Transactions of Nonferrous Metals Society of China (中国有色金属学报)•2026•DOI: 10.1016/S1003-6326(26)67069-0
The redox smelting of zinc leaching residue (ZLR) was investigated to determine the migration behavior and toxicity characteristics of zinc under varying anthracite addition, temperature, and holding time. The ZLR, containing 10–20 wt.% Zn, 0.5–5 wt.% Pb, and 0.1–0.5 wt.% Cd, generates TCLP leachate concentrations of Zn up to 4589.0 mg/L, far exceeding regulatory limits. Experimental results reveal that CaSO4 in the residue promotes the transformation of ZnFe2O4 into a ZnS–FeS eutectic, which hinders zinc recovery and elevates environmental risk due to its lower thermodynamic stability relative to (Fe,Zn)2SiO4, ZnFe2O4, and (ZnO)slag. At temperatures above 1573 K, the ZnS–FeS eutectic is oxidized by O2/(O)slag to ZnO(s), subsequently dissolved into the slag as chemically dissolved Zn, and finally reduced to Zn(g) by CO. Pre-desulfurization or increased oxygen potential enhances zinc volatilization. Under optimized conditions, the zinc recovery ratio reached 99.13%, and the residual zinc content in the slag decreased to 0.22 wt.%, substantially below the industrial range of 1.0–3.0 wt.%. A novel strategy integrating desulfurization pretreatment with redox smelting is proposed, which lowers the required smelting temperature and improves zinc recovery efficiency, offering a more economical and environmentally sustainable solution for ZLR treatment.
Nano-Micro Letters•2025•DOI: 10.1007/s40820-025-01741-0
Over recent decades, carbon-based chemical sensor technologies have advanced significantly. Nevertheless, significant opportunities persist for enhancing analyte recognition capabilities, particularly in complex environments. Conventional monovariable sensors exhibit inherent limitations, such as susceptibility to interference from coexisting analytes, which results in response overlap. Although sensor arrays, through modification of multiple sensing materials, offer a potential solution for analyte recognition, their practical applications are constrained by intricate material modification processes. In this context, multivariable chemical sensors have emerged as a promising alternative, enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition, while utilizing a single sensing material. Among various carbon-based materials, carbon nanotubes (CNTs) and graphene have emerged as ideal candidates for constructing high-performance chemical sensors, owing to their well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. This review examines the progress of carbon-based multivariable chemical sensors, focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition. The discussion encompasses fundamental aspects of these sensors, including sensing materials, sensor architectures, performance metrics, pattern recognition algorithms, and multivariable sensing mechanism. Furthermore, the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.
Journal of Semiconductors (半导体学报 - 中国科学院半导体研究所)•2024•DOI: 10.1088/1674-4926/24120026
All-perovskite tandem solar cells (ATSCs) have the potential to surpass the Shockley−Queisser efficiency limit of conventional single-junction devices. However, the performance and stability of mixed tin–lead (Sn–Pb) perovskite solar cells (PSCs), which are crucial components of ATSCs, are much lower than those of lead-based perovskites. The primary challenges include the high crystallization rate of perovskite materials and the susceptibility of Sn2+ oxidation, which leads to rough morphology and unfavorable p-type self-doping. To address these issues, we introduced ethylhydrazine oxalate (EDO) at the perovskite interface, which effectively inhibits the oxidation of Sn2+ and simultaneously enhances the crystallinity of the perovskite. Consequently, the EDO-modified mixed tin−lead PSCs reached a power conversion efficiency (PCE) of 21.96% with high reproducibility. We further achieved a 27.58% efficient ATSCs by using EDO as interfacial passivator in the Sn−Pb PSCs.
Int. Journal of Mining Science and Technology (采矿与安全工程)•2024•DOI: 10.1016/j.ijmst.2024.12.009
Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.