WO2023205246A1 - Characterization of inclusions using electron microscopy and x-ray spectrometry - Google Patents
Characterization of inclusions using electron microscopy and x-ray spectrometry Download PDFInfo
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- WO2023205246A1 WO2023205246A1 PCT/US2023/019113 US2023019113W WO2023205246A1 WO 2023205246 A1 WO2023205246 A1 WO 2023205246A1 US 2023019113 W US2023019113 W US 2023019113W WO 2023205246 A1 WO2023205246 A1 WO 2023205246A1
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- 238000012512 characterization method Methods 0.000 title description 6
- 238000000441 X-ray spectroscopy Methods 0.000 title description 3
- 238000001493 electron microscopy Methods 0.000 title description 3
- 238000000034 method Methods 0.000 claims abstract description 71
- 238000002149 energy-dispersive X-ray emission spectroscopy Methods 0.000 claims abstract description 38
- 239000002245 particle Substances 0.000 claims abstract description 18
- 239000000463 material Substances 0.000 claims abstract description 17
- 238000004626 scanning electron microscopy Methods 0.000 claims abstract description 14
- 238000000724 energy-dispersive X-ray spectrum Methods 0.000 claims abstract description 13
- 238000000386 microscopy Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 9
- 239000000203 mixture Substances 0.000 claims description 16
- 230000011218 segmentation Effects 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 14
- 238000005070 sampling Methods 0.000 claims description 9
- 238000010801 machine learning Methods 0.000 claims description 6
- 238000001228 spectrum Methods 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 238000013473 artificial intelligence Methods 0.000 claims description 2
- 230000002902 bimodal effect Effects 0.000 claims description 2
- 238000007621 cluster analysis Methods 0.000 claims description 2
- 238000003708 edge detection Methods 0.000 claims description 2
- 238000002362 energy-dispersive X-ray chemical map Methods 0.000 claims description 2
- 230000003628 erosive effect Effects 0.000 claims description 2
- 238000003709 image segmentation Methods 0.000 claims description 2
- 239000011159 matrix material Substances 0.000 claims description 2
- 238000001878 scanning electron micrograph Methods 0.000 claims description 2
- 238000004611 spectroscopical analysis Methods 0.000 claims description 2
- 238000007619 statistical method Methods 0.000 claims description 2
- 229910000831 Steel Inorganic materials 0.000 abstract description 10
- 239000010959 steel Substances 0.000 abstract description 10
- 229910052751 metal Inorganic materials 0.000 abstract description 4
- 239000002184 metal Substances 0.000 abstract description 4
- 150000002739 metals Chemical class 0.000 abstract description 4
- -1 steel Chemical class 0.000 abstract 1
- 238000000921 elemental analysis Methods 0.000 description 7
- 238000000635 electron micrograph Methods 0.000 description 6
- PNEYBMLMFCGWSK-UHFFFAOYSA-N aluminium oxide Inorganic materials [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 description 4
- 238000001889 high-resolution electron micrograph Methods 0.000 description 4
- 238000002441 X-ray diffraction Methods 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000009628 steelmaking Methods 0.000 description 3
- 238000002083 X-ray spectrum Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 238000010894 electron beam technology Methods 0.000 description 2
- 238000001433 helium-ion microscopy Methods 0.000 description 2
- 238000001000 micrograph Methods 0.000 description 2
- 230000006911 nucleation Effects 0.000 description 2
- 238000010899 nucleation Methods 0.000 description 2
- 238000005191 phase separation Methods 0.000 description 2
- 238000011002 quantification Methods 0.000 description 2
- 238000001350 scanning transmission electron microscopy Methods 0.000 description 2
- 229910052596 spinel Inorganic materials 0.000 description 2
- 239000011029 spinel Substances 0.000 description 2
- 238000004627 transmission electron microscopy Methods 0.000 description 2
- 239000013590 bulk material Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 150000004767 nitrides Chemical class 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 150000003568 thioethers Chemical class 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/22—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material
- G01N23/225—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material using electron or ion
- G01N23/2251—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material using electron or ion using incident electron beams, e.g. scanning electron microscopy [SEM]
Abstract
Provided herein is a method for recognizing the presence of multiple-phase inclusions in a base material, including but not limited to metals such as steel, using digital processing techniques with Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectrometry (EDX), or a combination thereof. Also provided herein is a method for recognizing the presence of a multiple-phase inclusion in a base material, comprising the steps of; acquiring a low-resolution scan of the sample with charged-particle microscopy; from the low-resolution scan, identifying an inclusion in the base material; acquiring a high-resolution scan of the inclusion with charged-particle microscopy; from the charged-particle microscopy, segmenting the inclusion into a plurality of domains; and acquiring EDX spectra of each of the plurality of domains.
Description
CHARACTERIZATION OF INCLUSIONS USING ELECTRON MICROSCOPY AND X-RAY SPECTROMETRY
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Serial No. 63/363,216, filed 19 Apr 2022, entitled “Characterization of Inclusions Using Electron Microscopy and X-ray Spectrometry”, the contents of which are incorporated herein by reference in their entirety.
FIELD OF THE INVENTION
[0002] The present invention relates to methods for identifying and characterizing multiplephase inclusions in a base material, including but not limited to metals such as steel.
BACKGROUND OF THE INVENTION
[0003] Inclusions are non-metallic features found within a base material, including but not limited to metals such as steel. The presence of these inclusions affect many properties of steel, including but not limited to castability, strength, formability, and surface finish. Inclusion formation is driven by the kinetics and thermodynamics of the steel-making process. Different phases of an inclusion can be formed at different times in the steelmaking process. The chemistry of inclusions can be dictated by the processing conditions. Due to this mechanism, inclusions can contain more than one phase, as inclusions are modified, agglomerate, or act as nucleation sites.
[0004] Similarly, many particles (e.g., environmental particulate and minerals) can act as nucleation sites during their formation. Particulate can form agglomerate processes any time multiple particles (of the same or differing phase) adhere to each other.
[0005] Inclusions in steel are typically oxides, sulfides, and nitrides, and can exist as a single phase or as a mixture of two or more phases.
[0006] Computer controlled scanning electron microscopy (“CCSEM”) can be used to identify and characterize inclusions present in bulk material. Along with providing the geometry of these inclusions, CCSEM can reveal information on chemical makeup.
[0007] Other microscopic techniques that can supplement the use of Scanning Electron Microscopy include charged-particle microscopy, Transmission Electron Microscopy, Scanning Transmission Electron Microscopy, and Scanning Helium Ion Microscopy.
[0008] CCSEM can be combined with energy dispersive spectrometry (“EDS”, also referred to as “EDX” and “XDS”), from which X-ray spectra are obtained. In turn, spectral information can identify the various elements present in the inclusion along with their concentrations. The X-ray analysis can be performed in a ‘point’ mode which involves determining the centroid of the inclusion and placing the electron beam at that location to generate the X-ray spectrum.
Alternatively, a ‘raster’ approach can be used to collect X-rays at over the entire cross-section of the inclusion. For small inclusions, typically less than 2 pm in size, a point analysis will provide an overall elemental composition of the inclusion due to the excitation volume of the electron beam. For larger inclusions, a raster analysis can be performed which will provide an overall X- ray analysis over the entire inclusion. Whereas an overall X-ray analysis for larger inclusions will be better than a point analysis from the perspective of overall composition of the inclusion, it will not provide details on the area fraction of the different phases within with the inclusion, which may be critical in assigning a proper classification. The following section provides an approach for characterizing multi-phase inclusions.
[0009] There remains, therefore, a need for methods of analysis of inclusions in metals.
SUMMARY
[0010] Provided herein is a method for recognizing the presence of multiple-phase inclusions in a base material using digital processing techniques with Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectrometry (EDX), or a combination thereof.
[0011] In some embodiments, the method further provides identification of each individual phase. In some embodiments, the method further provides quantification of each individual phase.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The disclosure will be described in conjunction with the following drawings in which like
reference numerals designate like elements and wherein:
[0013] FIG. 1 (a)(b) shows electron micrographs of two inclusions (left) and EDX elemental analyses (right).
[0014] FIG. 2 shows (a) low-resolution scan of a sample of steel and (b) - (g) corresponding high-resolution electron micrographs of the inclusions (left) and EDX elemental analyses (right).
[0015] FIG. 3 shows (a) single-site and (b) multiple-site sampling of an inclusion and (c)(d) corresponding EDX elemental analyses.
[0016] FIG. 4 shows electron micrographs of two inclusions.
[0017] FIG. 5 shows (a) electron micrograph of an inclusion (b) division of inclusion into two phases and (c)(d) corresponding EDX elemental analyses of the two domains.
[0018] FIG. 6 shows (a) - (f) high-resolution electron micrographs (left) and EDX elemental analyses (right) for six inclusions.
[0019] FIG. 7 shows (a) enlarged electron micrograph of an inclusion (b) corresponding EDX elemental analysis and (c) tabular compositional analyses.
[0020] FIG. 8 shows a representative analysis of an environmental particle segmented into distinct phases, using methods disclosed herein.
[0021] FIG. 9 shows a representative analysis of an environmental particle segmented into distinct phases, using methods disclosed herein.
[0022] FIG. 10 shows an analysis of gunshot residue, using methods disclosed herein. Left is an overview image, right is a high resolution electron micrograph showing inhomogeneous nature of a higher atomic number (brighter phase) segmented on a base material (gray phase)
DETAILED DESCRIPTION OF THE DISCLOSURE
[0023] Accordingly, provided herein is a method for recognizing the presence of multiple-phase inclusions in a base material using digital processing techniques with Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectrometry (EDX), or a combination thereof.
[0024] In some embodiments, the area of each phase is measured from the SEM image using image processing. In some embodiments, the multi-phase particle is isolated using a bitmap mask.
[0025] Also provided herein is a method for recognizing the presence of a multiple-phase inclusion in a base material, comprising the steps of: acquiring a low-resolution scan of the sample with charged-particle microscopy; from the low-resolution scan, identifying an inclusion in the base material; acquiring a high-resolution scan of the inclusion with charged-particle microscopy; from the charged-particle microscopy, segmenting the inclusion into a plurality of domains; and acquiring EDX spectra of each of the plurality of domains.
[0026] In some embodiments, the method further comprises the step of determining the identification of one or more of the plurality of domains. In some embodiments, the method further comprises the step of quantifying the composition of one or more of the plurality of domains.
Abbreviations and Definitions
[0027] CCSEM = computer controlled scanning electron microscopy or other computer controlled charged-particle micoscopes; EDS = energy-dispersive X-ray spectrometry, also referred to as EDX and XDS; SEM = scanning electron microscopy.
[0028] The articles “a” and “an” are used herein to refer to one or more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “a cell” means one cell or more than one cell.
[0029] ‘ ‘About” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±5%, preferably ±1%, and still more preferably ±0.1% from the specified value, as such variations are appropriate to perform the disclosed methods.
[0030] In some embodiments of any of the compositions or methods described herein, a range is
intended to comprise every integer or fraction or value within the range.
[0031] Embodiments described herein as “comprising” one or more features may also be considered as disclosure of the corresponding embodiments “consisting of’ and/or “consisting essentially of’ such features.
[0032] As used herein, the term “inclusion” refers to a non-metallic feature within a base material that is different in structure and / or composition.
[0033] As used herein, the term “segmentation” refers to a method for defining a plurality of domains in a single inclusion.
[0034] Provided herein is a method for recognizing the presence of multiple-phase inclusions in a base material using digital processing techniques with (a) a charged-particle microscopy technique selected from Scanning Electron Microscopy, Transmission Electron Microscopy, Scanning Transmission Electron Microscopy, or Scanning Helium Ion Microscopy, and (b) Energy Dispersive X-ray Spectrometry (EDX).
[0035] Also provided herein is a method for recognizing the presence of multiple-phase inclusions in a base material using digital processing techniques with Scanning Electron Microscopy and Energy Dispersive X-ray Spectrometry (EDX).
[0036] In some embodiments, the method further provides identification of each individual phase. In some embodiments, the method further provides quantification of each individual phase.
[0037] In some embodiments, the multi-phase particle is isolated using a bitmap mask. In some embodiments, the bitmap mask is subjected to one or multiple pixel erosions. In some embodiments, the bitmap mask is colorized with a specified grayscale.
[0038] In some embodiments, a spatial filter is applied to the image.
[0039] In some embodiments, like phases are merged.
[0040] In some embodiments, segmentation is performed with one or more grayscale thresholds. In some embodiments, segmentation is performed with clustering. In some embodiments,
segmentation is performed with edge detection. In some embodiments, segmentation is performed with statistical methods. In some embodiments, segmentation is performed with statistical region merging. In some embodiments, segmentation is performed with machine learning.
[0041] In some embodiments, the elemental composition of each phase is computed from EDX spectra.
[0042] In some embodiments, the spectra are collected from a single point in the interior of each phase. In some embodiments, the spectra are collected from a random sampling of multiple points within each phase.
[0043] In some embodiments, the species of each phase are classified based on hierarchical rules. In some embodiments, the species of each phase are classified based on fuzzy logic, cluster analysis, artificial intelligence or machine learning.
[0044] In some embodiments, the method is performed in an automated manner.
[0045] In some embodiments, characteristics of parent inclusions and their child phases are displayed in a hierarchical tabular format.
[0046] In some embodiments, stored images of the parent inclusions and their child phases are superimposed upon a global view of an entire sample.
[0047] In some embodiments, the outline of each phase is superimposed upon the global view.
[0048] In some embodiments, EDX is acquired in the center of each phase. In some embodiments, EDX for a phase is acquired as far removed from all other phases.
[0049] In some embodiments, the species of each phase is the weighted average of individual phases.
[0050] In some embodiments, weighted EDX of each phase are subtracted from the EDX acquired over the full multiphase inclusion.
[0051] In some embodiments, a (multi)phase below a size threshold is ignored. In some embodiments, the size threshold is based on a statistical property of the sample. In some
embodiments, the size threshold is based on machine learning. In some embodiments, the size threshold is based on a bimodal or multi-modal description of the phases. In some embodiments, the size threshold is based on chemical composition of the phase. In some embodiments, the size threshold is based on the classification of the phase.
[0052] In some embodiments, each phase is determined using EDX map.
[0053] In some embodiments, the spectra from the individual phases and surrounding matrix are used to determine the spectral contribution of each phase.
[0054] In some embodiments, EDX spectra are collected with photon position tagging.
[0055] In some embodiments, post analysis image segmentation is applied. In some embodiments, EDX spectra are calculated based on summation of counts within boundaries of individual phases.
[0056] In some embodiments, phase boundaries are adjusted manually.
[0057] In some embodiments, phase boundaries are adjusted post-analysis.
EXAMPLES
Example 1. SEM of inclusions in steel
[0058] FIG. 1 shows two representative inclusions in a sample of steel. FIG. 1(a) shows (left) a micrograph of the inclusion and (right) EDX analysis, from which the composition is determined to be MnS. FIG. 1(b) shows (left) a micrograph of the inclusion and (right) EDX analysis, from which the composition is determined to be spinel / CaS-MnS.
Example 2. Automated SEM of inclusions in steel
[0059] FIG. 2 shows (a) low-resolution scan of a sample of steel and (b) - (g) corresponding high-resolution electron micrographs of the inclusions (left) and EDX elemental analyses (right). From EDX, the inclusions are assigned the following compositions: (b) Al-CaS; (c) CaAl- CaS/MnS; (d) Al-CaS/MnS; (e) Al-MnS; (f) AlMg-CaS; (g) CaS-MnS.
Example 3. Variation of EDX with sampling location
[0060] FIG. 3 shows the effect of the choice of EDX sampling location(s) on the elemental composition. FIG. 3(a) depicts a single sampling site for an inclusion; FIG. 3(b) depicts sampling at nine different sites for the same inclusion. EDX from the single-site sampling suggests predominantly spinel (MgO / AI2O3) composition. EDX from the multiple-site sampling indicates the additional presence of both CaS and MnS.
Example 4. Classification of inclusion materials
[0061] To facilitate automated SEM, inclusions with similar chemistries are grouped together into classes. Table 1 provides one such classification scheme for these materials.
Table 1. Classification of phases.
Example 5. Variation of EDX with nature of inclusion
[0062] FIG. 4 shows the characterization of two different inclusions as the same material. Based on area fraction, the inclusion (a) consists of 10% AI2O3; Inclusion (b) consists of 85% AI2O3. Both inclusions would be characterized as AI2O3 - MnS.
Example 6. Phase Separation Analysis (“PSA ”)
[0063] Phase separation analysis is directed to providing accurate characterization of individual phases to the inclusion population. Overall inclusion is characterized at the “parent” level and the individual species are characterized at the “child” level. This will allow the user to adjust settings for segmentation, including a tab in the user interface that allows testing of different values.
Reporting can be customized to summarize results at either the overall level or at the level of individual phases.
[0064] The PSA process begins with the segmentation of an inclusion into two or more domains. FIG. 5(a) shows a representative inclusion which, in FIG. 5(b) has been segmented into two domains. From the EDX spectrum shown in FIG. 5(c), the rightmost phase can be assigned as AlMg. From the EDX spectrum shown in FIG. 5(d), the leftmost phase can be assigned as CaS.
[0065] FIG. 6(a) - (f) shows electron micrographs of six representative inclusions (left), along with EDX spectra (right). From the EDX spectra, the phases can be assigned as: (a) Al-MnS; (b)(d)(e)(f) MnS; and (c) Al.
[0066] FIG. 7 is directed at the inclusion shown in FIG. 6(a), with the electron micrograph and EDX spectra enlarged in FIGS. 7(a) and 7(b), respectively. FIG. 7(c) is a representative summary of composition, in tabular form, with the particular inclusion of FIG. 7, labeled “1153” highlighted in the table.
[0067] In summary, the PSA method provides a tool to resolve multi-phase inclusions into individual phases. Summary statistics can be generated for a sample at the overall parent inclusion level (parent) or individual species level (child). As individual phases and / or multiphase particles are formed during different steps in the steelmaking process, PSA is advancing inclusion characterization by providing a more accurate understanding of the nature of inclusion formation.
[0068] All publications and patents referred to herein are incorporated by reference. Whereas particular embodiments of this invention have been described herein for purposes of illustration, it will be evident to those skilled in the art that numerous variations of the details of the present invention may be made without departing from the invention as defined in the appended claims.
Claims
WHAT IS CLAIMED IS: A method for recognizing the presence of multiple-phase inclusions in a base material, and for identifying and quantifying each individual phase comprising: using digital processing techniques comprising Charged Particle Microscopy (such as Scanning Electron Microscopy (SEM)), Energy Dispersive x-ray Spectrometry (EDX), or a combination thereof. The method of claim 1, wherein characteristics of parent features (such as inclusions) and their child phases are displayed in a hierarchical tabular format. The method of claim 1, wherein each phase is determined using an EDX map. The method of claim 1, wherein stored images of the parent inclusions and their child phases are superimposed upon a global view of an entire sample. The method of claim 4, wherein the outline of each phase is superimposed upon the global view. The method of claim 1, wherein the area of each phase is measured from the SEM image using image processing. The method of claim 6, wherein a spatial filter is applied to the image. The method of claim 6, wherein like phases are merged. The method of claim 6, wherein segmentation is performed with one or more grayscale thresholds. The method of claim 6, wherein segmentation is performed with clustering. The method of claim 6, wherein segmentation is performed with edge detection. The method of claim 6, wherein segmentation is performed with statistical methods. The method of claim 6, wherein segmentation is performed with statistical region merging. The method of claim 6, wherein segmentation is performed with machine learning.
The method of claim 6, wherein the multi-phase particle is isolated using a bitmap mask. The method of claim 15, wherein the bitmap mask is subjected to one or multiple pixel erosions. The method of claim 15, wherein the bitmap mask is colorized with a specified grayscale. The method of claim 1, wherein the elemental composition of each phase is computed from EDX spectra. The method of claim 18, wherein the spectra are collected from a single point in the interior of each phase. The method of claim 18, wherein the spectra are collected from a random sampling of multiple points within each phase. The method of claim 18, wherein the species of each phase are classified based on hierarchical rules. The method of claim 18, wherein the species of each phase are classified based on fuzzy logic, cluster analysis, artificial intelligence or machine learning. The method of claim 18, wherein EDX is acquired in the center of each phase. The method of claim 18, wherein EDX for a phase is acquired as far removed from all other phase. The method of claim 18, wherein the species of each phase is the weighted average of individual phases. The method of claim 18, wherein weighted EDX of each phase are subtracted from the EDX acquired over the full multiphase inclusion. The method of claim 18, wherein the spectra from the individual phases and surrounding matrix are used to determine the spectral contribution of each phase. The method of claim 18, wherein EDX spectra are collected with tagging, such as photon position tagging (also known as position tagged spectroscopy).
The method of claim 1, wherein the method is performed in an automated manner. The method of claim 29, wherein post analysis image segmentation is applied and EDX spectra are calculated based on summation of counts within boundaries of individual phases. The method of claim 29, wherein phase boundaries are adjusted manually. The method of claim 29, wherein phase boundaries are adjusted post-analysis. The method of claim 1, wherein a (multi)phase below a size threshold is ignored. The method of claim 33, wherein the size threshold is based on a statistical property of the sample. The method of claim 33, wherein the size threshold is based on chemical composition of the phase. The method of claim 33, wherein the size threshold is based on the classification of the phase. The method of claim 33, wherein the size threshold is based on machine learning. The method of claim 37, wherein the size threshold is based on a bimodal or multimodal description of the phases.
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| US202263363216P | 2022-04-19 | 2022-04-19 | |
| US63/363,216 | 2022-04-19 |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6675106B1 (en) * | 2001-06-01 | 2004-01-06 | Sandia Corporation | Method of multivariate spectral analysis |
| US20120292503A1 (en) * | 2011-05-16 | 2012-11-22 | Fei Company | Charged-particle microscopy with occlusion detection |
| US20130094716A1 (en) * | 2011-10-14 | 2013-04-18 | Ingrain, Inc. | Dual Image Method And System For Generating A Multi-Dimensional Image Of A Sample |
| US20170200290A1 (en) * | 2016-01-11 | 2017-07-13 | Carl Zeiss X-Ray Microscopy Inc. | Multimodality Mineralogy Segmentation System and Method |
| US20180252087A1 (en) * | 2015-03-27 | 2018-09-06 | Schlumberger Technology Corporation | A method and a system for performing chemical treatment of a near wellbore area |
| CN111678919A (en) * | 2020-06-17 | 2020-09-18 | 中国人民解放军国防科技大学 | Strong laser element laser damage threshold prediction method, system and medium based on multi-mode in-situ test |
| US20210033548A1 (en) * | 2019-08-01 | 2021-02-04 | Fei Company | Method of examining a sample using a charged particle microscope |
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2023
- 2023-04-19 WO PCT/US2023/019113 patent/WO2023205246A1/en unknown
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6675106B1 (en) * | 2001-06-01 | 2004-01-06 | Sandia Corporation | Method of multivariate spectral analysis |
| US20120292503A1 (en) * | 2011-05-16 | 2012-11-22 | Fei Company | Charged-particle microscopy with occlusion detection |
| US20130094716A1 (en) * | 2011-10-14 | 2013-04-18 | Ingrain, Inc. | Dual Image Method And System For Generating A Multi-Dimensional Image Of A Sample |
| US20180252087A1 (en) * | 2015-03-27 | 2018-09-06 | Schlumberger Technology Corporation | A method and a system for performing chemical treatment of a near wellbore area |
| US20170200290A1 (en) * | 2016-01-11 | 2017-07-13 | Carl Zeiss X-Ray Microscopy Inc. | Multimodality Mineralogy Segmentation System and Method |
| US20210033548A1 (en) * | 2019-08-01 | 2021-02-04 | Fei Company | Method of examining a sample using a charged particle microscope |
| CN111678919A (en) * | 2020-06-17 | 2020-09-18 | 中国人民解放军国防科技大学 | Strong laser element laser damage threshold prediction method, system and medium based on multi-mode in-situ test |
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