Energy dispersive x-ray spectroscopy phase spectrum synthesis

Abstract

To provide a system for generating a synthesized spectrum of a target composition.SOLUTION: In a system 500, a material analysis system comprises: a memory that stores computer-executable components; and a processor that executes the computer-executable components stored in the memory. The computer-executable component includes: a determination component that determines a known composition having the same component as a target composition; and a generation component that, based on a synthesized spectrum of the known composition and on a first synthesized spectrum of the target composition, generates a second synthesized spectrum of the target composition.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to energy dispersive X-ray spectroscopy phase spectrum synthesis. [Background technology]

[0002] Scientific instruments for use in materials analysis can aid in determining the composition and properties of unknown compositions. In one or more examples, scientific instruments providing energy dispersive X-ray spectroscopy (EDS) or analyzing the results of an EDS device can aid in identifying unknown compositions by helping to provide one or more spectra that compare peak intensity readings with the incident energy of the electrons used. [Brief description of the drawings]

[0003] BRIEF DESCRIPTION OF THE DRAWINGS The embodiments will be readily understood from the following detailed description taken in conjunction with the accompanying drawings, in which: For ease of description, like reference numerals denote like structural elements, and in which: FIG. [Figure 1] 1 is a block diagram of an example scientific instrument for performing operations according to one or more embodiments described herein. [Diagram 2] 2 is a flow diagram of an example method of performing operations using the scientific instrument of FIG. 1 according to one or more embodiments described herein. [Diagram 3] FIG. 1 illustrates a graphical user interface (GUI) that can be used in performing one or more of the methods described herein, in accordance with one or more embodiments described herein. [Figure 4] FIG. 1 is a block diagram of an example computing device capable of performing one or more of the methods disclosed herein, in accordance with one or more embodiments described herein. [Diagram 5]FIG. 1 is a block diagram of an exemplary, non-limiting system that can provide a process for synthetically generating a synthetic spectrum of a target composition according to one or more embodiments described herein. [Figure 6] FIG. 2 is a block diagram of another example non-limiting system capable of providing a process for synthetically generating a synthetic spectrum of a target composition according to one or more embodiments described herein. [Figure 7] FIG. 6 illustrates a process for determining known compositions that are closest neighbor compositions to a target composition by the materials analysis system of FIG. 5, in accordance with one or more embodiments described herein. [Figure 8] FIG. 6 is a flow diagram of a set of processes for the composite determination of a composite spectrum of compositions by the material analysis system of FIG. 5, in accordance with one or more embodiments described herein. [Figure 9] FIG. 6 illustrates a general process for generating a composite spectrum obtained from a target composition by the material analysis system of FIG. 5, according to one or more embodiments described herein. [Figure 10A] FIG. 6 is a flow diagram of a set of processes for spectrum determination by the material analysis system of FIG. 5 in accordance with one or more embodiments described herein. [Figure 10B] FIG. 6 illustrates a set of arrays of photon quantities indexed across a set of spectral energy ranges used by the materials analysis system of FIG. 5 in accordance with one or more embodiments described herein. [Figure 10C] FIG. 10C shows an initial composite spectrum of a target composition from which one array of the set in FIG. 10B is obtained, according to one or more embodiments described herein. [Figure 10D] FIG. 10C shows the resulting composite spectrum of a target composition from which one array of the set in FIG. 10B is obtained, according to one or more embodiments described herein. [Figure 11] FIG. 1 illustrates a comparison of visual results provided by existing material analysis approaches and visual results that may be provided by one or more embodiments described herein. [Figure 12] 6 is a flow diagram of one or more processes that may be performed by the materials analysis system of FIG. 5 in accordance with one or more embodiments described herein. [Figure 13] 13 is a continuation of the flow diagram of FIG. 12 of one or more processes that may be performed by the materials analysis system of FIG. 5 in accordance with one or more embodiments described herein. [Figure 14] 6 is a flow diagram of one or more processes that may be performed by the materials analysis system of FIG. 5 in accordance with one or more embodiments described herein. [Figure 15] 15 is a continuation of the flow diagram of FIG. 14 of one or more processes that may be performed by the materials analysis system of FIG. 5 in accordance with one or more embodiments described herein. [Figure 16] 6 is a flow diagram of one or more processes that may be performed by the materials analysis system of FIG. 5 in accordance with one or more embodiments described herein. [Figure 17] 17 is a continuation of the flow diagram of FIG. 16 of one or more processes that may be performed by the materials analysis system of FIG. 5 in accordance with one or more embodiments described herein. [Figure 18] 1 is a block diagram of an example scientific instrument system capable of performing one or more of the methods described herein, in accordance with one or more embodiments described herein. [Figure 19] FIG. 1 is a block diagram of an exemplary operating environment in which embodiments of the subject matter described herein can be incorporated. [Figure 20] FIG. 1 is an exemplary schematic block diagram of a computing environment with which the subject matter described herein can at least partially interact and / or be implemented. Summary of the Invention

[0004] The following presents a summary to provide a basic understanding of one or more embodiments described herein. This summary is not intended to identify key or critical elements and / or to delineate the scope of particular embodiments or claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments, the systems, computer-implemented methods, apparatus, and / or computer programs described herein can provide a process for synthesizing a spectrum of a composition of interest using known compositions and their corresponding measured and synthesized spectra.

[0005] According to one embodiment, a system can include a memory that stores computer-executable components and a processor that executes the computer-executable components stored in the memory, including a determination component that determines known compositions having the same components as the target composition, and a generation component that generates a second synthetic spectrum of the target composition based on the synthetic spectrum of the known composition and the first synthetic spectrum of the target composition.

[0006] According to another embodiment, a computer-implemented method may include synthesizing a corrected spectrum of a target composition by a system operatively coupled to a processor, the system generating an intermediate synthetic relationship, the intermediate synthetic relationship being a relationship defining a delta between a synthetic spectrum of a known composition and a first synthetic spectrum of the target composition, aggregating the intermediate synthetic relationship with a measured spectrum of the known composition, and aggregating the corrected spectrum of the target composition and generating an aggregated synthetic spectrum based thereon by the system.

[0007] According to yet another embodiment, a computer program facilitates a process for synthesizing a spectrum of a target composition, the program instructions being executable by a processor, causing the processor to synthesize the spectrum of the target composition, where synthesizing includes causing the processor to determine a delta between a first photon amount of a portion of the synthetic spectrum of the known composition and a second photon amount of the portion of the first synthetic spectrum of the target composition to generate an intermediate synthetic relationship, aggregating a third photon amount of the portion of the intermediate synthetic relationship and a fourth photon amount of the portion of the measured spectrum of the known composition, and generating a second synthetic spectrum of the target composition based on a result of the aggregation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] The following detailed description is merely illustrative and is not intended to limit the embodiments and / or the application or utilization of the embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the Summary of the Invention section or the Detailed Description section above. One or more embodiments will now be described with reference to the drawings, wherein like reference numerals are utilized to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, it will be apparent that in various instances, one or more embodiments can be practiced without these specific details.

[0009] Various operations may be described as multiple separate actions or operations, in the order that is most helpful in understanding the subject matter disclosed herein. However, the order of description should not be construed as implying that these operations are necessarily order dependent. In particular, these operations may be performed in different orders than those presented. Operations that are described may be performed in different orders than in the described embodiment. Various additional operations may be performed and / or described operations may be omitted in additional embodiments.

[0010] Turning now to the subject of materials analysis and one or more embodiments described herein, when identifying an unknown material using a measured spectrum, such as from Energy Dispersive X-ray Spectroscopy (EDS) of known materials, or from a known database spectrum, such as obtained by accessing a known database of known materials, one or more spectra of known composition can be used to distinguish material phases (e.g., compositions), which may be used simply as a comparison, and may in fact be used by one or more embodiments described herein to generate a synthetic spectrum of the unknown composition.

[0011] As used herein, "synthetic spectrum" or "synthetically generated spectrum" refers to a spectrum that is not measured from an established providence sample, but instead is generated by a computing device without access to an established providence sample of an unknown material.

[0012] As used herein, a sample of "established providence" is one for which material composition and other information such as component weight percentages are known with a high degree of accuracy.

[0013] The synthetic spectra generated by one or more embodiments described herein can be generated even when the database used is not exhaustive and the known material information is limited. In fact, material databases are typically not exhaustive and are not exhaustive in nature. For example, many materials can be defined by a range of chemical formulas in which one cation can change to another cation. In another example, looking at minerals alone, there are about 5000 known minerals, most of which are rare or only encountered infrequently and therefore often missing from available databases.

[0014] Furthermore, a composite spectrum generated by one or more embodiments described herein can be generated even when such a composite spectrum has two or more (e.g., multiple) significant peaks (e.g., one or more peaks that are greater than other peaks in the spectrum). In one or more embodiments, these significant peaks can originate from the same component (e.g., atomic elements, molecular elements, phases of atomic or molecular elements, or combinations thereof). This is of particular interest, for example, when one of the materials in a multi-component composition is copper, iron, or zinc and / or when the unknown material contains a high proportion of these elements, and thus the composite spectrum generated can have discernible portions of the spectrum arising from these elements.

[0015] Since significant peaks can be used to define an unknown material, when two or more peaks have similar output values, differentiation by basic comparison alone can be difficult. This may be due, at least in part, to inherent errors in the values ​​being compared and / or when using statistical comparison techniques, etc. Rather, such multiple significant peaks should be differentiated with a high degree of accuracy.

[0016] To account for one or more inability or deficiencies of existing frameworks (e.g., existing materials analysis frameworks that use spectral comparisons), one or more embodiments are described herein that can use one or more known compositions having known material compositions similar to an unknown material composition (e.g., a target composition) to accurately identify the target composition and determine a synthetically generated spectrum that defines the target composition. Even when a measured spectrum of a target composition has multiple significant peaks, one or more embodiments described herein can accurately identify the target composition and enable synthetic generation of a spectrum of the target composition.

[0017] Furthermore, the synthetic spectra obtained for various target compositions can be used as future known compositions for use in generating synthetic spectra of other target compositions, i.e., a library of synthetically derived spectra can be built and used for further material analysis applications, thus allowing the system to self-improve by becoming more accurate for the analysis and identification of target compositions.

[0018] As used herein, an "atomic element" is a chemical species that exists as an individual atom.

[0019] As used herein, the phrase "based on" should be understood to mean "based at least in part on," unless otherwise specified.

[0020] As used herein, the term "component" can refer to an atomic element, a molecular element, a phase of atomic or molecular elements, or a combination thereof.

[0021] As used herein, the term "data" can include metadata.

[0022] As used herein, the terms “entity,” “requesting entity,” and “user entity” may refer to a machine, device, component, hardware, software, smart device, party, organization, individual, and / or human being.

[0023] As used herein, a "measured spectrum" is one that is generated based on measurements of a sample of an established providence of composition.

[0024] As used herein, a "molecular element" is a molecular substance that contains two or more atoms of a single element.

[0025] As used herein, the term "phase" can refer to changes in atomic or molecular elements.

[0026] As used herein, the term "spectrum" may refer to an X-ray spectrum.

[0027] Disclosed herein are scientific instrument systems, as well as associated methods, computing devices, and computer-readable media. For example, in one or more embodiments, the system may include a memory that stores computer-executable components and a processor that executes the computer-executable components stored in the memory. The computer-executable components include a determination component that determines known compositions having the same components as the target composition, and a generation component that generates a second synthetic spectrum of the target composition based on the synthetic spectrum of the known composition and the first synthetic spectrum of the target composition.

[0028] As noted above, the term "component" can refer to an atomic element, a molecular element, a phase of atomic or molecular elements, or a combination thereof. Thus, a component can refer to a multi-element material, such as NaOH.

[0029] One or more embodiments disclosed herein can achieve improved performance compared to existing approaches. For example, as described above, even when the measured spectrum of a target composition has multiple significant peaks, one or more embodiments described herein can accurately identify the target composition and enable synthetic generation of the spectrum of the target composition. Thus, the embodiments disclosed herein provide improvements to scientific instrument technology (e.g., improvements in the computer technology that supports such scientific instruments, among others).

[0030] Various embodiments disclosed herein can improve upon existing approaches to achieve the technical advantage of high accuracy identification of material compositions by using a known composition, particularly its measured and synthetically generated spectrum, as a baseline to generate a high accuracy spectrum that defines the target composition. This synthetically generated spectrum (i.e., not a measured spectrum) can then be used to accurately identify the target composition. As noted above, a "synthetically generated spectrum" refers to a spectrum that is not measured from an established providence sample, but instead is generated by a computing device without access to an established providence sample of an unknown material. That is, the generation of a synthetically generated spectrum can be accomplished without generating a measured spectrum of the target composition from an established providence sample, since the providence of the target composition itself has not yet been established.

[0031] Such technical advantages are not achievable by routine and existing approaches, and all user entities of a system incorporating such embodiments may benefit from these advantages (e.g., by assisting the user entity in performing technical tasks such as generating synthetic spectra as part of and / or separately from identifying a target composition through a guided human-machine interaction process).

[0032] Thus, the technical features of the embodiments disclosed herein (e.g., the ability to generate spectra to identify unknown compositions without established providence samples and use spectral data from known compositions) are clearly non-conventional in the field of materials analysis, as are combinations of the features of the embodiments disclosed herein. As an example, such combinations can also include determining the nearest known composition and using spectral data of known compositions (e.g., the target composition) to generate a synthetic spectrum of the unknown composition.

[0033] As discussed further herein, various aspects of the embodiments disclosed herein can improve the functionality of the computer itself. For example, synthetically generated spectra can be used for subsequent identification of the target composition next to known compositions. The computational and user interface features disclosed herein not only involve the collection and comparison of information, but also apply new analytical techniques and technological techniques to modify the operation of the computer analysis of the target composition. That is, a library of synthetically derived spectra can be built and used for further material analysis, thus allowing the system to self-improve by becoming more accurate for the analysis and identification of the target composition.

[0034] Thus, the present disclosure introduces functionality that neither existing computing devices nor humans can perform. Rather, such existing computing devices instead require an established providence material sample against which to identify an unknown target composition (e.g., when rapidly evaluating multiple samples, or hundreds or thousands of samples of unknown composition, as in mining operations, it is impractical to have an established providence material sample for each).

[0035] Thus, embodiments of the present disclosure may serve various technical purposes, such as controlling a particular technical system or process, determining how to control a machine from measurements, separating material sources in digital audio, image, or video enhancement or analytical mixed signals, generating data for reliable and / or efficient transmission or storage, providing estimates and confidence intervals for material samples, or providing faster processing of sensor data, etc. In particular, the present disclosure provides technical solutions to technical problems including, but not limited to, determining the closest known composition to a target composition, initially generating a baseline composite spectrum that defines the closest known composition, generating a resultant composite spectrum that defines the target composition, and identifying the target composition based on the resultant composite spectrum.

[0036] Thus, the embodiments disclosed herein provide improvements in materials analysis techniques (eg, improvements in computer techniques that aid in materials analysis, among other improvements).

[0037] One or more embodiments will now be described with reference to the drawings, wherein like reference numerals are used to refer to like drawing elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. It will be apparent, however, that in various instances one or more embodiments may be practiced without these specific details.

[0038] Furthermore, it should be understood that the embodiments illustrated in one or more of the figures described herein are for illustrative purposes only, and thus the architecture of the embodiments is not limited to the systems, devices and / or components illustrated therein, nor to any particular order, connection and / or coupling of the systems, devices and / or components illustrated therein.

[0039] Referring now in particular to one or more figures, initially to FIG. 1, there is shown a block diagram of a scientific instrument module 100 for producing synthetic generation and performing material analysis operations using compositional spectra measured from one or more spectroscopic techniques, according to various embodiments described herein. The scientific instrument module 100 may be implemented by a circuit (e.g., including electrical and / or optical components) such as a programmed computing device. The logic of the scientific instrument module 100 may be included in a single computing device or may be distributed across multiple computing devices that communicate with each other as needed. An example of a computing device in which the scientific instrument module 100 may be implemented, alone or in combination, is described herein with reference to the computing device 400 of FIG. 4, and an example of a system of interconnected computing devices in which the scientific instrument module 100 may be implemented across one or more of the computing devices is described herein with reference to the scientific instrument system 1800 of FIG. 18.

[0040] The scientific instrument module 100 may include a first logic 102, a second logic 104, and a third logic 106. As used herein, the term "logic" may include an apparatus that performs a set of operations associated with the logic. For example, any of the logic elements included in the module 100 may be implemented by one or more computing devices programmed with instructions that cause one or more processing devices of the computing devices to perform the associated set of operations. In certain embodiments, the logic elements may include one or more non-transitory computer-readable media having instructions that, when executed by one or more processing devices of the one or more computing devices, cause the one or more computing devices to perform the associated set of operations. As used herein, the term "module" may refer to a collection of one or more logic elements that together perform a function associated with the module. Different ones of the logic elements in a module may take the same form or different forms. For example, some logic in a module may be implemented by a programmed general-purpose processing device and other logic in the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different ones of the logic elements in a module may be associated with different sets of instructions executed by one or more processing devices. A module may omit one or more of the logic elements shown in an associated drawing. For example, a module may include a subset of the logic elements shown in an associated drawing when the module performs a subset of the operations discussed herein with reference to the module.

[0041] The first logic 102 can determine a closest composition of the set of known compositions to the target composition, i.e., the first logic 102 can determine the known composition of the set of compositions, in terms of component weight percent, etc., that has a closest material composition, such as atomic element weight percent (also referred to herein as element weight percent) or other component weight percent, to the target composition.

[0042] The second logic 104 can generate a baseline synthetic spectrum for the identified known compositions (i.e., nearest neighbor compositions) and the target composition, where the term "baseline" refers to the synthetic spectrum as an initial spectrum for use in generating further spectra.

[0043] The third logic 106 can use the initial composite spectrum of the target composition, the baseline spectrum of the known composition, and the measured spectrum of the known composition (e.g., based on a sample of known composition in known providence) to generate a final or resulting composite spectrum that defines the target composition.

[0044] 2 illustrates a flow diagram of a method 200 of performing operations by a scientific instrument module 100, according to various embodiments. The operations of method 200 may be described with reference to particular embodiments disclosed herein (e.g., the scientific instrument module 100 described herein with reference to FIG. 1, the GUI 300 described herein with reference to FIG. 3, the computing device 400 described herein with reference to FIG. 4, and / or the scientific instrument system 1800 described herein with reference to FIG. 18), although method 200 may be used in any suitable configuration to perform any suitable operations. Although the operations are illustrated in FIG. 2 once each and in a particular order, the operations may be suitably reordered and / or repeated as desired (e.g., different operations performed may be performed in parallel, as appropriate).

[0045] At 202, a first operation may be performed. For example, the first logic 102 of the module 100 may perform the first operation 202. The first operation 202 may include identifying a set of known compositions based on the composition of interest. The first operation 202 may further include determining a closest composition of the set of known compositions to the composition of interest.

[0046] At 204, a second operation can be performed. For example, the second logic 104 of the module 100 can perform the second operation 204. The second operation 204 can include generating a baseline composite spectrum of the identified known composition (i.e., the nearest composition) and generating an initial composite spectrum of the target composition. These composite spectra can be based on one scaled component spectrum of the composition by performing a matrix correction procedure to obtain a calculated peak ratio of each component. That is, the composite spectrum can be further based on one or more matrix correction procedures of the sum of the component weight percentages of the components of the composition. The composite spectrum can be further based on the scaling of the components based on the matrix correction procedures.

[0047] As used herein, a matrix correction procedure refers to a procedure, such as, but not limited to, ZAF analysis, inverse ZAF analysis, PAP analysis, Armstrong analysis, or XPHI analysis, to compensate for one or more deficiencies of conventional quantitative methods.

[0048] At 206, a third operation may be performed. For example, the third logic 106 of the module 100 may perform the third operation 206. The third operation 206 may include generating an intermediate composite relationship based on a baseline composite spectrum of the known composition and an initial composite spectrum of the target composition, more specifically based on the photon amounts of the portions of the spectrum. The third operation 206 may also include generating an aggregate composite spectrum based on the intermediate composite relationship and the measured spectrum of the known composition. The third operation 206 may further include generating a resultant composite spectrum of the target composition that is, or is based on, the aggregate composite spectrum.

[0049] The scientific instrument methods disclosed herein can include interactions with a user entity (e.g., via a user local computing device 1820 described herein with reference to FIG. 18). These interactions can include providing information to the user entity (e.g., information regarding the operation of a scientific instrument such as the scientific instrument 1810 of FIG. 18, information regarding a sample being analyzed or other tests or measurements performed by the scientific instrument, information obtained from a local or remote database, or other information), or providing options for the user entity to enter commands (e.g., to control the operation of a scientific instrument such as the scientific instrument 1810 of FIG. 18 or to control the analysis of data generated by the scientific instrument), queries (e.g., to a local or remote database), or other information. In some embodiments, these interactions may be performed through a graphical user interface (GUI) that includes a visual display on a display device (e.g., display device 410, described herein with reference to FIG. 4) that provides output to a user entity and / or directs the user entity to provide input (e.g., via one or more input devices, such as a keyboard, mouse, trackpad, or touch screen included in other I / O devices 412, described herein with reference to FIG. 4). The scientific instrument system 1800 disclosed herein may include any suitable GUI for interaction with a user entity.

[0050] 3, an exemplary GUI 300 is shown that may be used in performing some or all of the methods described herein, in accordance with various embodiments described herein. As noted above, the GUI 300 may be provided on a display device (e.g., display device 410 described herein with reference to FIG. 4) of a computing device (e.g., computing device 400 described herein with reference to FIG. 4) of a scientific instrument system (e.g., scientific instrument system 1800 described herein with reference to FIG. 18), and a user entity may interact with the GUI 300 using any suitable input device (e.g., any of the input devices included in other I / O devices 412 described herein with reference to FIG. 4) and input techniques (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button actuation, etc.).

[0051] GUI 300 may include a data display area 302, a data analysis area 304, a scientific instrument control area 306, and a settings area 308. The particular number and arrangement of areas shown in FIG. 3 is merely exemplary, and any number and arrangement of areas containing any desired features may be included in GUI 300.

[0052] The data display area 302 can display data generated by a scientific instrument (e.g., the scientific instrument 1810 described herein with reference to FIG. 18). For example, the data display area 302 can display a resulting composite spectrum or data representative of such a spectrum, as shown in spectrum 1160 of FIG. 11. The spectrum graph 1160 of FIG. 11 has a horizontal x-axis showing the energy of the incident photon in kiloelectron volts and is divided into 5 electron volt ranges. The vertical y-axis shows the number of photons acquired within a single range, with the sum of the spectrum normalized to 1 photon, displaying measured and composite spectra within the same vertical range to highlight the relative heights of the constituent peaks within that range.

[0053] The data analysis area 304 may display the results of the data analysis (e.g., the results of analyzing the data shown in the data display area 302 and / or other data). For example, the data analysis area 304 may display the subject composition or identification, such as a visual identification display as shown at 1150 in FIG. 11. In one or more embodiments, the data display area 302 and the data analysis area 304 may be combined in the GUI 300 (e.g., to include data output from a scientific instrument and some analysis of the data in a common graph or area).

[0054] The scientific instrument control area 306 may include options that enable a user entity to control a scientific instrument (e.g., the scientific instrument 1810 described herein with reference to FIG. 18 ). For example, the scientific instrument control area 306 may include one or more controls for selecting and / or validating a selection of known compositions for use in identifying the composition of interest.

[0055] Settings area 308 may include options that enable a user entity to control features and functions of GUI 300 (and / or other GUIs) and / or perform common computing operations related to data display area 302 and data analysis area 304 (e.g., saving data on a storage device, such as storage device 404 described herein with reference to FIG. 4, sending data to another user entity, labeling data, etc.). For example, settings area 308 may include one or more options for changing the color, fill, or form of an illustration, such as illustrations 1150 and 1160 of FIG. 11.

[0056] As noted above, the scientific instrument module 100 may be implemented by one or more computing devices. Accordingly, referring now to FIG. 4, FIG. 4 illustrates a block diagram of a computing device 400 capable of performing some or all of the scientific instrument methods disclosed herein, according to various embodiments. In one or more embodiments, the scientific instrument module 100 may be implemented by a single computing device 400, or by multiple computing devices 400. Additionally, as discussed below, a computing device 400 (or multiple computing devices 400) implementing the scientific instrument module 100 may be part of one or more of the scientific instrument 1810, user local computing device 1820, service local computing device 1830, or remote computing device 1840 of FIG. 18.

[0057] 4 is illustrated as having several components, any one or more of which may be omitted or duplicated as appropriate for the application and setting. As shown, these components may include one or more of a processor 402, a storage device 404, an interface device 406, a battery / power circuitry 408, a display device 410, and other input / output (I / O) devices 412, as described below.

[0058] In one or more embodiments, one or more of the components included in computing device 400 may be mounted on one or more motherboards and encapsulated in a housing (e.g., comprising plastic, metal, and / or other materials). In one or more embodiments, some of these components may be fabricated on a single system-on-a-chip (SoC) (e.g., an SoC may include one or more processors 402 and one or more storage devices 404). Additionally, in one or more embodiments, computing device 400 may omit one or more of the components shown in FIG. 4. In one or more embodiments, computing device 400 may include interface circuitry for coupling to one or more components using any suitable interface (e.g., a universal serial bus (USB) interface, a high-definition multimedia interface (HDMI) interface, a controller area network (CAN) interface, a serial peripheral interface (SPI) interface, an Ethernet interface, a wireless interface, or any other suitable interface). For example, computing device 400 may omit display device 410, but may include display device interface circuitry (eg, connectors and driver circuitry) to which display device 410 can be coupled.

[0059] Computing device 400 may include a processor 402 (e.g., one or more processing devices). As used herein, the term "processing device" may refer to any device or portion of a device that processes electronic data from registers and / or memory and converts the electronic data into other electronic data that may be stored in registers and / or memory. Processor 402 may be one or more digital signal processors (DSPs), application specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GSMUs), or other integrated circuits (ICs). The processing device may include a graphics processing unit (GPU), a cryptographic processor (a dedicated processor that executes cryptographic algorithms in hardware), a server processor, or any other suitable processing device.

[0060] The computing device 400 may include a storage device 404 (e.g., one or more storage devices). The storage device 404 may include random access memory (RAM) (e.g., static RAM). The storage device 404 may include one or more memory devices, such as a static RAM (SRAM) device, a magnetic RAM (MRAM) device, a dynamic RAM (DRAM) device, a resistive RAM (RRAM) device, or a conductive-bridging RAM (CBRAM) device, a hard drive-based memory device, a solid-state memory device, a network drive, a cloud drive, or any combination of memory devices. In one or more embodiments, the storage device 404 may include memory that shares a die with the processor 402. In such embodiments, the memory may be used as a cache memory and may include, for example, an embedded dynamic random access memory (eDRAM), or a spin transfer torque magnetic random access memory (STT-MRAM). In one or more embodiments, the storage device 404 may include a non-transitory computer-readable medium having instructions that, when executed by one or more processing devices (e.g., the processing device 402), cause the computing device 400 to perform any suitable method or portion of the methods disclosed herein.

[0061] Computing device 400 may include an interface device 406 (e.g., one or more interface devices 406). Interface device 406 may include one or more communication chips, connectors, and / or other hardware and software to manage communications between computing device 400 and other computing devices. For example, interface device 406 may include circuitry to manage wireless communications to transfer data to and from computing device 400. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc. that may communicate data through the use of modulated electromagnetic radiation over a non-solid medium. This term does not imply that the associated devices do not include wires, although in one or more embodiments, the associated devices may not include wires. The circuitry included in the interface device 406 for managing wireless communications may implement any of a number of wireless standards or protocols, including, but not limited to, Wi-Fi (IEEE 802.11 family), Institute for Electrical and Electronic Engineers (IEEE) standards including the IEEE 802.16 standard (e.g., the IEEE 802.16-2005 Amendment), the Long-Term Evolution (LTE) project with any amendments, updates, and / or revisions (e.g., the Advanced LTE project, the ultra-mobile broadband (UMB) project (also referred to as “3GPP®2”), etc.).In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may be adapted to support a variety of standards, including Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA (Evolved HSPA), and / or LTE. In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may operate in accordance with a GSM HSPA, E-HSPA, or LTE network. In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may operate in accordance with an Enhanced Data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may operate in accordance with a Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), or Evolved UTRAN (E-UTRAN) network. Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications Enhanced Cordless Telecommunications (DECT), Evolution-Data Optimized (EV-DO), and their derivatives, as well as any other wireless protocols designated as 3G, 4G, 5G, and beyond. In one or more embodiments, the interface device 406 may include one or more antennas (e.g., one or more antenna arrays) for receiving and / or transmitting wireless communications.

[0062] In one or more embodiments, the interface device 406 may include circuitry for managing wired communications, such as electrical, optical, or any other suitable communications protocol. For example, the interface device 406 may include circuitry to support communications according to Ethernet technology. In one or more embodiments, the interface device 406 may support both wireless and wired communications, and / or may support multiple wired and / or multiple wireless communications protocols. For example, a first set of circuits of the interface device 406 may be dedicated to short-range wireless communications, such as Wi-Fi or Bluetooth, and a second set of circuits of the interface device 406 may be dedicated to long-range wireless communications, such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, and the like. In one or more embodiments, the first set of circuits of the interface device 406 may be dedicated to wireless communications, and the second set of circuits of the interface device 406 may be dedicated to wired communications.

[0063] Computing device 400 may include battery / power circuitry 408. Battery / power circuitry 408 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 400 to an energy source separate from computing device 400 (e.g., AC line power).

[0064] Computing device 400 may include a display device 410 (e.g., multiple display devices). Display device 410 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touch screen display, a liquid crystal display (LCD), a light emitting diode display, or a flat panel display.

[0065] Computing device 400 may include other input / output (I / O) devices 412. Other I / O devices 412 may include, for example, one or more audio output devices (e.g., a speaker, a headset, an earpiece, an alarm, etc.), one or more audio input devices (e.g., a microphone or microphone array), a location device (e.g., a GPS device that communicates with a satellite-based system to receive the location of computing device 400, as is known in the art), an audio codec, a video codec, a printer, sensors (e.g., a thermocouple or other temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, an accelerometer, a gyroscope, etc.), an image capture device such as a camera, a cursor control device such as a keyboard, a mouse, a stylus, a trackball, or a touchpad, a barcode reader, a Quick Response (QR) code reader, or a radio frequency identification (RFID) device. The device may include a radio frequency identification (RFID) reader.

[0066] Computing device 400 may have any suitable form factor for the application and setting, such as a handheld or mobile computing device (e.g., a cell phone, a smartphone, a mobile Internet device, a tablet computer, a laptop computer, a netbook computer, an ultrabook computer, a personal digital assistant (PDA), an ultra-mobile personal computer, etc.), a desktop or server computing device, or other networked computing component.

[0067] 5 and 6, in one or more embodiments, the non-limiting systems 500 and / or 600 shown in Figures 5 and 6, and / or the systems thereof, may further comprise one or more computers and / or computation-based elements described herein with reference to a computing environment, such as the computing environment 2000 shown in Figure 20. In one or more described embodiments, the computers and / or computation-based elements may be used in connection with the implementation of one or more of the systems, devices, components and / or computer-implemented operations illustrated and / or described in connection with Figures 5 and / or 6 and / or other figures described herein.

[0068] 5, the figure illustrates a block diagram of an exemplary, non-limiting system 500 that may comprise a materials analysis system 502. The materials analysis system 502 may facilitate a process of generating a synthetic spectrum of a composition of interest based on one or more spectra defining a known composition, where the known composition has a material composition related to the composition of interest. In one or more embodiments, the materials analysis system 502 may be included at least in part in the computing device 400.

[0069] It should be noted that materials analysis system 502 is merely detailed in a simplified manner to provide an introduction to a more complex and / or more extensive materials analysis system 602 as shown in Figure 6. That is, further details regarding processes that may be performed by one or more embodiments described herein are provided below with respect to the non-limiting system 600 of Figure 6.

[0070] 5, materials analysis system 502 can include at least a memory 504, a bus 505, a processor 506, a determining component 514, and a generating component 520. Processor 506 can be the same as processor 402, can be included in a processor, or can be different from processor 402. Memory 504 can be the same as storage device 404, can be included in storage device 404, or can be different.

[0071] Using the components described above, materials analysis system 502 can use both synthetic and measured spectra of the target composition or selected known compositions to generate a more accurate synthetic spectrum (e.g., a derived synthetic spectrum) for the target composition than could be generated from the initial component composition data of the target composition alone. Indeed, the generation of the derived synthetic spectrum can be most particularly based on the identification of known compositions that are closest compositions to the target composition, having related material compositions, such as, for example, using component weight percentages.

[0072] The determination component 514 can generally determine a known composition 552 (e.g., a nearest neighbor composition) that has at least the same component 554 as a component 564 of the target composition 562. As used herein, components 554 and 564 refer to atomic elements. For example, the known composition 552 and the target composition 562 can each include a silicon (Si) component.

[0073] Generation component 520 may generally generate a resulting (e.g., second) synthetic spectrum 580 of the target composition 562 based on a baseline synthetic spectrum 558 of the known composition 552 (e.g., which is a baseline for the known composition 552) and an initial (e.g., first) synthetic spectrum 566 of the target composition 562. In one or more embodiments, the known composition 552 and data defining the known composition 552 may be obtained from a database 540 that may be communicatively connected to the material analysis system 502.

[0074] The determining component 514 and the generating component 520 can be operatively coupled to a processor 506, which can be operatively coupled to the memory 504. A bus 505 can provide the operative coupling. The processor 506 can facilitate the execution of the determining component 514 and the generating component 520. The determining component 514 and the generating component 520 can be stored in the memory 504.

[0075] In general, the non-limiting system 500 can use any suitable communication method (e.g., electronic, telecommunications, internet, infrared, fiber, etc.) to provide communication between the material analysis system 502, the source of data defining the target compositions and known compositions, and any devices associated with a user entity that use the resulting composite spectrum 580 of the target composition 562.

[0076] Referring now to Figure 6, a non-limiting system 600 is shown that may include a materials analysis system 602. Repeated descriptions of similar elements and / or processes used in each embodiment are omitted for brevity. The description of the embodiment of Figure 5 may be applicable to the embodiment of Figure 6. Similarly, the description of the embodiment of Figure 6 may be applicable to the embodiment of Figure 5.

[0077] In general, material analysis system 602 can facilitate the process of generating a synthetic spectrum of a composition of interest based on one or more spectra defining a known composition, where the known composition has a material composition related to the composition of interest. In one or more embodiments, material analysis system 602 can be included at least in part in computing device 400.

[0078] One or more communications between one or more components of the non-limiting system 600 may be provided by wired and / or wireless means, including but not limited to using a cellular network, a wide area network (WAN) (e.g., the Internet), and / or a local area network (LAN). Suitable wired or wireless technologies to support communication include, but are not limited to, Wireless Fidelity (Wi-Fi), Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), Enhanced General Packet Radio Service (enhanced GPRS), Third Generation Partnership Project (3GPP) Long Term Evolution (LTE), Third Generation Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed ​​Packet Access (HSPA), Zigbee and other 802.XX wireless technologies and / or legacy telecommunications technologies, BLUETOOTH, Session Initiation Protocol (SIP), ZIGBEE, RF4CE protocol, WirelessHART protocol, 6LoWPAN (Ipv 6over Low Power Wireless Area Networks), Z-Wave, Ultra Wideband (UWB) standard protocols and / or other proprietary and / or non-proprietary communication protocols.

[0079] Materials analysis system 602 may be associated with a cloud computing environment, such as being accessible via the cloud computing environment.

[0080] The materials analysis system 602 can include multiple components. The components can include a memory 604, a processor 606, a bus 605, a determination component 614, a linear combination component 616, a synthesis component 618, a generation component 620, and a database maintenance component 622. Using these components, the materials analysis system 602 can output, in response to a query regarding an unknown material (e.g., a target composition), one or more resulting synthetic spectra defining a target composition 662, or an identification of the target composition 662. The query can be requested by an entity, such as an administrator entity of a mining system or other system.

[0081] The processor 606, memory 604, and bus 605 of the materials analysis system 602 will now be described. For example, in one or more embodiments, the materials analysis system 602 can include a processor 606 (e.g., a computer processing unit, a microprocessor, a classical processor, a quantum processor, and / or the like). In one or more embodiments, the components associated with the materials analysis system 602 can include one or more computer and / or machine readable, writable, and / or executable components and / or instructions that can be executed by the processor 606 to provide performance of one or more processes defined by such components and / or instructions, as described herein with or without reference to one or more figures of one or more embodiments. In one or more embodiments, the processor 606 can include a determination component 614, a linear combination component 616, a synthesis component 618, a generation component 620, and a database maintenance component 622.

[0082] In one or more embodiments, materials analysis system 602 may comprise a computer-readable memory 604 that may be operatively coupled to processor 606. Memory 604 may store computer-executable instructions that, when executed by processor 606, cause processor 606 and / or one or more other components of materials analysis system 602 (e.g., determining component 614, linear combination component 616, synthesizing component 618, generating component 620, and database maintenance component 622) to perform one or more operations. In one or more embodiments, memory 604 may store computer-executable components (e.g., determining component 614, linear combination component 616, synthesizing component 618, generating component 620, and database maintenance component 622).

[0083] The materials analysis system 602 and / or components thereof described herein may be communicatively, electrically, operatively, optically, and / or otherwise coupled to one another via a bus 605. The bus 605 may comprise one or more of a memory bus, a memory controller, a peripheral bus, an external bus, a local bus, a quantum bus, and / or another type of bus that may use one or more bus architectures. One or more of these examples of the bus 605 may be used.

[0084] In one or more embodiments, materials analysis system 602 can be coupled (e.g., communicatively, electrically, operatively, optically, and / or similarly functional) to one or more external systems (e.g., an electrical output generating system, one or more output targets and / or output target controllers, not shown), sources and / or devices (e.g., classical and / or quantum computing devices, communication devices and / or similar devices), such as via a network. In one or more embodiments, materials analysis system 602 and / or one or more of the components of non-limiting system 600 can reside in the cloud and / or locally within a local computing environment (e.g., at a designated location).

[0085] In addition to the processor 606 and / or memory 604 described above, materials analysis system 602 may comprise one or more computer- and / or machine-readable, writeable, and / or executable components and / or instructions that, when executed by processor 606, can provide for the performance of one or more operations defined by such components and / or instructions.

[0086] Turning now to the additional components of material analysis system 602 (e.g., determination component 614, linear combination component 616, synthesis component 618, generation component 620, and database maintenance component 622), in general, material analysis system 602 can perform a set of processes that can be separated into three steps: nearest neighbor composition determination, initial synthetic spectrum generation, and resultant synthetic spectrum generation.

[0087] Turning first to the determination component 614, this component can generally determine a known composition 652 (e.g., a closest composition) that has one or more components 654 (e.g., component 654) that are the same as a component 664 (e.g., component 664) of the target composition 662.

[0088] More specifically, based on a set of known compositions 650, such as those obtained from database 640 or one or more other databases, determination component 614 can determine a composition in set 650 to be a nearest neighbor composition in set 650. A "nearest neighbor composition" can be a known composition having a composition / make-up most similar to the target composition 662. In one or more embodiments, this determination can be based on component weight percentages. That is, the selected nearest known composition 652 can have a component weight percentage that is closer to the component weight percentages of the target composition than other known compositions in set of known compositions 650. In one or more embodiments, the known compositions 652 and data defining the known compositions 652 can be obtained from database 640, which can be communicatively connected to materials analysis system 602.

[0089] The closest composition determination by determination component 614 may generally verify that the delta of component weight percentages of known composition 652 and target composition 662 satisfies a selected threshold that defines a selected compositional relationship between known composition 652 and target composition 662. The selected threshold may be defined by a user entity or by materials analysis system 602.

[0090] In one or more embodiments, the selected threshold may be defined as generally based on an error metric between pairs of vectors containing component weight percentages.

[0091] For example, in one or more embodiments, the selected threshold can be defined as the delta of the output of the sum of the absolute deviations of the components of the subject composition and the components of the known compositions. In one or more embodiments, the selected threshold can be defined as the delta of the output of the sum of the relative deviations of the components of the subject composition and the components of the known compositions. An exemplary relative error method can be defined, for example, for a single element, by: Error=abs(element1-element2) / element1.

[0092] As another example, and referring briefly to FIG. 7 and the closest composition determination process 700, this determination by the determination component 614 can be shown based on exemplary known compositions 652A, 652B, and 652C.

[0093] Target composition 662 is shown having component weight percentages 704 of 4% iron (Fe), 2% carbon (C), and 94% oxygen (O). Known composition 652A is shown having component weight percentages 702 of 10% Fe, 82% O, and 8% Ag. The delta of these component weight percentages 702 and 704 can be determined using absolute value deviations as shown in Equation 1 below in general and Equation 2 below in particular.

[0094] Formula 1: |%Fe T -%Fe K | and |%O T -%O K |= Δ%Fe and Δ%O; sum of deviations = Δ%Fe + Δ%O.

[0095] Formula 2: Fe |4%-10%|, O |94%-82%|, C |2%-0%| and A g |8%-0%| = 6% Fe, 12% O, 2% C and 8% Ag; sum of deviations = 6% + 1 2%+2%+8%=28%.

[0096] In one or more embodiments, elements C and Ag can be omitted.

[0097] Alternatively, known composition 652B is shown as having component weight percentages 702 of 3% Fe and 97% O compared to component weight percentages 704 of target composition 662 of 4% Fe and 2% C and 94% O. Thus, the delta of these component weight percentages 702 and 704 can be determined using the absolute value deviation, as specifically shown in Equation 3 below.

[0098] In one or more embodiments, element C can be omitted.

[0099] Equation 3: |4%-3%| of Fe, |94%-97%| of O, and |2%-0%| of C = 1% Fe, 3% O, and 2% C; total deviation = 1% + 3% + 2% = 6%.

[0100] Thus, the separate delta component weight percentages (e.g., separately for O and separately for Fe) of known composition 652B are each lower than the separate delta component weight percentages of known composition 652A. Thus, known composition 652 / closest composition 710 is known composition 652B.

[0101] Furthermore, the total variance of known composition 652B (6%) is lower than the total variance of known composition 652A (28%).

[0102] Note that known composition 652C has a total deviation of 184%.

[0103] In one or more other embodiments, another suitable method may be used that is based at least in part on component weight percentages, or that is not based on component weight percentages at all.

[0104] Referring now briefly to the linear combination component 616, if the sum of the deviations does not meet a selected threshold for the sum of the deviations and / or if the entity desires, the example known composition can be based on a combination (e.g., aggregation) of two or more known compositions. These two or more known compositions can be obtained from the same or different databases 640 by the determination component 614. Based on obtaining the two or more known compositions, the linear combination component 616 can perform an aggregation, such as a component weight percentage, of the two or more known compositions. In one or more embodiments, the aggregation can include averaging a first component weight percentage of a first element of the two or more known compositions, averaging a second component weight percentage of a second element of the two or more known compositions, etc. This can result in a combination of known compositions having component weight percentages averaged separately for each different element of the combination of known compositions.

[0105] For example, one synthesis of the spectrum of the mineral albite (NaAlSiO8) gives O=48.8%, Na=8.7%, Al=10.3%, Si=32.1%. This can be constructed from a set of three reference compositions (here minerals) SiO2, Na2O and Al2O3, with ratios of, for example, about 68.7% SiO2, 19.5% Al2O3 and 11.9% Na2O. These numbers can scale the measured spectra of these standards, thus resulting in a synthetic spectrum closer to the target. Given that Na2O is a rare compound, this composition may not be present in the available databases of known compositions, and the remaining contributions of Na and O may be synthetically derived. The other compounds SiO2 and Al2O3 are common and therefore more likely to be present in accessible databases.

[0106] In such cases, the combination known composition can be used as one of the comparison samples to determine the closest composition 710, as described above.

[0107] Determining the nearest neighbor composition 710 completes the first stage of identification, the first stage being nearest neighbor composition determination. The second stage involves the initial synthetic spectrum generation. In this second stage, the synthesis component 618 is now described.

[0108] The synthesis component 618 can generally synthetically generate a baseline synthetic spectrum 658 for the known composition 652 and an initial synthetic spectrum 665 for the target composition 662. These synthetically generated spectra can then be used to aid in the identification of the target composition 662. That is, the initial synthetic spectrum 665 of the target composition 662 becomes an initial estimate of the identification of the target composition 662, after which additional processing is performed by the generation component 620 to provide a more accurate identification of the target composition 662.

[0109] In general, the baseline composite spectrum 658 can be based on a sum of the component weight percentages of the components of known composition 652 and a matrix correction procedure, such as, but not limited to, ZAF analysis, PAP analysis, Armstrong analysis, or XPHI analysis, that allows for scaling of the sum over a set of iterations of summing and subsequent scaling. That is, with reference to FIG. 8 and composite spectrum determination process 800, a first step can include determining an initial weight percent composition 802 of the components of known composition 652. For example, given a known composition having component weight percentages of 3% Fe and 97% O, the 3% Fe spectrum and the 97% O spectrum can be aggregated. The result is a sum of the component weight percentages of the components of composition 804.

[0110] In one embodiment, a ZAF analysis may be performed on the results 804, resulting in an analysis output 806. A ZAF analysis may refer to a quantitative analysis that takes into account three effects of characteristic X-ray intensities, including the atomic number (Z) effect, the absorption (A) effect, and the fluorescence excitation (F) effect.

[0111] In one or more other embodiments, PAP analysis can be used, as described above. In energy dispersive X-ray spectroscopy (EDS), the PAP (Pouchou and Pichoir) matrix correction procedure can be used to improve the accuracy of quantitative analysis of thin samples. It addresses X-ray absorption and fluorescence effects that can affect measured X-ray intensity. The PAP method can involve iteratively calculating correction factors for each element in the sample based on theoretical models and experimental data, allowing researchers to obtain more reliable element composition information from EDS spectra of thin materials. The corrections provided by the PAP method help to compensate for these effects, resulting in more accurate and precise quantitative results in EDS analysis.

[0112] Both the PAP (Pouchou and Pichoir) matrix correction procedure and the ZAF (Z (atomic number), A (absorption), F (fluorescence)) method can be used in energy dispersive X-ray spectroscopy (EDS) for quantitative analysis. The difference between these methods lies in their approach to correcting for X-ray absorption and fluorescence effects. While the ZAF method addresses these corrections separately, the PAP method combines both absorption and fluorescence corrections into a single unified correction scheme. This integration enables the PAP method to provide more accurate and reliable element quantification, especially for thin samples.

[0113] As an example, using a ZAF analysis (e.g., ZAF method), an analysis output 806 can provide 1% Fe and 99% O. From this output 806, a set of scales 808 of the components of the composition can be generated. In the following example, the set of scales 808 is Fe3x and O0.9x. For example, the steps of Equation 4 and Equation 5 below can be performed, where Equation 4 is a general set of steps and Equation 5 continues with an exemplary composition of FE and O. It should be noted that the following notation of ZAF analysis can be replaced with one or more matrix correction procedures in general, or more specifically, with another type of matrix correction procedure.

[0114] Equation 4 (performed by the composition component 618): a) ZAF analysis output of component weight percentage of a given element; b) a given initial percentage determined for the element; c) Calculate the new scale for element 1: Initial Component Weight Percent / ZAF Analysis Percent = Scale of x (e.g., 0.1X, 2.4X, etc.); d) Calculate the new scale for element 2: Initial component weight percent / ZAF analysis percent=scale of x; e) Calculate the new scale of the added element; f) Apply the new scale to the original percentages: (Element 1: Initial Component Weight Percentage * Scale of x and element 2: Initial component weight percent * the scale of x; and g) Output the scaled sum = scaled element 1 and scaled element 2 h) Repeat steps af according to a threshold number of iterations.

[0115] Equation 5 (implemented by the composition component 618): a) Given ZAF analytical output 1% Fe and 99% O; b) given initial percentages 3% Fe and 97% O; c) Calculate the new Fe scale: 3% / 1%=3x; d) Calculate the new O scale: 97% / 99%=0.9x; e) Apply the new scale to the original percentages: (Fe: 3% * 3x and O: 97% * 0.9x); f) Output the scaled sum = 9% Fe and 95% O. g) Repeat steps af according to a threshold number of iterations.

[0116] As noted above, the scaled sum 810 of the component weight percentages of the components of known composition is 9% Fe and 95% O, which differs from the given initial percentages of 3% Fe and 97% O.

[0117] In another example, a PAP analysis can be performed to obtain an analysis output 806 , a scale 808 and a scaled sum 810 .

[0118] As discussed above, the calculation of the new scale 808 and resulting scaled sum 810 may be repeated any number of times until at least a selected threshold number of iterations is met. The threshold may be determined by a user entity or by the synthesis component 618. For example, as shown in FIG. 8 at a determination step 812, if the specified number of iterations is met, the synthetic spectrum determination process 800 may proceed to generate and output a scaled synthetic spectrum 814 of the known composition 652 based on the scaled sum 810 of component weight percentages. If the specified number of iterations is not met, the synthetic spectrum determination process 800 may proceed to re-perform the steps of outputting the analysis output 806, the scale 808, and the scaled sum 810.

[0119] Note that the output of the synthetic spectrum determination process 800 is the baseline synthetic spectrum 658 , which is the final scaled synthetic spectrum 814 after all completed iterations of the process 800 .

[0120] The same composite spectrum determination process 800 may be completed for the target composition 662 to output an initial composite spectrum 665 that becomes the final scaled composite spectrum 814 after all completed iterations of the process 800.

[0121] Upon determining the initial synthetic spectrum 665 of the target composition 662 and the baseline synthetic spectrum 658 of the known composition 652, a third stage of identification can be performed. The third stage can be the generation of a resultant synthetic spectrum 680 (e.g., a modified synthetic spectrum of the target composition 662).

[0122] That is, following generation component 620, this component can generally generate a resulting (e.g., second) synthetic spectrum 680 of the target composition 662 based on a baseline synthetic spectrum 658 of the known composition 652 (e.g., that is a baseline for the known composition 652) and an initial (e.g., first) synthetic spectrum 665 of the target composition 662. In one or more embodiments, the known composition 652 and data defining the known composition 652 can be obtained from a database 640 that can be communicatively connected to the materials analysis system 602.

[0123] However, two intermediate steps are first performed by the generation component 620 before outputting the resulting synthetic spectrum 680 of the target composition 662 .

[0124] Referring now to FIG. 9 and the process 900 for generating a resultant synthetic spectrum of a target composition, the first step involves the generation of an intermediate synthetic relation 678 by the generation component 620 .

[0125] That is, a baseline synthetic spectrum 658 was generated by synthesis component 618 of known composition 652 based on nearest known composition 710. Additionally, a measured spectrum 656 of known composition 652 can be obtained, such as from a database (e.g., database 640), such as by generation component 620. It should be noted that measured spectrum 656 is a spectrum measured by a scientific instrument, such as an EDS device, based on a sample of known providence of known composition 652 (e.g., a sample whose composition is precisely known).

[0126] The initial synthetic spectrum 665 of the target composition 662 and the baseline synthetic spectrum 658 of the known composition 652 can be used to generate the intermediate synthetic relationship 678. That is, the intermediate synthetic relationship 678 is determined as the relationship defined by the delta of these two synthetic spectra 665 and 658.

[0127] 10A-10D. Turning first to FIG. 10A to the illustrated process for intermediate composition relationship determination 1000, and then with further reference to FIG. 9, more specifically, intermediate composition relationship 678 is based on a delta of a first photon amount 1001 of a portion of a baseline synthetic spectrum 658 of known composition 652 and a delta of a second photon amount 1002 of a portion of an initial synthetic spectrum 665 of a target composition 662. That is, the portion of baseline synthetic spectrum 658 of known composition 652 represents a different energy range of baseline synthetic spectrum 658 of known composition 652, and the portion of initial synthetic spectrum 665 of target composition 662 represents a different energy range of initial synthetic spectrum 665 of target composition 662.

[0128] Even more specifically, determining the delta involves determining the difference between a first array of numbers (e.g., a column, or vector in FIG. 10B ) indexed across a set of energy ranges and corresponding to a first photon amount 1001, and a second array of numbers indexed across the same set of energy ranges and corresponding to a second photon amount 1002.

[0129] For example, referring to FIG. 10C, an exemplary initial synthetic spectrum 665 of a target composition 662 is shown. The x-axis indicates the energy of the spectrum (e.g., expressed in kiloelectron volts or keV), and the y-axis indicates the number of photons at each energy on the x-axis. Data from this exemplary initial synthetic spectrum 665 of a target composition 662 can be converted into an array of photon quantities at each of a number of energy ranges of energy on the x-axis. The energy ranges can be equal, and the default range for each energy range can cover any suitable quantity. As shown in column 1 of the set of arrays 1050 in FIG. 10B, the energy ranges are set to 0.02 keV per energy range. Each of the photon quantity arrays 1001-1006 is indexed over the same set of energy ranges in column 1.

[0130] Note that for simplicity, the array in column 1 only extends out to 0.6 keV rather than showing the full energy range of the spectrum in FIG. 10C (whose x-axis extends to 8.0 keV).

[0131] Additionally, although a visualization of the set of arrays 1050 may be useful to a user entity and may be generated by the generation component 620 for such purposes as display in the display area 302 (FIG. 3), the generation of a visual version of the set of arrays 1050 is not required for the determination of the intermediate synthesis relationship 678 or the aggregate synthetic spectrum 680 by the generation component 620.

[0132] 10B, column 2 shows an array of known photon quantities 1006 of measured spectra of target composition 662. While measured spectra or established provident samples of target compositions may not be available during use of materials analysis system 602 (and thus use of materials analysis system 602), in the example of FIGS. 10A-10D, the array in column 2 provides a proof of concept in comparison to the final results in column 7, which corresponds to the resulting composite spectrum 680 of target composition 662.

[0133] Column 3 of the array set 1050 in Figure 10B shows an array of second photon quantities 1002 for a portion (e.g., energy range) of an exemplary initial synthetic spectrum 665 of the target composition 662 obtained from the spectrogram in Figure 10C.

[0134] Also shown in column 4 of FIG. 10B is an array of first photon doses 1001 for portions (eg, energy ranges) of corresponding baseline synthetic spectra 658 of known compositions 652.

[0135] Column 5 of FIG. 10B is an array of third photon amounts 1003 of a portion (e.g., energy range) of the intermediate composite relationship 678. This array in column 5 is the result (e.g., delta) of subtracting the first photon amount 1001 (of the composite spectrum 658 of known composition 652) from the second photon amount 1002 (of the composite spectrum 665 of target composition 662). That is, the delta or subtraction performed by the generation component 620 is performed for columns 3 and 4 for each energy range (e.g., each row of the set of arrays 1050). That is, the individual photon amount of 1001 (column 4) is subtracted from the respective / corresponding (e.g., same row) individual photon amount 1002 (column 3) to obtain the photon amount 1003 of the array in column 5. In other words, column 3 - column 4 = column 5 or equation 6.

[0136] Equation 6: Initial synthetic spectrum 665 of target composition 662 - Baseline synthetic spectrum 658 of known composition 652 = Intermediate synthetic relationship 678.

[0137] Furthermore, column 5 + column 6 = column 7, or formula 7, as detailed below.

[0138] Equation 7: Intermediate composite relationship 678 - measured spectrum 656 of known composition 652 = aggregate composite spectrum 679 = resulting composite spectrum 680 of target composition 662.

[0139] More specifically, column 3 - column 4 + column 6 = column 7 or equation 8.

[0140] Equation 8: Initial synthetic spectrum 665 of target composition 662 − Baseline synthetic spectrum 658 of known composition 652 + Measured spectrum 656 of known composition 652 = Aggregated synthetic spectrum 679 = Resultant synthetic spectrum 680 of target composition 662.

[0141] Turning now to the second step above, the aggregate composite spectrum 679 may be generated by the generation component 620 based on the intermediate composite relation 678 .

[0142] The intermediate composite relationship 678 and the measured spectrum 656 of the known composition 652 can be used to generate an aggregate composite spectrum 679. That is, the aggregate composite spectrum 679 is determined as the sum (e.g., the aggregate) of these two aspects 678 and 656.

[0143] 9 and 10A-10D, more specifically, aggregate composite spectrum 679 is based on a sum of a third photon amount 1003 of the portion of intermediate composite relationship 678 and a fourth photon amount 1004 of the portion of measured spectrum 656 of known composition 652. That is, the portion of intermediate composite relationship 678 represents a set of energy ranges and the portion of measured spectrum 656 of known composition 652 represents different energy ranges of known composition 652 (e.g., the same set of energy ranges).

[0144] Even more specifically, determining the sum includes summing a third array of numbers indexed across a third energy range and corresponding to a third photon amount 1003 (column 5 of FIG. 10B ) and a fourth array of numbers indexed across a fourth energy range and corresponding to a fourth photon amount 1004 (column 6 of FIG. 10B ).

[0145] As shown in FIG. 10B, column 6 is an array of fourth photon amounts 1004 for a portion (eg, energy range) of a corresponding measured spectrum 656 of known composition 652 .

[0146] Column 7 is then an array of fifth photon amounts 1005 for a portion (e.g., energy range) of aggregate spectrum 679, which is the resulting composite spectrum 680 of target composition 662. Column 7 is the result of an aggregation (e.g., summation) performed by generation component 620 of each individual photon amount 1003 in column 5 (e.g., in each row of set of arrays 1050) with each corresponding individual photon amount 1004 (column 6). As above, column 5 + column 6 = column 7, or equation 7.

[0147] It will be appreciated that in one or more embodiments, the generation component 620 need not separately calculate the intermediate composition relation 678, rather omitting such a step with respect to the use of Equation 8.

[0148] Finally, from the photon quantity 1005 , a resulting composite spectrum 680 can be generated and output to a user entity of the materials analysis system 602 .

[0149] An optional fourth stage of identification may also be performed upon determination of the resulting composite spectrum 680 of the target composition 662. The fourth stage may include self-improvement of the materials analysis system 602 through use of the resulting composite spectrum 680.

[0150] That is, following the database maintenance component 622, this component can update the data in a database of known compositions (e.g., database 640). For example, database 640 can include measured spectrum 656 and synthetic spectrum 658 of known composition 652, and resultant synthetic spectrum 680 of target composition 662 can be added to database 640 by database maintenance component 662. In this manner, a user entity of material analysis system 602 can build a private library of known compositions for use in identifying future unknown compositions (e.g., target compositions). For example, upon a future iteration of use of material analysis system 602, determination component 614 can determine resultant synthetic spectrum 680 of target composition 662 as a second known composition to determine a resultant synthetic spectrum of the second target composition.

[0151] As a brief overview of one or more embodiments described above, diagrams 1100 and 1150 of FIG. 11 are now described. As shown, the material shown in results 1100 of the existing approach is identified as a single component 1102. This is incorrect compared to the actual physical sample. Rather, using the systems and methods described herein (e.g., materials analysis system 602), results 1150 of the described approach more accurately indicate that the unknown material includes multiple components (e.g., at least two components 1152 and 1154), and does not include a 100% weight percentage of only a single component. Indeed, this type of accurate identification can be important in mining and / or other material procurement fields.

[0152] As another overview, referring now to Figures 12 and 13, a flow diagram of an example non-limiting method 1200 is shown that can provide a process for generating a synthetic spectrum, where the synthetic spectrum accurately defines a composition of interest based on the nearest known composition, according to one or more embodiments described herein, such as non-limiting system 600 of Figure 6. Although non-limiting method 1200 is described with respect to non-limiting system 600 of Figure 6, non-limiting method 1200 may also be applicable to other systems described herein, such as non-limiting system 500 of Figure 5. Repeated descriptions of similar elements and / or processes used in each embodiment are omitted for the sake of brevity.

[0153] At 1202, the non-limiting method 1200 can include determining, by a system operably coupled to a processor (e.g., determination component 214), a known composition (e.g., known composition 652) having the same component (e.g., component 654) as the subject composition (e.g., subject composition 662 having component 664).

[0154] At 1204, the non-limiting method 1200 can include determining, by the system (e.g., linear combination component 616), the known composition based on identifying a linear combination of two or more other known compositions.

[0155] At 1206, non-limiting method 1200 can include determining, by the system (e.g., determining component 614), whether the known composition is a closest composition (e.g., closest composition 710) to the target composition of a set of known compositions (e.g., set of known compositions 650) that includes the known composition. In one or more embodiments, the closest composition is a composition having a component weight percentage that is closer to the component weight percentage of the target composition than other known compositions in the set of known compositions. If yes, the method can proceed to step 1208. If no, the method can return to step 1202.

[0156] At 1208, non-limiting method 1200 can include synthetically generating, by a system (e.g., synthesis component 618), a first synthetic spectrum of the target composition (e.g., initial synthetic spectrum 665) by performing steps 1208A and 1208B.

[0157] At 1208A, the non-limiting method 1200 can include determining, by the system (e.g., the composite component 618), a first sum of the component weight percentages of the components of the target composition based on the weight percentage composition of the components of the target composition.

[0158] At 1208B, non-limiting method 1200 can include applying, by the system (e.g., synthesis component 618), a scale of the components of the target composition to a weight percent composition of the components of the target composition based on the results of the matrix correction procedure for the first sum, thereby generating a scaled synthetic spectrum that is a first synthetic spectrum of the target composition.

[0159] At 1210, the non-limiting method 1200 can include synthetically generating, by a system (e.g., synthesis component 618), a synthetic spectrum of a known composition (e.g., baseline synthetic spectrum 658) by performing steps 1210A and 1210B.

[0160] At 1210A, non-limiting method 1200 can include determining, by a system (e.g., composite component 618), a first sum of component weight percentages of components of known composition based on the weight percentage compositions of the components of known composition.

[0161] At 1210B, non-limiting method 1200 can include applying, by a system (e.g., synthesis component 618), a scale for the component of known composition based on the results of the matrix correction procedure to the weight percent composition of the component of known composition, thereby generating a scaled synthetic spectrum that is a synthetic spectrum of the known composition.

[0162] At 1212, non-limiting method 1200 can include generating, by a system (e.g., generation component 620), a second synthetic spectrum of the target composition (e.g., resulting synthetic spectrum 680 of target composition 662) based on the synthetic spectrum of the known composition and the first synthetic spectrum of the target composition.

[0163] At 1214, the non-limiting method 1200 can include generating, by the system (e.g., generation component 620), a second synthetic spectrum of the target composition based on the measured spectrum of the known composition (e.g., measured spectrum 656).

[0164] At 1216, non-limiting method 1200 can include updating, by the system (e.g., database maintenance component 622), data in a database of known compositions (e.g., database 640), where the data includes the second composite spectra of the known compositions and the target composition.

[0165] At 1218, the non-limiting method 1200 can include determining, by the system (e.g., the determining component 614), a second composite spectrum of the target composition as the second known composition for determining a resulting composite spectrum of the second target composition.

[0166] As another overview, referring now to Figures 14 and 15, a flow diagram of an example non-limiting method 1400 is shown that can provide a process for synthesizing a modified synthetic spectrum (e.g., resulting synthetic spectrum 680) of a target composition (e.g., target composition 662) by a system operatively coupled to a processor according to one or more embodiments described herein, such as non-limiting system 600 of Figure 6. Although non-limiting method 1400 is described with respect to non-limiting system 600 of Figure 6, non-limiting method 1400 may also be applicable to other systems described herein, such as non-limiting system 500 of Figure 5. Repeated descriptions of similar elements and / or processes used in each embodiment are omitted for brevity.

[0167] At 1402, non-limiting method 1400 can include determining, by a system (e.g., determining component 614), a known composition (e.g., known composition 652) that includes the same components as the target composition (e.g., components of known composition 654 and components of target composition 664). The known composition can include atomic elements, phases of atomic elements, or a combination thereof.

[0168] At 1404, non-limiting method 1400 can include verifying, by the system (e.g., determination component 614), that a delta of the component weight percentages of the known composition and the target composition satisfies a selected threshold that defines a selected compositional relationship between the known composition and the target composition.

[0169] At 1406, the non-limiting method 1400 can include determining, by the system (e.g., the determination component 614), the delta as an output of an error score of the component weight percentage difference between the components of the target composition and the components of the known composition.

[0170] At 1408, non-limiting method 1400 can include determining, by the system (e.g., determining component 614), whether the known composition is a closest composition (e.g., closest composition 710) to the target composition of a set of known compositions (e.g., set of known compositions 650) that includes the known composition. In one or more embodiments, the closest composition is a composition having a component weight percentage that is closer to the component weight percentage of the target composition than other known compositions in the set of known compositions. If yes, the method can proceed to step 1410. If no, the method can return to step 1402.

[0171] At 1410, the non-limiting method 1400 can include generating, by a system (e.g., generation component 620), an intermediate synthetic relationship (e.g., intermediate synthetic relationship 678), which is a relationship that defines a delta between a synthetic spectrum of a known composition (e.g., baseline synthetic spectrum 658) and a first synthetic spectrum of a target composition (e.g., initial synthetic spectrum 665).

[0172] At 1412, non-limiting method 1400 can include determining, by the system (e.g., generation component 620), an intermediate composition relationship based on a first photon amount (e.g., first photon amount 1001) of the portion of the composite spectrum of the known composition and a second photon amount (e.g., second photon amount 1002) of the portion of the first composite spectrum of the target composition.

[0173] At 1414, the non-limiting method 1400 can include aggregating, by a system (eg, generation component 620), the intermediate composite relationship and the measured spectrum of the known composition (eg, measured spectrum 656).

[0174] At 1416, the non-limiting method 1400 can include aggregating, by the system (e.g., generation component 620), a third photon amount of the portion of the intermediate composite relationship (e.g., third photon amount 1003) and a fourth photon amount of the portion of the measured spectrum of the known composition (e.g., fourth photon amount 1004).

[0175] At 1418, the non-limiting method 1400 can include generating an aggregated synthetic spectrum (e.g., aggregated synthetic spectrum 679) based on the aggregation, which is a modified synthetic spectrum of the target composition (e.g., obtained synthetic spectrum 680) by the system (e.g., evaluation component 222).

[0176] As another overview, referring now to Figures 16 and 17, a flow diagram of an example non-limiting method 1600 is shown that can provide a process for synthesizing a spectrum of a target composition (e.g., target composition 662) by a system operatively coupled to a processor according to one or more embodiments described herein, such as non-limiting system 600 of Figure 6. Although non-limiting method 1600 is described with respect to non-limiting system 600 of Figure 6, non-limiting method 1600 may also be applicable to other systems described herein, such as non-limiting system 500 of Figure 5. Repeated descriptions of similar elements and / or processes used in each embodiment are omitted for brevity.

[0177] At 1602, non-limiting method 1600 may include determining, by the system (e.g., determining component 614), whether the known composition (e.g., known composition 652) is a closest composition (e.g., closest composition 710) to a target composition in a set of known compositions (e.g., set of known compositions 650) that includes the known composition. In one or more embodiments, the closest composition is a composition having a component weight percentage that is closer to the component weight percentage of the target composition than other known compositions in the set of known compositions. If yes, the method may proceed to step 1604. If no, the method may repeat step 1602.

[0178] At 1604, the non-limiting method 1600 can include determining, by the system (e.g., generation component 620), a delta between a first photon amount of a portion of a synthetic spectrum of a known composition (e.g., first photon amount 1001 of baseline synthetic spectrum 558 of known composition 652) and a second photon amount of a portion of a first synthetic spectrum of a target composition (e.g., second photon amount 1002 of initial synthetic spectrum 665 of target composition 662), resulting in an intermediate synthetic relationship (e.g., intermediate synthetic relationship 678). In one or more embodiments, the portion of the synthetic spectrum of the known composition represents a different energy range of the synthetic spectrum of the known composition and the portion of the synthetic spectrum of the target composition represents a different energy range of the synthetic spectrum of the target composition.

[0179] At 1606, non-limiting method 1600 can include determining, by the system (e.g., generating component 620), a delta by determining a difference between a first array of numbers indexed across a set of energy ranges and corresponding to a first photon amount of a portion of a synthetic spectrum of the known composition, and a second array of numbers indexed across the set of energy ranges and corresponding to a second photon amount of a portion of the first synthetic spectrum of the target composition.

[0180] At 1608, non-limiting method 1600 can include aggregating, by a system (e.g., generating component 620), a third photon amount of the portion of the intermediate composite relationship (e.g., third photon amount 1003) and a fourth photon amount of the portion of the measured spectrum of the known composition (e.g., fourth photon amount 1004 of measured spectrum 656 of known composition 652). In one or more embodiments, the portion of the intermediate composite relationship represents different energy ranges of the intermediate composite relationship and the portion of the measured spectrum of the known composition represents different energy ranges of the measured spectrum of the known composition.

[0181] At 1610, the non-limiting method 1600 can include determining, by the system (eg, determining component 614), a measured spectrum measured from a sample of the established providence of known composition.

[0182] At 1612, non-limiting method 1600 can include aggregating, by the system (e.g., generating component 620), an aggregation of a third array of numbers indexed across the set of energy ranges and corresponding to a third photon amount of the portion of the intermediate composite relationship, and a fourth array of numbers indexed across the set of energy ranges and corresponding to a fourth photon amount of the portion of the measured spectrum of the known composition.

[0183] At 1614, the non-limiting method 1600 can include generating, by the system (e.g., generation component 620), a second composite spectrum of the target composition (e.g., resulting composite spectrum 680) based on the results of the aggregation.

[0184] Additional Overview For ease of explanation, the computer-implemented and non-computer-implemented methodologies provided herein are depicted and / or described as a series of operations. It should be understood that the subject innovation is not limited by the illustrated operations and / or the order of operations, e.g., operations can occur in one or more orders and / or simultaneously, and can occur with other operations not shown and described herein. Moreover, not all illustrated operations may be utilized to implement the computer-implemented and non-computer-implemented methodologies in accordance with the described subject matter. In addition, the computer-implemented and non-computer-implemented methodologies may alternatively be represented as a series of interrelated states via state diagrams or events. Furthermore, the computer-implemented methodologies described below and throughout this specification may be stored in an article of manufacture for transmitting and transferring the computer-implemented methodologies to a computer. The term article of manufacture, as used herein, is intended to encompass a computer program accessible from any computer-readable device or storage medium.

[0185] Systems and / or devices are described (and / or further described) herein with respect to interactions between one or more components. Such systems and / or components may include those components or subcomponents designated therein, one or more of the designated components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components communicatively coupled to other components rather than being included within a parent component. One or more components and / or subcomponents may be combined into a single component that provides an aggregate function. Components may interact with one or more other components not specifically described herein for brevity but known to those skilled in the art.

[0186] In summary, one or more systems, computer programs, and / or computer-implemented methods provided herein relate to synthetically generating a synthetic spectrum of a target composition. The system may include a memory that stores computer-executable components and a processor that executes the computer-executable components stored in the memory. The computer-executable components may include a determination component that determines a known composition having the same components as the target composition, and a generation component that generates a second synthetic spectrum of the target composition based on the synthetic spectrum of the known composition and the first synthetic spectrum of the target composition.

[0187] Indeed, in view of one or more embodiments described herein, a practical application of one or more of the systems, computer-implemented methods and / or computer programs described herein may be the ability to rapidly and efficiently generate synthetic spectra of target compositions, even when the measured spectrum of such target compositions contains two or more significant peaks. One or more embodiments described herein may improve upon existing approaches to achieve the technical advantage of high accuracy identification of material compositions by using a known composition, and in particular its measured and synthetically generated spectrum, as a baseline for generating a high accuracy spectrum that defines the target composition. Then, by using this synthetically generated spectrum (i.e., not a measured spectrum), the target composition may be accurately identified. This may be accomplished without generating a measured spectrum of the target composition from a sample of established providence, since the providence of the target composition itself has not yet been established.

[0188] Furthermore, in one or more embodiments, the synthetically generated spectra can be used next to known compositions for subsequent identification of the composition of interest, i.e., a library of synthetically derived spectra can be built and used for further material analysis, thus allowing the system to self-improve by becoming more accurate for the analysis and identification of compositions of interest.

[0189] These are useful and practical applications of computers, thus providing enhanced (e.g., improved and / or optimized) data analysis and predictive output. Collectively, such computerized tools can constitute concrete and tangible technological improvements in the field of materials analysis, and more specifically, in materials analysis using EDS.

[0190] Furthermore, one or more embodiments described herein can be used in real-world systems based on the disclosed teachings. For example, as described above, one or more embodiments described herein can accurately identify a target composition and enable synthetic generation of a target composition spectrum even when the measured spectrum of the target composition has multiple significant peaks. Thus, the embodiments disclosed herein provide improvements to scientific instrument technology (e.g., improvements in computer technology supporting such scientific instruments, among others).

[0191] Additionally, the devices and / or methods described herein may be implemented in one or more domains to allow for scaled synthetic spectral output. Indeed, use of the systems described herein may be scalable, such as where multiple input target compositions can be evaluated and multiple resulting synthetic spectra of the target compositions can be generated at least partially simultaneously.

[0192] Systems and / or devices are described (and / or further described) herein with respect to interactions between one or more components. Such systems and / or components may include those components or subcomponents designated therein, one or more of the designated components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components communicatively coupled to other components rather than being included within a parent component. One or more components and / or subcomponents may be combined into a single component that provides an aggregate function. Components may interact with one or more other components not specifically described herein for brevity but known to those skilled in the art.

[0193] One or more embodiments described herein may, in one or more embodiments, be inherently and / or intimately linked to computer technology and cannot be implemented outside of a computing environment. For example, one or more processes performed by one or more embodiments described herein may provide for the execution of programs and / or program instructions more efficiently and more feasibly compared to existing systems and / or techniques and for material analysis based on nearest known compositions and their spectra. Systems, computer-implemented methods and / or computer programs providing the performance of these processes are highly useful in the field of material analysis, such as with EDS, and cannot be equally practically implemented in a reasonable manner outside of a computing environment.

[0194] One or more embodiments described herein may use hardware and / or software to solve problems that are highly technical, not abstract, and cannot be performed as a series of mental acts by a human. For example, one or more embodiments described herein may provide a process so that no human, or even thousands of humans, can efficiently, accurately, and / or effectively automatically determine the closest known composition, generate an initial synthetic spectrum, and / or generate a synthetic spectrum that is obtained in the absence of a known providence material sample. Moreover, neither a human mind nor a human with pen and paper can perform one or more of these processes as performed by one or more embodiments described herein.

[0195] In one or more embodiments, one or more of the processes described herein may be executed by one or more special-purpose computers (e.g., a special-purpose processing unit, a special-purpose classical computer, a special-purpose quantum computer, a special-purpose hybrid classical / quantum system, and / or another type of special-purpose computer) to perform defined tasks associated with one or more of the techniques described above. One or more of the embodiments described herein and / or components thereof may be used to solve new problems that arise due to advances in the technologies described above, quantum computing systems, cloud computing systems, computer architectures, and / or use of other techniques.

[0196] One or more of the embodiments described herein may be fully operational to perform one or more other functions (e.g., fully powered on, fully running, and / or another function) while performing one or more of the operations described herein.

[0197] To provide a further overview, a list of embodiments and their features is provided below.

[0198] The system includes a memory that stores computer-executable components and a processor that executes the computer-executable components stored in the memory, the computer-executable components including a determination component that determines known compositions having the same components as the target composition, and a generation component that generates a second synthetic spectrum of the target composition based on the synthetic spectrum of the known composition and the first synthetic spectrum of the target composition.

[0199] The system of the previous paragraph, wherein the generation component further generates a second synthetic spectrum of the target composition based on the measured spectrum of the known composition.

[0200] The system of any of the preceding paragraphs, wherein the determination component determines that the known composition is a closest composition to the target composition in a set of known compositions that includes the known composition, the closest composition being a composition having a component weight percentage that is closer to the component weight percentage of the target composition than other known compositions in the set of known compositions.

[0201] The system of any of the preceding paragraphs, further including a linear combination component that further determines the known composition based on identifying a linear combination of two or more other known compositions.

[0202] The system of any of the previous paragraphs further includes a synthesis component that synthetically generates a first synthetic spectrum of the target composition by performing steps including determining a first sum of component weight percentages of the components of the target composition based on a weight percentage composition of the components of the target composition, and applying a scale of the components of the target composition to the weight percentage composition of the components of the target composition based on a result of the matrix correction procedure for the first sum, whereby applying generates a scaled synthetic spectrum that is the first synthetic spectrum of the target composition.

[0203] The system of any of the preceding paragraphs further comprising a synthetic component that synthetically generates a synthetic spectrum of the known composition by performing steps including determining a first sum of component weight percentages of the components of the known composition based on a weight percentage composition of the components of the known composition, and applying a component scale of the known composition to the weight percentage composition of the components of the known composition based on a result of the matrix correction procedure to the first sum, whereby applying generates a scaled synthetic spectrum that is a synthetic spectrum of the known composition.

[0204] The system of any of the previous paragraphs, further comprising a database maintenance component that updates data in a database of known compositions including measured and synthetic spectra of known compositions and a second synthetic spectrum of a target composition, wherein the determination component determines the second synthetic spectrum of the target composition as a second known composition for determining a resulting synthetic spectrum of a second target composition.

[0205] The computer-implemented method includes synthesizing, by a system operatively coupled to a processor, a modified synthetic spectrum of the target composition, the system generating, by the system, an intermediate synthetic relationship, the relationship defining a delta of the synthetic spectrum between a synthetic spectrum of the known composition and a first synthetic spectrum of the target composition; aggregating, by the system, the intermediate synthetic relationship and a measured spectrum of the known composition; and generating, by the system, an aggregated synthetic spectrum based on the aggregating, the aggregated synthetic spectrum being the modified synthetic spectrum of the target composition.

[0206] The computer-implemented method of the previous paragraph, wherein the known composition comprises the same components as the subject composition, the same components comprising atomic elements, molecular elements, phases of atomic or molecular elements, or a combination thereof.

[0207] The computer-implemented method of any of the preceding paragraphs, further comprising determining, by the system, the known composition as a closest composition to the target composition in a set of known compositions that includes the known composition, the closest composition being a composition having a component weight percentage that is closer to the component weight percentage of the target composition than other known compositions in the set of known compositions.

[0208] The computer-implemented method of any of the previous paragraphs, wherein generating the intermediate composite relationship includes determining the intermediate composite relationship based on a first photon amount of the portion of the synthetic spectrum of the known composition and a second photon amount of the portion of the first synthetic spectrum of the target composition.

[0209] The computer-implemented method of any of the preceding paragraphs, wherein the aggregating further includes aggregating a third photon amount of the intermediate composite relationship portion and a fourth photon amount of the measured spectrum portion of the known composition.

[0210] The computer-implemented method of any of the preceding paragraphs, further comprising verifying by the system that a delta of the component weight percentages of the known composition and the component weight percentages of the target composition satisfies a selected threshold that defines a selected compositional relationship between the known composition and the target composition.

[0211] The computer-implemented method of any of the preceding paragraphs, wherein synthesizing the spectrum of the target composition further includes determining, by the system, a delta as an output of an error score of the component weight percentage difference between the components of the target composition and the components of the known composition.

[0212] 11. A computer program that facilitates a process for synthesizing a spectrum of a target composition, the program instructions being executable by a processor, causing the processor to synthesize a spectrum of the target composition, where synthesizing includes causing the processor to determine a first photon amount delta of a portion of the synthetic spectrum of a known composition and a second photon amount delta of the portion of the first synthetic spectrum of the target composition, resulting in and determining an intermediate synthesis relationship, aggregating a third photon amount of the portion of the intermediate synthesis relationship and a fourth photon amount of the portion of the measured spectrum of the known composition, and generating a second synthetic spectrum of the target composition based on the result of the aggregation.

[0213] The computer program of the previous paragraph, wherein the portions of the synthetic spectrum of the known composition represent different energy ranges of the synthetic spectrum of the known composition, the portions of the synthetic spectrum of the target composition represent different energy ranges of the synthetic spectrum of the target composition, the portions of the intermediate synthetic relations represent different energy ranges of the intermediate synthetic relations, and the portions of the measured spectrum of the known composition represent different energy ranges of the measured spectrum of the known composition.

[0214] The computer program of any of the previous paragraphs, wherein determining the delta includes determining a difference between a first array of numbers indexed across a set of energy ranges and corresponding to a first photon amount of a portion of the synthetic spectrum of the known composition, and a second array of numbers indexed across a set of energy ranges and corresponding to a second photon amount of a portion of the first synthetic spectrum of the target composition.

[0215] The computer program of any of the preceding paragraphs, wherein the aggregating includes determining an aggregation of a third array of numbers indexed across the set of energy ranges and corresponding to a third photon amount of the portion of the intermediate composite relationship, and a fourth array of numbers indexed across the set of energy ranges and corresponding to a fourth photon amount of the portion of the measured spectrum of known composition.

[0216] The computer program of any of the preceding paragraphs, wherein the measured spectrum is measured from an established providence sample of known composition.

[0217] The program instructions are further executable by the processor to cause the processor to: determining the known composition as a nearest neighbor of a set of known compositions, including the known composition, to the target composition; The computer program of any of the preceding paragraphs, wherein the closest composition is a composition having a component weight percentage closer to the component weight percentage of the target composition than other known compositions in the set of known compositions.

[0218] Scientific Instrument System Description Referring now to FIG. 18, a detailed description of additional context for one or more embodiments described herein in FIGS. 1-17 is provided. One or more computing devices implementing any of the scientific instrument modules or methods disclosed herein may be part of a scientific instrument system. FIG. 18 illustrates a block diagram of an exemplary scientific instrument system 1800 capable of performing one or more of the scientific instrument methods disclosed herein or other methods, according to various embodiments described herein. The scientific instrument modules and methods disclosed herein (e.g., scientific instrument module 100 of FIG. 1 and method 200 of FIG. 2) can be implemented by one or more of the scientific instrument 1810, user local computing device 1820, service local computing device 1830, or remote computing device 1840 of the scientific instrument system 1800.

[0219] Any of the scientific instrument 1810, the user local computing device 1820, the service local computing device 1830, or the remote computing device 1840 may include any of the embodiments of the computing device 400 described herein with reference to FIG. 4, and any of the scientific instrument 1810, the user local computing device 1820, the service local computing device 1830, and / or the remote computing device 1840 may take the form of one or more suitable embodiments of the computing device 400 described herein with reference to FIG. 4.

[0220] One or more of the scientific instruments 1810, the user local computing device 1820, the service local computing device 1830, and / or the remote computing device 1840 may include a processing device 1802, a storage device 1804, and / or an interface device 1806. The processing device 1802 may take any suitable form, including any form of the processor 402 described herein with reference to FIG. 4. The processing devices 1802 included in different ones of the scientific instruments 1810, the user local computing device 1820, the service local computing device 1830, and / or the remote computing device 1840 may take the same form or different forms. The storage device 1804 may take any suitable form, including any form of the storage device 404 described herein with reference to FIG. 4. The storage devices 1804 included in different ones of the scientific instruments 1810, the user local computing device 1820, the service local computing device 1830, and / or the remote computing device 1840 may take the same form or different forms. The interface device 1806 may take any suitable form, including any of the forms of the interface device 406 described herein with reference to Figure 4. The interface devices 1806 included in different ones of the scientific instrument 1810, the user local computing device 1820, the service local computing device 1830, and / or the remote computing device 1840 may take the same or different forms.

[0221] The scientific instruments 1810, the user local computing device 1820, the service local computing device 1830, and the remote computing device 1840 may communicate with other elements of the scientific instrument system 1800 via communication paths 1808. As shown, the communication paths 1808 may communicatively couple the interface devices 1806 of different ones of the elements of the scientific instrument system 1800, and may be wired or wireless communication paths (e.g., in accordance with any of the communication techniques described herein with reference to the interface device 406 of the computing device 400 of FIG. 4). Although the particular scientific instrument system 1800 shown in FIG. 18 includes communication paths between each pair of the scientific instruments 1810, the user local computing device 1820, the service local computing device 1830, and the remote computing device 1840, this "fully connected" implementation is merely exemplary, and various ones of the communication paths 1808 may be omitted in various embodiments. For example, in one or more embodiments, the service local computing device 1830 may omit the direct communication path 1808 between its interface device 1806 and the interface device 1806 of the scientific instrument 1810, but instead may communicate with the scientific instrument 1810 via a communication path 1808 between the service local computing device 1830 and the user local computing device 1820 and / or a communication path 1808 between the user local computing device 1820 and the scientific instrument 1810.

[0222] The scientific instrument 1810 may include any suitable scientific instrument, such as an EDS device or a device associated with or connected to an EDS device.

[0223] The user local computing device 1820 may be a computing device (e.g., according to any of the embodiments of computing device 400 described herein) that is near the user of the scientific instrument 1810. In one or more embodiments, the user local computing device 1820 may also be near the scientific instrument 1810, but need not be, e.g., a user local computing device 1820 associated with a home, office, or other building associated with a user entity may be remote from but in communication with the scientific instrument 1810 such that the user entity may control and / or access data from the scientific instrument 1810 using the user local computing device 1820. In one or more embodiments, the user local computing device 1820 may be a laptop, smartphone, or tablet device. In one or more embodiments, the user local computing device 1820 may be a portable computing device. In one or more embodiments, the user local computing device 1820 may be deployed in the field.

[0224] The service local computing device 1830 may be a computing device (e.g., according to any of the embodiments of computing device 400 described herein) that is proximate to an entity that provides a service to the scientific instrument 1810. For example, the service local computing device 1830 may be proximate to a manufacturer of the scientific instrument 1810 or a third party service company. In one or more embodiments, the service local computing device 1830 may communicate with the scientific instrument 1810, the user local computing device 1820, and / or the remote computing device 1840 (e.g., via a direct communication path 1808 or via multiple "indirect" communication paths 1808 as previously described) to receive data regarding the operation of the scientific instrument 1810, the user local computing device 1820, and / or the remote computing device 1840 (e.g., results of self-diagnostic tests of the scientific instrument 1810, calibration coefficients used by the scientific instrument 1810, measurements of sensors associated with the scientific instrument 1810, etc.). In one or more embodiments, the service local computing device 1830 can communicate with the scientific instrument 1810, the user local computing device 1820, and / or the remote computing device 1840 (e.g., via a direct communication path 1808 or multiple "indirect" communication paths 1808, as described above) and transmit data to the scientific instrument 1810, the user local computing device 1820, and / or the remote computing device 1840 (e.g., to update programmed instructions, such as firmware, in the scientific instrument 1810, to initiate the execution of a test or calibration sequence in the scientific instrument 1810, to update programmed instructions, such as software, in the user local computing device 1820 or the remote computing device 1840, etc.).A user entity of the scientific instrument 1810 may utilize the scientific instrument 1810 or user local computing device 1820 to communicate with the service local computing device 1830 to report problems with the scientific instrument 1810 or user local computing device 1820, to request a technician visit to improve the operation of the scientific instrument 1810, to order consumables or replacement parts associated with the scientific instrument 1810, or for other purposes.

[0225] The remote computing device 1840 may be a computing device (e.g., according to any of the embodiments of computing device 400 described herein) that is located remotely from the scientific instrument 1810 and / or from the user local computing device 1820. In one or more embodiments, the remote computing device 1840 may be included in a data center or other large-scale server environment. In one or more embodiments, the remote computing device 1840 may include a network-attached storage device (e.g., as part of the storage device 1804). The remote computing device 1840 may store data generated by the scientific instrument 1810, perform analysis of data generated by the scientific instrument 1810 (e.g., according to programmed instructions), facilitate communications between the user local computing device 1820 and the scientific instrument 1810, and / or facilitate communications between the service local computing device 1830 and the scientific instrument 1810.

[0226] In one or more embodiments, one or more of the elements of the scientific instrument system 1800 shown in FIG. 18 may be omitted. Additionally, in one or more embodiments, multiple elements of various ones of the elements of the scientific instrument system 1800 of FIG. 18 may be present. For example, the scientific instrument system 1800 may include multiple user local computing devices 1820 (e.g., different user local computing devices 1820 associated with different user entities or different locations). In another example, the scientific instrument system 1800 may include multiple scientific instruments 1810 that are all in communication with a servicing local computing device 1830 and / or a remote computing device 1840. In such an embodiment, the servicing local computing device 1830 may monitor these multiple scientific instruments 1810, and the servicing local computing device 1830 may cause updates or other information to be "broadcast" to the multiple scientific instruments 1810 at the same time. Different scientific instruments 1810 in the scientific instrument system 1800 may be located near each other (e.g., in the same room) or far from each other (e.g., different floors of a building, different buildings, different cities, etc.). In one or more embodiments, the scientific instruments 1810 may be connected to an Internet of Things (IoT) stack that allows for command and control of the scientific instruments 1810 through web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications may be accessed by a user entity operating a user local computing device 1820 that is in communication with the scientific instruments 1810 through an intervening remote computing device 1840. In one or more embodiments, the scientific instruments 1810 may be sold by a manufacturer together with one or more associated user local computing devices 1820 as part of a local scientific instrument computing unit 1812.

[0227] In one or more embodiments, different ones of the scientific instruments 1810 included in the scientific instrument system 1800 may be different types of scientific instruments 1810. For example, one scientific instrument 1810 may be an EDS device and another scientific instrument 1810 may be an analytical device that analyzes the results of the EDS device. In some such embodiments, the remote computing device 1840 and / or the user local computing device 1820 may combine data from the different types of scientific instruments 1810 included in the scientific instrument system 1800.

[0228] Example Operating Environment 19 is a schematic block diagram of an operating environment 1900 with which the described subject matter can interact. The operating environment 1900 includes one or more remote components 1910. The remote components 1910 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, the remote components 1910 can be a distributed computer system connected via a communication framework 1940 to a local autoscaling component and / or a program that uses resources of the distributed computer system. The communication framework 1940 can include wired network devices, wireless network devices, mobile devices, wearable devices, wireless access network devices, gateway devices, femtocell devices, servers, etc.

[0229] The operating environment 1900 also includes one or more local components 1920. The local components 1920 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, the local components 1920 can include auto-scaling components and / or programs that communicate / use remote resources 1910 and 1920, etc., connected to a remotely located distributed computing system via a communication framework 1940.

[0230] Possible communication between the remote component 1910 and the local component 1920 may be in the form of data packets adapted to be transmitted between two or more computer processes. Another possible communication between the remote component 1910 and the local component 1920 may be in the form of circuit-switched data adapted to be transmitted between two or more computer processes in a radio time slot. The operating environment 1900 comprises a communication framework 1940 that can be used to facilitate communication between the remote component 1910 and the local component 1920 and can comprise an air interface, such as an interface of a UMTS network over an LTE network. The remote component 1910 can be operatively connected to one or more remote data stores 1950, such as a hard drive, solid state drive, subscriber identity module (SIM) card, electronic SIM (eSIM), device memory, etc. that can be used to store information on the remote component 1910 side of the communication framework 1940. Similarly, the local component 1920 can be operatively connected to one or more local data stores 1930 that can be used to store information on the local component 1920 side of the communication framework 1940.

[0231] Exemplary Computing Environment To provide further context for the various embodiments described herein, Figure 20 and the following discussion are intended to provide a brief, general description of a suitable computing environment 2000 in which various embodiments of the embodiments described herein may be implemented. Although the embodiments have been described above in the general context of computer-executable instructions that may be executed on one or more computers, those skilled in the art will recognize that the embodiments may also be implemented in combination with other program modules and / or as a combination of hardware and software.

[0232] Generally, program modules include routines, programs, components, data structures, etc. that perform tasks or implement abstract data types. Furthermore, the methods may be practiced with other computer system configurations including single or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively coupled to one or more associated devices.

[0233] The illustrated embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0234] A computing device typically includes various media that may include computer-readable storage media, machine-readable storage media, and / or communication media, and these two terms are used differently from each other in this specification as follows. A computer-readable storage medium or machine-readable storage medium may be any available storage medium that can be accessed by a computer, and includes both volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, a computer-readable storage medium or machine-readable storage medium may be implemented in association with any method or technology for storing information, such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0235] A computer-readable storage medium may include, but is not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD ROM), digital versatile disk (DVD), Blu-ray disk (BD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device, solid state drive or other solid state storage device, or other tangible and / or non-transitory medium that can be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to a storage device, memory, or computer-readable medium as used herein as a modifier exclude only the propagating transitory signal itself, and do not waive the right to all standard storage devices, memories, or computer-readable media that do not merely propagate the transitory signal itself.

[0236] The computer-readable storage medium can be accessed by one or more local or remote computing devices for various operations on the information stored by the medium, for example, via access requests, queries, or other data retrieval protocols.

[0237] Communication media typically embodies computer readable instructions, data structures, program modules, or other structured or unstructured data in a data signal, such as a modulated data signal, e.g., carrier wave or other transport mechanism, and includes any information delivery or transmission media. The term "modulated data signal" refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal or signals. By way of example, and not limitation, communication media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared and other wireless media.

[0238] 20, an exemplary computing environment 2000 in which one or more embodiments described herein may be implemented includes a computer 2002 including a processing unit 2004, a system memory 2006, and a system bus 2008. The system bus 2008 couples system components including, but not limited to, the system memory 2006 to the processing unit 2004. The processing unit 2004 may be any of a variety of commercially available processors. Dual microprocessors and other multi-processor architectures may also be used as the processing unit 2004.

[0239] The system bus 2008 can be any of several types of bus structures that can be further interconnected to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 2006 includes a ROM 2010 and a RAM 2012. A basic input / output system (BIOS) can be stored in non-volatile memory such as a ROM, an erasable programmable read-only memory (EPROM), an EEPROM, etc., and the BIOS contains the basic routines that help to transfer information between elements within the computer 2002, such as during start-up. The RAM 2012 can also include a high-speed RAM, such as a static RAM, for caching data.

[0240] The computer 2002 further includes an internal hard disk drive (HDD) 2014 (e.g., EIDE, SATA) and may include one or more external storage devices 2016 (e.g., a magnetic floppy disk drive (FDD) 2016, a memory stick or flash drive reader, a memory card reader, etc.). Although the internal HDD 2014 is shown disposed within the computer 2002, the internal HDD 2014 may also be configured for external use in a suitable chassis (not shown). Additionally, although not shown in the computing environment 2000, a solid state drive (SSD) may be used in addition to or in place of the HDD 2014.

[0241] The other internal or external storage devices may include at least one other storage device 2020 having a storage medium 2022 (e.g., a solid-state storage device, a non-volatile memory device, and / or an optical disk drive that can read and write from removable media such as CD-ROM disks, DVDs, BDs, etc.). The external storage device 2016 may be realized by a networked virtual machine. The HDD 2014, the external storage device 2016, and the storage device (e.g., drive) 2020 may be connected to the system bus 2008 by a HDD interface 2024, an external storage interface 2026, and a drive interface 2028, respectively.

[0242] The drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of computer 2002, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the above description of computer-readable storage media refers to each type of storage device, other types of storage media readable by a computer, whether currently existing or developed in the future, may also be used in the exemplary operating environment, and further, any such storage media may include computer-executable instructions for performing the methods described herein.

[0243] A number of program modules may be stored in the drives and RAM 2012, including an operating system 2030, one or more application programs 2032, other program modules 2034, and program data 2036. All or portions of the operating system, applications, modules, and / or data may also be cached in RAM 2012. The systems and methods described herein may be implemented using various commercially available operating systems or combinations of operating systems.

[0244] The computer 2002 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate the hardware environment of the operating system 2030, where the emulated hardware may optionally differ from the hardware shown in FIG. 20. In such an embodiment, the operating system 2030 may comprise a virtual machine (VM) of a number of VMs hosted on the computer 2002. Additionally, the operating system 2030 may provide a runtime environment, such as the Java Runtime Environment or the .NET Framework, for the application 2032. The runtime environment is a consistent execution environment that allows the application 2032 to run on any operating system that includes the runtime environment. Similarly, the operating system 2030 may support containers, where the application 2032 may be in the form of a container, which is a lightweight, standalone, executable package of software that includes, for example, code, runtime, system tools, system libraries, and settings for the application.

[0245] Additionally, computer 2002 can be enabled with a security module, such as a Trusted Processing Module (TPM). For example, in a TPM, a boot component may hash the boot component in time and wait for the result to match a secure value before loading the next boot component. This process can occur at any layer in the code execution stack of computer 2002, for example, applied at the application execution level or at the operating system (OS) kernel level, thereby enabling security at any level of code execution.

[0246] A user entity may enter commands and information into the computer 2002 via one or more wired / wireless input devices, such as a keyboard 2038, a touch screen 2040, and a pointing device, such as a mouse 2042. Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and / or a virtual reality headset, a game pad, a stylus pen, an image input device, such as a camera, a gesture sensor input device, a visual motion sensor input device, an emotion or face detection device, a biometric input device, such as a fingerprint or iris scanner, etc. These and other input devices are often connected to the processing unit 2004 via an input device interface 2044, which may be coupled to the system bus 2008, but may also be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH interface, etc.

[0247] A monitor 2046 or other type of display device can also be connected to the system bus 2008 via an interface, such as a video adapter 2048. In addition to the monitor 2046, computers typically include other peripheral output devices (not shown), such as speakers, printers, etc.

[0248] The computer 2002 can operate in a networked environment using logical connections via wired and / or wireless communication to one or more remote computers, such as a remote computer 2050. The remote computer 2050 can be a workstation, a server computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other common network node, and typically includes many or all of the elements described above with respect to the computer 2002, although for simplicity, only a memory / storage device 2052 is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) 2054 and / or a wide area network (e.g., a wide area network (WAN) 2056). Such LAN and WAN networking environments are commonplace in offices and businesses, facilitating enterprise-wide computer networks, such as intranets, all of which can be connected to a global communications network, such as the Internet.

[0249] When used in a LAN networking environment, the computer 2002 can be connected to the local network 2054 via a wired and / or wireless communication network interface or adapter 2058. The adapter 2058 can facilitate wired or wireless communication to the LAN 2054, and the LAN can also include a wireless access point (AP) disposed thereon for communicating with the adapter 2058 in a wireless mode.

[0250] When used in a WAN networking environment, the computer 2002 may include a modem 2060 or may be connected via other means for establishing communications over the WAN 2056, such as the Internet, to a communications server on the WAN 2056. The modem 2060 may be internal or external and a wired or wireless device and may be connected to the system bus 2008 via the input device interface 2044. In a networked environment, program modules depicted relative to the computer 2002, or portions thereof, may be stored in the remote memory / storage device 2052. The network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

[0251] When used in either a LAN or WAN networking environment, the computer 2002 can access a cloud storage system or other network-based storage systems in addition to or in place of the external storage device 2016, as described above. In general, a connection between the computer 2002 and a cloud storage system can be established via the LAN 2054 or WAN 2056, for example, by an adapter 2058 or modem 2060, respectively. Upon connecting the computer 2002 to an associated cloud storage system, the external storage interface 2026, with the aid of the adapter 2058 and / or modem 2060, can manage the storage devices provided by the cloud storage system as well as other types of external storage devices. For example, the external storage interface 2026 can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer 2002.

[0252] The computer 2002 may be operable to communicate with any wireless device or entity operatively disposed in wireless communication, such as printers, scanners, desktop and / or portable computers, portable data assistants, communications satellites, any equipment or location associated with a radio detectable tag (e.g., a kiosk, a newsstand, a store shelf, etc.), and a telephone. This may include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, communication may be a defined structure similar to an existing network, or simply an ad-hoc communication between at least two devices.

[0253] Additional Closing Information The embodiments described herein may be directed to one or more of a system, a method, an apparatus, and / or a computer program at any possible level of technical detail integration. A computer program may include a computer readable storage medium having computer readable program instructions for causing a processor to execute aspects of one or more of the embodiments described herein. A computer readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a superconducting storage device, and / or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media can also include: portable computer diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), static random access memories (SRAMs), portable compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or raised structures in grooves on which instructions are recorded, and / or any suitable combination of the above. As used herein, computer-readable storage media should not be construed as being ephemeral signals per se, such as electric signals transmitted over wires, electromagnetic waves and / or other freely propagating electromagnetic waves, electromagnetic waves propagating through wave guides and / or other transmission media (e.g., light pulses passing through fiber optic cables), and / or other signals transmitted over wires.

[0254] The computer readable program instructions described herein can be downloaded from a computer readable storage medium to the respective computing / processing device and / or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical transmission fiber, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing / processing device. The computer readable program instructions for performing the operations of one or more embodiments described herein can be source code and / or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, and / or object oriented programming languages, such as Smalltalk, C++, and / or procedural programming languages, such as the "C" programming language and / or similar programming languages. The computer readable program instructions may execute entirely on the computer, partially on the computer, as a stand-alone software package, partially on the computer, and / or partially on a remote computer, or entirely on a remote computer and / or server. In the latter scenario, the remote computer may be connected to the computer via any type of network, including a local area network (LAN) and / or a wide area network (WAN), and / or may be connected to an external computer (e.g., via the Internet using an Internet Service Provider).In one or more embodiments, electronic circuitry including, for example, programmable logic circuitry, field programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs) may execute computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry to perform aspects of one or more embodiments described herein.

[0255] Aspects of one or more embodiments described herein will be described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer programs according to one or more embodiments described herein. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, and / or other programmable data processing apparatus to produce a machine, and the instructions executed via the processor of the computer or other programmable data processing apparatus can create means for implementing the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions can also be stored in a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that a computer-readable storage medium having instructions stored thereon can include a product including instructions that can implement aspects of the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams. The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, and / or other device to cause the computer, other programmable apparatus, and / or other device to perform a series of operable operations to generate a computer-implemented process, such that the instructions, executing on the computer, other programmable data processing apparatus, and / or other device, implement the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0256] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and / or operation of possible implementations of systems, computer-executable methods, and / or computer programs according to one or more embodiments described herein. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, and / or portion of an instruction, which includes one or more executable instructions for implementing a specified logical function or functions. In one or more alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the figures. For example, two blocks shown in succession may be executed substantially simultaneously and / or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, and / or combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented by a dedicated hardware-based system capable of executing the specified functions and / or operations and / or one or more combinations of dedicated hardware and / or computer instructions.

[0257] Although the subject matter has been described above in the general context of computers and / or computer-executable instructions for computer programs running on computers, those skilled in the art will appreciate that one or more embodiments herein may also be implemented at least partially in parallel with one or more other program modules. Generally, a program module includes routines, programs, components, and / or data structures that perform particular tasks and / or implement particular abstract data types. Furthermore, the computer-implemented methods described above may be practiced with single-processor and / or multi-processor computer systems, mini-computing devices, mainframe computers, and other computer system configurations including computers, handheld computing devices (e.g., PDAs, phones), and / or microprocessor-based or programmable consumer and / or industrial electronic devices. The illustrated aspects may also be practiced in distributed computing environments where tasks are performed by remote processing devices linked through a communications network. However, one or more aspects, if not all aspects, of one or more of the embodiments described herein may be practiced on stand-alone computers. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0258] As used in this application, the terms "component," "system," "platform," and / or "interface" may refer to and / or include a computer-related entity or entity associated with an operating machine having one or more specific functions. The entities described herein may be either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, a thread of execution, a program, and / or a computer. As an example, both an application running on a server and the server may be a component. One or more components may reside within a process and / or thread of execution, and a component may be localized on one computer and / or distributed between two or more computers. In another example, each component may execute from various computer readable media having various data structures stored thereon. The components may communicate via local and / or remote processes, such as according to signals having one or more data packets (e.g., data from one component interacting with another system via a network such as a local system, a distributed system, and / or the Internet via signals). As another example, a component may be a device having a particular functionality provided by mechanical parts operated by electrical or electronic circuitry operated by software and / or firmware applications executed by a processor. In such cases, the processor may be internal and / or external to the device and may execute at least a portion of the software and / or firmware applications. As yet another example, a component may be a device that provides a particular functionality without mechanical parts through electronic components, where the electronic components may include a processor and / or other means for executing software and / or firmware that at least partially provides the functionality of the electronic components.In one aspect, the component may emulate an electronic component via a virtual machine, for example, in a cloud computing system.

[0259] As used herein, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X uses A or B" means all natural inclusive permutations. That is, if X uses A, then X uses B, or if X uses both A and B, then "X uses A or B" is satisfied in any of the foregoing cases. Furthermore, the articles "a" and "an" as used in this specification and the accompanying drawings should be generally interpreted to mean "one or more" unless otherwise specified or clear from the context to indicate the singular form. As used herein, the terms "example" and / or "exemplary" are utilized to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter described herein is not limited by such examples. Moreover, any aspect or design described herein as "example" and / or "exemplary" should not necessarily be construed as preferred or advantageous over other aspects or designs, nor is it meant to exclude equivalent exemplary structures and techniques known to those skilled in the art.

[0260] As used herein, the term "processor" may refer to substantially any computing processing unit and / or device, including, but not limited to, a single-core processor, a single processor with software multi-threaded execution capability, a multi-core processor, a multi-core processor with software multi-threaded execution capability, a multi-core processor with hardware multi-threading technology, a parallel platform, and / or a parallel platform with distributed shared memory. Additionally, a processor may refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic, discrete hardware components, and / or any combination thereof designed to perform the functions described herein. Additionally, a processor may utilize nanoscale architectures, such as, but not limited to, molecular and quantum dot-based transistors, switches, and / or gates, to optimize space usage and / or improve the performance of associated devices. A processor may be implemented as a combination of computing processing units.

[0261] As used herein, terms such as "store," "memory," "data store," "data storage," "database," and substantially any other information storage component associated with the operation and functionality of a component are utilized to refer to a "memory component," an entity embodied in a "memory," or a component that includes a memory. The memory and / or memory components described herein may be either volatile or non-volatile memory, or may include both volatile and non-volatile memory. By way of example and not limitation, non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory may include, for example, RAM, which may function as an external cache memory. By way of example, and not limitation, RAM may come in many forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SyncLink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and / or Rambus dynamic RAM (RDRAM). Additionally, the memory components of the systems and / or computer-implemented methods described herein are intended to include, but are not limited to, these and / or any other suitable types of memory.

[0262] The foregoing includes merely examples of systems and computer-implemented methods. Of course, for purposes of describing one or more embodiments, it is not possible to describe every possible combination of components and / or computer-implemented methods, but one of ordinary skill in the art can recognize that many further combinations and / or permutations of one or more embodiments are possible. Moreover, to the extent that terms such as "includes," "has," "possesses," and the like are used in the detailed description, claims, appendices, and / or drawings, such terms are intended to be as inclusive as the term "comprising" as "comprising" is interpreted when used as a transitional term in the claims.

[0263] The description of various embodiments may use the phrases "one embodiment," "various embodiments," "one or more embodiments," and / or "some embodiments," each of which may refer to one or more of the same or different embodiments.

[0264] The description of various embodiments is presented for illustrative purposes, but is not intended to be exhaustive or limited to the embodiments described herein. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terms used herein have been selected to best explain the principles of the embodiments, practical applications and / or technical improvements to the art found in the marketplace, and / or to enable those skilled in the art to understand the embodiments described herein.

Claims

1. 1. A system comprising: a memory for storing computer executable components; a processor for executing the computer-executable components stored in the memory, the computer-executable components comprising: a determination component for determining a known composition having the same components as the composition of interest; a generation component that generates a second synthetic spectrum of the target composition based on the synthetic spectra of the known compositions and the first synthetic spectrum of the target composition; Including, the system.

2. The system of claim 1 , wherein the generation component further generates the second synthetic spectrum of the target composition based on a measured spectrum of the known composition.

3. the determination component determines the known composition as a closest composition to the target composition in a set of known compositions that includes the known composition; 2. The system of claim 1, wherein the closest composition is a composition having a component weight percentage that is closer to the component weight percentage of the target composition than other known compositions in the set of known compositions.

4. The system of claim 1 , further comprising a linear combination component that further determines the known composition based on identifying a linear combination of two or more other known compositions.

5. a synthesis component for synthetically generating the first synthetic spectrum of the target composition, determining a first sum of component weight percents of the components of the target composition based on the weight percent compositions of the components of the target composition; applying a scale of the components of the target composition to the weight percent composition of the components of the target composition based on the results of a matrix correction procedure for the first sum, thereby synthetically generating a first synthetic spectrum of the target composition; The system of claim 1 , wherein said applying causes generation of a scaled synthetic spectrum, said first synthetic spectrum of said target composition.

6. determining a first sum of component weight percents of the components of the known composition based on the weight percent compositions of the components of the known composition; applying a scale of the components of the known composition to the weight percent composition of the components of the known composition based on the results of a matrix correction procedure for the first sum, thereby synthetically generating a synthetic spectrum of the known composition; The system of claim 1 , wherein said applying causes the generation of a scaled synthetic spectrum that is the synthetic spectrum of the known composition.

7. a database maintenance component that updates data in the database of known compositions, the data including the measured spectra and the synthetic spectra of the known compositions and the second synthetic spectrum of the target composition; The system of claim 1 , wherein the determination component determines the second composite spectrum of the target composition as a second known composition for determining a resultant composite spectrum of a second target composition.

8. 1. A computer-implemented method comprising: synthesizing, by a system operatively coupled to a processor, a modified synthetic spectrum of the target composition, generating an intermediate synthetic relationship by the system, the intermediate synthetic relationship being a relationship defining a synthetic spectral delta between a synthetic spectrum of a known composition and a first synthetic spectrum of the target composition; aggregating, by the system, the intermediate composition relationships and the measured spectra of the known compositions; and generating, by the system, an aggregated synthetic spectrum based on the aggregating, the aggregated synthetic spectrum being the modified synthetic spectrum of the target composition.

9. 10. The computer-implemented method of claim 8, wherein the known composition comprises the same components as the target composition, the same components comprising atomic elements, molecular elements, phases of atomic or molecular elements, or a combination thereof.

10. determining, by the system, the known composition as a closest neighbor to the target composition in a set of known compositions that includes the known composition; 9. The computer-implemented method of claim 8, wherein the closest composition is a composition having component weight percentages that are closer to the component weight percentages of the target composition than other known compositions in the set of known compositions.

11. 9. The computer-implemented method of claim 8, wherein generating the intermediate composition relationship comprises determining the intermediate composition relationship based on a first photon amount of the portion of the synthetic spectrum of the known composition and a second photon amount of the portion of the first synthetic spectrum of the target composition.

12. 10. The computer-implemented method of claim 8, wherein the aggregating further comprises aggregating a third photon amount of the intermediate composite relationship portion and a fourth photon amount of the measured spectrum portion of the known composition.

13. 10. The computer-implemented method of claim 8, further comprising verifying by the system that a delta of component weight percentages of the known composition and the target composition satisfies a selected threshold that defines a selected compositional relationship between the known composition and the target composition.

14. said synthesizing said modified synthetic spectrum of said target composition further comprises:

14. The computer-implemented method of claim 13, further comprising determining, by the system, the delta as an output of an error score of a component weight percent difference between the components of the target composition and the components of the known compositions.

15. 1. A computer program that facilitates a process for synthesizing a spectrum of a target composition, the program instructions being executable by a processor, the processor comprising: synthesizing a spectrum of a target composition, said synthesizing comprising: determining a first photon amount delta of a portion of a composite spectrum of a known composition and a second photon amount delta of a portion of a first composite spectrum of the target composition, resulting in and determining an intermediate composite relationship; aggregating a third amount of photons in the intermediate composition relationship portion and a fourth amount of photons in the measured spectrum portion of the known composition; generating a second composite spectrum of the target composition based on results of the aggregation.

16. the portions of the composite spectrum of the known compositions represent different photon energy ranges of the composite spectrum of the known compositions; the portions of the composite spectrum of the target composition represent different photon energy ranges of the composite spectrum of the target composition; the portions of the intermediate composition relationship represent different photon energy ranges of the intermediate composition relationship; 16. The computer program of claim 15, wherein the portions of the measured spectrum of the known composition represent different photon energy ranges of the measured spectrum of the known composition.

17. 16. The computer program product of claim 15, wherein determining the delta comprises determining a difference between a first array of numbers indexed across a set of energy ranges and corresponding to the first photon amount of the portion of the synthetic spectrum of the known composition and a second array of numbers indexed across the set of energy ranges and corresponding to the second photon amount of the portion of the first synthetic spectrum of the target composition.

18. 18. The computer program product of claim 17, wherein the aggregating comprises determining an aggregation of a third array of numbers indexed across the set of energy ranges and corresponding to the third photon amount of the portion of the intermediate composite relationship, and a fourth array of numbers indexed across the set of energy ranges and corresponding to the fourth photon amount of the portion of the measured spectrum of the known composition.

19. 20. The computer program of claim 18, wherein the measured spectrum is measured from an established providence sample of the known composition.

20. The program instructions are further executable by the processor to cause the processor to: determining the known composition as a nearest neighbor of a set of known compositions that includes the known composition to the target composition; 16. The computer program of claim 15, wherein the closest composition is a composition having a component weight percentage that is closer to the component weight percentage of the target composition than other known compositions in the set of known compositions.

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