Explore the vast range of titles available.

Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is inherently challenging due to the multi-phase, multi-component, reactive dynamics within multi-scale, multi-layered porous media. In addition, currently inadequate imaging and modelling capabilities are limiting simulations to small areas (<1 mm 2 ) or simplified architectures. Herein, an advancement in water modelling is achieved using X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multi-phase simulation. The resulting image is the most resolved domain (16 mm 2 with 700 nm voxel resolution) and the largest direct multi-phase flow simulation of a fuel cell. This generalisable approach unveils multi-scale water clustering and transport mechanisms over large dry and flooded areas in the gas diffusion layer and flow fields, paving the way for next generation proton exchange membrane fuel cells with optimised structures and wettabilities. Accurate liquid water modelling is challenging. Here the authors use X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multiphase simulation to simulate fuel cell and guide fuel cell design.

Digital imaging and modeling are essential tools for characterizing rock structures and understanding fluid flow behavior. These efforts often rely on X‐ray micro‐computed tomography (micro‐CT), which faces an inherent trade‐off between resolution and field‐of‐view (FOV). Deep learning super‐resolution (SR) methods have been developed to overcome this limitation, but their application to carbonate rocks is challenged by complex micro‐nanometer features. Due to the resolution limits, micro‐CT fails to capture sub‐micrometer features such as micropores in carbonates, and using such data as high‐resolution (HR) training images limits the SR model's ability to accurately reconstruct the micropore structures. We introduce a cascading SR pipeline designed to address these challenges and reveal sub‐micrometer features in carbonate rocks. The approach integrates multi‐stage 2D SR networks to progressively enhance low‐resolution (LR) images toward the HR domain, followed by a third‐plane SR network for 3D reconstruction. We evaluate this method on a three‐stage SR task: starting from a 3 μ ${\\upmu }$m resolution micro‐CT image, super‐resolving to an intermediate 1 μ ${\\upmu }$m resolution, and ultimately reaching 0.1 μ ${\\upmu }$m resolution based on scanning electron microscopy (SEM), achieving a 30× ${\\times} $ scale factor. Validation with unseen SEM demonstrates that the reconstructed domains retain essential structural and physical properties. This approach provides a practical solution to current imaging limitations and enables the integration of multi‐resolution modalities for improved rock characterization.

Net‐zero carbon targets drive the development of new underground activities such as hydrogen storage and in situ critical mineral recovery, all of which involve geochemical reactions between minerals and fluid/ion transport. Understanding these processes is key to optimizing efficiency and minimizing environmental impacts. However, the fundamental mechanisms of ion transport, mineral dissolution, and secondary precipitation remain poorly understood, particularly at the pore scale. This gap partly arises from the challenges of characterizing samples at such a fine scale, where fluid/ion transport and reactions occur simultaneously. Herein, a core‐to‐pore‐scale experimental approach, combined with time‐lapse three‐dimensional (3D) imaging, is designed to characterize fluid/ion transport, dissolution, and precipitation processes. We implemented this workflow in an electrokinetic in situ recovery (EK‐ISR) system. Time‐lapse 3D micro‐computed tomography (micro‐CT) images were acquired during the experiment to observe dissolution and precipitation dynamics and to measure pore‐scale physical parameters. Findings indicate uniform reactive ion transport and mineral dissolution under EK conditions, with over 78% of the target mineral dissolved. Time‐lapse images reveal multiple dissolution and precipitation patterns that influence reactive transport processes. Geochemical modeling based on pore‐scale parameters demonstrates over 90% correlation with core‐scale experimental data. Our workflow demonstrates a promising capability for characterizing reactive transport processes across pore‐to‐core scales.

The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (10 11 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks. This study introduces P3T-Net, a pseudo-3D deep learning model that enables accurate and efficient cross-domain transfer of large 3D material images, improving image quality and ensuring image consistency across diverse imaging conditions.

Carbon catalysts have shown promise as an alternative to the currently available energy-intensive approaches for nitrogen fixation (NF) to urea, NH3, or related nitrogenous compounds. The primary challenges for NF are the natural inertia of nitrogenous molecules and the competitive hydrogen evolution reaction (HER). Recently, carbon-based materials have made significant progress due to their tunable electronic structure and ease of defect formation. These properties significantly enhance electrocatalytic and photocatalytic nitrogen reduction reaction (NRR) activity. While transition metal-based catalysts have solved the kinetic constraints to activate nitrogen bonds via the donation-back-π approach, there is a problem: the d-orbital electrons of these transition metal atoms tend to generate H-metal bonds, inadvertently amplifying unwanted HER. Because of this, a timely review of defective carbon-based electrocatalysts for NF is imperative. Such a review will succinctly capture recent developments in both experimental and theoretical fields. It will delve into multiple defective engineering approaches to advance the development of ideal carbon-based electrocatalysts and photocatalysts. Furthermore, this review will carefully explore the natural correlation between the structure of these defective carbon-based electrocatalysts and photocatalysts and their NF activity. Finally, novel carbon-based catalysts are introduced to obtain more efficient performance of NF, paving the way for a sustainable future.

Energy-based materials, such as hydrogen fuel cells and metal ore, play an essential role in reducing greenhouse gas emissions, facilitating the clean energy transition, and designing renewable energy devices. Image techniques, such as x-ray microcomputed tomography (micro-CT), scanning electron microscopy (SEM), and quantitative evaluation of materials by scanning electron microscopy (QEMSCAN), provide a means to characterize the physical and chemical properties of porous materials. Conventionally, 2-dimensional (2D) image techniques are used to evaluate the bulk properties of a material’s surface, such as QEM-SCAN for mineral liberation analysis. However, 2D analysis of 3D materials causes sampling error and stereological effects, and therefore is insufficient for accurate materials characterization. Micro-CT provides a 3-dimensional (3D) vision of the internal material’s structure at the length scale at which materials interfaces are well resolved, and therefore is used to extract detailed information by coupling with other 2D imaging techniques. However, due to the limits of micro-CT, including resolution limits, scan artifacts, similar attenuation values for different materials, etc. Image processing techniques are required to accurately quantify the information from micro-CT. However, traditional image processing methods, such as thresholding and gradient-based methods for image segmentation, are insufficient to fully characterize the 3D porous material structure and in addition contain significant human-judgement.Machine learning (ML) methods are utilized to alleviate the limitations associated with traditional methods. Firstly, for iron ore sinter bed particles that contain various minerals with different textures and geometry, a supervised ML workflow is proposed to segment micro-CT image sinter images. Instead of voxel-level segmentation methods that cannot segment the particles containing various minerals; the geometric, texture, and grayscale features of each particle are generated as input to a ML model. XGBoost and LightGBM as two gradient descent ensemble ML models are utilised to classify particles based on these features. Accuracy over 90 % is achieved for iron ores that are morphologically domain-distinct in their feature space, while lower accuracy in the order of 40-80 % is achieved between particles that derive from different mine sources. Additionally, several CNN architectures are proposed to perform multi-phase segmentation on sandstone, iron ore, and a hydrogen fuel cell. A novel network called EfficientU-Net and its variance EfficientU-Net-cGAN with an additional image-to-image GAN is proposed that demonstrates a higher pixelwise and physical accuracy than the commonly used U-ResNet. With the assistance of these networks, materials characterization is extended from 2D to 3D without user judgement by coupling 3D micro-CT with other 2D image techniques, such as QEMSCAN and micro-XRF. Meanwhile, two GAN-based networks, PH-GAN and CycleGAN, are presented to increase the generalizability of ML techniques when dealing with a large number of images under diverse scanning conditions. Large 3D images are downscaled using PH-GAN in a way that preserves the topological feature. Lastly, CycleGAN executes unpaired domain transfer to eliminate the domain inconsistency of various scanning situations.Overall, this dissertation demonstrates how ML methods and workflows, along with their coupling with imaging techniques are useful for 3D full-feature characterization of energy-based materials, surpassing the capabilities of conventional characterization techniques and minimizing the influence of scanning conditions for cross-image characterization.

Electrokinetic in-situ recovery is an alternative to conventional mining, relying on the application of an electric potential to enhance the subsurface flow of ions. Understanding the pore-scale flow and ion transport under electric potential is essential for petrophysical properties estimation and flow behavior characterization. The governing physics of electrokinetic transport is electromigration and electroosmotic flow, which depend on the electric potential gradient, mineral occurrence, domain morphology (tortuosity and porosity, grain size and distribution, etc.), and electrolyte properties (local pH distribution and lixiviant type and concentration, etc.). Herein, mineral occurrence and its associated zeta potential are investigated for EK transport. The new Ek model which is designed to solve the EK flow in complex porous media in a highly parallelizable manner includes three coupled equations: (1) Poisson equation, (2) Nernst–Planck equation, and (3) Navier–Stokes equation. These equations were solved using the lattice Boltzmann method within X-ray computed microtomography images. The proposed model is validated against COMSOL multiphysics in a two-dimensional microchannel in terms of fluid flow behavior when the electrical double layer is both resolvable and unresolvable. A more complex chalcopyrite-silica system is then obtained by micro-CT scanning to evaluate the model performance. The effects of mineral occurrence, zeta potential, and electric potential on the three-dimensional chalcopyrite-silica system were evaluated. Although the positive zeta potential of chalcopyrite can induce a flow of ferric ion counter to the direction of electromigration, the net effect is dependent on the occurrence of chalcopyrite. However, the ion flux induced by electromigration was the dominant transport mechanism, whereas advection induced by electroosmosis made a lower contribution. Overall, a pore-scale EK model is proposed for direct simulation on pore-scale images. The proposed model can be coupled with other geochemical models for full physicochemical transport simulations. Meanwhile, electrokinetic transport shows promise as a human-controllable technique because the electromigration of ions and the applied electric potential can be easily controlled externally. Graphical abstract

Carbon catalysts have shown promise as an alternative to the currently available energy-intensive approaches for nitrogen fixation (NF) to urea, NH , or related nitrogenous compounds. The primary challenges for NF are the natural inertia of nitrogenous molecules and the competitive hydrogen evolution reaction (HER). Recently, carbon-based materials have made significant progress due to their tunable electronic structure and ease of defect formation. These properties significantly enhance electrocatalytic and photocatalytic nitrogen reduction reaction (NRR) activity. While transition metal-based catalysts have solved the kinetic constraints to activate nitrogen bonds via the donation-back-π approach, there is a problem: the d-orbital electrons of these transition metal atoms tend to generate H-metal bonds, inadvertently amplifying unwanted HER. Because of this, a timely review of defective carbon-based electrocatalysts for NF is imperative. Such a review will succinctly capture recent developments in both experimental and theoretical fields. It will delve into multiple defective engineering approaches to advance the development of ideal carbon-based electrocatalysts and photocatalysts. Furthermore, this review will carefully explore the natural correlation between the structure of these defective carbon-based electrocatalysts and photocatalysts and their NF activity. Finally, novel carbon-based catalysts are introduced to obtain more efficient performance of NF, paving the way for a sustainable future.

Electrokinetic in-situ recovery is an alternative to conventional mining, relying on the application of an electric potential to enhance the subsurface flow of ions. Understanding the pore-scale flow and ion transport under electric potential is essential for petrophysical properties estimation and flow behavior characterization. The governing physics of electrokinetic transport is electromigration and electroosmotic flow, which depend on the electric potential gradient, mineral occurrence, domain morphology, and electrolyte properties. Herein, mineral occurrence and its associated zeta potential are investigated for EK transport. The governing model includes three coupled equations: (1) Poisson equation, (2) Nernst--Planck equation, and (3) Navier--Stokes equation. These equations were solved using the lattice Boltzmann method within X-ray computed microtomography images. The proposed model is validated against COMSOL Multiphysics in a 2-dimensional microchannel in terms of fluid flow behavior when the electrical double layer is both resolvable and unresolvable. A more complex chalcopyrite-silica system is then obtained by micro-CT scanning to evaluate the model performance. The effects of mineral occurrence, zeta potential, and electric potential on the 3-dimensional chalcopyrite-silica system were evaluated. Although the positive zeta potential of chalcopyrite can induce a flow of ferric ion counter to the direction of electromigration, the net effect is dependent on the occurrence of chalcopyrite. However, the ion flux induced by electromigration was the dominant transport mechanism, whereas advection induced by electroosmosis made a lower contribution. Overall, a pore-scale EK model is proposed for direct simulation on pore-scale images. The proposed model can be coupled with other geochemical models for full physicochemical transport simulations.

Groundwater pollution poses a significant threat to environmental sustainability during urbanization. Existing remediation methods like pump-and-treat and electrokinetics have limited ion transport control. This study introduces a coupled advection-diffusion-electromigration system for controlled ion transport in the subsurface. Using the Lattice-Boltzmann-Poisson method, we simulate ion transport in various two- and three-dimensional porous media. We establish an ion transport regime classification based on the Peclet number (Pe) and a novel Electrodiffusivity index (EDI). By manipulating the electric potential, hydrostatic pressure, and ion concentration, we identify four transport regimes: large channeling, uniform flow, small channeling, and no flow. Large channeling occurs when advection dominates, while uniform flow arises when diffusion and electromigration are more prevalent. Small channeling happens when the advection opposes electromigration and diffusion, and no flow occurs when the advection or electromigration impedes ion transport via diffusion. Simulations in heterogeneous models confirm these transport regimes, highlighting the influence of pore size variation on transport regimes. Consequently, \\(Pe\\) and \\(EDI\\) must be tailored for optimal transport control. These findings enable better control over ion transport, optimizing processes such as heavy metal removal, bioremediation, and contaminant degradation in groundwater management.