ALBERT

Description: In this work, the impact of temperature is investigated on the electrical characteristics of charge plasma-based doping-less double gate tunnel FET (DL-DG-TFET) with a low bandgap source material i.e., Si0.5Ge0.5. The influence of temperature (from 250 K to 450 K) is analysed on several performance parameters of the device such as bandgap, threshold voltage, SS, switching current ratio, ID-VGS, ID-VDS, gate current. The small change in energy bandgap with temperature reflects that device is minimally dependent on temperature. Temperature impact is significant on the sub-threshold region of transfer characteristics due to SRH recombination and trap-assisted tunnelling current. Insignificant variation of gate current with temperature signifies the better reliability of the device. Further, temperature effect is observed on analog parameters such as cut-off frequency, gate capacitance, trans-conductance, output conductance, power delay product (PDP). The minimal variation of analog parameters with temperature assures application of device in high-temperatures.

Description: Purpose. To investigate indirect radiation-induced changes in airways as precursors to atelectasis post radiation therapy (RT). Methods. Three Wisconsin Miniature Swine (WMS TM ) underwent a research course of 60 Gy in 5 fractions delivered to a targeted airway/vessel in the inferior left lung. The right lung received a max point dose

Description: The compression behavior of osmium metal was investigated up to 280 GPa (volume compression V/Vo =0.725) under nonhydrostatic conditions at ambient temperature using angle dispersive axial x-ray diffraction (A-XRD) with a diamond anvil cell (DAC). In addition, shear strength of osmium was measured to 170 GPa using radial x-ray diffraction (R-XRD) technique in DAC. Both diffraction techniques in DAC employed platinum as an internal pressure standard. Density functional theory (DFT) calculations were also performed, and the computed lattice parameters and volumes under compression are in good agreement with the experiments. DFT predicts a monotonous increase in axial ratio (c/a) with pressure and the structural anomalies of less than 1 % in (c/a) ratio below 150 GPa were not reproduced in theoretical calculations and hydrostatic measurements. The measured value of shear strength of osmium (τ) approaches a limiting value of 6 GPa above a pressure of 50 GPa in contrast to theoretical predictions of 24 GPa and is likely due to imperfections in polycrystalline samples. DFT calculations also enable the studies of shear and tensile deformations. The theoretical ideal shear stress is found along the (001)[1-10] shear direction with the maximal shear stress ~24 GPa at critical strain ~0.13.

Description: This article discusses the fabrication and performance of a multi-channel fibre switch, consisting of 19 single-mode fibres, with enhanced coupling efficiency due to micro-optics, directly printed via two-photon-polymerization on the end-face of each fibre. The use of high-resolution two-photon-polymerization not only allows the enhancement of the coupling efficiency with respect to the coupling device in use but likewise offers great freedom in the arrangement of the used fibres. This letter gives a thorough explanation of the fabrication method as well as the optical simulations for the lenses on the fibre assembly.

Description: Quantum metrology requires a stable single-photon emission and a high single-photon purity. Since nitrogen-vacancy (NV-) centers provide both features at room temperature, they are promising candidates for the application in this field [1, 2]. The knowledge about a suitable sample preparation technique is crucial, because the quality of the single-photon emission strongly depends on the sample purity and on the spatial resolvability of the emitters. This work presents the comparison and optimization of various sample fabrication techniques of nitrogen vacancy center doped nanodiamonds on standard cover glasses. The preparation is followed by a comparative characterization of the centers of the various samples. The sample fabrication includes the removal of contaminants on the cover glass surface. This was carried out by using peroxymonosulfuric acid (piranha solution, H2SO5) in comparison to the commercially available lye Hellmanex III (by Hellma Analytics). After cleaning the cover glasses, volumes of various nanodiamond dilutions were applied via spin coating. In subsequent steps, the nanodiamonds themselves were cleaned with peroxymonosulfuric acid, too, to remove contaminants resulting from the manufacturing process, e.g. graphite. The samples were analyzed by using a confocal laser scanning microscope with an oil immersion objective. Single-photon purity was determined by measuring the second order correlation function with a Hanbury Brown and Twiss setup. Spectral analysis revealed the presence of NV-- and NV0-centers. It was shown that a suitable cleansing method has an immense impact on single-photon emission, as was proven by a comparative characterization of differently manufactured nanodiamonds.

Description: This paper presents a molecular structure-informed viscoelastic constitutive equation that adopts the Doi-Edward’s tube model with coarse-grained molecular dynamics (MD) simulation and primitive path analysis. Since this model contains polymer physics-related parameters directly obtained from molecular simulations, it can reflect molecular information in predictions of the viscoelastic behavior of elastomers, unlike other empirical models. The proposed incremental formulations and constitutive stiffness matrix were implemented into implicit finite element analysis (FEA) codes as a user-supplied material model and viscoelastic properties (storage, loss modulus, and tan⁡δ) were calculated from the constitutive equation. While obtaining polymer dynamics parameter of the molecular system, a relationship between self-diffusivity coefficient (D_c) and the polymerization degree of the polymer was confirmed. Furthermore, a series of parametric studies showed that increase of the primitive path length (L) and decrease of D_c have led to the strengthening of moduli and decrease of tan⁡δ peak. Moreover, under the same condition, the shift of tan⁡δ peak to low-frequency domain was observed, which implies a decline in free volume in the molecular system and an increase in the glass transition temperature.

Description: Purpose The aim of this study was to assess the feasibility of the development and training of a deep learning object detection model for automating the assessment of fiducial marker migration and tracking of the prostate in radiotherapy patients. Methods and Materials A fiducial marker detection model was trained on the YOLO v2 detection framework using approximately 20,000 pelvis kV projection images with fiducial markers labelled. The ability of the trained model to detect marker positions was validated by tracking the motion of markers in a respiratory phantom and comparing detection data with the expected displacement from a reference position. Marker migration was then assessed in 14 prostate radiotherapy patients using the detector for comparison with previously conducted studies. This was done by determining variations in intermarker distance between the first and subsequent fractions in each patient. Results On completion of training, a detection model was developed that operated at a 96% detection efficacy and with a root mean square error of 0.3 pixels. By determining the displacement from a reference position in a respiratory phantom, experimentally and with the detector it was found that the detector was able to compute displacements with a mean accuracy of 97.8% when compared to the actual values. Interfraction marker migration was measured in 14 patients and the average and maximum ± standard deviation marker migration were found to be 2.0±0.9 mm and 2.3±0.9 mm, respectively. Conclusion This study demonstrates the benefits of pairing deep learning object detection, and image-guided radiotherapy and how a workflow to automate the assessment of organ motion and seed migration during prostate radiotherapy can be developed. The high detection efficacy and low error make the advantages of using a pre-trained model to automate the assessment of the target volume positional variation and the migration of fiducial markers between fractions.

Description: We report the double-fold enhancement of piezoelectric nanogenerator output voltage with a simple design strategy. The piezoelectric nanogenerator is fabricated with ZnO nanosheets coated on both sides of the aluminum substrate in this new design strategy with necessary electrodes. The cost-effective hydrothermal method is employed to synthesize two-dimensional (2D) ZnO nanosheets on both sides of the aluminum substrate at a low growth temperature of 80˚C for 4 hours. The ZnO nanosheets were characterized for their morphology, crystallinity, and photoluminescence property. The nanogenerator is fabricated with a double-side coated aluminum substrate and compared its performance with a single-side coated aluminum substrate. The nanogenerators fabricated only with one side coating produced an output voltage of ~ 170 mV. In contrast, the nanogenerators fabricated with a double side coating produced an output voltage of ~ 285 mV. The nanogenerator with double-side coating produced ~1.7 times larger voltage output compared to the voltage output from one side coated nanogenerators fabricated with each side of the substrate. The enhancement in the output

Description: This study conducted a systematic literature review of the technical aspects and methodological choices in life cycle assessment (LCA) studies of using hydrogen for road transport. More than 70 scientific papers published during 2000–2021 were reviewed, in which more than 350 case studies of use of hydrogen in the automotive sector were found. Only some studies used hybrid LCA and energetic input-output LCA, whereas most studies addressed attributional process-based LCA. A categorization based on the life cycle scope distinguished case studies that addressed the well-to-tank (WTT), well-to-wheel (WTW), and complete life cycle approaches. Furthermore, based on the hydrogen production process, these case studies were classified into four categories: thermochemical, electrochemical, thermal-electrochemical, and biochemical. Moreover, based on the hydrogen production site, the case studies were classified as centralized, on-site, and on-board. The fuel cell vehicle passenger car was the most commonly used vehicle. The functional unit for the WTT studies was mostly mass or energy, and vehicle distance for the WTW and complete life cycle studies. Global warming potential (GWP) and energy consumption were the most influential categories. Apart from the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model and the Intergovernmental Panel on Climate Change for assessing the GWP, the Centrum voor Milieukunde Leiden method was most widely used in other impact categories. Most of the articles under review were comparative LCA studies on different hydrogen pathways and powertrains. The findings provide baseline data not only for large-scale applications, but also for improving the efficiency of hydrogen use in road transport.

Description: Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) from thoracic CBCTs. A deep-learning model which integrates histogram matching (HM) into a cycle-consistent adversarial network (Cycle-GAN) framework, called HM-Cycle-GAN, was trained to learn mapping between thoracic CBCTs and paired planning CTs. Perceptual supervision was adopted to minimize blurring of tissue interfaces. An informative maximizing loss was calculated by feeding CBCT into the HM-Cycle-GAN to evaluate the image histogram matching between the planning CTs and the sCTs. The proposed algorithm was evaluated using data from 20 SBRT patients who each received 5 fractions and therefore 5 thoracic CBCTs. To reduce the effect of anatomy mismatch, original CBCT images were pre-processed via deformable image registrations with the planning CT before being used in model training and result assessment. We used planning CTs as ground truth for the derived sCTs from the correspondent co-registered CBCTs. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC) indices were adapted as evaluation metrics of the proposed algorithm. Assessments were done using Cycle-GAN as the benchmark. The average MAE, PSNR, and NCC of the sCTs generated by our method were 66.2 HU, 30.3 dB, and 0.95, respectively, over all CBCT fractions. Superior image quality and reduced noise and artifact severity were seen using the proposed method compared to the results from the standard Cycle-GAN method. Our method could therefore improve the accuracy of IGRT and corrected CBCTs could help improve online adaptive RT by offering better contouring accuracy and dose calculation.

Description: Purpose: Since guidance based on X-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the X-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures. Method: We present a robust learning-based denoising strategy involving model- based simulations of low-dose X-ray images during the training phase. The method also utilizes a data-driven normalization step - based on an X-ray imaging model - to stabilize the mixed signal-dependent noise associated with X-ray images. We thoroughly analyze the method's sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose X-ray images on the denoising results. Results: A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band co efficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy. Conclusion: The proposed learning-based denoising strategy provides scope for significant X-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training ph