Investigating the densification of Li6PS5Cl solid electrolyte through multi-scale characterization techniques

All-solid-state lithium-sulfur batteries (ASSLSBs), as an energy storage system for achieving the high energy density target of 600 Wh kg-1, hold significant importance in driving in next-generation battery technologies. This review focuses on the key challenges of cathode materials for high energy density ASSLSBs and systematically summarizes the recent research progress. First, the interfacial reaction mechanisms among active materials, conductive agents, and solid electrolytes in sulfur cathodes are analyzed in depth, revealing the fundamental causes of interface failure. Second, the advancements in composite cathodes are summarized, including the influence of preparation processes, material design strategies, and the structure-performance regulation mechanisms of mixed conductors. Next, the role of interface engineering strategies in enhancing reaction kinetics is discussed in detail. Furthermore, recently developed solutions for critical technical bottlenecks, such as high sulfur loading and low-temperature adaptability, are reviewed. Finally, future research directions are envisioned from the dimensions of multiscale interface engineering, material systems, and characterization techniques. This review aims to move beyond conventional single-component optimization approaches, developing a multicomponent framework for cathode design. The review further provides references for developing high-energy-density, long-cycle-life ASSLSBs, offering a comprehensive reference for advancing the practical application of this energy storage technology.

Accurate prediction of particulate matter (PM) using time series data is a challenging task. The recent advancements in sensor technology, computing devices, nonlinear computational tools, and machine learning (ML) approaches provide new opportunities for robust prediction of PM concentrations. In this study, we develop a hybrid model for forecasting PM10 and PM2.5 based on the multiscale characterization and ML techniques. At first, we use the empirical mode decomposition (EMD) algorithm for multiscale characterization of PM10 and PM2.5 by decomposing the original time series into numerous intrinsic mode functions (IMFs). Different individual ML algorithms such as random forest (RF), support vector regressor (SVR), k-nearest neighbors (kNN), feed forward neural network (FFNN), and AdaBoost are then used to develop EMD-ML models. The air quality time series data from Masfalah air station Makkah, Saudi Arabia are utilized for validating the EMD-ML models, and results are compared with non-hybrid ML models. The PMs (PM10 and PM2.5) concentrations data of Dehli, India are also utilized for validating the EMD-ML models. The performance of each model is evaluated using root mean square error (RMSE) and mean absolute error (MAE). The average bias in the predictive model is estimated using mean bias error (MBE). Obtained results reveal that EMD-FFNN model provides the lowest error rate for both PM10 (RMSE = 12.25 and MAE = 7.43) and PM2.5 (RMSE = 4.81 and MAE = 3.02) using Misfalah, Makkah data whereas EMD-kNN model provides the lowest error rate for PM10 (RMSE = 20.56 and MAE = 12.87) and EMD-AdaBoost provides the lowest error rate for PM2.5 (RMSE = 15.29 and MAE = 9.45) using Dehli, India data. The findings also reveal that EMD-ML models can be effectively used in forecasting PM mass concentrations and to develop rapid air quality warning systems.

There is an increasing interest in studying the high Ni-content layered oxide materials for Li-ion batteries, especially for their application in electrical vehicles, since they are promising candidates for next-generation energy storage materials. Compared to traditional layered oxide materials such as LiCoO2 (LCO) or LiNi1/3Mn1/3Co1/3 O2 (NMC333), high Ni-content materials can provide higher specific capacity and energy density due to the two electron transfer per Ni by the Ni2+/Ni4+ redox couple. However, these materials are still suffering from the problems of capacity fading. It is quite important to understand the relationship between performance deterioration and structural degradation. The in-depth understanding of such relation can provide valuable guidance for future material design. The results obtained from our newly developed multi-scale characterization techniques will be reported, including the state-of-the-art aberration-corrected scanning transmission microscopy (STEM) imaging, STEM-electron energy loss spectroscopy (EELS), X-ray imaging, X-ray diffraction (XRD), and X-ray absorption spectroscopy (XAS). These results show that such multi-scale combined characterization tools can help us to study the formation of internal nano-pore structure at primary particle level and understand its impact on the formation of micro-cracks at secondary particle level. This structure degradation is highly correlated to the irreversible cation migration and intermixing at atomic level during extensive electrochemical cycling. Also, we found that the surface plays a vital role in the function of these materials. By combining the synchrotron based X-ray techniques with electron microscopy and spectroscopy, this presentation will report the degradation pathway of high Ni-content cathode materials for Li-ion batteries.

Multiscale structural characterization of yttria dispersed copper alloys fabricated by hot isostatic processing of mechanically alloyed powders

Laser cladding of a Ni based powder on Cu-Ni-Al or cast iron was performed with a 4kW continuous Nd: YAG laser. The Cu-Ni-Al and cast-iron substrates are used for their thermal properties in glass mold industry. But the issue of these materials is their lack of resistance on corrosion and abrasion. The role of the Ni based alloy is to protect the mold without affecting its thermal properties (Heat Affected Zone (HAZ)). The purpose of this research is to produce a well bonded Ni based melted powder without pores or cracks on a non-planar surface (curvilinear section). An investigation of the impact of the processing parameters, power (1500-3200 W), scanning speed (2.5-10 mm/s) and powder feeding rate (24.5-32.5 g/min) on the bonding quality, the porosity propagation and HAZ appearance is performed. The used methods are neutronography, Scanning Electron Microscopy, Energy Dispersive Spectroscopy and Electron BackScatter Diffraction (EBSD). These multi-scale techniques are obviously complementary. Neutronography is a well-adapted non-destructive method to observe the porosity in the volume thanks to the contrast between materials. EBSD analysis allows us to analyze the microstructural evolution of the coating notably by observing the dendrites growth. This same method also permits to observe the HAZ nature according to the laser cladding parameters. Those methods allowed to optimize the processing parameters in a way to obtain perfect bonding, to avoid porosity propagation and to limit the HAZ emergence.

Machine learning-facilitated multiscale imaging for energy materials

The study of mechanical and chemical phenomena arising within a material that is being subjected to external stress is termed mechanochemistry (MC). Recent advances in MC have revealed the prospect not only to enable a greener route to chemical transformations but also to offer previously unobtainable opportunities in the production and screening of biomaterials. To date, the field of MC has been constrained by the inability of current characterisation techniques to provide essential localised multiscale chemically mapping information. A potential method to overcome this is secondary electron hyperspectral imaging (SEHI). SEHI is a multiscale material characterisation technique applied within a scanning electron microscope (SEM). Based on the collection of secondary electron (SE) emission spectra at low primary beam energies, SEHI is applicable to the chemical assessment of uncoated polymer surfaces. Here, we demonstrate that SEHI can provide in situ MC information using poly(glycerol sebacate)-methacrylate (PGS-M) as an example biomaterial of interest. This study brings the use of a bespoke in situ SEM holder together with the application of SEHI to provide, for the first time, enhanced biomaterial mechanochemical characterisation.

Predicting the macroscopic and microscopic mechanical response of metals and alloys subjected to complex loading conditions necessarily requires a synergistic combination of multiscale material models and characterization techniques. This article focuses on the use of a multiscale approach to study the difference between intergranular lattice strain evolution for various grain families measured during in situ neutron diffraction on dog bone and cruciform 316L samples. At the macroscale, finite element simulations capture the complex coupling between applied forces and gauge stresses in cruciform geometries. The predicted gauge stresses are used as macroscopic boundary conditions to drive a mesoscale full-field elasto-viscoplastic fast Fourier transform crystal plasticity model. The results highlight the role of grain neighborhood on the intergranular strain evolution under uniaxial and equibiaxial loading.

SummaryThe Monteith Formation is an important tight gas reservoir in the Deep Basin, Alberta, and consists of a progradational succession of shallow marine sediments, nonmarine carbonaceous and coaly, coastal plain facies, and coarse-grained fluvial deposits, from base to top, respectively. This study is based on multiscale description and characterization techniques with cores and drill cuttings, including multimethods laboratory measurements of key reservoir parameters such as porosity and permeability. A second stage of the study involves the use of laboratory measurements obtained from cores and drill cuttings and their integration with well logs to construct a numerical 3D model of the study area. The 3D model is used to history match gas production, and forecast performance of new wells in those areas where the geologic model indicates potential for gas production.The ultimate goal is to provide a better understanding of the distribution of reservoir properties in the study area for developing drilling prospects and their production potential in areas where reliable data are scarce. The reservoir-modeling stage is carried out by implementing a recently developed methodology that integrates a variable shape distribution (VSD) model, capable of capturing different reservoir properties throughout the whole scale spectrum without any data truncation. Truncation is the excuse generally used for eliminating information that does not fit a given distribution. The claim is that the data are of poor quality, something that is not true in many cases. This new methodology eliminates the need for truncation, and introduces an extension of the VSD approach for reservoir-simulation purposes that reduces uncertainty in the generation of drilling prospects.Core analysis shows that the Monteith A member is composed of complex fluvial-dominated deposits with better rock quality than the shallow marine sandstones of the Monteith C member. This is most likely because of larger pore-throat apertures that range between 0.5 and 1 μm, and a relatively higher proportion of preserved intergranular pore space within these coarser-grained framework grains. Furthermore, the best production performance is from wells that are producing from the Monteith A. Variability of production rates also seems to be controlled by the presence of natural fractures. It is anticipated that the resulting 3D reservoir model will allow improving field-development strategies for this and other similar unconventional gas reservoirs in the Deep Basin of Alberta and elsewhere.

The Monteith Formation is an important tight gas reservoir in the Deep Basin, Alberta, and consists of a progradational succession of shallow marine sediments, non-marine carbonaceous and coaly, coastal plain facies, and coarse-grained fluvial deposits, from base to top, respectively. This study compares rock properties and production performance of the uppermost lithostratigraphic unit ("Monteith A") and the lowermost portion ("Monteith C") of the Monteith Formation in the Western Canada Sedimentary Basin (WCSB) in Alberta. The study is based on multi-scale description and characterization techniques using cores and drill cuttings, including multiple laboratory measurements of key reservoir parameters such as porosity and permeability. A second stage of the study involves the use of laboratory measurements obtained from cores and drill cuttings and their integration with well logs to produce a numerical 3D model of the study area. The 3D model is used to history match gas production, and forecast performance of new wells in those areas where the geologic model indicates potential for gas production. The ultimate goal is to provide a better understanding of the distribution of reservoir properties in the study area, for developing drilling prospects and their production potential. In addition to that, the reservoir modeling stage is carried out by implementing a recently developed methodology that integrates a Variable Shape Distribution (VSD) model, capable of capturing different reservoir properties through the whole scale spectrum without any data truncation. This new methodology introduces an extension of the VSD approach for reservoir simulation purposes. The results are showing that the Monteith A unit has better rock quality than the shallow marine sandstones of the Monteith C interval. This is most likey due to larger pore throat apertures ranging between 0.5 and 1 microns, relatively higher proportion of preserved intergranular pore space within these coarser-grained framework grains. Furthermore, the best production performance is found from wells that are actually producing from the uppermost interval. The resulting 3D reservoir model will allow to improve field development strategies for this and other similar unconventional gas reservoir in the Deep Basin of Alberta.

Multi-scale morphological characterization of Ni foams with directional pores

Understanding the oxidation behaviors of a Ni-Co-based superalloy at elevated temperatures through multiscale characterization

Multiscale characterizations of painted surface appearance by continuous wavelet transform

Multiscale characterization of an additively manufactured property graded Ni-base alloy for molten-salts\\supercritical-CO2 heat exchangers

Heterogeneous functional materials for energy-conversion and –storage, such as fuel cells, electrolyzers and batteries are hierarchical, porous, electroactive materials that rely on multi-scale imaging techniques for accurate morphological characterization. These materials are building blocks for the catalyst layers, gas diffusion layers and electrodes in fuel cells, porous transport layers (PTLs) in electrolyzers and porous electrodes within batteries. Functional materials electrochemical functionality can be assessed with various in-situ and operando techniques that probe chemical state during operation, such as x-ray absorption near edge structure (XANES). Synchrotron bright x-ray sources allow for nano-scale imaging combined with XANES, which state-of-the-art lab-scale systems are still lacking. Furthermore, these bright sources allow ultra-fast imaging on sub-second and micro-second temporal resolution. Optimization of temporal, spatial and chemical dimensions is critical to answer the morphological and chemical questions of interest [1]. Transmission x-ray microscopy (TXM) and its three-dimensional analog x-ray computed tomography (CT) allows 30 nm resolution with nano-CT set-up and 1 um resolution with micro-CT beamlines. The nano-CT beamlines have precise energy resolution to enable a combination of 3D imaging and XANES. The generated imaging data sizes exceed terabytes of space and requires modeling frameworks to interpret the findings. We have developed a scale-bridging algorithm to incorporate nano-scale findings into micro-scale using direct numerical simulations and coupling algorithms via effective properties and boundary conditions. In this work we will present several examples of operando data collected on fuel cells, electrolyzers and batteries using nano and micro x-ray CT and in some instances with XANES or with sub-second radiography. Full-field x-ray imaging beamline 18-ID at National Synchrotron Light Source II has achieved 3 minutes nano-CT scan, what enables full 3D XANES scan within reasonable time-frame. Some of the questions that we attempt to answer include activity and water management of NiCu/KB electrocatalyst used in alkaline fuel cells as anode; electrocatalyst activity and its interface with the PTL within operating electrolyzers, and Li metal dendrite growth in solid polymer electrolyte batteries during cycling. Scale-bridging framework is applied to fuel cell electrodes to understand oxygen transport and reaction-diffusion processes for these multi-scale catalyst layers.