Metal Recovery from Sludge through the Combination of Hydrothermal Sulfidation and Flotation

AbstractThe heavy metal in the waste can react with sulfur and be converted to metal sulfide through the hydrothermal sulfidation. For metal recovery, the synthetic metal sulfide can be enriched through subsequent flotation process. It is a novel way for the recovery of heavy metal from the sludge. In this study, the effects of liquid/solid ratio, mineralizer concentration, precursor concentration and dosage of sulfur on the sulfidation extent and floatation index were investigated. Result shows that with a precursor concentration of 15%, a Zn/S molar ratio of 1:1.2, a liquid/solid ratio of 3:1, the sulfidation extent of zinc in the sludge was greater than 92%, while the flotation recovery of zinc reached up to 45.34%. The toxicity characteristic leaching procedure (TCLP) revealed that stabilization and detoxification of heavy metals occurred during sulfidation

Microstructure and Mechanical Properties of Magnetron Sputtering TiN-Ni Nanocrystalline Composite Films

In this paper, TiN-Ni nanostructured composite films with different Ni contents are prepared using the magnetron sputtering method. The composition, microstructure, and mechanical properties of composite films are analyzed using an X-ray energy spectrometer (EDS), a scanning electron microscope (SEM), X-ray diffraction technology (XRD), a transmission electron microscope (TEM), and nanoindentation. All the films grow in a columnar crystal structure. There are only TiN diffraction peaks in the XRD spectrum, and no diffraction peaks of Ni and its compounds are observed. The addition of the Ni element disrupts the integrity of TiN lattice growth, resulting in a decrease in the grain size from 60 nm in TiN to 25 nm at 20.6% Ni. The film with a Ni content of 12.4 at.% forms a nanocomposite structure in which the nanocrystalline TiN phase (nc-TiN) is surrounded by the amorphous Ni (a-Ni) phase. The formation of nc-TiN/a-Ni nanocomposite structures relies on the good wettability of Ni on TiN ceramics. The hardness and elastic modulus of the film gradually decrease with the increase in Ni content, but the toughness is improved. The hardness and elastic modulus decrease from 19.9 GPa and 239.5 GPa for TiN film to 15.4 GPa and 223 GPa at 20.6 at.% Ni film, respectively, while the fracture toughness increases from 1.5 MPa m1/2 to 2.0 MPa m1/2. The soft and ductile Ni phase enriched at the TiN grain boundaries hinders the propagation of cracks in the TiN phase, resulting in a significant increase in the film’s toughness. The research results of this paper provide support for the design of TiN-Ni films with high strength and toughness and the understanding of the formation mechanism of nanocomposite structures.info:eu-repo/semantics/publishedVersio

H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation

Human hands possess remarkable dexterity and have long served as a source of inspiration for robotic manipulation. In this work, we propose a human $\textbf{H}$and$\textbf{-In}$formed visual representation learning framework to solve difficult $\textbf{Dex}$terous manipulation tasks ($\textbf{H-InDex}$) with reinforcement learning. Our framework consists of three stages: (i) pre-training representations with 3D human hand pose estimation, (ii) offline adapting representations with self-supervised keypoint detection, and (iii) reinforcement learning with exponential moving average BatchNorm. The last two stages only modify $0.36\%$ parameters of the pre-trained representation in total, ensuring the knowledge from pre-training is maintained to the full extent. We empirically study 12 challenging dexterous manipulation tasks and find that H-InDex largely surpasses strong baseline methods and the recent visual foundation models for motor control. Code is available at https://yanjieze.com/H-InDex .Comment: NeurIPS 2023. Code and videos: https://yanjieze.com/H-InDe

Rigid-body inverse dynamics of a spatial redundantly actuated parallel mechanism constrained by two point contact higher kinematic pairs

This paper presents a comparative study of the rigid-body inverse dynamics of a spatial redundantly actuated parallel mechanism constrained by two point contact higher kinematic pairs (HKPs). Firstly, its constrained motions are analysed comprehensively, then four different models are formulated by the generalized momentum approach and the Lagrange-D'Alembert formulation to explore its inverse dynamics. In each method, the first model is built by employing the method directly to the mechanism. In the second model, the dynamic model of its non-redundantly actuated counterpart free of HKPs is built by this approach first, then the constraints from HKPs are modelled, to finally reach the model of the redundantly actuated parallel mechanism (RAPM) where that of its counterpart is utilised as the core. The four models give rise to equivalent numerical results, and the second model in both methods of the RAPM can alleviate the strong coupling between the parasitic motion variables and degrees of freedom (DOFs), boosting the computational speed as fast as that of its non-redundantly actuated counterpart without simplification or loss of accuracy. The comparisons between the mechanism and its counterpart validate that the HKP constraints greatly increase the computational complexity, and the torques required by the parasitic motions of the end effector are significantly smaller than those by the corresponding DOFs

DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds. In this paper, we identify a major shortcoming in existing visual RL methods that is the agents often exhibit sustained inactivity during early training, thereby limiting their ability to explore effectively. Expanding upon this crucial observation, we additionally unveil a significant correlation between the agents' inclination towards motorically inactive exploration and the absence of neuronal activity within their policy networks. To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network. Empirically, we also recognize that the dormant ratio can act as a standalone indicator of an agent's activity level, regardless of the received reward signals. Leveraging the aforementioned insights, we introduce DrM, a method that uses three core mechanisms to guide agents' exploration-exploitation trade-offs by actively minimizing the dormant ratio. Experiments demonstrate that DrM achieves significant improvements in sample efficiency and asymptotic performance with no broken seeds (76 seeds in total) across three continuous control benchmark environments, including DeepMind Control Suite, MetaWorld, and Adroit. Most importantly, DrM is the first model-free algorithm that consistently solves tasks in both the Dog and Manipulator domains from the DeepMind Control Suite as well as three dexterous hand manipulation tasks without demonstrations in Adroit, all based on pixel observations

Ferroptosis: new insight into the mechanisms of diabetic nephropathy and retinopathy

Diabetic nephropathy (DN) and diabetic retinopathy (DR) are the most serious and common diabetes-associated complications. DN and DR are all highly prevalent and dangerous global diseases, but the underlying mechanism remains to be elucidated. Ferroptosis, a relatively recently described type of cell death, has been confirmed to be involved in the occurrence and development of various diabetic complications. The disturbance of cellular iron metabolism directly triggers ferroptosis, and abnormal iron metabolism is closely related to diabetes. However, the molecular mechanism underlying the role of ferroptosis in DN and DR is still unclear, and needs further study. In this review article, we summarize and evaluate the mechanism of ferroptosis and its role and progress in DN and DR, it provides new ideas for the diagnosis and treatment of DN and DR

Design of a flexure-based mechanism possessing low stiffness and constant force

This paper presents a novel design of a flexure-based constant force mechanism with a long travel stroke. Unlike the conventional force control method using a force sensor and feedback controller to obtain constant force output, the proposed compliant mechanism provides a constant force utilizing the unique mechanical property of the mechanical structure. The constant force is generated by using the combination of a negative-stiffness and a positive-stiffness mechanism. In order to achieve a low driving force, the negative stiffness is realized by a special bistable beam, which is a step beam with structural holes. Meanwhile, the positive stiffness is generated by the leaf flexure hinges with structural holes. The regular structural holes can reduce the mass and stiffness of the whole mechanism. Furthermore, the elliptic integral method and the pseudo-rigid-body approach are utilized to establish the model of the constant force mechanism. Based on the established model, the performance of the constant force mechanism is evaluated computationally. Additionally, the parametric model of the proposed mechanism is investigated using the nonlinear finite element analysis. Finally, a prototype is fabricated using 3D printing technique. The open-loop and the closed-loop experimental tests are implemented to investigate the performance of the developed constant force mechanism. It is noted that the constant force mechanism can be robustly controlled by a proportional-integral-derivative control method. Experimental results demonstrate that the developed constant force mechanism has a constant force with slight fluctuation with a range of 500 μm

Design of a novel 3D tip-based nanofabrication system with high precision depth control capability

The design, analysis, and experimental investigation of a novel 3D tip-based nanofabrication system with high precision depth control capability is presented in this paper. Based on this system, a new depth control method, namely tip displacement-based closed-loop (DC) depth control methodology is proposed to improve the depth control capability. As the force-depth prediction with the commonly-used depth control method, i.e. the normal force-based closed-loop (FC) method, may depend on the machining speed, the machining direction, and the material properties, etc. Compared with the FC method, the DC method decreases the complexity and the high uncertainty. The tip feed system utilizes a non-contact force, i.e. the electromagnetic force, to adjust the tip displacement. Therefore, the tip support mechanism can be used to accomplish the tip-sample contact detection. Additionally, an active compensation method is proposed to eliminate the tilt angle between the sample surface and the horizontal plane. Otherwise the machining depth will change gradually, i.e. getting deeper or lower. Furthermore, a series of patterns have been fabricated on silicon sample surface with the proposed system and method. The maximum machining depth of a single scan reaches 300 nm, which is much larger than that of an atomic force microscope (AFM)-based nanofabrication system. The experimental results demonstrate that the system has advantages of distinguished depth control capability, high machining accuracy, and excellent repeatability, which diminishes the influence of above-mentioned factors on the machining depth. Also, the method has the potential of machining arbitrary 2D/3D patterns with well-controlled depth and high accuracy