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In recent years, there has been growing demand for the monitoring of ascorbic acid levels, especially in underdeveloped populations where ascorbic acid deficiency affects up to 74% of individuals. To facilitate widespread ascorbic acid screening, we have developed a highly scalable conductive polymer nanocomposite with excellent ascorbic acid sensing performance. The material is based on polyaniline, which is deposited in a single step in the presence of polystyrene sulfonate and multi-walled carbon nanotubes onto carbon paper. The modified electrodes take advantage of the electrocatalytic properties of polyaniline toward ascorbic acid, which are boosted by the proton donating polystyrene sulfonate polymer and the high surface area of the multi-walled carbon nanotubes. The morphology and composition of the composite are characterized using field-emission scanning electron microscopy, transmission electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy and the electrochemical characteristics are examined using cyclic voltammetry and electrochemical impedance spectroscopy. The modified electrode shows good ascorbic acid sensing characteristics, with a linear range of 1-400 mu M, a sensitivity of 546 mu A mM-1 cm-2, and a limit of detection of 0.11 mu M. High performance and low cost results in a promising platform to support the widespread, cheap monitoring of ascorbic acid deficiency.

Tetrodotoxin (TTX), a potent neurotoxin, has recently been detected in marine fauna and waters in multiple locations outside its historical range and is projected to increase in both stability and bioavailability in acidifying oceans. We report the first biosensor designed to detect sub-ng ml-1 concentrations of TTX directly in natural seawater. The compact sensor chip employs a competitive enzyme strategy with a glucose oxidase-TTX conjugate that generates an amperometric response in the presence of a ferricyanide redox mediator. The biorecognition layer synthesis is confirmed by attenuated total reflection Fourier transform infrared spectroscopy. Cyclic voltammetry and electrochemical impedance spectroscopy are used to probe the stability and hydrophilicity of the biorecognition layer. We investigate the effects of nonspecific adsorption on sensor results and find that bovine serum albumen is an excellent blocking agent for preventing NSA when incubating with filtered seawater samples. The biosensor has a limit of detection of 0.053 ng ml-1 and a sensitivity of 87 nA ng-1 ml. Requiring no extraction or preconcentration steps with a time-to-results of under an hour, our biosensor provides a new tool for detecting TTX in natural waters, which can lead to more proactive monitoring and higher throughput testing.

Gel polymer electrolytes (GPEs) are promising candidates for next generation lithium metal batteries (LMBs) due to their reassuring safety and high ionic conductivity at room temperature. However, they are still plagued by incompatible interfaces and poor compatibility with Ni-rich cathodes, imposing great restrictions on the scalability of high-voltage cathodes. To overcome these problems, a GPE with a polyacrylonitrile porous membrane, together with 2,2,3,4,4,4-hexafluorobutyl acrylate grafted pentaerythritol tetraacrylate, is fabricated via in-situ thermal polymerization. By virtue of the effective transport channels provided by PAN and PETEA molecules, our GPE exhibits both a high lithium-ion transference number and a high ionic conductivity of 0.87 and 4.06 × 10−3 S cm−1, respectively at 30 °C. Moreover, the GPE ensures a high electrochemical window, stable at potentials up to 5.35 V, enabling the application of LiNi0.8Co0.1Mn0.1O2 (NCM811) cathodes. Due to the uniform cathode electrolyte interface (CEI) formed by our GPE on the surface of NCM811, the assembled NCM811\GPE\Li batteries present an excellent cycling life of 300 cycles in the voltage range of 3 ∼ 4.5 V with a capacity retention of 84% at 2C. Our study provides a novel strategy for constructing fast Li+ transport channels and stable CEI membranes in LMBs operating at high voltages.

Background: Frequent binge drinking is a known contributor to alcohol-related harm, but its impact on systemic and hepatic inflammation is not fully understood. We hypothesize that changes in immune markers play a central role in adverse effects of acute alcohol intake, especially in patients with early liver disease. Aim: To investigate the effects of acute alcohol intoxication on inflammation-related markers in hepatic and systemic venous plasma in people with alcohol-related liver disease (ArLD), non-alcoholic fatty liver disease (NAFLD) and healthy controls. Methods: Thirty-eight participants (13 with ArLD, 15 with NAFLD and 10 healthy controls) received 2.5 mL of 40% ethanol per kg body weight via a nasogastric tube. Seventy-two inflammation-related markers were quantified in plasma from hepatic and systemic venous blood, at baseline, 60 and 180 min after intervention. Results: Alcohol intervention altered the levels of 31 of 72 and 14 of 72 markers in the systemic and hepatic circulation. All changes observed in the hepatic circulation were also identified in the systemic circulation after 180 min. Only FGF21 and IL6 were increased after alcohol intervention, while the remaining 29 markers decreased. Differences in response to acute alcohol between the groups were observed for 8 markers, and FGF21 response was blunted in individuals with steatosis. Conclusion: Acute alcohol intoxication induced changes in multiple inflammation-related markers, implicated in alcohol metabolism and hepatocellular damage. Differences identified between marker response to binge drinking in ArLD, NAFLD and healthy controls may provide important clues to disease mechanisms and potential targets for treatment. Clinical trial number: NCT03018990.

This thesis concerns labelling unlabelled motion capture (mocap) data using a Dynamic Graph Convolutional Neural Network (DGCNN) [46]. The most common type of motion-capture system, i.e. passive motion-capture, records the 3D position of multiple reflective markers using multiple infrared cameras with overlapping fields of view. To make the recorded points useful, each point must be assigned a label uniquely identifying what it was attached to in the real world. For human subjects, the correspondence between recorded markers and the human body is usually established by having the subject perform a calibration pose at the beginning of the recording. This thesis investigates if this labelling process can be performed using a DGCNN originally devised for general point cloud segmentation and recognition. In order to evaluate this, a DGCNN was implemented and trained on synthetically generated mocap data. When applied to non-synthetic mocap data released as part of the state-of-the-art transformer-based SOMA [15] auto labelling system, the DGCNN could correctly label 99% of all recorded points, only slightly below the 100% state of the art performance on the same dataset., Det här arbetet handlar om uppmärkning av motion capture (mocap) data med ett dynamiska graf-faltningsnätverk (DGCNN) [46]. Den vanligaste typen av motion-capture-system, passiv motion-capture, spelar in reflekterande markörer med flera överlappande IR-kameror och beräknar markörernas 3D-positioner. För att den de inspelade positionerna (punkter) ska vara användbara måste de identifieras med vilken inspelad markör de härrör från. När mänsklig rörelse spelas in etableras vanligen kopplingen mellan de inspelade och verkliga markörerna genom att aktören börjar inspelningen med att intar en kalibreringspose. I det här arbetet undersöks om uppmärknings processen kan utföras av ett DGCNN, en algorithm ursprungligen skapad för att segmentera och kategorisera punktmoln generellt. För att undersöka detta implementerades en DGCNN och tränades på syntetiskt genererad mocap data. Applicerad på den genuina mocap datan publicerad som en del av SOMA [15] systemet för mocap uppmärkning, kunde DGCNN-nätverket korrekt ange den korrekta markören för 99% av de inspelade punkterna, bara lite det bästa resultatet i literaturen 100% för datasetet.