Spatial Distribution Of Phases Research Articles

Editors' Choice—Visualizing the Impact of the Composite Cathode Microstructure and Porosity on Solid-State Battery Performance

Abstract The kinetics of composite cathodes for solid-state batteries (SSBs) relies heavily on their micro-structure. Spatial distribution of the different phases, porosity, interface areas, and tortuosity factors are important descriptors that need accurate quantification for models to predict the elec-trochemistry and mechanics of SSBs. In this study, high-resolution focused ion beam-scanning electron microscopy tomography was used to investigate the microstructure of cathodes com-posed of a nickel-rich cathode active material (NCM) and a thiophosphate-based inorganic solid electrolyte (ISE). The influence of the ISE particle size on the microstructure of the cathode was visualized by 3D reconstruction and charge transport simulation. By comparison of experimen-tally determined and simulated conductivities of composite cathodes with different ISE particle sizes, the electrode charge transport kinetics is evaluated. Porosity is shown to have a major influence on the cell kinetics and the evaluation of the active mass of electrochemically active particles reveals a higher fraction of connected NCM particles in electrode composites utilizing smaller ISE particles. The results highlight the importance of homogeneous and optimized mi-crostructures for high performance SSBs, securing fast ion and electron transport.

Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials

The microstructure is a critical factor governing the functionality of ceramic materials. Meanwhile, microstructural analysis of electron microscopy images of polycrystalline ceramics, which are geometrically complex and composed of countless crystal grains with porosity and secondary phases, has generally been performed manually by human experts. Objective pixel-based analysis (semantic segmentation) with high accuracy is a simple but critical step for quantifying microstructures. In this study, we apply neural network-based semantic segmentation to secondary electron images of polycrystalline ceramics obtained by three-dimensional (3D) imaging. The deep-learning-based models (e.g., fully convolutional network and U-Net) by employing a dataset based on a 3D scanning electron microscopy with a focused ion beam is found to be able to recognize defect structures characteristic of polycrystalline materials in some cases due to artifacts in electron microscopy imaging. Owing to the training images with improved depth accuracy, the accuracy evaluation function, intersection over union (IoU) values, reaches 94.6% for U-Net. These IoU values are among the highest for complex ceramics, where the 3D spatial distribution of phases is difficult to locate from a 2D image. Moreover, we employ the learned model to successfully reconstruct a 3D microstructure consisting of giga-scale voxel data in a few minutes. The resolution of a single voxel is 20 nm, which is higher than that obtained using a typical X-ray computed tomography. These results suggest that deep learning with datasets that learn depth information is essential in 3D microstructural quantifying polycrystalline ceramic materials. Additionally, developing improved segmentation models and datasets will pave the way for data assimilation into operando analysis and numerical simulations of in situ microstructures obtained experimentally and for application to process informatics.

The systematic structural studies of some Byzantine ceramic fragments from Dobrudja region of Romania: Raman spectroscopy, neutron diffraction, and imaging data

AbstractThe systematic studies of composition and spatial distribution of main phases inside volume of 25 fragments of Byzantine ceramic obtained in archeological works in the Dobrudja region, Romania, have been performed using neutron diffraction and tomography, and Raman spectroscopy. The obtained structural data on the content of phases, the presence of calcite grains and pores, and the uniformity of phase spatial distribution made it possible to systematize the studied fragments and correlate them with clay sources or pottery workshop. The experimental possibilities of neutron methods of nondestructive structural diagnostics as well as the structural markers provided by them in the studies of ceramic samples are discussed.

Contradict mechanism of long-term magnesium and sodium sulfate attacks of nano silica-modified cement mortars – Experimental and thermodynamic modeling

The mechanisms of external sulfate attack on cement mortars containing nano silica have been studied under full immersion conditions after 3 years. The sulfate degradation processes were compared between sodium sulfate and magnesium sulfate solutions with nano-silica replacements of 0, 1, and 8 wt% in CEM I Portland cement. Expansion, mass/strength loss, and microstructural alterations (spatial distribution of secondary phases and chemical compositions on C–S–H) of the degraded mortar were experimentally studied. The solution information was estimated from thermodynamic modeling, which was further used to predict the phase assemblage and the formation and expansion pressure of ettringite. The results showed that the degradation mechanisms were completely contradicted between the exposure solutions: the addition of nano silica increased the sulfate resistance in sodium sulfate solutions, the predicted low crystallization pressure resulted in low expansion, whereas the reduced sulfate resistance in magnesium sulfate solutions occurred where decalcification played a major role.

Development of the concept of a multiphase flowmeter

In a number of industries: oil and gas, chemical and nuclear industries, the task of controlling multiphase flow regimes arises. In the nuclear and chemical industries, the flow regime directly affects the nature of technological processes and their safety. In the oil and gas industry, the products extracted from wells are usually a mixture of oil, water and gas, and the task of monitoring the flow regime is related to compliance with the permissible parameters of pumping and control equipment. When using multiphase flowmeters of the flow type, the algorithms for calculating phase flow rates in a multiphase flow are very sensitive to a violation of the uniformity and homogeneity of the measured flow. Excessive noise of the signal of pressure sensors, volume content and flow caused by projectile, cork or stratified modes can negatively affect the accuracy of measurements. As a rule, flow mode maps are used when determining the current mode. This approach is based on the calculation of a number of dimensionless flow parameters (Froude number, Lockhart–Martinelli parameter, etc.). In the case of a dynamically changing flow, this approach may not be suitable. For a more accurate and reliable determination of flow modes, it is proposed to use a direct method of analyzing the spatial distribution of phases in the flow and recognizing the type of flow using artificial convolutional neural networks. This approach allows you to get rid of classification errors and get more accurate information about the flow. The aim of the study is to develop a technique for neural network analysis of images of a multiphase flow with subsequent determination of its type. In the course of the work, approaches to the formation of a training sample are considered, the search for the optimal structure of the neural network is carried out and an accuracy assessment is given for the classification of multiphase flow modes by a convolutional neural network. The study was carried out on two types of data: 1) synthetic images obtained using numerical simulation of multiphase flows, and 2) experimentally obtained flow images on a multiphase flow stand.

Segmentability evaluation of back-scattered SEM images of multiphase materials

Segmentation methods are very useful tools in the Electron Microscopy inspection of materials, enabling the extraction of quantitative results from microscopy images. Back-Scattered Electron (BSE) images carry information of the mean atomic number in the interaction volume and hence can be used to quantify the phase composition in multiphase materials. Since phase composition and proportion affects the material properties and hence its applications, the segmentation accuracy of such images rendered of critical importance for material science. In this work, the notion of segmentability for BSE images is proposed to define the ability of an image to be segmented accurately. This notion can be used to guide the image acquisition process so that segmentability is maximized and segmentation accuracy is ensured. An index is devised to quantify segmentability based on a combination of the modified Fisher Discrimination Ratio and of the second Minkowski functional capturing intensity and spatial aspects of BSE images respectively. The suggested Segmentability Index (SI) is validated in synthetic BSE images which are generated with a novel algorithm allowing the independent control of spatial distribution of phases and their grayscale intensity histograms. Additionally, SI is applied in real-synthetic BSE images, where the real greyscale distributions of Ordinary Portland Cement (OPC) clinker crystallographic phases are used, to demonstrate the ability of SI to indicate the optimum choice of critical image acquisition settings leading to the more accurate segmentation output.

Scanning precession electron diffraction data analysis approaches for phase mapping of precipitates in aluminium alloys

Mapping the spatial distribution of crystal phases with nm-scale spatial resolution is an important characterisation task in studies of multi-phase materials. One popular approach is to use scanning precession electron diffraction which enables semi-automatic phase mapping at the nanoscale by collecting a single precession electron diffraction pattern at every probe position over regions spanning up to a few micrometers. For a successful phase mapping each diffraction pattern must be correctly identified. In this work four different approaches for phase mapping of embedded precipitates in an Al-Cu-Li alloy are compared on a sample containing three distinct crystal phases. These approaches are based on: non-negative matrix factorisation, vector matching, template matching and artificial neural networks. To evaluate the success of each approach a ground truth phase map was manually created from virtual images based on characteristic phase morphologies and compared with the deduced phase maps. The percentage accuracy of all methods when compared to the ground truth was satisfactory, with all approaches obtaining scores above 98%. The optimal method depends on the specific task at hand. Non-negative matrix factorisation is suitable with limited prior data knowledge but performs best with few unique diffraction patterns and requires substantial post-processing. It has the advantage of reducing the dimensionality of the dataset and handles weak diffracted intensities well given that they occur repeatedly. The current vector matching implementation is fast, simple, based only on the Bragg spot geometry and requires few parameters. It does however demand that each Bragg spot is accurately detected in each pattern and the current implementation is limited to zone axis patterns. Template matching handles a large range of orientations, including off-axis patterns. However, achieving successful and reliable results often require thorough data pre-processing and do require adequate diffraction simulations. For artificial neural networks a substantial setup effort is demanded but once trained it excels for routine tasks, offering fast predictions. The implemented codes and the data used are available open-source. These resources and the detailed assessment of the methods will allow others to make informed decisions when selecting a data analysis approach for 4D-STEM phase mapping tasks on other material systems.

Understanding the Effect of Electrochemical Properties and Microstructure on the Microgalvanic Corrosion of Mg Alloys via Phase-Field Simulations

In this work, we apply a phase-field model to gain insights into the corrosion behavior of Mg alloys. Specifically, we study the effect of electrochemical properties of the materials and their environment and the spatial distribution of second phases in a magnesium alloy. To this end, we perform sensitivity analyses in which we separately vary the exchange current density and corrosion potential of the second phase, as well as the electrolyte conductivity. We describe the effects of these parameters on the corrosion current and the resulting corrosion-front morphology. We also confirm that the ratio between the Wagner length, calculated for the anodic phase, and the initial width of the anodic domain is a good predictor of the morphological characteristic of the corroding surface. In addition, we examine the effect of the microstructure on the corrosion behavior in both 2D and 3D systems by varying the shape, size, and distribution of the second-phase regions. Our findings provide insights into the influence of the two-phase microstructure on the corrosion behavior in a magnesium alloy.

Bridging the Gap: Electrode Microstructure and Interphase Characterization by Combining ToF‐SIMS and Machine Learning

AbstractThis article presents a new analytical methodology to analyze large (hundreds of µm) battery electrode microstructures by mapping the spatial distribution of the main phases (e.g., active material and carbon‐binder domain) and degradation products (solid‐ or cathode‐electrolyte interphase) formed during cycling. The methodology can be used for a better understanding of the relationships between electrode architecture and degradation, paving the way toward the analysis of interphases spatial distribution and their correlations to the electrode formulation, microstructure, and cycling conditions. This work is based on time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), and focuses on analyzing large 2D electrode cross‐sections at both the microstructure and single particle/agglomerate level. It also shows that this analysis can be expanded to 3D electrode microstructures when combining ToF‐SIMS and devoted machine learning procedures, which can be of particular interest to the 3D electrochemical modeling community.

Coupling grid nanoindentation and surface chemical analysis to infer the mechanical properties of shale mineral phases

Shale is a low-permeability, multi-phase, and multi-scale composite material with intrinsic heterogeneity in micro-texture and mineralogical composition. In this paper, relationships between the microscale texture and constituent phases of shale were investigated using SEM (scanning electron microscopy)/BSE (backscatter electron imaging)-EDS (energy-dispersive x-ray spectroscopy) methods, together with grid nanoindentation experiments. The mechanical properties of the constituent phases of the carbonate-rich Longmaxi shale samples were extracted based on the spatial distribution of mineral phases and the indentation interaction volume. We analyze the effects of particle size on the interpreted mechanical properties and establish a characteristic length scale based on a probabilistic analysis. The identified characteristic length for extracting the mechanical properties of constituent mineral phases from the grid nanoindentation technique is about 5.8–11.7 μm, i.e., up to 10 times greater than that proposed in prior research. A multi-scale mechanical model was established with considerations of a self-consistent scheme for granular morphology to link the microscopic characteristics with the multi-scale mechanical properties of shales. The modeling results show that the stiffness and the strength of the homogenized nano-porous illite/quartz aggregates in Longmaxi shale can be assessed from the nano-scale mechanical properties of the mineral phases and the nano-scale porosity. This study paves the way to accessing the mechanical properties of the constituent phases of composite materials based on the properties of their building blocks and provides extensive insights into their complex mechanical behavior, representing a major step towards developing reliable multi-scale models for engineering applications.

Exploiting Solid-State Dynamic Nuclear Polarization NMR Spectroscopy to Establish the Spatial Distribution of Polymorphic Phases in a Solid Material

Exploiting Solid-State Dynamic Nuclear Polarization NMR Spectroscopy to Establish the Spatial Distribution of Polymorphic Phases in a Solid Material

Nb Phase Position Marking for Clarifying the Formation Process of Cu-Al Composite Interfacial Phases in Continuous Composite Casting

Cu-Al composites are widely applied materials exhibiting advanced properties of both matrix metals. Controlling the brittle interfacial phases is a key factor in improving the interfacial strength of Cu-Al composites. This paper studied the interfacial formation process of Cu-Al composites fabricated by continuous composite casting. The phase formation sequence, growth direction and formation mechanism were clarified via element marking and thermodynamic calculations. The spatial distribution of the interfacial phases from the aluminum side to the copper side is as follows: the α + θ layer (α-Al + CuAl2), the θ layer (CuAl2) and the γ layer (Cu9Al4). Moreover, insular η phases (CuAl) and δ phases (Cu3Al2) exist in the γ phase sublayer. The formation sequence of interfacial phases is as follows: the θ phase, the η phase, the δ phase and the γ phase. The θ layer and α + θ layer are transformed from a liquid diffusion layer formed by scouring the surface of copper with liquid aluminum, the η and δ phases grow towards the θ layer and the γ phase simultaneously grows towards both the copper matrix and the θ layer.

Spatial distribution of manganese oxide minerals in the natural ferromanganese nodule of the Arctic Sea: A view from Raman spectroscopy

Knowledge of the spatial distribution of manganese oxide minerals in the natural ferromanganese nodules provides improved prospects for understanding the geochemical processes involved in nodule formation. In this study, we report a series of micro-laser Raman spectra along with a line profile from the center to the rim of a spherical ferromanganese nodule from the East Siberian Sea in the Arctic region. For the Raman spectra obtained at the center of the nodule, the characteristic Raman bands for todorokite at ∼ 733, ∼ 508, and ∼ 240 cm‐1 were clearly observed, suggesting that the todorokite is predominant in the central part of the nodule. As the data acquisition point moved away from the center to the rim (D), the Raman bands corresponding to the todorokite decreased, while the intensity of the 588 cm‐1 band corresponding to the layered manganate minerals (i.e., birnessite) increased, suggesting that the proportion of manganate phases with a tunnel structure decreases with nodule growth. In addition, the distinct Raman bands at ∼ 280 and 403 cm‐1 indicated that the triclinic birnessite is prevalent in the outer regions of the nodule. Semi-quantitative measurement of the intensity of diagnostic Raman bands for todorokite (I733) allows us to effectively observe the spatial distribution of manganese oxide minerals in the ferromanganese nodule. At the center of the nodule, I733 was ∼ 0.7. Despite a few fluctuations, I733 clearly decreased from ∼ 0.7 to ∼ 0.2 with increasing D from 0 to 7500 μm, indicating that the relative fraction of todorokite in nodules significantly decreases with nodule growth. This study highlights that a highly resolved spatial distribution of manganate mineral phases in ferromanganese nodules can provide a historical record of the formation process and geochemical conditions.

Effect of Wettability on Two-Phase Flow Through Granular Porous Media: Fluid Rupture and Mechanics of the Media

A key parameter with considerable influence on the spatial distribution of the fluid phases in porous media is the wettability. Understanding the effect of wettability on fluids’ distribution in three-dimensional (3D) porous media is critical to any phenomenon that involves multiphase flow in such media. To address the problem, we have carried out extensive computer simulations of two-phase flow in a 3D granular packing of grains and studied the effect of the wettability by varying the contact angle. To this end, the Immersed Boundary and Volume-of- Fluid approaches are coupled with the discrete element method (DEM). Five contact angles, covering a full range of wettability, are considered. We find that the wettability affects particle dynamics, such as the displacement of the grains, the contact force, and fluid velocity, and that rupture of the fluid can also occur in the pore space. Our results indicate that the volume of invading fluid injected into the system, the pressure on walls, and the injection surface decrease for larger contact angles. Most importantly, increasing the contact angle reduces the inter-particle interactions, whereas the drag force exhibits an opposite trend, leading to larger displacement of the particles. We also demonstrate and emphasize that some aspects of the effect of the wettability can only be observed in the 3D models of porous media and, thus, 2D models are inadequate.

Exploiting solid-state dynamic nuclear polarization NMR spectroscopy to establish the spatial distribution of polymorphic phases in a solid material.

Solid-state DNP NMR can enhance the ability to detect minor amounts of solid phases within heterogenous materials. Here we demonstrate that NMR contrast based on the transport of DNP-enhanced polarization can be exploited in the challenging case of early detection of a small amount of a minor polymorphic phase within a major polymorph, and we show that this approach can yield quantitative information on the spatial distribution of the two polymorphs. We focus on the detection of a minor amount (<4%) of polymorph III of m-aminobenzoic acid within a powder sample of polymorph I at natural isotopic abundance. Based on proposed models of the spatial distribution of the two polymorphs, simulations of 1H spin diffusion allow NMR data to be calculated for each model as a function of particle size and the relative amounts of the polymorphs. A comparison between simulated and experimental NMR data allows the model(s) best representing the spatial distribution of the polymorphs in the system to be established.

Stochastic (re)constructions of non-stationary material structures: Using ensemble averaged correlation functions and non-uniform phase distributions

Depending on the spatial distribution of mass or phases within the studied materials, they can be classified into two distinct property types: stationary and non-stationary. Stochastic reconstruction with simulated annealing and correlation functions is a powerful technique to produce material structural images from limited input data, but they usually deal with statistically homogeneous images only. In this work we show that the alteration of the initial phase distributions before starting annealing can produce non-stationary structures. The degree of such non-stationarity depends on numerous parameters of the annealing, including maximum correlation length, the choice of correlation function weights, annealing cooling schedule, in a convoluted manner—this makes it impossible to segregate the influence of each parameter. In general, we have shown that it is possible to choose such parameters in order to suppress the inhomogeneity of the resulting replicas. The degree of stationarity of the stochastic reconstructions had a significant influence on the physical properties of the reconstructed binary structures—computed full permeability tensors showed different degree of anisotropy and off-diagonal terms values depending on the reconstruction parameters. The proposed approach to produce non-stationary structures from ensemble averaged set of correlation functions opens numerous ways to attack theoretical and practical problems with natural and artificial materials with statistically inhomogeneous structure.

Interplay between solidification microsegregation and complex precipitation in a γ/γ’ cobalt-based superalloy elaborated by directed energy deposition

This study examines the segregations and the precipitation appearing during the solidification path of a Co-Ni-Al-W-Ta-Ti-Cr γ/γ’ cobalt-based superalloy processed by directed energy deposition (DED). Observations reveal characteristic elements partitioning in the liquid during additive manufacturing. Due to this microsegregation, complex multiphase precipitation occurs and the various precipitates are identified and characterized in a cobalt-based superalloy fabricated by DED. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) are used to investigate the spatial distribution and nature of the various phases detected in the as-fabricated microstructure. Energy dispersive X-ray spectrometry (EDS), wavelength dispersive X-ray spectrometry (WDS), and electron energy loss spectroscopy (EELS) are coupled with a fine analysis of the diffraction patterns to identify the different phases decorating the interdendritic regions. These characterizations allow the identification of different submicronic precipitates: Al 2 O 3 , (Ta,Ti)(N,C), HfO 2 , Cr 3 B 2 and (Ti,Zr,Hf) 2 SC. The solidification sequence is discussed at light of the experimental results. This work provides a first understanding of the interplay between solidification segregations and the formation of second phase precipitation in a cobalt-based superalloy processed by DED. • A cobalt-based superalloy was processed by Direct Energy Deposition. • Solidification segregations lead to the precipitation of interdendritic phases. • Automated crystallographic orientation mapping and chemical analyses were used. • Various interdendritic phases have been identified: oxides (Al 2 O 3 and HfO 2 ), carbonitrides (Ta, Ti)(N, C), sulphocarbides (Ti, Zr, Hf) 2 SC, and borides Cr 3 B 2 . • The solidification path can be established based on the experimental observations.

Identification of Horizontal Gas-Liquid Two-Phase Flow Regime using Deep Learning

Two-phase flow is of great importance in various industrial processes. A characteristic feature of two-phase flow is that it can acquire various spatial distribution of phases to form different flow patterns/regimes. The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems and it holds great significance in petrochemical and thermonuclear industries today. The objective of this study is to develop a methodology for identification of flow regime using dynamic pressure signals and deep learning techniques. Stratified, slug and annular flow regimes were simulated using a Level-Set (LS) method coupled with Volume of Fluid (VOF) method in a 6 m horizontal pipe with 0.050 m inner diameter. Dynamic pressure signals were collected at a strategic location. These signals were converted to scalograms and used as inputs in deep learning architectures like ResNet-50 and ShuffleNet. Both architectures were effective in classifying different flow regime and recorded testing accuracies of 85.7% and 82.9% respectively. According to our knowledge no similar research has been reported in literature, where various Convolutional Neural Networks are used along with dynamic pressure signals to identify flow regime in horizontal pipe. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This study can be extended by collecting data over broader range of flow parameters and different geometries.

Polarization contrast neutron imaging of magnetic crystallographic phases

Non-destructive characterization methods to observe phase transformations and thus, gain insights to transformation mechanisms in representative volumes are key for the development of advanced materials and manufacturing. Conventional methods are constrained to the surface and small sizes, thus, access to the bulk often implies tedious destructive approaches and hinders in-situ observations of phase evolution. In this work, we introduce a non-destructive technique that overcomes key limitations prevailing today for mapping the spatial distribution of magnetic phases in bulk materials. The use of polarized neutrons, being sensitive to sub-percent fractions of ferromagnetic phases and able to penetrate centimeter sized samples, enables micrometer-scale spatial and second-scale time resolutions. We demonstrate ex-situ and in-situ quantitative mapping of magnetic phases, in particular the evolution of martensite induced by uniaxial- and biaxial deformation in metastable 304 steel. The quantitative results obtained during in-situ deformation testing prove polarization contrast neutron imaging to be particularly effective in detecting small fractions of martensite. Especially during early stages of deformation where neutron diffraction, which in contrast does not provide full field spatial resolution, fails. The short exposure times and high sensitivity renders the method well suited for rapid 3D tomographic mapping and/or operando investigations of phase distributions.

Detection of incipient aqueous alteration in carbonaceous chondrites

We discuss if the detection of aqueous alteration depends on the techniques that are used. We apply different methods to estimate the extent of aqueous alteration on four ungrouped carbonaceous chondrites showing limited aqueous alteration and thermal metamorphism: Chwichiya 002, El Médano (EM) 200, Northwest Africa (NWA) 12957 and NWA 11750, classified as C3 or C3.00-ung. The aim is to propose a reliable methodology to identify the most primitive chondrites. Chwichiya 002, NWA 11750 and NWA 12957 display very primitive matrices and could be amongst the most primitive chondrites currently known, similar to the least altered lithologies of the CM chondrites Paris (CM2.9) and Asuka (A) 12085 (CM2.8), A 12236 (CM2.9) and A 12169 (CM3.0). The structure of organic matter and Cr2O3 in ferroan olivines show that the four meteorites have been less heated than the least metamorphosed standard/reference type 3 chondrite, Semarkona (LL3.00), with Chwichiya 002, NWA 12957 and NWA 11750 similar to the CO3.0 s, Acfer 094 (C2-ung) and Paris meteorites. Chwichiya 002 and NWA 12957 show similar alteration phases and degree of alteration, with high abundances of amorphous material with embedded metal and sulfide, resembling Glass with Embedded Metal and Sulfide (GEMS)-like materials, and tochilinite-cronstedtite intergrowths (TCIs) as the major alteration phases. The matrix in NWA 11750 contains aggregates of nanoscale olivine crystals and abundant carbonates, observed as micrometer-sized carbonate veins surrounding chondrules, and as nanoscale carbonates mixed with the fine-grained materials. It also contains abundant grains of metal and a low abundance of phyllosilicates. El Medano 200 shows a high abundance of magnetite (∼10 vol%), nanoscale phyllosilicates, troilite, and organic matter. The variability of the secondary alteration phases in the meteorites suggests different alteration mechanisms, likely depending on both the starting composition of the meteorites and the composition of the fluids of alteration.Scanning and transmission electron microscopy (SEM and TEM) allow the identification of primitive phases and the composition and spatial distribution of the secondary phases. X-ray diffraction (XRD) can detect alteration products, including some amorphous phases, although this is limited by the small coherence domains of small TCIs and other phyllosilicates. Transmission infrared (IR) spectroscopy can detect phyllosilicate and carbonate, but is ineffective for the detection of amorphous phases, metal, or sulfide. Both matrix defocused electron microprobe analyses (EMPA) and thermogravimetric analysis (TGA) allow detection of hydrated minerals, such as phyllosilicates and carbonates, but are strongly influenced by the presence of organic matter and do not reflect the overall alteration state of a meteorite. We conclude that the assessment of the primitivity of a chondrite is highly technique dependent. We propose a combination of XRD and the Cr2O3 in ferroan olivines or Raman spectroscopy for a rapid characterization of the alteration state of a chondrite and the detection of the most primitive meteorites. Finally, the combination of XRD and TEM allows for the detection of all primary and secondary phases and represents an ideal methodology for the characterization and detailed study of primitive chondrites and the different types of incipient aqueous alteration.