Lithium slag is a silica-aluminum solid waste, and its leaching activity in alkali solution determines its application prospect in alkali-activated cementitious materials. In this study, based on the leaching characteristics of lithium slag in sodium hydroxide solution, combined with microscopic testing methods, investigates the leaching activity of Si, Al, and Ca in lithium slag under different alkali concentrations, leaching times, and leaching temperatures. The interrelationships between the physicochemical properties, leaching behavior, and reaction activity of lithium slag were revealed, and a leaching model for lithium slag was established to elucidate the leaching kinetics process. The results indicate that compared to leaching time, alkali concentration and leaching temperature have a more significant impact on the leaching of Si and Al in lithium slag. The leached Si and Al in lithium slag mainly participate in the formation of N-A-S-H and C-A-S-H reaction products. The optimal leaching scheme was obtained through a predictive model for Si and Al leaching from lithium slag. The leaching process of lithium slag in sodium hydroxide solution is controlled by the diffusion behavior of the product layer, with activation energies for leaching Si and Al being 23.9562 kJ/mol and 24.3278 kJ/mol, respectively.

The durability of concrete is significantly affected by the chloride solidification property of the binder. This study investigated the chloride-binding capacity and penetration process of metallurgical slag (MS)-based binder and mortar. It also analysed the influence of the ratio of steel slag (SS) to granulated blast furnace slag (GBFS). The results showed that the C–S–H gels and Friedel’s salt (Fs) bound more chloride ions in high- and low-concentration NaCl solutions, respectively. In a 0.1 mol/L NaCl solution, Fs captured 0.01–0.04 mmol/g Cl–. However, in a 5 mol/L NaCl solution, the chloride bound by the C–S–H gels was 0.07–0.20 mmol/g. In addition, the increase of the SS to GBFS ratio (SGR) promoted the generation of Fs through the activity promoting of GBFS and reduced the Cl content captured by the C–S–H gels by improving calcium ions and their unstable connections with silicate chain oxygen. The increasing SGR decreased the percentage of pores smaller than 10 nm, reaching a minimum of 22.6 % at R3. However, the chloride resistance of R3 also exhibited the highest Cs of 1.01 % and the lowest Da of 8.54 × 10–12 m2/s among MS-based mortars due to a 0.10 % decrease in porosity. Furthermore, the chloride-binding capacity and resistance performance of MS-based binders were all better than those of ordinary Portland cement.

Geopolymer is a kind of aluminosilicate material with three-dimensional network structure, which has excellent physical and chemical properties. It is application in wastewater treatment, particularly in pollutant removal, is extensive. This paper comprehensively reviews the advancements in geopolymer-based adsorption of typical contaminants including heavy metal ions, organic compounds, and radionuclides across various water environments. The influence of different raw materials and experimental parameters on the adsorption efficacy of geopolymers were studied, alongside an elucidation of the adsorption mechanisms pertinent to typical pollutants. The analysis underscores the pivotal role of silica-alumina source in determining the adsorption efficiency of geopolymers, with composite variants incorporating metakaolin or fly ash demonstrating notable effectiveness. Additionally, the morphology of the adsorbent emerges as a critical factor, with powdered geopolymer exhibiting superior adsorption capabilities over block forms. Furthermore, the paper delves into the extensive research conducted on the optimal pH conditions and adsorption mechanisms, which predominantly involve ion exchange supplemented by physical adsorption. Finally, the discussion extends to the prospects and challenges facing geopolymer and its composites, aiming to facilitate their broader adoption in engineering applications.

Utilizations of industrial byproducts and wastes as much as possible, together with desirable durability, are essential to sustainable development of building materials. In this work, the chloride diffusion behavior of alkali-activated steel slag (SS) and ultrafine blast furnace slag (UFS) is studied, towards deeper insights into the effect of electrical double layer on the resistance to chloride penetration. The microstructure improvement of alkali-activated SS with increasing addition of UFS was examined by means of X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric analysis, mercury intrusion porosimetry and nitrogen sorption. The zeta potentials above the pore surface of various alkali-activated SS-UFS systems were measured and compared. The electrostatic resistance of the electrical double layer to chloride diffusion was analyzed and discussed. Results indicate that UFS dosages above 40% substantially refines the pore structure of alkali-activated SS and then the mechanism governing chloride penetration shifts from capillary transport to gel transport. The exposure of higher chloride sodium concentration results in the pore surface of alkali-activated SS-UFS systems to be less negatively charged and the zeta potential is reversed to be positive value after continuous ion exchange between sodium and calcium. The presence of UFS can remarkably increase the proportion of physically bound chloride that attains approximately 80% of the total binding capacity. The impediment of electrical double layer to chloride diffusivity becomes increasingly pronounced with pore structure refinement, especially for mixtures with diffusion coefficients below 5 × 10−12 m2/s obtained based on Fick's law of diffusion.

Vacancy engineering offers an appealing strategy for modifying the electronic structure of transition metals. Transition metals with abundant sulfur vacancies can significantly contribute to the microwave absorption capabilities of absorbers. In this study, an NixSy@De composite material was synthesized through a straightforward hydrothermal synthesis technique. The effective absorption bandwidth (EAB) of this composite material reached 9.86 GHz at 1.44 mm. A minimum reflection loss (RLmin) of -33.61 dB at 1 mm was achieved, and after mild etching, the RLmin further improved to -93.53 dB at 1.16 mm to achieve a high-attenuation microwave absorption. The exceptional performance of NixSy@De for the absorption of electromagnetic waves (EMWs) is based on its high dielectric loss, substantial magnetic loss, and excellent impedance matching. This work combines transition metal sulfides with three-dimensional biotemplated diatomite, providing valuable insights into the design of advanced EMW absorbing materials.

The heavy-metal ion critical role in γ-dicalcium silicate (γ-C2S) both in terms of solidification mechanism and hydration is still unclear. In this work, the solidification mechanism and the effect on initiating hydration of these three heavy-metal ions (Ba, Cd, and Cr) in γ-C2S is systemically studied by well-defined ab initio calculations. The calculated results show that the solid solution tendency of ions originates from the charge contribution, and the charge localization caused by the doping of Cr ions weakens the surface water adsorption. These insights will provide theoretical guidance for the low-carbon cement development by γ-C2S.

Category-level 6-D object pose estimation plays a crucial role in achieving reliable robotic grasp detection. However, the disparity between synthetic and real datasets hinders the direct transfer of models trained on synthetic data to real-world scenarios, leading to ineffective results. Additionally, creating large-scale real datasets is a time-consuming and labor-intensive task. To overcome these challenges, we propose CatDeform, a novel category-level object pose estimation network trained on synthetic data but capable of delivering good performance on real datasets. In our approach, we introduce a transformer-based fusion module that enables the network to leverage multiple sources of information and enhance prediction accuracy through feature fusion. To ensure proper deformation of the prior point cloud to align with scene objects, we propose a transformer-based attention module that deforms the prior point cloud from both geometric and feature perspectives. Building upon CatDeform, we design a two-branch network for supervised learning, bridging the gap between synthetic and real datasets and achieving high-precision pose estimation in real-world scenes using predominantly synthetic data supplemented with a small amount of real data. To minimize reliance on large-scale real datasets, we train the network in a self-supervised manner by estimating object poses in real scenes based on the synthetic dataset without manual annotation. We conduct training and testing on CAMERA25 and REAL275 datasets, and our experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) techniques in both self-supervised and supervised training paradigms. Finally, we apply CatDeform to object pose estimation and robotic grasp experiments in real-world scenarios, showcasing a higher grasp success rate.