CN111505101B - Uranium ore producing area classification method based on principal component analysis - Google Patents
Uranium ore producing area classification method based on principal component analysis Download PDFInfo
- Publication number
- CN111505101B CN111505101B CN202010352579.3A CN202010352579A CN111505101B CN 111505101 B CN111505101 B CN 111505101B CN 202010352579 A CN202010352579 A CN 202010352579A CN 111505101 B CN111505101 B CN 111505101B
- Authority
- CN
- China
- Prior art keywords
- uranium ore
- principal component
- sample
- component analysis
- classifying
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 229910052770 Uranium Inorganic materials 0.000 title claims abstract description 112
- JFALSRSLKYAFGM-UHFFFAOYSA-N uranium(0) Chemical compound [U] JFALSRSLKYAFGM-UHFFFAOYSA-N 0.000 title claims abstract description 112
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000000513 principal component analysis Methods 0.000 title claims abstract description 23
- 239000000523 sample Substances 0.000 claims abstract description 66
- 229910052761 rare earth metal Inorganic materials 0.000 claims abstract description 34
- 239000012488 sample solution Substances 0.000 claims abstract description 27
- 238000004519 manufacturing process Methods 0.000 claims abstract description 9
- 238000009616 inductively coupled plasma Methods 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims description 20
- 229910052702 rhenium Inorganic materials 0.000 claims description 18
- WUAPFZMCVAUBPE-UHFFFAOYSA-N rhenium atom Chemical compound [Re] WUAPFZMCVAUBPE-UHFFFAOYSA-N 0.000 claims description 18
- 238000009826 distribution Methods 0.000 claims description 13
- 239000000843 powder Substances 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000000576 coating method Methods 0.000 claims description 9
- 239000011248 coating agent Substances 0.000 claims description 8
- 238000010438 heat treatment Methods 0.000 claims description 8
- -1 polytetrafluoroethylene Polymers 0.000 claims description 8
- 229920001343 polytetrafluoroethylene Polymers 0.000 claims description 8
- 239000004810 polytetrafluoroethylene Substances 0.000 claims description 8
- QWPPOHNGKGFGJK-UHFFFAOYSA-N hypochlorous acid Chemical compound ClO QWPPOHNGKGFGJK-UHFFFAOYSA-N 0.000 claims description 7
- 239000002245 particle Substances 0.000 claims description 5
- 239000002253 acid Substances 0.000 claims description 4
- 239000012159 carrier gas Substances 0.000 claims description 4
- 238000004090 dissolution Methods 0.000 claims description 4
- 239000007789 gas Substances 0.000 claims description 4
- 238000001095 inductively coupled plasma mass spectrometry Methods 0.000 claims description 4
- 238000005303 weighing Methods 0.000 claims description 4
- 238000005485 electric heating Methods 0.000 claims description 3
- 238000000227 grinding Methods 0.000 claims description 3
- 239000011824 nuclear material Substances 0.000 abstract description 10
- 238000004364 calculation method Methods 0.000 abstract 1
- 238000007619 statistical method Methods 0.000 abstract 1
- 150000002500 ions Chemical class 0.000 description 19
- 238000005259 measurement Methods 0.000 description 11
- 230000000875 corresponding effect Effects 0.000 description 9
- 235000013619 trace mineral Nutrition 0.000 description 6
- 239000011573 trace mineral Substances 0.000 description 6
- 230000001186 cumulative effect Effects 0.000 description 4
- 239000000203 mixture Substances 0.000 description 4
- 230000000630 rising effect Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 229910052765 Lutetium Inorganic materials 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000029087 digestion Effects 0.000 description 2
- 238000005194 fractionation Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000012857 radioactive material Substances 0.000 description 2
- 239000004215 Carbon black (E152) Substances 0.000 description 1
- 229910052693 Europium Inorganic materials 0.000 description 1
- 229910052779 Neodymium Inorganic materials 0.000 description 1
- 241000337007 Oceania Species 0.000 description 1
- 229910052772 Samarium Inorganic materials 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 238000004821 distillation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 229930195733 hydrocarbon Natural products 0.000 description 1
- 150000002430 hydrocarbons Chemical class 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 229910052746 lanthanum Inorganic materials 0.000 description 1
- 238000001819 mass spectrum Methods 0.000 description 1
- 238000005272 metallurgy Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000002133 sample digestion Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000010407 vacuum cleaning Methods 0.000 description 1
- 238000009849 vacuum degassing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
- G01N27/626—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode using heat to ionise a gas
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a uranium ore producing area classification method based on principal component analysis. The method comprises the following steps: (1) Collecting a uranium ore sample, and pretreating to obtain a uranium ore sample solution; (2) Measuring the content of rare earth elements in a uranium ore sample by adopting an inductively coupled plasma mass spectrometer; (3) Analyzing the Nd isotope ratio in the uranium ore sample by adopting a thermal surface ionization mass spectrometer; (4) And (3) performing principal component analysis on the content of the rare earth elements and the Nd isotope ratio, and classifying the uranium ore samples according to production places according to calculation results. Aiming at the problems of nuclear material responsibility tracing and the like, the invention adopts a multivariate statistical analysis method, improves the classification accuracy of the uranium ores and adapts to the requirements of nuclear evidence obtaining application.
Description
Technical Field
The invention relates to the technical field of ore classification, in particular to a uranium ore producing area classification method based on principal component analysis.
Background
In recent years, the nuclear activities of countries and regions around China have become a focus of international attention, countries such as korea, japan, india and pakistan have a sufficient amount of nuclear materials, the risk of the nuclear materials flowing into the surrounding countries and regions is high, china should pay attention to the nuclear diffusion situation of the surrounding regions, and once the nuclear materials are diffused to the country, the nuclear activities are a great hidden danger of national stability and safety. Therefore, mastering the nuclear evidence obtaining technology is beneficial to protecting the nuclear safety benefits of China and maintaining the international reputation of China.
For the reasons, a comprehensive subject involving multiple subjects, namely the nuclear evidence collection, is generated. The purpose of nuclear forensics is to provide characteristic quantities and process history information of intercepted nuclear materials as much as possible, provide evidences for tracing the origin, production process and transportation route of the materials, and provide technical support for proving the responsibility of exploring illegal possession of the nuclear materials and radioactive materials. Among them, the traceability analysis of the geographical source of nuclear materials is the key point of the nuclear evidence research, and uranium ore becomes an important analysis object of the nuclear evidence research as a basic raw material of the nuclear materials.
In nuclear evidence research, a characteristic fingerprint that provides accurate and reliable geographic traceability information is generally referred to as a regional indicator. At present, the traditional uranium ore producing area classification method mainly adopts trace element content or stable isotope composition as a regional indicator for research, however, if the stable isotope composition is independently used as the regional indicator, the defects of extremely limited contained information and low accuracy exist; if the trace elements are used as the regional indicators independently, the method generally classifies the producing areas by comparing the distribution curves of the trace elements, and because the trace elements have various types and large content difference, the method is difficult to directly classify the trace elements according to the producing areas, has strong subjectivity, is difficult to quantify and has large errors. In addition, because the chemical properties of the rare earth elements in the trace elements are relatively close, the correlation coefficient is relatively large, namely, the contained information is relatively overlapped, the traditional method for classifying the nuclear material producing areas by taking the rare earth elements as the regional indicators has the problems of relatively large calculated amount and low analysis efficiency.
Disclosure of Invention
In view of the above, the invention aims to provide an effective and high-accuracy uranium ore production place classification method based on principal component analysis.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a uranium ore producing area classification method based on principal component analysis is characterized by comprising the following steps:
(1) Collecting m uranium ore samples, and carrying out pretreatment to obtain uranium ore sample solutions;
(2) Respectively measuring the content of rare earth elements in m uranium ore samples by adopting an inductively coupled plasma mass spectrometer;
(3) Respectively measuring Nd isotope ratios in the m uranium ore samples by adopting a thermal surface ionization mass spectrometer;
(4) Performing principal component analysis according to the rare earth element content and Nd isotope ratio of each uranium ore sample, and classifying the uranium ore samples according to the production place sources; wherein, the step (4) specifically comprises the following steps:
step (4.1) utilizes the content of rare earth elements and Nd isotope ratio of each uranium ore sample 143 Nd/ 144 Nd, solving a covariance matrix;
step (4.2) combining the covariance matrix with the extreme value theorem to obtain a principal component coefficient matrix, and solving the characteristic root and the variance contribution rate of each principal component;
step (4.3) sorting the variance contribution rates from large to small, and selecting principal components of which the cumulative variance contribution rate is more than 80% and the characteristic root is more than 1;
determining corresponding principal component coefficients of the selected principal components according to the principal component coefficient matrix, multiplying the content of each rare earth element and the Nd isotope ratio with the corresponding principal component coefficients of the selected principal components respectively, and summing the products to obtain corresponding principal component values of each uranium ore sample respectively;
and (4.5) drawing a uranium ore sample distribution map by taking each principal component as a coordinate axis, and classifying the geographical sources of the uranium ore samples according to the spatial positions of the sample points in the distribution map.
Further, the step (1) specifically comprises the following steps:
step (1.1) grinding a uranium ore sample into powder with uniform particles by using a spherical grinder;
weighing a uranium ore powder sample and placing the uranium ore powder sample in a polytetrafluoroethylene beaker;
digesting: sequentially adding HNO into a polytetrafluoroethylene beaker 3 HF and HClO 4 Heating the beaker on an electric heating plate for a certain time;
step (1.4) after the sample solution is completely evaporated to dryness, utilizing concentrated HNO 3 Dissolving;
step (1.5) repeating operation step (1.4) for multiple times until the dissolution is complete;
step (1.6) after the sample solution is evaporated to dryness again, HCl and H are used 3 BO 3 And dissolving the mixed acid again to obtain a uranium ore sample solution.
Further, in the step (1.3), HNO 3 The concentration is 15mol/L;
further, in the step (1.3), the concentration of HF is 20mol/L;
further, in the step (1.3), HClO 4 The concentration is 12.4mol/L;
further, in the step (1.6), H 3 BO 3 The concentration is 0.5mol/L;
further, the inductively coupled plasma mass spectrometry of step (2) is performed under the following conditions: the sample introduction rate was 0.3rps, and was stable for 35 seconds before analysis; in the He gas mode, the number of acquisition points of the acquisition number of the unit mass number is 3, and the data acquisition repetition frequency is 3; the radio frequency power is 1550W, the flow rate of the carrier gas is 5.5mL/min, and the temperature of the atomizing chamber is 2 ℃.
Further, the step (3) of measuring the Nd isotope ratio in the uranium ore sample by adopting a thermal surface ionization mass spectrometer specifically comprises the following steps:
step (3.1) coating the sample solution on the center of a rhenium strip lamp wire by using a micropipette;
step (3.2) the filament after sample coating is loaded into an ion source, the filament is heated, and a central Faraday cup is adopted for receiving 144 Nd + Adjusting ion current peak shape and peak center;
step (3.3) adjusting the focusing parameters of the electron lens of the thermal surface ionization mass spectrometer, and heating the filament at a low speed to obtain a stable and strong filament 143 Nd + And 144 Nd + ion current intensity;
step (3.4) when the vacuum degree of the ion source reaches a certain value, obtaining 143 Nd/ 144 And obtaining the Nd isotope ratio.
Further, the step (3.4) is specifically that the vacuum degree of the ion source is more than or equal to 1.1 multiplied by 10 -7 mbar, is obtained 143 Nd/ 144 The ratio of Nd.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. according to the invention, on the selection of the regional indicator, the regional indicator of the uranium ore is formed by adopting the content of the rare earth element and the Nd isotope, so that the problems that other common regional indicator elements are easily influenced by the mining and metallurgy processes or have poor synchronism and the like are solved;
2. according to the method, the classification research of the producing areas of the uranium ores is carried out by using a principal component analysis method, most information of original variables is kept, data are effectively compressed, and the rapid and accurate producing area classification can be realized.
3. The uranium ore producing area classification method provided by the invention realizes accurate producing area classification of the uranium ore by combining the rare earth element content and the Nd isotope composition, and can be used for tracing analysis on nuclear materials, radioactive materials and other related materials.
Drawings
FIG. 1 is a graph showing the distribution of the main components based on the content of rare earth elements and the ratio of Nd isotope in one example;
fig. 2 is a partial diagram of the distribution of the main component based on the content of the rare earth element and the Nd isotope ratio provided in the first embodiment.
Detailed Description
The present invention will now be described more fully hereinafter with reference to the accompanying examples, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A uranium ore producing area classification method based on principal component analysis is characterized by comprising the following steps:
(1) Collecting m uranium ore samples, and carrying out pretreatment to obtain uranium ore sample solutions;
(2) Respectively measuring the content of rare earth elements in m uranium ore samples by adopting an inductively coupled plasma mass spectrometer;
(3) Respectively measuring Nd isotope ratios in the m uranium ore samples by adopting a thermal surface ionization mass spectrometer;
(4) And (3) performing principal component analysis according to the rare earth element content and Nd isotope ratio of each uranium ore sample, and classifying the uranium ore samples according to the origin of the production area.
And (4) performing principal component analysis according to the rare earth element content and Nd isotope ratio of each uranium ore sample, and classifying the uranium ore samples according to the production place sources. The method specifically comprises the following steps:
step (4.1) utilizes the content of rare earth elements and Nd isotope ratio of each uranium ore sample 143 Nd/ 144 Nd, solving a covariance matrix;
step (4.2) combining the covariance matrix with the extreme value theorem to obtain a principal component coefficient matrix, and solving the characteristic root and the variance contribution rate of each principal component;
step (4.3) sorting the variance contribution rates from large to small, and selecting principal components with the accumulated variance contribution rate larger than 80% and the characteristic root larger than 1;
step (4.4) determining corresponding principal component coefficients of the selected principal components according to the principal component coefficient matrix, multiplying the content of each rare earth element and the Nd isotope ratio with the corresponding principal component coefficients of the selected principal components respectively, and summing the products to obtain corresponding principal component values of each uranium ore sample respectively;
and (4.5) drawing a uranium ore sample distribution map by taking each principal component as a coordinate axis, and carrying out production place classification on the uranium ore sample according to the spatial position of a sample point in the distribution map.
Further, m uranium ore samples are collected in the step (1) and are subjected to pretreatment, and uranium ore sample solutions are obtained.
The method specifically comprises the following steps:
step (1.1) grinding a uranium ore sample into powder with uniform particles with the particle size of about 10 microns by using a spherical grinder;
step (1.2) weighing a uranium ore powder sample with a certain mass by using a high-precision balance and placing the uranium ore powder sample into a polytetrafluoroethylene beaker;
digesting: sequentially adding HNO into a polytetrafluoroethylene beaker 3 HF and HClO 4 Heating the beaker on an electric heating plate for a certain time;
step (1.4) after the sample solution is completely evaporated to dryness, utilizing concentrated HNO 3 Dissolving;
step (1.5) repeating operation step (1.4) for multiple times until the dissolution is complete;
step (1.6) after the sample solution is evaporated to dryness again, HCl and H are used 3 BO 3 The mixed acid is dissolved again to eliminate fluorinated complexes possibly generated in the uranium ore sample digestion process, and a uranium ore sample solution is obtained.
Further, in the step (1.3), HNO 3 The concentration is 15mol/L;
further, in the step (1.3), the concentration of HF is 20mol/L;
further, in the step (1.3), HClO 4 The concentration is 12.4mol/L;
further, in the step (1.6), H 3 BO 3 The concentration is 0.5mol/L;
further, the inductively coupled plasma mass spectrometry of step (2) is performed under the following conditions: the sample introduction rate was 0.3rps, and was stable for 35 seconds before analysis; in the He gas mode, the number of acquisition points of the acquisition number of the unit mass number is 3, and the data acquisition repetition frequency is 3; the radio frequency power is 1550W, the flow rate of the carrier gas is 5.5mL/min, and the temperature of the atomizing chamber is 2 ℃.
Further, the step (3) adopts a thermal surface ionization mass spectrometer to measure the Nd isotope ratio in the uranium ore sample. The method specifically comprises the following steps:
step (3.1) coating the sample solution on the center of a rhenium strip lamp wire by using a micropipette, which is favorable for improving the ionization efficiency of the sample;
step (3.2) the filament after sample coating is loaded into an ion source, the filament is heated, and a central Faraday cup is adopted for receiving 144 Nd + Adjusting ion flow peak shape and peak center;
step (3.3) adjusting the focusing parameters of the electron lens of the thermal surface ionization mass spectrometer, and heating the filament at a low speed to obtain a stable and strong filament 143 Nd + And 144 Nd + ion current intensity;
step (3.4) when the vacuum degree of the ion source reaches a certain value, adopting a Faraday cup to receive the ion source simultaneously 143 Nd + And 144 Nd + ion flow to obtain 143 Nd/ 144 And obtaining the Nd isotope ratio.
Further, the rhenium filament current rising speed in the step (3.2) is 200 mA/min-800 mA/min;
further, the rhenium filament current rising speed in the step (3.3) is 30 mA/min-80 mA/min, the rhenium filament current rising speed is not too fast, otherwise, the too fast evaporation consumption of the sample is easily caused;
further, in the step (3.4), the vacuum degree of the ion source is more than or equal to 1.1 multiplied by 10 -7 mbar, is obtained 143 Nd/ 144 The Nd ratio;
further, each measurement in the step (3.4) consists of 10-20 blocks, and each block contains 10-16 cycles;
furthermore, the rhenium filament current increasing speed in the step (3.2) is 400mA/min;
further, the rhenium filament current rising speed in the step (3.3) is 60mA/min;
further, each measurement in step (3.4) consists of 16 blocks, each block containing 11 cycles.
Example 1
In this embodiment, a uranium ore origin classification method based on principal component analysis specifically includes the following steps:
collecting a uranium ore sample, and carrying out pretreatment to obtain a uranium ore sample solution;
the total of 25 uranium ore samples collected in the laboratory, which are respectively from four continents of europe, africa, north america and oceania, wherein 10 uranium ore samples produced in the united states, 7 uranium ore samples produced in congo, 3 uranium ore samples produced in australia, 2 uranium ore samples produced in macagasco, 1 uranium ore sample produced in germany, 1 uranium ore sample produced in mexico and 1 uranium ore sample produced in nanobira are contained.
Pre-treating a uranium ore sample: it was first ground to a uniform particle size of about 10 microns using a ball mill and the digestion process was as follows:
(1) Weighing a 500mg uranium ore powder sample by using a high-precision balance and placing the sample in a polytetrafluoroethylene beaker;
(2) 9mL of HNO with the concentration of 15mol/L are added in sequence 3 6mL of HF at a concentration of 20mol/L and 3mL of HClO at a concentration of 12.4mol/L 4 Digesting, namely heating the polytetrafluoroethylene beaker on an electric hot plate to 210 ℃, and keeping the temperature for 24 hours;
(3) After the sample solution is completely evaporated to dryness, 10mL of concentrated HNO is used 3 Dissolving;
(4) Repeating the operation step (1.4) for multiple times until the dissolution is complete;
(5) After the sample solution was evaporated to dryness again, 4mL of 2.5mol/L HCl and 2mL of 0.5mol/L H were added 3 BO 3 The mixed acid is redissolved at 90 ℃ to eliminate fluorinated complexes which may remain during digestion of the uranium ore sample.
And (4) completely digesting the uranium ore powder sample to obtain a sample solution.
And (2) measuring the content of rare earth elements in 25 uranium ore samples by adopting an inductively coupled plasma mass spectrometer:
measuring the content of 14 rare earth elements La, ce, pr, nd, sm, eu, gd, tb, dy, ho, er, tm, yb and Lu in each uranium ore sample solution by adopting an inductively coupled plasma mass spectrometer, wherein the conditions of the inductively coupled plasma mass spectrometer are set as follows: sample introduction rate 0.3rps, stable 35 seconds before analysis; in the He gas mode, the number of acquisition points of the acquisition number of the unit mass number is 3, and the data acquisition repetition frequency is 3; the radio frequency power is 1550W, the flow rate of the carrier gas is 5.5mL/min, and the temperature of the atomizing chamber is 2 ℃. The ICP-MS measurement results of the rare earth element contents of the 25 uranium ore samples are shown in table 1.
TABLE 1 measurement of the content (ppm) of each rare earth element in uranium ore samples and the Nd isotope ratio
TABLE 1 measurement of the ratio of the content (ppm) of each rare earth element to Nd isotope in uranium ore samples
And (3) measuring the Nd isotope ratio in the uranium ore sample by adopting a thermal surface ionization mass spectrometer:
step (3.1) in the sample coating process of the thermal ionization mass spectrum, a rhenium band is usually adopted as a sample band, the length of the rhenium band is about 10mm, the width of the rhenium band is about 0.1mm, a micropipette is adopted to dropwise add a sample solution on the rhenium band, and the sample solution is heated and evaporated to dryness under appropriate current. Since the rhenium band is very narrow, only 1. Mu.L of sample solution can be added dropwise at a time, and therefore, the sample addition needs to be repeated several times.
Before the sample is coated, the blank rhenium belt is placed in vacuum of 10 -5 Pa, vacuum cleaning and degassing with a heating current through the rhenium strip, typically 5.5A for a duration of about 30min. The program can effectively reduce the measurement background caused by hydrocarbon, reduce the interference of isobaric ions with the same quantity, improve the vacuum degree of the ion source during measurement and be beneficial to maintaining a good measurement environment. It is noted that to increase the ionic current strength, improve ionization efficiency and reduce isotope fractionation, micropipettes should be used to coat the sample solution in the center of the rhenium band as much as possible.
The specific sample coating conditions are as follows: 1 μ L of the sample solution was added dropwise to the rhenium ribbon at a current of 0.9A. After the mixture is dried by distillation, adding current to 1.3A, and keeping for 2min; then adding current to 1.8A, and keeping for 30s; then adding current to 2A and keeping for 2s; slowly reducing the current to 1.3A, and keeping for 30s; and slowly reducing the current to 0A, and taking down the rhenium band to be tested.
Step (3.2) the filament after sample coating is loaded into an ion source, the filament is heated to about 1000 ℃ at the speed of 200mA/min, and a central Faraday cup is adopted for receiving 144 Nd + Adjusting ion flow peak shape and peak center;
step (3.3) repeatedly adjusting the gathering parameters of the electron lens of the thermal surface ionization mass spectrometer, and optimizing the state of the electron lens of the thermal surface ionization mass spectrometer; the filament is heated at a low speed of 60mA/min to obtain the stable and strong filament 143 Nd + And 144 Nd + ion current intensity;
step (3.4) when the vacuum degree of the ion source is better than 1.1 multiplied by 10 -7 measurement at mbar Nd + Ion flow, obtaining 143 Nd/ 144 The ratio of Nd. Each measurement consists of 16 blocks, each containing 11 cycles. The integration time and dead time for each cycle were set to 4.914s and 3s, respectively.
To correct for mass fractionation effects, to 146 Nd/ 144 Nd =0.7219 is a correction reference value, and the measured Nd isotope ratio is corrected by adopting an exponential law to obtain the Nd isotope ratio in each uranium ore sample 143 Nd/ 144 The measurement results of the ratio of Nd (hereinafter, ε Nd was used instead) are shown in Table 1.
And (4) performing principal component analysis according to the rare earth element content and Nd isotope ratio of each uranium ore sample, and classifying the uranium ore samples according to the production place sources.
And (3) calculating covariance of all the quantities by using SPSS 20 statistical analysis software and taking the content information of the 14 rare earth elements and the Nd isotope ratio of each uranium ore sample as input quantities to obtain covariance matrixes of 25 uranium ore samples (table 2). As can be seen from the matrix: except for epsilon Nd, sm and Eu, the absolute values of correlation coefficients of the epsilon Nd and the contents of the rest 12 rare earth elements are less than 0.2, which shows that the fingerprint information contained in the Nd isotope has smaller compressible space.
TABLE 2 covariance matrix based on rare earth element content and Nd isotope ratio
| La | Ce | Pr | Nd | Sm | Eu | Gd | Tb | |
| La | 1.000 | .907 | .506 | .292 | .070 | .129 | .483 | .274 |
| Ce | .907 | 1.000 | .820 | .651 | .279 | .109 | .805 | .612 |
| Pr | .506 | .820 | 1.000 | .957 | .540 | .063 | .998 | .913 |
| Nd | .292 | .651 | .957 | 1.000 | .715 | .056 | .964 | .985 |
| Sm | .070 | .279 | .540 | .715 | 1.000 | .000 | .557 | .819 |
| Eu | .129 | .109 | .063 | .056 | .000 | 1.000 | .051 | .051 |
| Gd | .483 | .805 | .998 | .964 | .557 | .051 | 1.000 | .923 |
| Tb | .274 | .612 | .913 | .985 | .819 | .051 | .923 | 1.000 |
| Dy | .231 | .456 | .676 | .807 | .971 | -.011 | .690 | .892 |
| Ho | .268 | .451 | .620 | .745 | .966 | -.033 | .633 | .844 |
| Er | .317 | .625 | .885 | .958 | .852 | .002 | .895 | .983 |
| Tm | .325 | .678 | .959 | .989 | .695 | -.053 | .970 | .972 |
| Yb | .224 | .605 | .937 | .977 | .643 | -.063 | .953 | .950 |
| Lu | .203 | .495 | .778 | .898 | .929 | -.071 | .796 | .955 |
| εNd | .114 | .130 | .086 | -.006 | -.243 | -.396 | .078 | -.072 |
TABLE 2 covariance matrix based on rare earth element content and Nd isotope ratio
The covariance matrix is combined with the extreme value principle to obtain principal components, the characteristic root and variance contribution rate of each principal component are calculated to obtain a table 3, and the table shows that: the characteristic root values corresponding to the first three principal components are all larger than 1, wherein the characteristic root value of the first principal component reaches 10.222, and the contribution rate reaches 68.148%; the second principal component characteristic root value is 1.974, the contribution rate is 13.161%, the third principal component is 1.424, and the contribution rate is 9.496%, wherein the cumulative contribution rate of the first two principal components reaches 81.309%, which indicates that the first two principal components already contain most of the original information. And selecting the first two principal components for analysis according to the requirements that the accumulated contribution rate of the principal components is more than 80% and the characteristic root is more than 1.
TABLE 3 characteristic root and contribution rate of each main component based on rare earth element content and Nd isotope ratio
| Principal component | Characteristic root | Contribution ratio (%) | Cumulative contribution ratio (%) |
| 1 | 10.222 | 68.148 | 68.148 |
| 2 | 1.974 | 13.161 | 81.309 |
| 3 | 1.424 | 9.496 | 90.805 |
| 4 | .864 | 5.763 | 96.568 |
| 5 | .468 | 3.117 | 99.685 |
| 6 | .020 | .133 | 99.818 |
| 7 | .015 | .098 | 99.916 |
| 8 | .012 | .082 | 99.998 |
| 9 | .000 | .001 | 99.999 |
| 10 | 7.629E-005 | .001 | 100.000 |
| 11 | 1.762E-005 | .000 | 100.000 |
| 12 | 6.711E-006 | 4.474E-005 | 100.000 |
| 13 | 3.146E-006 | 2.097E-005 | 100.000 |
| 14 | 4.184E-007 | 2.789E-006 | 100.000 |
| 15 | 1.726E-007 | 1.151E-006 | 100.000 |
And (3) obtaining a coefficient matrix of the first two selected principal components by combining a covariance matrix with an extreme value principle as shown in a table 4, multiplying the content of each rare earth element and the Nd isotope ratio with the corresponding principal component coefficient in the principal component coefficient matrix respectively, and summing the products to obtain the corresponding principal component value of each uranium ore sample respectively.
Expression of the first principal component:
PC1＝0.039×La+0.068×Ce+0.091×Pr+0.095×Nd+0.079×Sm+0.001×Eu+0.091×Gd+0.097×Tb+0.088×Dy+0.084×Ho+0.097×Er+0.095×Tm+0.091×Yb+0.093×Lu–0.004×εNd；
expression of the second principal component:
PC2＝0.356×La+0.325×Ce+0.161×Pr+0.020×Nd–0.261×Sm+0.006×Eu+0.147×Gd–0.041×Tb–0.176×Dy–0.180×Ho–0.045×Er+0.044×Tm+0.034×Yb–0.135×Lu+0.265×εNd；
TABLE 4 principal component coefficient matrix based on rare earth element content and Nd isotope ratio
| Coefficient of first principal component | Coefficient of second principal component | |
| La | .039 | .356 |
| Ce | .068 | .325 |
| Pr | .091 | .161 |
| Nd | .095 | .020 |
| Sm | .079 | -.261 |
| Eu | .001 | .006 |
| Gd | .091 | .147 |
| Tb | .097 | -.041 |
| Dy | .088 | -.176 |
| Ho | .084 | -.180 |
| Er | .097 | -.045 |
| Tm | .095 | .044 |
| Yb | .091 | .034 |
| Lu | .093 | -.135 |
| εNd | -.004 | .265 |
PC1 is positively correlated with other statistical variables except for ε Nd; PC2 is in negative correlation with Sm, tb, dy, ho, er and Lu, and is in positive correlation with other statistical variables. Since the cumulative contribution rates of the first two principal components exceed 80%, the two-dimensional distribution map of the uranium ore sample is drawn by using the first principal component and the second principal component as coordinate axes in the present embodiment.
As can be seen from the principal component distribution diagram 1:
(1) The uranium ore sample distribution method includes the steps that 25 uranium ore samples from different geographical sources are obviously aggregated, the uranium ore samples are mainly divided into 5 types, and the specific distribution is shown in a circle surrounded by a figure;
(2) The uranium ore samples from the motor Gassaka, australia, germany and Nanbia have better aggregation condition and are easy to distinguish;
(3) Samples of uranium ore from congo, usa, mexico are grouped together in a class and closely spaced from each other.
For the sake of easy observation, the uranium ore sample accumulation region from congo, usa is enlarged to obtain fig. 2, and it can be clearly seen that 10 U.S. samples, 7 congo samples and 1 mexican sample are mixedly distributed in the region of-0.6-plus pc1-0.35, -0.5-plus pc2-plus 0.8, which can be distinguished by the dotted line in the figure.
Claims (9)
1. A uranium ore producing area classification method based on principal component analysis is characterized by comprising the following steps:
(1) Collecting m uranium ore samples, and carrying out pretreatment to obtain uranium ore sample solutions;
(2) Respectively measuring the content of rare earth elements in m uranium ore samples by adopting an inductively coupled plasma mass spectrometer;
(3) Respectively measuring Nd isotopes in m uranium ore samples by adopting thermal surface ionization mass spectrometer 143 Nd and 144 the ratio of Nd;
(4) According to the content of rare earth elements and Nd isotope of each uranium ore sample 143 Nd and 144 performing principal component analysis on the ratio of Nd, and classifying uranium ore samples according to the origin of the production area;
wherein, the step (4) specifically comprises the following steps:
step (4.1) utilizes the content of rare earth elements and Nd isotope of each uranium ore sample 143 Nd and 144 the ratio of Nd is used for solving a covariance matrix;
step (4.2) combining the covariance matrix with the extreme value theorem to obtain a principal component coefficient matrix, and solving the characteristic root and the variance contribution rate of each principal component; step (4.3) sorting the variance contribution rates from large to small, and selecting principal components with the accumulated variance contribution rate larger than 80% and the characteristic root larger than 1;
step (4.4) determining the corresponding principal component coefficients of the selected principal components according to the principal component coefficient matrix, and determining the contents of the respective rare earth elements and Nd isotopes 143 Nd and 144 multiplying the ratio of Nd by the corresponding principal component coefficient of the selected principal component respectively, and summing the products to obtain the corresponding principal component value of each uranium ore sample respectively;
and (4.5) drawing a uranium ore sample distribution map by taking each principal component as a coordinate axis, and performing geographical source classification on the uranium ore sample according to the spatial position of a sample point in the distribution map.
2. The method for classifying uranium ore producing regions based on principal component analysis according to claim 1, wherein: the step (1) specifically comprises the following steps:
step (1.1) grinding a uranium ore sample into powder with uniform particles by using a ball grinder;
weighing a uranium ore powder sample and placing the uranium ore powder sample in a polytetrafluoroethylene beaker;
digesting: sequentially adding HNO into a polytetrafluoroethylene beaker 3 HF and HClO 4 Heating the beaker on an electric heating plate for a certain time;
step (1.4) after the sample solution is completely evaporated to dryness, utilizing concentrated HNO 3 Dissolving;
step (1.5) repeating operation step (1.4) for multiple times until the dissolution is complete;
step (1.6) after the sample solution is evaporated to dryness again, HCl and H are used 3 BO 3 And dissolving the mixed acid again to obtain a uranium ore sample solution.
3. The method for classifying uranium ore origins based on principal component analysis according to claim 2, wherein: in the step (1.3), the HNO 3 The concentration was 15mol/L.
4. The method for classifying uranium ore producing regions based on principal component analysis according to claim 2, wherein: in the step (1.3), the concentration of HF is 20mol/L.
5. The method for classifying uranium ore producing regions based on principal component analysis according to claim 2, wherein: in the step (1.3), the HClO 4 The concentration was 12.4mol/L.
6. The method for classifying uranium ore producing regions based on principal component analysis according to claim 2, wherein: in the step (1.3), the H 3 BO 3 The concentration was 0.5mol/L.
7. The method for classifying uranium ore producing regions based on principal component analysis according to claim 1, wherein: the inductively coupled plasma mass spectrometry in the step (2) has the following conditions: the sample introduction rate was 0.3rps, and was stable for 35 seconds before analysis; in the He gas mode, the number of acquisition points of the acquisition number of the unit mass number is 3, and the data acquisition repetition frequency is 3; the radio frequency power is 1550W, the flow rate of the carrier gas is 5.5mL/min, and the temperature of the atomizing chamber is 2 ℃.
8. The method for classifying uranium ore producing regions based on principal component analysis according to claim 1, wherein: the step (3) of measuring the Nd isotope ratio in the uranium ore sample by adopting a thermal surface ionization mass spectrometer specifically comprises the following steps:
step (3.1) coating a sample solution on the center of a rhenium strip filament by using a micropipette;
step (3.2) the filament after sample coating is loaded into an ion source, the filament is heated, and a central Faraday cup is adopted for receiving 144 Nd + Adjusting ion flow peak shape and peak center;
step (3.3) adjusting the focusing parameters of the electron lens of the thermal surface ionization mass spectrometer, and heating the filament at a low speed to obtain a stable and strong filament 143 Nd + And 144 Nd + ion current intensity;
step (3.4) when the vacuum degree of the ion source reaches a certain value, obtaining 143 Nd and 144 and obtaining the Nd isotope ratio.
9. The method for classifying uranium ore origins based on principal component analysis according to claim 8, wherein: the step (3.4) is specifically that the vacuum degree of the ion source is more than or equal to 1.1 multiplied by 10 -7 mbar, is obtained 143 Nd and 144 the ratio of Nd.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010352579.3A CN111505101B (en) | 2020-04-29 | 2020-04-29 | Uranium ore producing area classification method based on principal component analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010352579.3A CN111505101B (en) | 2020-04-29 | 2020-04-29 | Uranium ore producing area classification method based on principal component analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111505101A CN111505101A (en) | 2020-08-07 |
| CN111505101B true CN111505101B (en) | 2023-04-18 |
Family
ID=71870363
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010352579.3A Active CN111505101B (en) | 2020-04-29 | 2020-04-29 | Uranium ore producing area classification method based on principal component analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111505101B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112710725B (en) * | 2020-12-08 | 2024-04-16 | 核工业二一六大队 | Sandstone type uranium ore deep penetration geochemical ionization weak information extraction method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106548920A (en) * | 2015-09-17 | 2017-03-29 | 塞莫费雪科学（不来梅）有限公司 | Mass spectrograph |
| CN106560841A (en) * | 2016-10-20 | 2017-04-12 | 中国计量大学 | Wuyi rock tea production place identification method based on deep learning |
| CN108761564A (en) * | 2018-08-07 | 2018-11-06 | 青海省地质调查院（（青海省地质矿产研究所）） | A Cenozoic magmatic copper-polymetallic ore prospecting method |
| CN110687189A (en) * | 2019-10-08 | 2020-01-14 | 浙江省检验检疫科学技术研究院 | Method for distinguishing milk producing area tracing and identifying based on multiple elements and multiple indexes |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050255606A1 (en) * | 2004-05-13 | 2005-11-17 | Biospect, Inc., A California Corporation | Methods for accurate component intensity extraction from separations-mass spectrometry data |
| CN101743604A (en) * | 2007-06-04 | 2010-06-16 | 微软公司 | Finding paired isotope groups |
| US8429153B2 (en) * | 2010-06-25 | 2013-04-23 | The United States Of America As Represented By The Secretary Of The Army | Method and apparatus for classifying known specimens and media using spectral properties and identifying unknown specimens and media |
| DK2590206T3 (en) * | 2011-11-02 | 2015-06-29 | Vito Nv Vlaamse Instelling Voor Technologisch Onderzoek Nv | Method and apparatus for estimating the elemental composition of a molecule from an isotope distribution |
| CN106248810A (en) * | 2016-06-08 | 2016-12-21 | 深圳出入境检验检疫局食品检验检疫技术中心 | A kind of wine place of production based on multielement and stable isotope source tracing method |
| US10734209B2 (en) * | 2016-10-07 | 2020-08-04 | New York University | Reagents and methods for simultaneously detecting absolute concentrations of a plurality of elements in a liquid sample |
-
2020
- 2020-04-29 CN CN202010352579.3A patent/CN111505101B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106548920A (en) * | 2015-09-17 | 2017-03-29 | 塞莫费雪科学（不来梅）有限公司 | Mass spectrograph |
| CN106560841A (en) * | 2016-10-20 | 2017-04-12 | 中国计量大学 | Wuyi rock tea production place identification method based on deep learning |
| CN108761564A (en) * | 2018-08-07 | 2018-11-06 | 青海省地质调查院（（青海省地质矿产研究所）） | A Cenozoic magmatic copper-polymetallic ore prospecting method |
| CN110687189A (en) * | 2019-10-08 | 2020-01-14 | 浙江省检验检疫科学技术研究院 | Method for distinguishing milk producing area tracing and identifying based on multiple elements and multiple indexes |
Non-Patent Citations (2)
| Title |
|---|
| Origin of uranium deposits revealed by their rare earth element Origin of uranium deposits revealed by their rare earth element;Julien Mercadier et al;《Terra Nova》;20111231;第264-269页 * |
| 鞍山 本溪地区条带状铁建造的铁同位素与稀土元素特征及其对成矿物质来源的指示;李志红 等;《岩石矿物学杂质》;20080731;第27卷(第4期);第285-290页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111505101A (en) | 2020-08-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kennedy et al. | Grenville skarn titanite: potential reference material for SIMS U–Th–Pb analysis | |
| Tanimizu et al. | Development of rapid and precise Pb isotope analytical techniques using MC-ICP-MS and new results for GSJ rock reference samples | |
| CN111505101B (en) | Uranium ore producing area classification method based on principal component analysis | |
| CN111487309B (en) | Uranium ore geographic tracing method based on discriminant analysis | |
| Liu et al. | Precise and accurate boron and lithium isotopic determinations for small sample-size geological materials by MC-ICP-MS | |
| Bai et al. | Simultaneous measurement stable and radiogenic Nd isotopic compositions by MC-ICP-MS with a single-step chromatographic extraction technique | |
| Li et al. | Bastnaesite K-9: A homogenous natural reference material for in-situ U-Pb and Th-Pb dating | |
| CN113340975A (en) | Method for simultaneously determining 18 elements in copper ore | |
| Kennedy et al. | Apatite reference materials for SIMS microanalysis of isotopes and trace elements | |
| CN110632162B (en) | A kind of identification method of local wild astragalus and cultivated astragalus | |
| Walczyk et al. | Iridium isotope ratio measurements by negative thermal ionization mass spectrometry and atomic weight of iridium | |
| CN113340978A (en) | Method and system for measuring concentration of elements of unnatural isotope abundance | |
| Wang et al. | The absolute isotopic composition and atomic weight of ytterbium using multi-collector inductively coupled plasma mass spectrometry and development of an SI-traceable ytterbium isotopic certified reference material | |
| CN111505100B (en) | Uranium ore producing area classification method based on principal component-cluster analysis | |
| Gou et al. | Determination of barium isotopic ratios in river water on the multiple collector inductively coupled plasma mass spectrometer | |
| CN115144459A (en) | A method for detecting trace metals on the surface of silicon carbide epitaxial wafers | |
| CN111505102B (en) | Uranium ore producing area classification method based on cluster analysis | |
| CN118483171A (en) | Method for detecting heavy metal and harmful element contents in drynaria medicinal materials in different producing areas and pretreatment method | |
| CN113514586B (en) | Soybean origin tracing identification method based on combination of MALDI-TOF/TOF and multi-element analysis technology | |
| Chang et al. | The absolute isotopic composition of europium | |
| CN117451465A (en) | Preparation method of standard substance, standard substance and application method of standard substance | |
| CN117491125A (en) | Method for measuring rare earth elements in cassiterite by laser in-situ micro-area | |
| CN104931575A (en) | Analysis method for identifying gold, platinum and palladium in chemical sample | |
| CN113390858A (en) | Method for measuring lithium in coal by high-pressure closed digestion-inductively coupled plasma mass spectrometry | |
| Lee et al. | Determination of lanthanoids and yttrium in JGb2 and JR3 by inductively coupled plasma-mass spectrometry after cation-exchange pretreatment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |