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Abstract Given the crucial role of lithium (Li) in clean energy transition through effective decarbonization of various energy sectors, enhancing and diversifying the source of Li is regarded as an urgent priority. Producing Li from formation brines is a promising solution due to their abundant resources and environmental friendlessness to extract. In this study, we focus on Li extraction with an ion-sieve method utilizing Li/aluminum-layered double hydroxide chlorides (Li/Al-LDH), by its significant stability, great scalability, and favorable techno-economic feasibility. In this regard, we set our goal to numerically quantify the adsorption performance of granulated Li/Al-LDH adsorbent for Li+ by quantitatively analyzing the impacts of controlling factors. To achieve the goal, we develop our numerical capability of addressing brine injection, fluid flow, component transport, and adsorption in column chromatography application, based on lattice Boltzmann method (LBM) modeling. To quantify the impact of operational conditions of Li+ adsorption performance with granulated Li/Al-LDH adsorbent, various values of porosity and radius of granule, Li+ concentration in injected brine, and brine injection velocity are considered. From the numerical simulations and coupled local sensitivity analysis, the radius of the adsorbent granule is found to be most influential on the adsorption performance, followed by granule porosity, concentration of Li+ in injected brine, and injection velocity. This study provides the conceptual and essential information on the quantified impact of various operational conditions on Li+ adsorption performance that can be used to optimize the design of Li/Al-LDH adsorbent granule and column chromatography strategy, as achieving the techno-economically feasible Li+ extraction from formation brines. 

Reactive transport modeling of subsurface environments plays an important role in addressing critical problems of geochemical processes, such as dissolution and precipitation of minerals. Current transport models for porous media span various scales, ranging from pore-scale to continuum-scale. In this study, we established an upscaling method connecting pore-scale and continuum-scale models by employing a deep learning methodology of Convolutional Neural Networks (CNNs). We applied Darcy-Brinkmann-Stokes (DBS) method to simulate the fluid flow and reactive transport in pore-scale models, which would act as constituents of a continuum-scale model. The datasets of spatial pore distribution of subvolume samples were used as the input for the deep learning model, and the continuum (Darcy)-scale parameters such as permeability, effective surface area, and effective diffusion coefficient were figured out as outputs (i.e., labels). By applying the trained models of the subvolumes in the entire sample volume, we generated the initial field of porosity, permeability, effective diffusion coefficient, and effective surface area for continuum-scale simulation of a mineral dissolution problem. We took an acid dissolution case as an example to utilize the outcomes of trained deep learning models as input data in the continuum-scale simulation. This work presents a comprehensive upscaling workflow, as bridging the findings of microscale simulations to the continuum-scale simulations of a reactive transport problem. 

Switching of magnetization by spin–orbit torque in the (Ga,Mn)(As,P) film was studied with currents along ⟨100⟩ crystal directions and an in-plane magnetic field bias. This geometry allowed us to identify the presence of two independent spin–orbit-induced magnetic fields: the Rashba field and the Dresselhaus field. Specifically, we observe that when the in-plane bias field is along the current (I[Formula: see text]H bias ), switching is dominated by the Rashba field, while the Dresselhaus field dominates magnetization reversal when the bias field is perpendicular to the current (I ⊥ H bias ). In our experiments, the magnitudes of the Rashba and Dresselhaus fields were determined to be 2.0 and 7.5 Oe, respectively, at a current density of 8.0 × 10 5 A/cm 2 .