Removal mechanism of Pb(ii) from soil by biochar-supported nanoscale zero-valent iron composite materials.

As an adsorbent, biochar has a highly porous structure and strong adsorption capacity, and can effectively purify the environment. In response to the increasingly serious problem of heavy metal pollution in water, this study used nano zero valent iron and rice husk biochar to prepare a new type of magnetic sheet-like biochar loaded nano zero valent iron (BC-nZVI) composite material through rheological phase reaction, showing remarkable advantages such as low cost, easy preparation, and superior environmental remediation effect. The physical and chemical properties and structure of the material were extensively characterized using various methods such as HRTEM, XPS, FESEM, EDS, XRD, FTIR, and RAMAN. Concurrently, batch experiments were undertaken to assess the removal efficiency of Pb(ii) by BC-nZVI, with investigations into the influence of pH value, temperature, soil water ratio, and initial concentration of heavy metal ion solution on its removal efficiency. The results indicate that the removal of Pb(ii) by BC-nZVI reaches an equilibrium state after around 120 minutes. Under the conditions of pH 6, temperature 20 °C, soil water ratio 1 : 5, and BC-nZVI dosage of 1 g L-1, BC-nZVI can reduce the Pb(ii) content in wastewater with an initial concentration of 30 mg L-1 to trace levels, and the treatment time is about 120 minutes. The analysis of adsorption kinetics and isotherms indicates that the adsorption process of Pb(ii) by BC-nZVI adheres to the quasi-second-order kinetic model and Langmuir model, suggesting a chemical adsorption process. Thermodynamic findings reveal that the adsorption of Pb(ii) by BC-nZVI is spontaneous. Furthermore, BC-nZVI primarily accumulates Pb(ii) through adsorption co-precipitation. BC-nZVI serves as an eco-friendly, cost-effective, and highly efficient adsorbent, showing promising capabilities in mitigating Pb(ii) heavy metal pollution. Its recoverability and reusability facilitated by an external magnetic field make it advantageous for remediating and treating lead-contaminated sites.

3D hand pose and mesh estimation via a generic Topology-aware Transformer model.

In Human-Robot Interaction (HRI), accurate 3D hand pose and mesh estimation hold critical importance. However, inferring reasonable and accurate poses in severe self-occlusion and high self-similarity remains an inherent challenge. In order to alleviate the ambiguity caused by invisible and similar joints during HRI, we propose a new Topology-aware Transformer network named HandGCNFormer with depth image as input, incorporating prior knowledge of hand kinematic topology into the network while modeling long-range contextual information. Specifically, we propose a novel Graphformer decoder with an additional Node-offset Graph Convolutional layer (NoffGConv). The Graphformer decoder optimizes the synergy between the Transformer and GCN, capturing long-range dependencies and local topological connections between joints. On top of that, we replace the standard MLP prediction head with a novel Topology-aware head to better exploit local topological constraints for more reasonable and accurate poses. Our method achieves state-of-the-art 3D hand pose estimation performance on four challenging datasets, including Hands2017, NYU, ICVL, and MSRA. To further demonstrate the effectiveness and scalability of our proposed Graphformer Decoder and Topology aware head, we extend our framework to HandGCNFormer-Mesh for the 3D hand mesh estimation task. The extended framework efficiently integrates a shape regressor with the original Graphformer Decoder and Topology aware head, producing Mano parameters. The results on the HO-3D dataset, which contains various and challenging occlusions, show that our HandGCNFormer-Mesh achieves competitive results compared to previous state-of-the-art 3D hand mesh estimation methods.

From picture to 3D hologram: end-to-end learning of real-time 3D photorealistic hologram generation from 2D image input.

In this Letter, we demonstrate a deep-learning-based method capable of synthesizing a photorealistic 3D hologram in real-time directly from the input of a single 2D image. We design a fully automatic pipeline to create large-scale datasets by converting any collection of real-life images into pairs of 2D images and corresponding 3D holograms and train our convolutional neural network (CNN) end-to-end in a supervised way. Our method is extremely computation-efficient and memory-efficient for 3D hologram generation merely from the knowledge of on-hand 2D image content. We experimentally demonstrate speckle-free and photorealistic holographic 3D displays from a variety of scene images, opening up a way of creating real-time 3D holography from everyday pictures.

Efficient capture of radioactive iodine by ZIF-8 derived porous carbon.

Due to the rapid diffusion of radioactive iodine, the demand for safe and efficient capture and storage of radioactive iodine is increasing worldwide. The use of porous carbon materials to capture iodine has aroused great interest. This work prepared porous carbon materials derived from polymetallic oxides of the zeolitic imidazolate framework (ZIF) by pyrolysis at 1000 °C. The carbon materials (CZIF-1000) have a high specific surface area of about 1110 m2/g and a total pore volume of 0.92 cm3/g. Adsorption studies have shown that the CZIF-1000 had significant adsorption performance for iodine, and the adsorption capacity can reach 790.8 mg/g at 8h. The potential mechanism of adsorption is that the carbonization causes the charge-transfer interaction and pore size distribution. Compared with the conventional adsorbents, the adsorbents showed faster kinetics and high extraction capacity for iodine. This experiment provides an effective method for designing a highly efficient adsorbent for iodine and broadens the ideas for developing new iodine extraction adsorbents.

Three-dimensional computer holography enabled from a single 2D image.

To compute a high-quality computer-generated hologram (CGH) for true 3D real scenes, a huge amount of 3D data must be physically acquired and provided depending on specific devices or 3D rendering techniques. Here, we propose a computational framework for generating a CGH from a single image based on the idea of 2D-to-3D wavefront conversion. We devise a deep view synthesis neural network to synthesize light-field contents from a single image and convert the light-field data to the diffractive wavefront of the hologram using a ray-wave algorithm. The method is able to achieve extremely straightforward 3D CGH generation from hand-accessible 2D image content and outperforms existing real-world-based CGH computation, which inevitably relies on a high-cost depth camera and cumbersome 3D data rendering. We experimentally demonstrate 3D reconstructions of indoor and outdoor scenes from a single image enabled phase-only CGH.

Deep-learning-based computer-generated hologram from a stereo image pair.

We propose a deep-learning-based approach to producing computer-generated holograms (CGHs) of real-world scenes. We design an end-to-end convolutional neural network (the Stereo-to-Hologram Network, SHNet) framework that takes a stereo image pair as input and efficiently synthesizes a monochromatic 3D complex hologram as output. The network is able to rapidly and straightforwardly calculate CGHs from the directly recorded images of real-world scenes, eliminating the need for time-consuming intermediate depth recovery and diffraction-based computations. We demonstrate the 3D reconstructions with clear depth cues obtained from the SHNet-based CGHs by both numerical simulations and optical holographic virtual reality display experiments.

Single RGB Image 6D Object Grasping System Using Pixel-Wise Voting Network.

A robotic system that can autonomously recognize object and grasp it in a real scene with heavy occlusion would be desirable. In this paper, we integrate the techniques of object detection, pose estimation and grasping plan on Kinova Gen3 (KG3), a 7 degrees of freedom (DOF) robotic arm with a low-performance native camera sensor, to implement an autonomous real-time 6 dimensional (6D) robotic grasping system. To estimate the object 6D pose, the pixel-wise voting network (PV-net), is applied in the grasping system. However, the PV-net method can not distinguish the object from its photo through only RGB image input. To meet the demands of a real industrial environment, a rapid analytical method on a point cloud is developed to judge whether the detected object is real or not. In addition, our system shows a stable and robust performance in different installation positions with heavily cluttered scenes.

Superhydrophobic and superaerophilic hierarchical Pt@MIL-101/PVDF composite for hydrogen water isotope exchange reactions.

A hierarchical porous composite of Pt@MIL-101/ployvinylidene fluoride (Pt@MIL-101/PVDF) was successfully prepared through a solution-processed method. This composite possesses advanced superhydrophobic and superaerophilic performance which makes it a promising catalyst facilitating liquid phase catalytic exchange techniques (LPCE) in hydrogen-water isotope exchange process. Its superhydrophobic property results in the repellence of water drops from flooding the catalytic surface with a relatively large contact angle in the exchange reaction, and its superaerophilic surface broke hydrogen bubbles into thin film so as to reach higher catalytic reactive efficiency. High reactivity and long-term stability in the reaction process can also be achieved by the configuration of mesoporous cages of MIL-101 confining Pt nanoparticles and preventing them from sintering.

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

Robust Stereo Visual Odometry Using Improved RANSAC-Based Methods for Mobile Robot Localization.

In this paper, we present a novel approach for stereo visual odometry with robust motion estimation that is faster and more accurate than standard RANSAC (Random Sample Consensus). Our method makes improvements in RANSAC in three aspects: first, the hypotheses are preferentially generated by sampling the input feature points on the order of ages and similarities of the features; second, the evaluation of hypotheses is performed based on the SPRT (Sequential Probability Ratio Test) that makes bad hypotheses discarded very fast without verifying all the data points; third, we aggregate the three best hypotheses to get the final estimation instead of only selecting the best hypothesis. The first two aspects improve the speed of RANSAC by generating good hypotheses and discarding bad hypotheses in advance, respectively. The last aspect improves the accuracy of motion estimation. Our method was evaluated in the KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) and the New Tsukuba dataset. Experimental results show that the proposed method achieves better results for both speed and accuracy than RANSAC.