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A novel and efficient palladium-catalyzed C-H activation reaction of [60]fullerene with arylphosphinic acids has been developed for the synthesis of [60]fullerene-fused phosphinolactones. A possible reaction mechanism is proposed to explain the generation of the obtained products. A representative product can be further electrochemically transformed into bis-benzylated 1,2- and 1,4-adducts of [60]fullerene. In addition, a [60]fullerene-fused phosphinolactone with a 12-membered ring can also be synthesized from the electrochemical ring expansion of the employed phosphinolactone with a 6-memebered ring with 1,2-bis(bromomethyl)benzene.

The electrosynthesis of decorated basket molecules, that is, [60]fullerene-fused 12-membered macrolactones, has been achieved efficiently for the first time by the electrochemical reduction of [60]fullerene-fused 6-membered lactones and subsequent ring expansion with 1,2-bis(1-bromoalkyl)benzenes. The observed isomeric distributions of the obtained macrolactones are elucidated by theoretical calculations. The product structures have been firmly established by single-crystal X-ray analyses.

A novel and efficient Cu(I)-catalyzed radical heteroannulation reaction of [60]fullerene (C60) with α-bromo acetamides has been disclosed for the direct synthesis of diverse C60-fused lactams. Furthermore, the formed C60-fused lactams can be served as a versatile platform for further electrochemical functionalization to prepare 1,2-, 1,4-, 1,2,3,16-, and 1,4,9,25-adducts of C60. In addition, a representative fullerene product has been applied as an overcoating layer of the electron-transporting layer in n-type perovskite solar cell.

Endohedral clusterfullerenes exhibit unique chemical properties due to intramolecular electron transfer of the encaged metal cluster to the outer fullerene cages. Herein, we report the synthesis of two Sc 3 N@ D 3 h -C 78 monoadducts 2a and 2b through the 1, 3-dipolar reaction of Sc 3 N@ D 3 h -C 78 with carbonyl ylide bearing anomalous cis-conformation regioselectivity. The molecular structures of these monoadducts are unambiguously confirmed by single-crystal X-ray crystallography, revealing that both of 2a and 2b have cis-conformations within furan moiety grafted via [6,6]-closed addition patterns. Under the same conditions, the control reaction of C 60 with carbonyl ylide affords two monoadducts 3a and 3b , which however exhibit respectively the cis - and trans -conformations with [6,6]-closed addition patterns. According to theoretical calculations, the exclusive formation of the cis -only Sc 3 N@ D 3 h -C 78 monoadducts is interpreted by the conjunct effects of thermodynamic stability of adducts, the reactivity of the addition site, and the cis-dipole intermediate from trans 1 .

While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary example. The structural details of the cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 mm) provided by current medical CT scanners. In this paper, we propose a clinical micro-CT (CMCT) system design integrating conventional spiral cone-beam CT, contemporary interior tomography, deep learning techniques, and technologies of micro-focus X-ray source, photon-counting detector (PCD), and robotic arms for ultrahigh resolution localized tomography of a freely-selected volume of interest (VOI) at a minimized radiation dose level. The whole system consists of a standard CT scanner for a clinical CT exam and VOI specification, and a robotic-arm based micro-CT scanner for a local scan at much higher spatial and spectral resolution as well as much reduced radiation dose. The prior information from global scan is also fully utilized for background compensation to improve interior tomography from local data for accurate and stable VOI reconstruction. Our results and analysis show that the proposed hybrid reconstruction algorithm delivers superior local reconstruction, being insensitive to the misalignment of the isocenter position and initial view angle in the data/image registration while the attenuation error caused by scale mismatch can be effectively addressed with bias correction. These findings demonstrate the feasibility of our system design. We envision that deep learning techniques can be leveraged for optimized imaging performance. With high resolution imaging, high dose efficiency and low system cost synergistically, our proposed CMCT system has great potentials in temporal bone imaging as well as various other clinical applications., Comment: 10 pages, 13 figures, 3 tables

Electrochemical alkylations of a [60]fulleroindoline with different bulky alkyl bromides exhibit different reaction behaviors. The hydroalkylation and dialkylation of the electrochemically generated dianionic [60]fulleroindoline with bulky 2,4,6-tris(bromomethyl)mesitylene give rise to 1,2,3,16-adducts. In comparison, the hydroalkylation of the dianionic [60]fulleroindoline with bulkier diphenylbromomethane still affords a 1,2,3,16-adduct, while the corresponding dialkylation provides a sterically favoured 1,4,9,12-adduct, which is scarcely investigated, as the major product along with the isomeric 1,2,3,16-adduct as the minor product. The structures of these products have been determined by spectroscopic data and single-crystal X-ray diffraction analysis. A plausible reaction mechanism has been proposed to explain the formation of the observed products.

An efficient synthesis of [60]fullerene-fused furochromenones has been developed by the palladium-catalyzed heteroannulation of [60]fullerene with 4-hydroxycoumarins. The formed [60]fullerene-fused furochromenones can be further derivatized via an electrochemical method to synthesize 1,2,3,4-adducts in high yields.

The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey-Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function. Here we design a convolutional neural network to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function. Specifically, we use a Gaussian mixture model as an example to represent the phase function generally and learn the model parameters accurately. The Gaussian mixture model is selected because it provides the analytic expression of phase function to facilitate deflection angle sampling in MC simulation, and does not significantly increase the number of free parameters. Our proposed method is validated on MC-simulated reflectance images of typical biological tissues using the Henyey-Greenstein phase function with different anisotropy factors. The effects of field of view (FOV) and spatial resolution on the errors are analyzed to optimize the estimation method. The mean squared error of the phase function is 0.01 and the relative error of the anisotropy factor is 3.28%., Comment: 16 pages, 8 figures

The conversion of the [60]fullerene-fused lactones to ketones with triflic anhydride as an unusual reductant under aerobic conditions has been achieved in excellent yields. The present thermal retro Baeyer-Villiger reaction from lactones to ketones has no precedents. In addition, the unique ring-opened peroxy [60]fullerene derivatives can be obtained by the electrochemical reactions of the synthesized [60]fullerene-fused ketones.

Electrochemical benzylation of [60]fullerene-fused lactones with different motifs unexpectedly affords three types of ring-opened benzylated adducts under different conditions. Both [60]fullerene-fused δ- and γ-lactones can be benzylated to generate ring-opened mono- and bis-benzylated adducts in good yields. In addition, representative fullerene products have been applied as efficient electron-transport materials in perovskite solar cells.

The width of auroral arc is one of the most important factors in understanding and examining its physical mechanisms. In this letter, we propose a fully automatic method for computing the width of auroral arcs based on the instance segmentation of auroral images. To accurately detect and segment auroral arcs with oriented bounding boxes, we adapt a state-of-the-art instance segmentation model, Mask region-based convolutional neural network, by designing a two-stage inference process combined with an indispensable random rotation training strategy and designing an effective feature extraction architecture. Given the segmented masks of individual auroral arcs, we present a method for computing the arc width automatically. In our experiments, the instance segmentation model achieves 86.8% of mean average precision on the human-labeled data set. By automatically evaluating the width of 29 938 detected auroral arcs in 18 417 auroral arc images, we obtain a similar arc width distribution to that evaluated by the semiautomatic approach, which demonstrates the effectiveness of our proposed method.

An unprecedented retro Baeyer–Villiger reaction has been achieved by the electrochemical reduction of [60]fullerene-fused lactones in the presence of acetic acid at room temperature, affording [60]fullerene-fused ketones in excellent yields within a short time., A highly efficient electrochemical reduction of [60]fullerene-fused lactones to [60]fullerene-fused ketones, a formal process of retro Baeyer–Villiger reaction, has been achieved for the first time. The electrochemically generated dianionic [60]fullerene-fused lactones can be transformed into [60]fullerene-fused ketones in the presence of acetic acid in 85–91% yields. Control experiments have been performed to elucidate the reaction mechanism. The products have been characterized with spectroscopic data and single-crystal X-ray analysis. Moreover, the electrochemical properties have also been investigated.

Endohedral clusterfullerenes exhibit unique chemical properties due to intramolecular electron transfer of the encaged metal cluster to the outer fullerene cages. We report the synthesis of two Sc

Recently proposed neural architecture search (NAS) algorithms adopt neural predictors to accelerate architecture search. The capability of neural predictors to accurately predict the performance metrics of the neural architecture is critical to NAS, but obtaining training datasets for neural predictors is often time-consuming. How to obtain a neural predictor with high prediction accuracy using a small amount of training data is a central problem to neural predictor-based NAS. Here, a new architecture encoding scheme is first devised to calculate the graph edit distance of neural architectures, which overcomes the drawbacks of existing vector-based architecture encoding schemes. To enhance the predictive performance of neural predictors, two self-supervised learning methods are proposed to pre-train the architecture embedding part of neural predictors to generate a meaningful representation of neural architectures. The first method designs a graph neural network-based model with two independent branches and utilizes the graph edit distance of two different neural architectures as a supervision to force the model to generate meaningful architecture representations. Inspired by contrastive learning, the second method presents a new contrastive learning algorithm that utilizes a central feature vector as a proxy to contrast positive pairs against negative pairs. Experimental results illustrate that the pre-trained neural predictors can achieve comparable or superior performance compared with their supervised counterparts using only half of the training samples. The effectiveness of the proposed methods is further validated by integrating the pre-trained neural predictors into a neural predictor guided evolutionary neural architecture search (NPENAS) algorithm, which achieves stateof-the-art performance on NASBench-101, NASBench-201, and DARTS benchmarks

Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray energy spectrum is divided into several bins, each energy-bin-specific projection has a low signal-noise-ratio (SNR) than the current-integrating counterpart, which makes image reconstruction a unique challenge. Traditional wisdom is to use prior knowledge based iterative methods. However, this kind of methods demands a great computational cost. Inspired by deep learning, here we first develop a deep learning based reconstruction method; i.e., U-net with L_p^p-norm, Total variation, Residual learning, and Anisotropic adaption (ULTRA). Specifically, we emphasize the Various Multi-scale Feature Fusion and Multichannel Filtering Enhancement with a denser connection encoding architecture for residual learning and feature fusion. To address the image deblurring problem associated with the $L_2^2$-loss, we propose a general $L_p^p$-loss, $p>0$ Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the $L_p^p$-loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods. Finally, the anisotropically weighted total variation is employed to characterize the sparsity in the spatial-spectral domain to regularize the proposed network. In particular, we validate our ULTRA networks on three large-scale spectral CT datasets, and obtain excellent results relative to the competing algorithms. In conclusion, our quantitative and qualitative results in numerical simulation and preclinical experiments demonstrate that our proposed approach is accurate, efficient and robust for high-quality spectral CT image reconstruction.

Tomographic image reconstruction with deep learning is an emerging field, but a recent landmark study reveals that several deep reconstruction networks are unstable for computed tomography (CT) and magnetic resonance imaging (MRI). Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missing in a deeply reconstructed image, and (3) decreased imaging performance with increased input data. On the other hand, compressed sensing (CS) inspired reconstruction methods do not suffer from these instabilities because of their built-in kernel awareness. For deep reconstruction to realize its full potential and become a mainstream approach for tomographic imaging, it is thus critically important to meet this challenge by stabilizing deep reconstruction networks. Here we propose an Analytic Compressed Iterative Deep (ACID) framework to address this challenge. ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement. Our study demonstrates that the deep reconstruction using ACID is accurate and stable, and sheds light on the converging mechanism of the ACID iteration under a Bounded Relative Error Norm (BREN) condition. In particular, the study shows that ACID-based reconstruction is resilient against adversarial attacks, superior to classic sparsity-regularized reconstruction alone, and eliminates the three kinds of instabilities. We anticipate that this integrative data-driven approach will help promote development and translation of deep tomographic image reconstruction networks into clinical applications., 78 pages, 30 figures, 149 references

An efficient one-pot three-component domino coupling reaction of phenols, C60, and bromoalkanes was developed, resulting in the highly regioselective synthesis of 1,4-asymmetrical C60 bisadducts. The reaction utilizes KOtBu as a promoter and likely proceeds by an oxyanion/carbanion rearrangement/nucleophilic addition cascade. This new methodology is particularly effective for the synthesis of 1,4-asymmetrical C60 electron transport materials. Its utility is demonstrated by the synthesis of a new efficient 1,4-C60 ETM, which possesses better performance, easier synthesis, and a lower cost compared with the commercially available PCBM.

A series of cyclopentafullerenes have been synthesized in high stereoselectivity by the thermal reaction of [60]fullerene with aldehydes and secondary amines. Both α,β-unsaturated aldehydes and saturated aldehydes can be utilized to synthesize cyclopentafullerenes as the cis isomers. The possible reaction mechanisms for the formation of cyclopentafullerenes are proposed on the basis of the experimental results.

With the recent advance in chemical modification of fullerenes, electrosynthesis has demonstrated increasing importance in regioselective synthesis of novel fullerene derivatives. Herein, we report successively regioselective synthesis of stable tetra- and hexafunctionalized [60]fullerene derivatives. The cycloaddition reaction of the electrochemically generated dianions from [60]fulleroindolines with phthaloyl chloride regioselectively affords 1,2,4,17-functionalized [60]fullerene derivatives with two attached ketone groups and a unique addition pattern, where the heterocycle is rearranged to a [5,6]-junction and the carbocycle is fused to an adjacent [6,6]-junction. This addition pattern is in sharp contrast with that of the previously reported biscycloadducts, where both cycles are appended to [6,6]-junctions. The obtained tetrafunctionalized compounds can be successively manipulated to 1,2,3,4,9,10-functionalized [60]fullerene derivatives with an intriguing “ S ”-shaped configuration via a novel electrochemical protonation. Importantly, the stability of tetrafunctionalized [60]fullerene products allows them to be applied in planar perovskite solar cells as efficient electron transport layers.

Bulk heterojunction (BHJ) perovskite solar cells (PVKSCs) are advantageous in passivating defects and facilitating electron extraction/transport. Most of the reported fullerene derivatives involved in BHJ-PVKSCs render single-site interactions, limiting their passivation effect. Herein, we developed a perfluoroalkyl and pyridine-cofunctionalized fullerene derivative (C60-PyF15), and applied it as an additive of CH3NH3PbI3 layer to construct inverted (p-i-n) BHJ-PVKSCs, affording double-site defect passivation of perovskite film and a champion power conversion efficiency of 20.10%, which is among the highest values for fullerene-incorporated inverted BHJ-PVKSCs. To elucidate the crucial role of the perfluoroalkyl group in efficiency enhancement, another analogous novel non-fluoro-substituted pyridine-functionalized fullerene derivative with the same chain length of alkyl group (C60-PyH15) was also synthesized, which afforded a champion PCE of 19.22% and is lower than that of C60-PyF15. The double-site defect passivation ability of C60-PyF15 is resulted from the coordination interaction of the pyridine moiety with Pb2+ ions and hydrogen bonding between the fluorine atom within the perfluoroalkyl group and the CH3NH3+ cations. These enable ordered orientation of CH3NH3+ and suppressed ion migration, leading to efficiency enhancement and improved ambient and thermal stabilities.

The electrochemical cyclopropanation of [60]fullerobenzofurans with diethyl dibromomalonate has been investigated. Controlled by the steric effect, the sterically favored e bisadducts are obtained as the major products along with two trans-3 bisadducts as minor products. The addition sites and patterns of this reaction are very different from those of our previously reported reaction with benzyl bromide, providing insights into the controlling factors for the electrophilic reactions of dianionic fullerene derivatives.

The regioselective electrosynthesis of tetra- and hexa-functionalized [60]fullerene derivatives with unprecedented 1,2,4,17-(cis-3′) and 1,2,3,4,9,10-(“S”-shaped) addition patterns is achieved., The efficient and regioselective electrosynthesis of tetra- and hexa-functionalized [60]fullerene derivatives with unprecedented addition patterns has been achieved. The tetra-functionalized [60]fullerene derivative with an intriguing 1,2,4,17-addition pattern is regioselectively obtained by cyclization reaction of the dianionic species generated electrochemically from a [60]fulleroindoline with 1,2-bis(bromomethyl)benzene at 0 °C, and can be converted to the more stable 1,2,3,4-adduct at 25 °C. Furthermore, the hexa-functionalized [60]fullerene derivative with the 1,2,3,4,9,10-addition pattern displaying a unique “S”-shaped configuration can be synthesized by protonation of the electrochemically generated dianion of the obtained tetra-functionalized 1,2,4,17-adduct. The structures of the tetra- and hexa-functionalized products have been determined by spectroscopic data and single-crystal X-ray analysis.

Fullerene is the first molecular form of the elemental carbon, and its discovery more than three decades ago substantially revolutionalized nanoscience. One of the most intriguing structural feature of fullerene is its hollow interior which forms a closed cage; such a sub-nanomater inner space offers unique opportunities to entrap atoms, ions, or clusters, affording endohedral fullerenes. Meanwhile, the spherical skeleton of fullerene consists of a conjugated π system, enabling the feasibility of exohedral functionalizations of fullerene to utilize its outer space. Herein, we present a brief overview on recent advance in both endohedral and exohedral functionalizations of fullerenes as well as their potential applications in versatile fields.

Deep learning has become the most powerful tool for action recognition in recent years. The aim of this paper is to investigate the proper convolutional network architecture to classify poleward moving auroras (PMAs). The first challenge is that the auroral images have complex morphological and motion characteristics, so it is difficult to get the discriminative motion information to recognize the PMA events. Second, the imbalanced dataset will cause the serious problems, such as the network tends to identify a given sequence as the majority of events (non-PMAs). To address these issues, we use 3D ResNet-18 to get the motion information of auroral image sequence based on optical flow and also use the spatial attention mechanism to make the network pay more attention to PMA events. The empirical results demonstrate that our method achieves significant performance improvement compared with the previous method.

[60]Fullerene-fused lactones can be electrochemically reduced under controlled potential electrolysis (PCE) to generate the corresponding dianionic species. The generated dianionic [60]fullerene-fused lactones are then treated with acids and different electrophiles to afford the [60]fullerene-fused ketones, alkylated 1,2- and 1,4-adducts, and cyclic adducts with a medium or large ring.

Non-isothermal oxidation kinetics of the high-speed steel (HSS) were studied by thermal gravimetric analysis. The surface and cross-sectional morphology of the HSS oxide film formed at different temperatures and durations were observed by scanning electron microscopy, and the corresponding chemical composition was analyzed by using energy dispersive spectrometer. The composition and structure of the oxide film were also investigated by X-ray diffraction. The results showed that the oxide scale of the HSS is mainly composed of Fe2O3, Fe3O4 and FeCr2O4. Temperature is the main factor on the quality of the oxide film. Below 600 °C, the oxidation rate of the steel is slow and the thickness of the oxide film is below 5 µm. However, the oxidation rate sharply increased as the temperature reaches 600 °C. Cr, Mo and V tend to concentrate at the scale/steel interface and form FeCr2O4 and other oxides.