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The efficient use of renewable X/γ-rays or accelerated electrons for chemical transformation of CO 2 and water to fuels holds promise for a carbon-neutral economy; however, such processes are challenging to implement and require the assistance of catalysts capable of sensitizing secondary electron scattering and providing active metal sites to bind intermediates. Here we show atomic Cu-Ni dual-metal sites embedded in a metal-organic framework enable efficient and selective CH 3 OH production (~98%) over multiple irradiated cycles. The usage of practical electron-beam irradiation (200 keV; 40 kGy min −1 ) with a cost-effective hydroxyl radical scavenger promotes CH 3 OH production rate to 0.27 mmol g −1  min −1 . Moreover, time-resolved experiments with calculations reveal the direct generation of CO 2 •‒ radical anions via aqueous electrons attachment occurred on nanosecond timescale, and cascade hydrogenation steps. Our study highlights a radiolytic route to produce CH 3 OH with CO 2 feedstock and introduces a desirable atomic structure to improve performance. Most approaches for CH 3 OH production focus on thermochemical, electrolytic, and photolytic processes. Here the authors report a radiolytic route to produce CH 3 OH from CO 2 and water by atomic Cu-Ni dual sites embedded in a metal-organic framework.

p-Nitrophenol (p-NP), a well-known organic pollutant in industrial and agricultural wastewater, is difficult to degrade. Therefore, an exploiting efficient and economical reductant is of great significance for environmental remediation and human health. Herein, MoS2 nanocrystals with different morphologies were successfully prepared by a simple hydrothermal method in the mixed solution of ethylenediamine and ethylene glycol. The morphology can be readily controlled by tuning the volume ratio of ethylenediamine to the solvent (R). Moreover, well-crystallized MoS2 nanocrystals were developed by the following heat treatment process. The as-prepared MoS2 nanocrystals were employed as catalysts for the reduction of toxic p-NP to industrially beneficial p-aminophenol (p-AP). It was clearly revealed that both the morphology and crystallinity play critical roles in the catalytic reduction efficiency. Among the MoS2 samples, the optimized R75 sample with a rough surface exhibits the highest rate constant of kapp = 0.3906 min−1 for the reduction of p-NP, while improved crystallinity will decrease the catalytic efficiency. These novel findings can promote the development of a new non-noble metal catalyst which can be used for wastewater reductive treatment.

Nowadays, discovering new skeleton antifungal drugs is the direct way to address clinical fungal infections. Pyrylium salt SM21 was screened from a library containing 50,240 small molecules. Several studies about the antifungal activity and mechanism of SM21 have been reported, but the structure–activity relationship of pyrylium salts was not clear. To explore the chemical space of antifungal pyrylium salt SM21, a series of pyrylium salt derivatives were designed and synthesized. Their antifungal activity and structure-activity relationships (SAR) were investigated. Compared with SM21, most of the synthesized compounds exhibited equivalent or improved antifungal activities against Candida albicans in vitro. The synthesized compounds, such as XY10, XY13, XY14, XY16 and XY17 exhibited comparable antifungal activities against C. albicans with MIC values ranging from 0.47 to 1.0 μM. Fortunately, a compound numbered XY12 showed stronger antifungal activities and lower cytotoxicity was obtained. The MIC of compound XY12 against C. albicans was 0.24 μM, and the cytotoxicity decreased 20-fold as compared to SM21. In addition, XY12 was effective against fluconazole-resistant C. albicans and other pathogenic Candida species. More importantly, XY12 could significantly increase the survival rate of mice with a systemic C. albicans infection, which suggested the good antifungal activities of XY12 in vitro and in vivo. Our results indicated that structural modification of pyrylium salts could lead to the discovery of new antifungal drugs.

Photoreduction of CO2 into high-value chemicals is one of the promising strategies to mitigate CO2 emission and alleviate energy challenges, while the design and development of high-performance photocatalysts are recognized as the pivotal part. Herein, CdS-xAu catalysts with abundant S vacancies and Au nanoparticles were successfully synthesized via one-step γ-ray radiation method. The characterization results showed that the CdS-γ had smaller spherical particles compared to the CdS-H catalyst prepared by hydrothermal method, which was favorable for exposing more active sites. Besides, the Au was reduced by solvated electrons and anchored on the in situ generated S vacancies within CdS. By acting as the photogenerated electrons extractors, the generated Au nanoparticles synergized with S vacancies to promote the charge separation and inhibit the recombination of photogenerated carriers, thus enhancing the photocatalytic activities. Consequently, the CdS-2.5Au photocatalyst with appropriate loading amount presented the optimal performance for achieving the CO yield of 12.48 μmol g−1 h−1, which was superior to CdS-γ and even 2.8 times that of the hydrothermal synthesized CdS-H. This study provided new insights into the applications of γ-ray radiation in the preparation of high-performance photocatalytic materials.

A radiation-self-Fenton system without the addition of H 2 O 2 was developed for the efficient degradation of organic pollutants. We synthesized nickel-based metal–organic framework (Ni-BTC) as a catalyst for the in-situ generation of abundant hydroxyl radicals (·OH) and H 2 O 2 under X-ray excitation, and the latter was further converted to ·OH in the Fenton reaction to synergistically accelerate the Methylene blue (MB) degradation. The embedded nickel sites and confined layered structure contributed to the stabilization and enrichment of ·OH within the framework. Consequently, the system could completely decolorize 150 mL of MB (20 mg/L) by absorbing 90 Gy dose. This study provided a novel technology for wastewater treatment.

The efficient use of renewable X/γ-rays or accelerated electrons for chemical transformation of CO and water to fuels holds promise for a carbon-neutral economy; however, such processes are challenging to implement and require the assistance of catalysts capable of sensitizing secondary electron scattering and providing active metal sites to bind intermediates. Here we show atomic Cu-Ni dual-metal sites embedded in a metal-organic framework enable efficient and selective CH OH production (~98%) over multiple irradiated cycles. The usage of practical electron-beam irradiation (200 keV; 40 kGy min ) with a cost-effective hydroxyl radical scavenger promotes CH OH production rate to 0.27 mmol g  min . Moreover, time-resolved experiments with calculations reveal the direct generation of CO radical anions via aqueous electrons attachment occurred on nanosecond timescale, and cascade hydrogenation steps. Our study highlights a radiolytic route to produce CH OH with CO feedstock and introduces a desirable atomic structure to improve performance.

In the context of rapid advancements in industrial automation, vision-based robotic grasping plays an increasingly crucial role. In order to enhance visual recognition accuracy, the utilization of large-scale datasets is imperative for training models to acquire implicit knowledge related to the handling of various objects. Creating datasets from scratch is a time and labor-intensive process. Moreover, existing datasets often contain errors due to automated annotations aimed at expediency, making the improvement of these datasets a substantial research challenge. Consequently, several issues have been identified in the annotation of grasp bounding boxes within the popular Jacquard Grasp. We propose utilizing a Human-In-The-Loop(HIL) method to enhance dataset quality. This approach relies on backbone deep learning networks to predict object positions and orientations for robotic grasping. Predictions with Intersection over Union (IOU) values below 0.2 undergo an assessment by human operators. After their evaluation, the data is categorized into False Negatives(FN) and True Negatives(TN). FN are then subcategorized into either missing annotations or catastrophic labeling errors. Images lacking labels are augmented with valid grasp bounding box information, whereas images afflicted by catastrophic labeling errors are completely removed. The open-source tool Labelbee was employed for 53,026 iterations of HIL dataset enhancement, leading to the removal of 2,884 images and the incorporation of ground truth information for 30,292 images. The enhanced dataset, named the Jacquard V2 Grasping Dataset, served as the training data for a range of neural networks.

With the advent of the era of big data, artificial intelligence has attracted continuous attention from all walks of life, and has been widely used in medical image analysis, molecular and material science, language recognition and other fields. As the basis of artificial intelligence, the research results of neural network are remarkable. However, due to the inherent defect that electrical signal is easily interfered and the processing speed is proportional to the energy loss, researchers have turned their attention to light, trying to build neural networks in the field of optics, making full use of the parallel processing ability of light to solve the problems of electronic neural networks. After continuous research and development, optical neural network has become the forefront of the world. Here, we mainly introduce the development of this field, summarize and compare some classical researches and algorithm theories, and look forward to the future of optical neural network.

Fragmentation and reduced continuity of habitat patches threaten the environment and biodiversity. Recently, ecological networks are increasingly attracting the attention of researchers as they provide fundamental frameworks for environmental protection. This study suggests a set of procedures to construct an ecological network. First, we proposed a method to construct a landscape resistance surface based on the assessment of habitat quality. Second, to analyze the effect of the resistance surface on corridor simulations, we used three methods to construct resistance surfaces: (1) the method proposed in this paper, (2) the entropy coefficient method, and (3) the expert scoring method. Then, we integrated habitat patches and resistance surfaces to identify potential corridors using graph theory. These procedures were tested in Changzhou, China. Comparing the outputs of using different resistance surfaces demonstrated that: (1) different landscape resistance surfaces contribute to how corridors are identified, but only slightly affect the assessment of the importance of habitat patches and potential corridors; (2) the resistance surface, which is constructed based on habitat quality, is more applicable to corridor simulations; and (3) the assessment of the importance of habitat patches is fundamental for ecological network optimization in the conservation of critical habitat patches and corridors.