The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory

Motivated by the recent experimental study on hydrogen storage in MXene multilayers [Nature Nanotechnol. 2021, 16, 331], for the first time we propose a workflow to computationally screen 23,857 compounds of MXene to explore the general relation between the activated H2 bond length and adsorption distance. By using density functional theory (DFT), we generate a dataset to investigate the adsorption geometries of hydrogen on MXenes, based on which we train physics-informed atomistic line graph neural networks (ALIGNNs) to predict adsorption parameters. To fit the results, we further derived a formula that quantitatively reproduces the dependence of H2 bond length on the adsorption distance from MXenes within the framework of Pauling's resonating valence bond (RVB) theory, revealing the impact of transition metal's ligancy and valence on activating dihydrogen in H2 storage

YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging problem due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg, based on image synthesis. A background generator delivers image backgrounds that closely match real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.Comment: 11 pages, 10 figures, submitted to IEEE Transactions on Medical Imaging (TMI

Influence of Indentation on the Fatigue Strength of Carbonitrided Plain Steel

To study the influence of indentation on the fatigue strength of untreated and carbonitrided specimens of S38C steel, the fatigue limit of specimens with and without indentations was tested. Fracture surfaces were observed using scanning electron microscopy (SEM). The results show that the fatigue strength of the untreated specimen decreases with increasing dimension of indentation, without significant difference compared to the predicted results. Compared to the fatigue limit of the untreated specimen, those of the carbonitrided specimen and the carbonitrided specimen whose compound layer was polished were improved by 12% and 40%, respectively. The fatigue strength of the carbonitrided specimen decreased sharply with increasing indentation size because of the presence of microcracks in the compound layer. When the compound layer was removed, the fatigue limit was observed to be less sensitive to indentation than that of the carbonitrided specimen

A demand-response method to balance electric power-grids via HVAC systems using active energy-storage: simulation and on-site experiment

With the increasing popularity of renewable energy sources and the globally increasing electricity demand, the task of balancing the intermittent energy supply with varying demand becomes increasingly difficult. Instead of adjusting the supply, improving the demand response (DR) can be a more efficient way to optimize power balance. HVAC (heating, ventilation, and air-conditioning) systems, which operate on the demand side of power-grids, have a huge potential to improve the power balance. To assess their potential in a variable air volume (VAV) air-conditioning system with energy storage tank we introduce a demand response method that combines active cool-energy storage (ACES) with global temperature adjustment (GTA). To confirm the effectiveness of this combined ACES+GTA approach, we conduct measurements with the help of a full-scale VAV air-conditioning test setup. The experimental results are compared with a TRNSYS simulation. The measurements indicate that an energy-storing water-tank can effectively reduce the number of starts and stops for the heat pump. The simulation confirms that the ACES+GTA method can also effectively reduce the peak load of the power grid with little impact on the thermal comfort of the energy consumers. The cost-saving rate, compared to the conventional operating mode (no energy-storage during other periods), reaches 7.02% for an entire cooling season if the GTA method (with DR) is used

Research on Secure Localization Model Based on Trust Valuation in Wireless Sensor Networks

Secure localization has become very important in wireless sensor networks. However, the conventional secure localization algorithms used in wireless sensor networks cannot deal with internal attacks and cannot identify malicious nodes. In this paper, a localization based on trust valuation, which can overcome a various attack types, such as spoofing attacks and Sybil attacks, is presented. The trust valuation is obtained via selection of the property set, which includes estimated distance, localization performance, position information of beacon nodes, and transmission time, and discussion of the threshold in the property set. In addition, the robustness of the proposed model is verified by analysis of attack intensity, localization error, and trust relationship for three typical scenes. The experimental results have shown that the proposed model is superior to the traditional secure localization models in terms of malicious nodes identification and performance improvement

Efficient gene editing in adult mouse livers via adenoviral delivery of CRISPR/Cas9

AbstractWe developed an adenovirus-based CRISPR/Cas9 system for gene editing in vivo. In the liver, we demonstrated that the system could reach the level of tissue-specific gene knockout, resulting in phenotypic changes. Given the wide spectrum of cell types susceptible to adenoviral infection, and the fact that adenoviral genome rarely integrates into its host cell genome, we believe the adenovirus-based CRISPR/Cas9 system will find applications in a variety of experimental settings

Functionalizing tetraphenylpyrazine with perylene diimides (PDIs) as high-performance nonfullerene acceptors

Perylene diimide (PDI)-based small molecular acceptors with a three-dimensional structure are thought to be essential for efficient photocurrent generation and high power conversion efficiencies (PCEs). Herein, a couple of new perylene diimide acceptors (PPDI-O and PPDI-Se) have been designed and successfully synthesized using pyrazine as the core-flanking pyran and selenophene-fused PDIs, respectively. Compared to PPDI-O, PPDI-Se exhibits a blue-shifted absorption in the 400–600 nm range, a comparable LUMO level, and a more distorted molecular geometry. The PPDI-Se-based organic solar cell device with PDBT-T1 as the donor achieved the highest PCE of 7.47% and a high open-circuit voltage (Voc) of up to 1.05 V. The high photovoltaic performance of PPDI-Se-based devices can be attributed to its high LUMO energy level, complementary absorption spectra with donor materials, favorable morphology and balanced carrier transport. The results demonstrate the potential of this type of fullerene-free acceptor for high efficiency organic solar cells