Materials That Enhance Efficiency and Radiation Resistance of Solar Cells

A thin layer (approximately 10 microns) of a novel "transparent" fluorescent material is applied to existing solar cells or modules to effectively block and convert UV light, or other lower solar response waveband of solar radiation, to visible or IR light that can be more efficiently used by solar cells for additional photocurrent. Meanwhile, the layer of fluorescent coating material remains fully "transparent" to the visible and IR waveband of solar radiation, resulting in a net gain of solar cell efficiency. This innovation alters the effective solar spectral power distribution to which an existing cell gets exposed, and matches the maximum photovoltaic (PV) response of existing cells. By shifting a low PV response waveband (e.g., UV) of solar radiation to a high PV response waveband (e.g. Vis-Near IR) with novel fluorescent materials that are transparent to other solar-cell sensitive wavebands, electrical output from solar cells will be enhanced. This approach enhances the efficiency of solar cells by converting UV and high-energy particles in space that would otherwise be wasted to visible/IR light. This innovation is a generic technique that can be readily implemented to significantly increase efficiencies of both space and terrestrial solar cells, without incurring much cost, thus bringing a broad base of economical, social, and environmental benefits. The key to this approach is that the "fluorescent" material must be very efficient, and cannot block or attenuate the "desirable" and unconverted" waveband of solar radiation (e.g. Vis-NIR) from reaching the cells. Some nano-phosphors and novel organometallic complex materials have been identified that enhance the energy efficiency on some state-of-the-art commercial silicon and thin-film-based solar cells by over 6%

A Bayesian optimization tunning integrated multi-stacking classifier framework for the prediction of radiodermatitis from 4D-CT of patients underwent breast cancer radiotherapy

PurposeIn this study, we aimed to develop a novel Bayesian optimization based multi-stacking deep learning platform for the prediction of radiation-induced dermatitis (grade ≥ two) (RD 2+) before radiotherapy, by using multi-region dose-gradient-related radiomics features extracted from pre-treatment planning four-dimensional computed tomography (4D-CT) images, as well as clinical and dosimetric characteristics of breast cancer patients who underwent radiotherapy.Materials and methodsThe study retrospectively included 214 patients with breast cancer who received radiotherapy after breast surgeries. Six regions of interest (ROIs) were delineated based on three PTV dose -gradient-related and three skin dose-gradient-related parameters (i.e., isodose). A total of 4309 radiomics features extracted from these six ROIs, as well as clinical and dosimetric characteristics, were used to train and validate the prediction model using nine mainstream deep machine learning algorithms and three stacking classifiers (i.e., meta-learners). To achieve the best prediction performance, a Bayesian optimization based multi-parameter tuning technology was adopted for the AdaBoost, random forest (RF), decision tree (DT), gradient boosting (GB) and extra tree (XTree) five machine learning models. The five parameter -tuned learners and the other four learners (i.e., logistic regression (LR), K-nearest neighbors (KNN), linear discriminant analysis (LDA), Bagging) whose parameters cannot be tuned, all as the primary week learners, were fed into the subsequent meta-learners for training and learning the final prediction model.ResultsThe final prediction model included 20 radiomics features and eight clinical and dosimetric characteristics. At the primary learner level, on base of Bayesian parameter tuning optimization, the RF, XGBoost, AdaBoost, GBDT, and LGBM models with the best parameter combinations achieved AUC of 0.82, 0.82, 0.77, 0.80, and 0.80 prediction performance in the verification data set, respectively. In the secondary meta-learner lever, compared with LR and MLP meta-learner, the best predictor of symptomatic RD 2+ for stacked classifiers was the GB meta-learner with an area under the curve (AUC) of 0.97 [95% CI: 0.91-1.0] and an AUC of 0.93 [95% CI: 0.87-0.97] in the training and validation datasets, respectively and the 10 top predictive characteristics were identified.ConclusionA novel multi-region dose-gradient-based Bayesian optimization tunning integrated multi-stacking classifier framework can achieve a high-accuracy prediction of symptomatic RD 2+ in breast cancer patients than any other single deep machine learning algorithm

Triazole–Au(I) complex as chemoselective catalyst in promoting propargyl ester rearrangements

Triazole–Au (TA–Au) catalysts were employed in several transformations involving propargyl ester rearrangement. Good chemoselectivity was observed, which allowed the effective activation of the alkyne without affecting the reactivity of the allene ester intermediates. These results led to the investigation of the preparation of allene ester intermediates with TA–Au catalysts under anhydrous conditions. As expected, the desired 3,3-rearrangement products were obtained in excellent yields (generally \u3e90% yields with 1% loading). Besides the typical ester migrating groups, carbonates and carbamates were also found to be suitable for this transformation, which provided a highly efficient, practical method for the preparation of substituted allenes

Mutual Coupling Effects on Pattern Diversity Antennas for MIMO Femtocells

Diversity antennas play an important role in wireless communications. However, mutual coupling between multiple ports of a diversity antenna has significant effects on wireless radio links and channel capacity. In this paper, dual-port pattern diversity antennas for femtocell applications are proposed to cover GSM1800, UMTS, and WLAN frequency bands. The channel capacities of the proposed antennas and two ideal dipoles with different mutual coupling levels are investigated in an indoor environment. The relation between mutual coupling and channel capacity is observed through investigations of these antennas

A COVID-19 Risk Score Combining Chest CT Radiomics and Clinical Characteristics to Differentiate COVID-19 Pneumonia From Other Viral Pneumonias

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective

Disruption of RFX family transcription factors causes autism, attention-deficit/hyperactivity disorder, intellectual disability, and dysregulated behavior

Purpose We describe a novel neurobehavioral phenotype of autism spectrum disorder (ASD), intellectual disability, and/or attention-deficit/hyperactivity disorder (ADHD) associated with de novo or inherited deleterious variants in members of the RFX family of genes. RFX genes are evolutionarily conserved transcription factors that act as master regulators of central nervous system development and ciliogenesis. Methods We assembled a cohort of 38 individuals (from 33 unrelated families) with de novo variants in RFX3, RFX4, and RFX7. We describe their common clinical phenotypes and present bioinformatic analyses of expression patterns and downstream targets of these genes as they relate to other neurodevelopmental risk genes. Results These individuals share neurobehavioral features including ASD, intellectual disability, and/or ADHD; other frequent features include hypersensitivity to sensory stimuli and sleep problems. RFX3, RFX4, and RFX7 are strongly expressed in developing and adult human brain, and X-box binding motifs as well as RFX ChIP-seq peaks are enriched in the cis-regulatory regions of known ASD risk genes. Conclusion These results establish a likely role of deleterious variation in RFX3, RFX4, and RFX7 in cases of monogenic intellectual disability, ADHD and ASD, and position these genes as potentially critical transcriptional regulators of neurobiological pathways associated with neurodevelopmental disease pathogenesis

Computational studies and optimization of wakefield accelerators

Laser- and particle beam-driven plasma wakefield accelerators produce accelerating fields thousands of times higher than radio-frequency accelerators, offering compactness and ultrafast bunches to extend the frontiers of high energy physics and to enable laboratory-scale radiation sources. Large-scale kinetic simulations provide essential understanding of accelerator physics to advance beam performance and stability and show and predict the physics behind recent demonstration of narrow energy spread bunches. Benchmarking between codes is establishing validity of the models used and, by testing new reduced models, is extending the reach of simulations to cover upcoming meter-scale multi-GeV experiments. This includes new models that exploit Lorentz boosted simulation frames to speed calculations. Simulations of experiments showed that recently demonstrated plasma gradient injection of electrons can be used as an injector to increase beam quality by orders of magnitude. Simulations are now also modeling accelerator stages of tens of GeV, staging of modules, and new positron sources to design next-generation experiments and to use in applications in high energy physics and light sources

Couplage spectro-électrochimique Raman-Impédance (application à la polyaniline)

La spectroscopie électrochimique d'impédance (SEI) renseigne sur les mécanismes à l interface électrode-électrolyte, mais ne peut pas identifier la nature des espèces adsorbées à la surface de l'électrode. La spectroscopie Raman est une technique puissante pour la recherche structurale ; elle peut fournir les informations requises au niveau moléculaire, qui sont absentes des mesures de SEI. Le but de ce travail a été de mettre au point la méthode de couplage dynamique de ces deux techniques. Cette nouvelle méthode -La spectro-électrochimie Raman-Impédance- a ensuite été appliquée à l étude d un film de polymère conducteur.Le principe de la méthode consiste à analyser le spectre émis par l interface, et à convertir l'intensité des bandes choisies en une tension électrique. Plusieurs fonctions de transfert V/ I, Raman/ V, Raman/ I ; sont enregistrées simultanément. Les premiers résultats de cette méthode appliquée à l étude d un film mince de polyaniline prouvent que l'équilibre acido-basique à l'intérieur du polymère doit être pris en considération sur la base d un mécanisme cinétique impliquant deux réactions rédox et une réaction chimique.PARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Genetic Diversity and Population Structure of the Peanut Worm (Sipunculus nudus) in Southern China as Inferred from Mitochondrial 16S rRNA Sequences

Genetic diversity and population structure of the peanut worm (Sipunculus nudus) were investi- gated by using 536 base-pair fragments of the mitochondrial 16S ribosomal gene. Populations were collected from three locations along the southern coast of China - Beihai, Sanya, and Xiamen. Amplified polymerase chain reaction products were sequenced in both directions and data were analyzed using ClustalX, Arlequin, and MEGA. A total of 69 polymorphic sites and 21 distinct haplotypes were revealed. The mean haplotype and nucleotide diversity of the three pop- ulations were 0.814% and 0.37%, respectively. The Beihai population had the greatest haplo- type and nucleotide diversity, followed by the Xiamen and Sanya populations. Analysis of mole- cular variance (AMOVA) showed significant genetic differentiation among the three populations (Fst = 0.0619, p<0.05) and distinct population structures among the three sites

Graphdiyne Ink for Ionic Liquid Gated Printed Transistor

Graphdiyne‐based electronic devices have recently attracted a lot of research interest due to their excellent performance and promising application prospects in carbon electronics. Here, graphdiyne (GDY) inks are prepared by solution processing of newly grown GDY material, which is suitable for fabricating fully printed thin‐film field‐effect transistor (FET). An ionic liquid gate dielectric is used as a gate to maintain stable on/off ratios at different V SD compared to conventional SiO2 dielectric. Significantly, the GDY network combined with ionic liquid allows a general and cheap approach to achieve printed FET devices containing 2D carbon materials. Furthermore, a flexible FET on polyethylene terephthalate is developed, which still reaches a repeatable on/off ratio of more than 102. These results enable the design of wearable or large‐area carbon‐based electronics involving graphdiyne semiconductors, suggesting a promising new carbon material for novel electronic devices.This study was supported by the National Natural Science Foundation of China (51802324, 21790050, 21790051, 51822208, 21771187), the Frontier Science Research Project (QYZDB-SSW-JSC052) of the Chinese Academy of Sciences, and the Taishan Scholars Program of Shandong Province (tsqn201812111)