Search for: All records

Reclamation of coal fly ash, a legacy waste material, provides an alternative pathway for the recovery of rare earth elements (REEs) while reducing the environmental stresses that stem from traditional mining. The reactive transport processes underlying the recovery of REEs from ash wastes, however, are yet to be fully elucidated owing to the physicochemical complexity of the micro/nanoscale fly ash particles, including the crystallinity of the particulate matrix. In this work, we use transmission electron microscopy to characterize the material properties of ash particles and reveal the impact of crystallinity on the reactive transport processes governing access to and recovery of the encapsulated REEs. Our results show, for the first time, two distinct crystalline structures of REE-bearing aluminosilicate particles: dense amorphous matrices that facilitate the exchange of chemical species through their lattice interstices and porous polycrystalline matrices characterized by connected intraparticle pores and chemical inertness to leaching solutions. Notably, the presence of matrix crystallinity, or the lack thereof, governs the extent of reagents consumed parasitically by secondary reactions with the aluminosilicate matrix. Our work reveals how the variability of crystalline structures of the ash matrices hosting REEs defines the pathways for the recovery of REEs, providing key insights required for the development of targeted recovery processes. 

Bimetallic nanoparticles often show properties superior to their single-component counterparts. However, the large parameter space, including size, structure, composition, and spatial arrangement, impedes the discovery of the best nanoparticles for a given application. High-throughput methods that can control the composition and spatial arrangement of the nanoparticles are desirable for accelerated materials discovery. Herein, we report a methodology for synthesizing bimetallic alloy nanoparticle arrays with precise control over their composition and spatial arrangement. A dual-channel nanopipet is used, and nanofluidic control in the nanopipet further enables precise tuning of the electrodeposition rate of each element, which determines the final composition of the nanoparticle. The composition control is validated by finite element simulation as well as electrochemical and elemental analyses. The scope of the particles demonstrated includes Cu–Ag, Cu–Pt, Au–Pt, Cu–Pb, and Co–Ni. We further demonstrate surface patterning using Cu–Ag alloys with precise control of the location and composition of each pixel. Additionally, combining the nanoparticle alloy synthesis method with scanning electrochemical cell microscopy (SECCM) allows for fast screening of electrocatalysts. The method is generally applicable for synthesizing metal nanoparticles that can be electrodeposited, which is important toward developing automated synthesis and screening systems for accelerated material discovery in electrocatalysis. 

Abstract 2D memristors have demonstrated attractive resistive switching characteristics recently but also suffer from the reliability issue, which limits practical applications. Previous efforts on 2D memristors have primarily focused on exploring new material systems, while damage from the metallization step remains a practical concern for the reliability of 2D memristors. Here, the impact of metallization conditions and the thickness of MoS₂films on the reliability and other device metrics of MoS₂‐based memristors is carefully studied. The statistical electrical measurements show that the reliability can be improved to 92% for yield and improved by ≈16× for average DC cycling endurance in the devices by reducing the top electrode (TE) deposition rate and increasing the thickness of MoS₂films. Intriguing convergence of switching voltages and resistance ratio is revealed by the statistical analysis of experimental switching cycles. An “effective switching layer” model compatible with both monolayer and few‐layer MoS₂, is proposed to understand the reliability improvement related to the optimization of fabrication configuration and the convergence of switching metrics. The Monte Carlo simulations help illustrate the underlying physics of endurance failure associated with cluster formation and provide additional insight into endurance improvement with device fabrication optimization.