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Nanostructural modification of two-dimensional (2D) materials has attracted significant attention for enhancing hydrogen evolution reaction (HER) activity. selleck compound In this study, the nanostructure of TaS2 films was controlled by controlling the Ar/H2S gas ratio used in plasma-enhanced chemical vapor deposition (PECVD). At a high Ar/H2S gas ratio, vertically aligned TaS2 (V-TaS2) films were formed over a large area (4 in) at a temperature of 250 °C, which, to the best of our knowledge, is the lowest temperature reported for PECVD. Furthermore, the plasma species formed in the injected gas at various Ar/H2S gas ratios were analyzed using optical emission spectroscopy to determine the synthesis mechanism. In addition, the 4 in wafer-scale V-TaS2 was analyzed by X-ray photoelectron spectroscopy, transmission electron microscopy, and atomic force microscopy, and the HER performance of the as-synthesized TaS2 fabricated with various Ar/H2S ratios was measured. The results revealed that, depending on the film structure of TaS2, the HER performance can be enhanced owing to its structural advantage. Furthermore, the excellent stability and robustness of V-TaS2 was confirmed by conducting 1,000 HER cycles and post-HER material characterization. This study provides important insights into the plasma-assisted nanostructural modification of 2D materials for application as enhanced electrocatalysts.The convexity of objectives and constraints in fluence map optimization (FMO) for radiation therapy has been extensively studied. Next to convexity, there is another important characteristic of optimization functions and problems, which has thus far not been considered in FMO literature conic representation. Optimization problems that are conically representable using quadratic, exponential and power cones are solvable with advanced primal-dual interior point algorithms. These algorithms guarantee an optimal solution in polynomial time and have good performance in practice. In this paper, we construct conic representations for most FMO objectives and constraints. Thus, this paper is the first that shows that FMO problems containing multiple biological evaluation criteria can be solved in polynomial time. For fractionation-corrected functions for which no exact conic reformulation is found, we provide an accurate approximation that is conically representable. We present numerical results on the TROTS data set, which demonstrate very stable numerical performance for solving FMO problems in conic form. With ongoing research in the optimization community, improvements in speed can be expected, which makes conic optimization a promising alternative for solving FMO problems.Color centers in hexagonal boron nitride (hBN) have emerged as promising candidates for single-photon emitters (SPEs) due to their bright emission characteristics at room temperature. In contrast to mono- and few-layered hBN, color centers in multi-layered flakes show superior emission characteristics such as higher saturation counts and spectral stability. Here, we report a method for determining both the axial position and three-dimensional dipole orientation of SPEs in thick hBN flakes by tuning the photonic local density of states using vanadium dioxide (VO2), a phase change material. Quantum emitters under study exhibit a strong surface-normal dipole orientation, providing some insight on the atomic structure of hBN SPEs, deeply embedded in thick crystals. Next, we optimized a hot pickup technique to reproducibly transfer the hBN flake from VO2/Sapphire substrate onto SiO2/Si substrate and relocated the same emitters. Our approach serves as a practical method to systematically characterize SPEs in hBN prior to integration in quantum photonics systems.By altering some synthesis variables, the morphology and structural properties of anodic TiO2 Nanotube Arrays (TiO2NTs) can be tailored to a specific application. This study aims to investigate the effect of electrolyte-containing ions from human plasma and annealing temperature on structural, morphological, and mechanical parameters of TiO2NTs films, targeting its potential biomedical applications. Bio-inspired TiO2NTs were grown from Ticp and its Ti6Al4V alloy by potentiostatic anodization in the recently developed SBF-based electrolyte, maintained at 10 °C and 40 °C. The thermal investigation was performed by TGA/DSC and used to define the phase transition temperatures used for annealing (450 and 650 °C). Morphological and structural parameters were evaluated by FE-SEM, XRD, contact angle measurements, and nanoindentation. Results show that self-organized as-formed TiO2NTs were grown under all synthesis conditions with different wettability profiles for each substrate group. At 450 °C annealing temperature, the beginning of nanostructures collapse starts, becoming evident at 650 °C. The nanoindentation characterization reveals that both electrolyte and thermal annealing exhibited low effects on the hardness and Young's modulus. The tailoring of specific properties by different synthesis conditions could allow the individualization of treatments and better performance in vivo.Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is to develop a framework, which given an initial state defined by patient-attributes, can predict future states based on patterns from a well-defined patient population. Here, we investigate the feasibility of predicting patient anatomical changes, defined as a joint state of volume and daily setup changes, across a fractionated treatment schedule using two approaches. The first is based on a new framework employing quantum mechanics in combination with deep recurrent neural networks, denoted QRNN. The second approach is developed based on a classical framework, which models patient changes as a Markov process, denoted MRNN. We evaluated the performance of these two approaches on a dataset of 125 head and neck cancer patients, which was supplemented by synthetic data generated using a generative adversarial network. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) scores. The MRNN framework had slightly better performance, with MRNN(QRNN) validation AUC scores of 0.742 ± 0.021 (0.675 ± 0.036), 0.709 ± 0.026 (0.656 ± 0.021), 0.724 ± 0.036 (0.652 ± 0.044), and 0.698 ± 0.016 (0.605 ± 0.035) for system state vector sizes of 4, 6, 8, and 10, respectively. Of these, the results from the two higher order states had statistically significant differences (p less then 0.05). A similar trend was observed when the models were applied to an external testing dataset of 20 patients, yielding MRNN(QRNN) AUC scores of 0.707 (0.623), 0.687 (0.608), 0.723 (0.669), and 0.697 (0.609) for states vectors sizes of 4, 6, 8, and 10, respectively. These results suggest that both models have potential value in predicting patient changes during the course of adaptive radiotherapy.Imaging of tissue engineered three-dimensional (3D) specimens is challenging due to their thickness. We propose a novel multimodal imaging technique to obtain multi-physical 3D images and the electrical conductivity spectrum of tissue engineered specimens in vitro. We combine simultaneous recording of rotational multifrequency electrical impedance tomography (R-mfEIT) with optical projection tomography (OPT). Structural details of the specimen provided by OPT are used here as geometrical priors for R-mfEIT. This data fusion enables accurate retrieval of the conductivity spectrum of the specimen. We demonstrate experimentally the feasibility of the proposed technique using a potato phantom, adipose and liver tissues, and stem cells in biomaterial spheroids. The results indicate that the proposed technique can distinguish between viable and dead tissues and detect the presence of stem cells. This technique is expected to become a valuable tool for monitoring tissue engineered specimens' growth and viability in vitro.In 2020, the UK environmental regulators and food safety agencies, published the 25th edition of the Radioactivity in Food and the Environment (RIFE) report. This marks a quarter of a century since the landmark RIFE report was first published by the Ministry of Agriculture, Fisheries and Food in 1996, which represented the first joint monitoring and assessment report for the United Kingdom. This paper provides a summary of the RIFE report, how it has evolved and presents some case studies from over the 25-year period.Accurate dosimetry plays a key role in evaluating the radiation-induced health risks of radiation workers. The National Dose Registry in Korea contains the dose records of radiation workers in nuclear-related occupations since 1984. Thus, radiation doses for workers before 1984 are often sparse or missing. This study aimed to estimate the historical radiation dose before 1984 for radiation workers in Korea based on dose reconstruction models. The dose reconstruction models were derived from the nationwide self-administered questionnaire survey and the personal badge doses for workers in the cohort of the Korean Radiation Worker Study (KRWS). The mean estimated annual doses between 1984 and 2016 from the dose reconstruction model were 4.67-0.6 mSv, which closely matched the reported doses of 4.51-0.43 mSv. The mean estimated annual doses between 1961 and 1983 based on the exposure scenarios developed by factors associated with radiation doses ranged from 11.08-4.82 mSv. The mean estimated annual doses of individuals in the cohort from 1961-1983 ranged from 11.15-4.88 mSv. Although caution needs to be exercised in the interpretation of these estimations due to uncertainty owed to the nature of extrapolation beyond the range of observed data, this study offers a sense of the radiation doses for workers during Korea's early period of radiation-related activities, which can be a useful piece of information for radiation-induced health risk assessments.The mechanical properties of soft tissues play a key role in studying human injuries and their mitigation strategies. While such properties are indispensable for computational modelling of biological systems, they serve as important references in loading and failure experiments, and also for the development of tissue simulants. To date, experimental studies have measured the mechanical properties of peripheral tissues (e.g., skin) in-vivo and limited internal tissues ex-vivo in cadavers (e.g., brain and the heart). The lack of knowledge on a majority of human tissues inhibit their study for applications ranging from surgical planning, ballistic testing, implantable medical device development, and the assessment of traumatic injuries. The purpose of this work is to overcome such challenges through an extensive review of the literature reporting the mechanical properties of whole-body soft tissues from head to toe. Specifically, the available linear mechanical properties of all human tissues were compiled. Non-linear biomechanical models were also introduced, and the soft human tissues characterized using such models were summarized. The literature gaps identified from this work will help guide future biomechanical studies on soft human tissue characterization and the development of accurate medical models for the study and mitigation of injuries.