical, machine vision, and process-level considerations. In this work, we present an approach that builds upon prior work at an automotive gear facility located in Guelph, Ontario, which is looking to expand its defect detection capabilities. We outline a set of inspection-cell changes, which has led to full-gear surface scanning and inspection at a rate of every 7.5 s, and which is currently able to detect three common types of surface-level defects.

In a randomized trial of PCI in patients with stable angina who were receiving little or no antianginal medication and had documented ischemia, PCI resulted in a better health status with respect to angina than placebo at 12 weeks.