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Controllable synthesis of large-scale and high-quality graphene film is a basis for development of electronic and spintronic devices. Chemical vapor deposition (CVD) has been considered as a potent...

The use of total marrow and lymphoid irradiation (TMLI) as part of conditioning regimens for bone marrow transplantation is trending due to its advantages in disease control and low toxicity. Accurate contouring of target structures such as bone and lymph nodes plays an important role in irradiation planning. However, this process is often time-consuming and prone to inter-observer variation. Recently, deep learning methods such as convolutional neural networks (CNNs) and vision transformers have achieved tremendous success in medical image segmentation, therefore enabling fast semiautomatic radiotherapy planning. In this paper, we propose a dual-encoder U-shaped model named DE-Net, to automatically segment the target structures for TMLI. To enhance the learned features, the encoder of DE-Net is composed of parallel CNNs and vision transformers, which can model both local and global contexts. The multi-level features from the two branches are progressively fused by intermediate modules, therefore effectively preserving low-level details. Our experiments demonstrate that the proposed method achieves state-of-the-art results and a significant improvement in lymph node segmentation compared with existing methods.

Controllable nitrogen doping, an effective way to regulate the electronic property of graphene, has been achieved on metal catalyst which needs to be removed by additional processes when fabricating electronic devices, giving rise to structural damages and contaminations. Here, by using pyridine as the sole source of carbon and nitrogen, a metal-free thermal chemical vapor deposition method is demonstrated to synthesize nitrogen-doped graphene (NG) on dielectrics (SiO2/Si, quartz, rock crystal, and sapphire). It is discovered that increasing H2 flow could promote the quality of graphene and the proportion of graphitic-N. At an optimal growth temperature of 1000 °C, we realize the controllable synthesis of graphitic-N doped graphene. Impressively, field-effect transistors directly fabricated from the N-doped graphene (∼1.1%) films on SiO2/Si exhibit strong n-type semiconducting behaviors with electron mobilities of 110–283 cm2 V−1 s−1 in air, rivaling heavily doped NG grown on metals. As revealed by density functional theory calculations, compared with NG with multiple types of nitrogen atoms, single graphitic-N doping leads to more prominent n-type transport behaviors, owing to the absence of p-type pyrrolic-N and pyridinic-N.

Organic ultralong room temperature phosphorescence (OURTP) materials having stimuli-responsive attributes have attracted great attention due to their great potential in a wide variety of advanced applications. It is of fundamental importance but challengeable to develop stimuli-responsive OURTP materials, especially such materials with modulated optoelectronic properties in a controlled manner probably due to the lack of an authentic construction approach. Here, we propose an effective strategy for OURTP materials with controllably regulated stimuli-responsive properties by engineering the resonance linkage between flexible chain and phosphor units. A quantitative parameter to demonstrate the stimuli-responsive capacity is also established by the responsivity rate constant. The designed OURTP materials demonstrate efficient photoactivated OURTP with lifetimes up to 724 ms and tunable responsivity rate constants ranging from 0.132 to 0.308 min

Radiation therapy is an essential treatment modality for cervical cancer, while accurate and efficient segmentation methods are needed to improve the workflow. In this study, a three-dimensional V-net model is proposed to automatically segment clinical target volume (CTV) and organs at risk (OARs), and to provide prospective guidance for low lose area.A total of 130 CT datasets were included. Ninety cases were randomly selected as the training data, with 10 cases used as the validation data, and the remaining 30 cases as testing data. The V-net model was implemented with Tensorflow package to segment the CTV and OARs, as well as regions of 5 Gy, 10 Gy, 15 Gy, and 20 Gy isodose lines covered. The auto-segmentation by V-net was compared to auto-segmentation by U-net. Four representative parameters were calculated to evaluate the accuracy of the delineation, including Dice similarity coefficients (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD).The V-net and U-net achieved the average DSC value for CTV of 0.85 and 0.83, average JI values of 0.77 and 0.75, average ASD values of 2.58 and 2.26, average HD of 11.2 and 10.08, respectively. As for the OARs, the performance of the V-net model in the colon was significantly better than the U-net model (p = 0.046), and the performance in the kidney, bladder, femoral head, and pelvic bones were comparable to the U-net model. For prediction of low-dose areas, the average DSC of the patients' 5 Gy dose area in the test set were 0.88 and 0.83, for V-net and U-net, respectively.It is feasible to use the V-Net model to automatically segment cervical cancer CTV and OARs to achieve a more efficient radiotherapy workflow. In the delineation of most target areas and OARs, the performance of V-net is better than U-net. It also offers advantages with its feature of predicting the low-dose area prospectively before radiation therapy (RT).

PurposeDosimetric parameters (e.g., mean lung dose (MLD), V20, and V5) can predict radiation pneumonitis (RP). Constraints thereof were formulated before the era of combined immune checkpoint inhibitors (ICIs) and radiotherapy, which could amplify the RP risk. Dosimetric predictors of acute RP (aRP) in the context of ICIs are urgently needed because no data exist thus far.Methods and MaterialsAll included patients underwent thoracic intensity-modulated radiotherapy, previously received ICIs, and followed-up at least once. Logistic regression models examined predictors of aRP (including a priori evaluation of MLD, V20, and V5), and their discriminative capacity was assessed by receiver operating characteristic analysis.ResultsMedian follow-up of the 40 patients was 5.3 months. Cancers were lung (80%) or esophageal (20%). ICIs were PD-1 (85%) or PD-L1 (15%) inhibitors (median 4 cycles). Patients underwent definitive (n=19), consolidative (n=14), or palliative (n=7) radiotherapy; the median equivalent dose in 2 Gy fractions (EQD2) was 60 Gy (IQR, 51.8-64 Gy). Grades 1-5 aRP occurred in 25%, 17.5%, 15%, 2.5%, and 5%, respectively. The only variables associated with any-grade aRP were V20 (p=0.014) and MLD (p=0.026), and only V20 with grade ≥2 aRP (p=0.035). Neither the number of prior ICI cycles nor the delivery of concurrent systemic therapy significantly associated with aRP risk. Graphs were constructed showing the incrementally increasing risk of aRP based on V20 and MLD (continuous variables).ConclusionsThis is the first study illustrating that V20 and MLD may impact aRP in the setting of prior ICIs. However, these data should not be extrapolated to patients without pre-radiotherapy receipt of prior ICIs, or to evaluate the risk of chronic pulmonary effects. If these results are validated by larger studies with more homogeneous populations, the commonly accepted V20/MLD dose constraints could require revision if utilized in the setting of ICIs.

Objective. We investigated dosimetry effect of gating latency in cine magnetic resonance image (cine MRI) guided breath-hold pancreatic cancer radiotherapy. Approach. The gating latency was calculated based on cine MRI obtained from 17 patients who received MRI guided radiotherapy. Because of the cine MRI-related latency, beam overshoot occurs when beam remains on while the tracking target already moves out of the target boundary. The number of beam on/off events was calculated from the cine MRI data. We generated both IMRT and VMAT plans for all 17 patients using 33 Gy prescription, and created motion plans by applying isocenter shift that corresponds to motion-induced tumor displacement. The GTV and PTV coverage and dose to nearby critical structures were compared between the motion and original plan to evaluate the dosimetry change caused by cine MRI latency. Main results. The time ratio of cine MRI imaging latency over the treatment duration is 6.6 ± 3.1%, the mean and median percentage of beam-on events p = 0.861 and 0.397 for IMRT and VMAT planning techniques, respectively). However, the PTV V33Gy and stomach Dmax for the motion plans are significantly lower; duodenum V12.5 Gy and V18Gy are significantly higher when compared with the original plans, for both IMRT and VMAT planning techniques. Significance. The cine MRI gating latency can significantly decrease the dose delivered to the PTV, and increase the dose to the nearby critical structures. However, no significant difference is observed for the GTV coverage. The dosimetry impact can be mitigated by implementing additional beam-on control techniques which reduces unnecessary beam on events and/or by using faster cine MRI sequences which reduces the latency period.

PurposeAccurate segmentation of liver and liver tumors is critical for radiotherapy. Liver tumor segmentation, however, remains a difficult and relevant problem in the field of medical image processing because of the various factors like complex and variable location, size, and shape of liver tumors, low contrast between tumors and normal tissues, and blurred or difficult-to-define lesion boundaries. In this paper, we proposed a neural network (S-Net) that can incorporate attention mechanisms to end-to-end segmentation of liver tumors from CT images.MethodsFirst, this study adopted a classical coding-decoding structure to realize end-to-end segmentation. Next, we introduced an attention mechanism between the contraction path and the expansion path so that the network could encode a longer range of semantic information in the local features and find the corresponding relationship between different channels. Then, we introduced long-hop connections between the layers of the contraction path and the expansion path, so that the semantic information extracted in both paths could be fused. Finally, the application of closed operation was used to dissipate the narrow interruptions and long, thin divide. This eliminated small cavities and produced a noise reduction effect.ResultsIn this paper, we used the MICCAI 2017 liver tumor segmentation (LiTS) challenge dataset, 3DIRCADb dataset and doctors’ manual contours of Hubei Cancer Hospital dataset to test the network architecture. We calculated the Dice Global (DG) score, Dice per Case (DC) score, volumetric overlap error (VOE), average symmetric surface distance (ASSD), and root mean square error (RMSE) to evaluate the accuracy of the architecture for liver tumor segmentation. The segmentation DG for tumor was found to be 0.7555, DC was 0.613, VOE was 0.413, ASSD was 1.186 and RMSE was 1.804. For a small tumor, DG was 0.3246 and DC was 0.3082. For a large tumor, DG was 0.7819 and DC was 0.7632.ConclusionS-Net obtained more semantic information with the introduction of an attention mechanism and long jump connection. Experimental results showed that this method effectively improved the effect of tumor recognition in CT images and could be applied to assist doctors in clinical treatment.

Three phosphine sulfide-based bipolar host materials, viz&nbsp, CzPhPS, DCzPhPS, and TCzPhPS, were facilely prepared through a one-pot synthesis in excellent yields. The developed hosts exhibit superior thermal stabilities with the decomposition temperatures (Td) all exceeding 350 °C and the melting temperatures (Tm) over 200 °C. In addition, their triplet energy (ET) levels are estimated to be higher than 3.0 eV, illustrating that they are applicable to serve as hosts for blue phosphorescent organic light-emitting diodes (PhOLEDs). The maxima luminance, current efficiency (CE), power efficiency (PE), and external quantum efficiency (EQE) of 17223 cd m−2, 36.7 cd A−1, 37.5 lm W−1, and 17.5% are achieved for the blue PhOLEDs hosted by CzPhPS. The PhOLEDs based on DCzPhPS and TCzPhPS show inferior device performance than that of CzPhPS, which might be ascribed to the deteriorated charge transporting balance as the increased number of the constructed carbazole units in DCzPhPS and TCzPhPS molecules would enhance the hole-transporting ability of the devices to a large extent. Our study demonstrates that the bipolar hosts derived from phosphine sulfide have enormous potential applications in blue PhOLEDs, and the quantity of donors should be well controlled to exploit highly efficient phosphine sulfide-based hosts.

Direct chemical vapor deposition growth of high quality graphene on dielectric substrates holds great promise for practical applications in electronics and optoelectronics. However, graphene growth on dielectrics always suffers from the issues of inhomogeneity and/or poor quality. Here, we first reveal that a novel precursor-modification strategy can successfully suppress the secondary nucleation of graphene to evolve ultrauniform graphene monolayer film on dielectric substrates. A mechanistic study indicates that the hydroxylation of silica substrate weakens the binding between graphene edges and substrate, thus realizing the primary nucleation-dominated growth. Field-effect transistors based on the graphene films show exceptional electrical performance with the charge carrier mobility up to 3800 cm2 V-1 s-1 in air, which is much higher than those reported results of graphene films grown on dielectrics.

This paper presents a new approach to investigate the bubbles movements and their interaction in the boiling heat transfer proess. Based on the study of the wake flow of single rising bubble, the interaction of bubble pairs, such as interaction and coalescenceboiling, is experimentally and numerically simulated. The validity of the numerical simulation was verified by the experimental pictures captured by high-speed CCD camera. The wakes of two bubbles was revealed by obtatined velocity field, and the interaction between two bubbles was analyzed in fluid dynamics. The simulations shows that the coalescence of bubbles may happen when one bubble enters the wake influence area of the main bubble. The velocity vector field shows that wake flow of the bubble is the major influencing factor determining the interaction between bubbles.

Achieving afterglow organic light-emitting diodes (OLEDs) that exhibit the organic ultralong room temperature phosphorescence (OURTP) emission after switching off the applied voltage is highly attractive. However, it is difficult to obtain appropriate emitting layers that are of excellent charge transport ability and OURTP properties simultaneously to fabricate highly efficient afterglow OLEDs. Here, we report an easy but effective strategy to construct afterglow OLEDs via host–guest doping by adopting the excellent carrier transporting materials as rigid host and the commendable OURTP emitters as guest. The resultant green afterglow OLEDs exhibit the state-of-the-art maximum external quantum efficiency, luminance, and OURTP lifetimes of up to 1.47%, 743 cd m−2, and 356 ms, respectively, with the low turn-voltage of 4.4 V. Due to the inherent stable afterglow properties and outstanding carrier transport ability of the emitting layer, the OLEDs show admirable afterglow emission stability with the intensity and lifetimes keeping almost the same for more than ten repeated voltage pulses. The current work paves the way to develop highly efficient and stable afterglow OLEDs by host–guest doping.

Accurate segmentation of clinical target volume (CTV) in computed tomography (CT) images is of great significance for planning cervical cancer radiotherapy. However, manual delineation is time-consuming and subjective. In this study, we propose a novel method for the auto-segmentation of cervical cancer CTV based on the convolutional neural network (CNN). We adopt 3D UNet ++ as the backbone and develop the global multi-level attention (GML) module, which can help effectively establish global contextual attention and enhance the ability to learn differential representations. Besides, the atrous spatial pyramid pooling (ASPP) is employed at the central bottleneck to handle the multi-scale objects in CTV regions. We train and evaluate the proposed approach on a dataset of 87 CT scans. The experimental results show that our method has superior performance compared with typical methods. Meanwhile, the ablation experiments further validate the effectiveness of the GML module.

Intrinsic graphene features semi-metallic characteristics that limit its applications in electronic devices, whereas graphene nanoribbons (GNRs) are promising semiconductors because of their bandgap-opening feature. However, the controllable mass-fabrication of high-quality GNR arrays remains a major challenge. In particular, the in situ growth of GNR arrays through template-free chemical vapor deposition (CVD) has not been realized. Herein, we report a template-free CVD strategy to grow large-area, high-quality and self-aligned GNR arrays on liquid copper surface. The width of as-grown GNR could be optimized to sub-10 nm with aspect ratio up to 387, which is higher than those of reported CVD-GNRs. The study of the growth mechanism indicates that a unique comb-like etching-regulated growth process caused by a trace hydrogen flow guides the formation of the mass-produced self-aligned GNR arrays. Our approach is operationally simple and efficient, offering an assurance for the use of GNR arrays in integrated circuits., A comb-like etched patterns triggered by the trace hydrogen flow was used to in-situ grow large-area and self-aligned graphene nanoribbon arrays on liquid copper.

Accurate segmentation of organs at risk (OARs) in computed tomography (CT) is an essential step in radiation therapy for thoracic cancer treatment. However, the manual segmentation of OARs is time-consuming and subject to inter-observer variation. In this paper, we propose a novel U-like deep convolutional neural network (CNN) architecture, which adopts the encoder-decoder design, to automatically segment the OARs in thoracic CT images. In our method, hybrid dilated convolution (HDC) is employed to enlarge the receptive field of the encoder part, and a pyramid backbone with lateral connections between encoder and decoder is utilized to capture contextual information at multiple scales. To reduce the false-positive segmentation results, we use the multi-task learning strategy to add an auxiliary classifier branch to the network. The experiments demonstrate that the proposed method outperforms other state-of-the-art models and the results have a good consistency with that of experienced radiologists.

Owing to the distinctively morphological and structural features, vertically-aligned graphene nanosheets (VGs) possess many unique properties and hold great promise for applications in various fields. For controllable preparation and wide application of VGs, the establishing reliable growth method and profound understanding of the growth mechanism are of vital significance. Up to date, VGs are normally produced by plasma-enhanced chemical vapor deposition (PECVD) and it's considered that plasma is an indispensible factor for the vertical alignment of graphene sheets. Herein, for the first time, we report the facile and controllable VGs growth via a thermal CVD by precisely tuning growth parameters. Experimental observations in combination with detailed energy calculations reveal that the flow rate of carbon precursor determines the growth dynamics of graphene in CVD. This work offers a novel and reliable technique for VGs preparation and provides new insights into the intrinsic mechanism of vertical graphene growth. Furthermore, benefiting from the ultra-high density of edge sites, thin thickness, and outstanding electrical conductivity of VGs, the as-prepared VGs exhibit excellent field-emission performance such as ultra-low turn-on electric field and threshold field down to 1.07 and 1.65 V μm −1 , respectively.

The composite of graphene and carbon nanotubes (CNTs) can increase the electrical conductivity and the electrochemical active sites in favor of the application in energy storage and conversion. However, the fabrication of graphene/CNT hybrid material requires multiple steps to introduce two kinds of catalysts for separate growth of graphene and CNTs. Here, we report a one-step process to in situ grow a seamless graphene/CNT heterojunction using a single catalyst of copper foil. Through precise tuning of the evaporation temperature of ethanol, creation of a method for seamlessly bonding CNTs on the graphene surface without assistance of metal particles has been realized. A study of the growth mechanism shows that the self-limited surface reaction of copper foil leads to many defects in multilayer graphene films, which provides the nucleation sites for the seamless growth of CNTs. The as-grown graphene/CNT heterojunctions have a higher electrical conductivity than the single graphene film, and its optical transmittance is almost equal to that of graphene film.

Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on pCT and sCT following the same protocol. Dose distributions were evaluated for 4 patients by comparing the dose-volume-histogram (DVH) and 2D gamma index analysis. Results: The average MAE and RMSE values between sCT by three models and pCT reduced by 15.4 HU and 26.8 HU at least, while the mean PSNR and SSIM metrics between sCT by different models and pCT added by 10.6 and 0.05 at most, respectively. There were only slight differences for DVH of selected contours between different plans. The passing rates of 2D gamma index analysis under 3 mm/3% 3 mm/2%, 2 mm/3%and 2 mm/2% criteria were all higher than 95%. Conclusions: All the sCT had achieved better evaluation metrics than those of original CBCT, while the performance of CycleGAN model was proved to be best among three methods. The dosimetric agreement confirmed the HU accuracy and consistent anatomical structures of sCT by deep learning methods.

The effects of hydrogen related defect on the microstructure and optical property of ZnO thin films were investigated by slow positron beam, in combination with x-ray diffraction, infrared and photoluminescence spectroscopy. The defects were introduced by 90 keV proton irradiation with doses of 1×10 15 and 1×10 16 ions cm −2 . Zn vacancy and OH bonding ( V Zn +OH) defect complex were identified in hydrogen implanted ZnO film by positron annihilation and infrared spectroscopy. The formation of these complexes led to lattice disorder in hydrogen implanted ZnO film and suppressed the luminescence process.

Hydrogen- and corrosion-induced microstructure and defects of ZnO single crystal under acidic conditions have been studied by using slow positron beam spectroscopy and microscopy techniques. Slow positron beam measurements revealed that a near-surface damaged region with a high concentration of defects was formed after hydrogen charging, but not after corrosion. The formation of hexagonal pyramids was found after electrochemical charging with hydrogen, while the formation of stepped hexagonal pits was observed after direct immersion in the solution. The incorporation of hydrogen by electrochemical charging enhanced the near-band-edge (NBE) emission intensity.