Ultratrace Platinum Group Elements in Geological Samples by Inductively Coupled Plasma-Mass Spectrometry with Nickel Sulfide Fire Assay
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摘要:
铂族元素(PGEs)六项元素钌、铑、钯、锇、铱和铂的物理化学性质相近，在地壳中丰度极低且分布不均匀，且具有明显的粒金效应，长期以来准确测定其含量始终是岩矿测试的难题。镍锍试金取样量大，可定量分离富集铂族元素，通常被应用于PGEs分析，但将其应用于超痕量PGEs分析的关键问题是流程空白高，质谱干扰严重。本文报道了一种同时测定样品中超痕量铂钯铑铱锇和钌的方法。检查全流程试剂空白后，使用镍锍试金富集样品中的PGEs，经杂质分离，利用电感耦合等离子体质谱(ICP-MS)动能歧视模式测定六项元素，有效地降低了质谱干扰。结果表明，方法的空白主要来自盐酸和捕集剂镍粉，选择合适厂家的试剂或对试金配料进行提纯，可降低全流程空白。同时，使用ICP-MS法测定六项元素时，在标准模式下，铂和钯的检出限小于0.2ng/g，铑、铱和锇的检出限小于0.02ng/g，钌的检出限大于0.1ng/g，钌的检出限无法满足超痕量PGEs的测定要求。使用动能歧视模式后，钌的背景等效浓度比标准模式降低近两个数量级，从而消除了镍对钌的质谱干扰，钌的检出限降低至0.005ng/g，使六项元素检出限同时满足超痕量PGEs测定要求。该方法用于分析土壤(GBW07288、GBW07294)、水系沉积物标准物质(GBW07289)，六项元素的结果与标准值符合，相对误差为−10.9%～11.8%，相对标准偏差为3.85%～9.37%，加标回收率为92%～110%。该方法流程较短、操作简便，满足大批量地质样品中超痕量PGEs的检测要求。
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关键词:
- 镍锍试金 /
- 电感耦合等离子体质谱法 /
- 动能歧视模式 /
- 铂族元素；土壤；水系沉积物
要点(1)通过对锍镍试金配料及各类溶剂空白值的检验，确定空白来源及试剂提纯方法，并采用羰基镍粉有效地降低全流程空白，使痕量铂族六项元素的检出限满足地球化学调查需求。
(2)通过添加羰基铁粉，优化试金配方，使锍扣在水浸泡的条件下即可自行粉化松散，不仅简化了分析流程，而且避免了因机械碎扣而产生污染的风险。
(3)采用动能歧视模式测定，有效地消除镍元素的质谱干扰，降低钌元素的背景等效浓度约两个数量级，提高了检测结果的准确性。
HIGHLIGHTS(1) The source of the blank was identified by checking reagent blanks and the purification methods for reagents were given. Carbonyl nickel powder was used as a nickel collector, with the lower reagent blank, and the detection limits for PGEs required by geochemical exploration were achieved.
(2) Carbonyl iron powder was added to ensure the NiS bead could be smashed in deionized water. In this way, the analysis process was simplified, and the risk of contamination caused by mechanical breakage of NiS bead was avoided.
(3) Using ICP-MS to determine the solution in the kinetic energy discrimination model effectively eliminated the matrix effect of Ni. The results showed that the background equivalent concentration of ruthenium in kinetic energy discrimination mode was two orders of magnitude lower than that in the standard mode.
Abstract:Pt, Pd, Rh, Ir, Os and Ru are platinum group elements (PGEs) with similar properties. Due to the low abundance as well as the nugget effect, the accurate determination of PGEs has been a challenge for rock and mineral analysis. Fire assay methods with large sample weights were developed to separate and preconcentrate PGEs, however, there are still difficulties to accurately determine ultratrace PGEs because of the high reagent blanks and the matrix effect. A method of nickel sulfide fire assay combined with ICP-MS simultaneous determination of ultratrace PGEs in samples was established. The results showed that the blank mainly comes from hydrochloric acid and nickel collector when using nickel sulfide fire assay to capture PGEs. The intensities of PGEs were detected by ICP-MS in standard mode and kinetic energy discrimination. In standard mode, the detection limits were 0.2ng/g for Pt and Pd, and 0.02ng/g for Rh, Ir and Os, but it couldn’t reach 0.1ng/g for Ru. In kinetic energy discrimination, the background equivalent concentration of Ru was two orders of magnitude lower than that in the standard mode. With the matrix effect of Ni effectively eliminated, the detection limits reached 0.005ng/g for Ru. The detection limits for PGEs required by geochemical exploration were achieved. The certified reference materials of soil (GBW07288, GBW07294) and stream sediment (GBW07289) were analyzed to test the method. The determined values were in good agreement with the certified values. The relative errors were between −10.9% and 11.8%, the relative standard deviations (RSD, n=12) were 3.88%−9.37%, and the spiked recoveries were 92%−110%. This method is simple, rapid and meets the requirements of ultratrace PGEs determination in large quantities of geological samples. The BRIEF REPORT is available for this paper at http://www.ykcs.ac.cn/en/article/doi/10.15898/j.ykcs.202407180159.
BRIEF REPORTSignificance: Platinum-group elements (PGEs), such as Pt, Pd, Rh, Ir, Os and Ru exhibit similar physico-chemical properties and significant features, and have been used widely in geochemistry and environmental chemistry. As a result, the accurate determination of the concentration of PGEs in geological samples is very important. Although various analytical methods for PGEs have been developed in the past, accurate and simultaneous determination of PGEs concentrations on the same sample digestion remains a significant challenge. This is mainly due to: (1) their extremely low abundance, sample heterogeneity and the nugget effect, making it necessary to analyze large sample sizes to obtain representative analyses; (2) During sample dissolving, Os and Ru are easy to form volatile OsO4 and RuO4, making it difficult to simultaneously measure the concentrations of PGEs accurately.
Improved Carius tube combined high-pressure asher (HPA-S) techniques have been widely used for the determination of PGEs to avoid the loss of volatile OsO4 and RuO4. However, the relatively complex procedure does not meet the requirements of ultratrace PGEs determination in large quantities of geological samples. Nickel sulfide (NiS) fire assay is a classical method to completely extract the PGEs from a large sample size into a nickel sulphide button and easily separate it from the slag. This method combined with highly sensitivity instrumental measurement, i.e., inductively coupled plasma-mass spectrometry (ICP-MS), can be used to simultaneously determine multi-elements with high sensitivity and effectively reduce the detection limit. However, it is still difficult to determine ultratrace PGEs because of the high procedural blank that mainly derives from the commercial nickel reagents and polyatomic ion interferences formed by interaction of a sample aerosol with components of the plasma-forming gas (such as C, Ar) and residual Ni and Cu ions in solution. Therefore, the improvement of the method for simultaneous determination of PGEs by ICP-MS combined with nickel sulfide fire assay preconcentration is important.
An analytical method for accurate and simultaneous determination of ultratrace PGEs in geological samples by ICP-MS combined with nickel sulfide fire assay is proposed. At the same time, it can effectively reduce the procedural blank and suppress/eliminate interferences. More importantly, it provides a practical method for the accurate determination of ultratrace PGE concentrations on the same sample digestion for surveying a large number of geological samples.
Methods: In the experiment, it is important to reduce the procedural blank and suppress/eliminate interferences. The data show that the high reagent blank is primarily from hydrochloric acid (Table 3) and nickel collector (Table 5). Therefore, the hydrochloric acid after purification or obtained from other manufacturers can be used in the experiment. At the same time, carbonyl nickel powder rather than other type of nickel powder is used as the fire assay collector.
Before analysis, the PGEs concentrations in the reagent (including hydrochloric acid and nickel powder) were measured. The reagent was not suitable to analyze the ultratrace PGEs, when the results exceeded 0.02ng/mL. (1) 50mL hydrochloric acid was evaporated at low temperature, and 5mL of aqua regia prepared from the same bottle of hydrochloric acid was added. After being extracted at low temperature and adding 20mL of deionized water, the solution was determined by ICP-MS. (2) 1.6g nickel power was mixed well with 2g sulfur, 4g carbonyl iron powder, 25g Na2B4O7∙10H2O, 25g Na2CO3, 4g SiO2, and 1g of edible flour and transferred into a 500mL fire-clay crucible, and fused in a furnace at approximately 1000℃. The furnace door was opened to cool to 800℃. Then the door was closed, and temperature gradually rose to 1050℃ and maintained for 30min. The fluid in the crucible was poured into a cast iron mold. After cooling, the NiS bead was separated from the slag, and placed into a 200mL Triangle bottle with 20mL deionized water until smashed. Then 20mL concentrated HCl was added into the Triangle bottle and heated for 1h until no bubbles formed. Filtering out the sediment through a filter membrane, sediment was transferred into a 200mL Triangle bottle and 5mL aqua regia was added. Next the air-cooled tube was loaded, and the solution was heated to boiling. The cooling solution was diluted to 25mL by the addition of deionized water. Lu (50ng/mL) was added as an internal standard for the determination of Pt, Pd, Ru, Rh and Ir by ICP-MS (Table 1) in kinetic energy discrimination mode.
Ultratrace PGEs in geological samples were determined after the reagent blanks were checked. 20g sample was mixed well with the above-mentioned flux and collectors (proration shown in Table 2). The fusion procedure was also described above. The solution was determined by ICP-MS in kinetic energy discrimination mode, which can effectively eliminate the matrix effect.
Data and Results: Determination of ruthenium in geological samples by ICP-MS, showed that the background equivalent concentration of ruthenium in kinetic energy discrimination mode was two orders of magnitude lower than that in the standard mode (Fig.1). In kinetic energy discrimination mode, the detection limit of the method was 0.005ng/g (Table 8). The certified reference materials of soil (GBW07288, GBW07294) and stream sediment (GBW07289) were analyzed to test the method. The determined values were in good agreement with the certified values. The relative errors were between −10.9% and 11.8%, the relative standard deviations (RSD, n=12) were 3.85%−9.37%, the spiked recoveries were between 92% and 110%.
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X射线荧光光谱定性分析技术经过长期的应用及发展，其应用范围也越来越广泛[1-4]。目前，XRF所带的定性分析软件(SQX)可自动对扫描谱图进行搜索和匹配，包括确定峰位、背景和峰位的净强度[5-7]，并从XRF特征谱线数据库中配对确定元素的谱线，这对从事XRF的分析者而言非常便利[8-10]。近年来刘岩等[11]采用XRF无标样分析法检测催化剂，测定结果的相对标准偏差小于1.3%；张红菊等[12]采用XRF无标样分析法检测轻合金铝合金中的主量元素，其测量值与认定值的相对误差低于±5%，测量结果都具有很好的可靠性和准确度。
自然界矿物种类复杂，应用XRF半定量分析软件(SQX)分析未知样品时，由于SQX软件仅对样品中9F~92U元素进行半定量分析，而对H2O、C这些参数不能直接测定。对于烧失量(LOI)、结晶水(H2O+)含量较高的铝土矿，二氧化碳含量较高的碳酸盐矿物，硫、碳含量较高的硫化物金属矿这类高烧失量矿物样品，平衡归一化计算时对未知样品中的Al2O3、SiO2、CaO、MgO、Fe等主要元素分析结果影响较大，半定量分析数据准确度较低。这就要求XRF分析人员需要掌握未知样品的来源及基本情况，根据测定结果对各元素在样品中的结构状态进行评估，选用更为合理的校正模式，提高半定量分析的准确性[13-15]。为了解决这个问题，本文提出了一种校正模式。该校正模式根据半定量分析初步结果，采用重量法、碘量法、酸碱测定法、红外光谱法有选择性地对未知样品中的LOI、S、C、H2O+等项目进行定量分析，然后将定量分析结果输入SQX该参数的固定结果中，二次平衡归一计算得出新的半定量分析结果。应用该校正模式校正后，铝土矿、碳酸盐矿物、硫化物金属矿等高烧失量矿物的半定量分析结果的准确度得到大幅度提高。
1. 实验部分
1.1 仪器与测量条件
ZSX PrimusⅣ型顺序扫描波长色散X射线荧光光谱仪(日本理学电机工业株式会社)，端窗铑靶X射线管，工作电压20~60kV，工作电流2~160mA，铍窗厚度30μm，视野光栏0.5~30mm，准直器: S2/S4，探测器: PC/SC，分光晶体:RX 25/Ge/PET/LiF200[16-19]。测量元素范围9F~92U。BP-1型压样机(丹东北方科学仪器公司)。各元素具体的测量条件见表 1。
表 1 仪器测量条件Table 1. Measuring conditions of the XRF equipment分析元素 数据库 靶材 电流
(kV)电压
(mA)滤光片 衰减器 准直器 晶体 探测器 PHA 重元素 Standard Rh 50 60 OUT 1/1 S2 LiF(200) SC 100~300 重元素(1) Sta-Ni400 Rh 50 60 Ni-400 1/1 S2 LiF(200) SC 150~250 Ca-Kα Standard Rh 40 75 OUT 1/1 S4 LiF(200) PC 100~300 K-Kα Standard Rh 40 75 OUT 1/1 S2 LiF(200) PC 100~300 Cl-Kα Standard Rh 30 100 OUT 1/1 S4 Ge PC 150~300 S-Kα Standard Rh 30 100 OUT 1/1 S4 Ge PC 150~300 P-Kα Standard Rh 30 100 OUT 1/1 S4 Ge PC 150~300 Si-Kα Standard Rh 30 100 OUT 1/1 S4 PET PC 100~300 Al-Kα Standard Rh 30 100 OUT 1/1 S4 PET PC 100~250 Mg-Kα Standard Rh 30 100 OUT 1/1 S4 RX25 PC 100~250 Na-Kα Standard Rh 30 100 OUT 1/1 S4 RX25 PC 100~250 F-Kα Standard Rh 40 75 OUT 1/1 S4 RX25 PC 100~300 1.2 SQX分析模拟计算流程
XRF半定量分析可选择测定未知样品中F~U或Ti~U之间的元素，分析测试程序完成后会自动报出大于仪检出限的各元素的分析结果，这时应根据测试结果作一个初步判断是否需要进行SQX计算；如不需要，则可以直接报出测定结果；如测定结果与样品实际结构状态有较大差别，则需选用更为合适的校正模式、平衡组分或添加其他方法测试结果后进行SQX计算，以得到更为合理的测定结果。定性分析的基本流程见图 1。
1.3 样品制备及实验方法
为验证本文提出的半定量分析模式分析校正效果，选用国家标准物质铝土矿GBW(E)70036、碳酸盐矿物GBW07131、硫化物多金属矿GBW07166作为待测样品，在105℃下烘干2h，称取4.5±0.1g，倒入放置于平板模具上的PVC塑料环(外径40mm，内径35mm，高5mm)中，在30t压力下加压30s压制成型，编号，置于样品盒内，用X射线荧光光谱仪半定量分析方法进行测试[20-22]。仪器自动计算出各元素的含量。
根据XRF半定量初步分析结果，按化学标准方法YS/T 575.19—2007、GB/T 3286.8—2014、GB/T 3286.7—2014、GB/T 14353.12—2010、GB/T 8151.2—2012、SN/T 3598—2013、GB/T 2469—1996、YS/T 575.18—2007选择性分析未知样品中的烧失量(LOI)、硫(S)、碳(C)、结晶水(H2O+)，计算出定量结果，备用。
将化学分析结果作为XRF半定量分析软件(SQX)中该元素的固定结果，重新进行平衡计算出新的半定量结果。
2. 结果与讨论
2.1 烧失量对铝土矿类型矿物半定量分析结果的影响
铝土矿是一种土状矿物，化学组成为Al2O3·nH2O，含水不定，多为单水或三水矿物[23-24]。由于XRF的局限性，对于H2O、C这些未定量的参数，其含量在铝土矿中较高[25]，平衡归一化计算时会对Al2O3、SiO2、Fe2O3等元素的影响较大。这时可采用烧失量校正的方法，添加烧失量(LOI)作为该样品的固定值，运行半定量分析软件(SQX)重新计算出新的结果。将GBW(E)70036作为未知样品用XRF定性分析方法进行分析，各种校正模式的计算值与认定值对照结果见表 2。
表 2 铝土矿标准物质GBW(E)70036各种校正模式计算值与认定值对比Table 2. Calculated values and standard values of bauxite standard material GBW(E)70036 in various correction models分析元素 氧化物模式测试
结果(%)添加LOI校正结果
(%)H2O作平衡
校正结果(%)GBW(E)70036
认定值(%)氧化物模式测试结果
相对误差(%)LOI校正结果
相对误差(%)MgO 0.136 0.121 0.116 0.120 13.33 0.83 Al2O3 76.94 67.51 64.46 69.74 10.32 -3.20 SiO2 7.91 6.62 6.12 4.88 62.09 35.66 P2O5 0.159 0.132 0.121 0.120 32.50 10.00 SO3 0.182 0.00 0.139 0.047 - - K2O 1.07 0.880 0.810 0.710 50.70 23.94 CaO 0.258 0.212 0.195 0.180 43.33 17.78 TiO2 5.10 4.17 3.81 3.97 28.46 5.04 Fe2O3 7.42 5.97 5.35 6.09 21.84 -1.97 LOI * 13.70 △ 13.74 - - H2O * △ 18.26 - - - 注：“*”表示XRF不能直接分析该参数，无数据；“△”表示在LOI或H2O其中一项参数有测量结果时，另一项结果不参与校正计算；“-”表示未定值或未统计计算。 据表 2可知，GBW(E)70036以氧化物模式的测试结果与认定值误差较大，当添加LOI校正计算后，其多个元素的平均相对误差由32.8%降至12.3%，准确度大幅提高。此外，在确定未知样品是未经高温灼烧的情况下，还可以采用H2O作为平衡组分直接计算，其计算结果也与认定值较为相近。
2.2 二氧化碳与烧失量对碳酸盐类型矿物半定量分析结果的影响
碳酸盐矿物中CO2的占比较高, 而CO2是SQX软件未能定量参数之一，给定性分析结果带来较大误差。为提高定性分析的准确度，可对CO2或烧失量进行定量分析[26]，添加烧失量或CO2定量分析结果作为该样品的固定值，运行SQX重新计算出新的结果。将GBW07131作为未知样品用XRF定性分析方法进行分析，各种校正模式的计算值与认定值对照结果见表 3。
表 3 碳酸盐标准物质GBW07131各种校正模式计算值与认定值对比Table 3. Calculated values and standard values of carbonate standard material GBW07131 in various correction models分析元素 氧化物模式
测试结果(%)CO2平衡
校正结果(%)添加LOI
校正结果(%)钙镁元素以碳酸盐
计平衡计算(%)GBW07131
认定值(%)氧化物模式测试
结果相对误差(%)LOI校正结果
相对误差(%)MgO 29.73 19.57 19.18 20.4 20.14 47.62 4.77 Al2O3 0.759 0.454 0.449 0.451 0.290 161.72 -54.83 SiO2 2.18 1.29 1.27 1.28 1.15 89.57 -10.43 P2O5 0.051 0.030 0.030 0.030 0.016 218.75 -87.50 SO3 0.442 0.256 0.00 0.254 - - - K2O 0.292 0.161 0.160 0.160 0.160 82.50 0.00 CaO 64.54 31.76 32.07 31.50 30.93 108.66 -3.69 TiO2 0.045 0.018 0.0186 0.0178 0.013 246.15 -43.08 MnO 0.038 0.015 0.016 0.011 0.012 216.67 -33.33 Fe2O3 0.435 0.169 0.176 0.167 0.170 155.88 -3.53 CO2 * 45.66 △ - - - - LOI * △ 45.67 - 45.73 - 0.13 注：“*”表示XRF不能直接分析该参数，无数据；“△”表示在LOI或CO2其中一项参数有测量结果时，另一项结果不参与校正计算；“-”表示未定值或未统计计算。 据表 3可知，GBW07131以氧化物模式测试结果较认定值误差较大。当添加LOI校正计算后，其多个元素的平均相对误差由122.4%降至27.2%，准确度大幅提高。此外，采用滴加稀盐酸确定未知样品是碳酸盐矿物的情况下，可以采用CO2作为平衡组分直接计算或者将CaO、MgO换算成为CaCO3、MgCO3计算模式重新平衡计算，其结果也与认定值较为相近。
2.3 碳硫元素对硫化物多金属矿类型矿物半定量分析结果的影响
硫化物多金属矿中的碳、硫元素含量较高，以氧化物模式对该类型样品进行半定量分析时误差较大。当采用化学法测定这类样品的烧失量时，硫化物金属矿中的硫在高温下会被空气中的氧替换，不仅会出现烧蚀减量，还会出现烧蚀增量，使得烧失量的结果是不准确的[27-28]，因此不能把烧失量校作为该未知样品的固定值对测定结果进行平衡计算。这时可以采用化学法测定该未知样品中的C、S元素，作为该样品的固定值，运行半定量分析软件(SQX)重新计算出新的结果。将GBW07166作为未知样品用XRF半定量程序进行分析，各种校正模式的计算值与认定值对照结果见表 4。
表 4 硫化矿多金属矿标准物质GBW07166各种校正模式计算值与认定值对比Table 4. Calculated values and standard values of sulfide polymetallic ore standard material GBW07166 in various correction models分析元素 氧化物模式测试
结果(%)总硫、总碳固定
平衡计算(%)LOI平衡计算
(%)Sulfide模式
校正结果(%)GBW07166
认定值(%)氧化物模式测试
结果相对误差(%)总硫、总碳校正
结果相对误差(%)MgO 0.360 0.350 0.675 0.505 0.310 16.13 12.90 Al2O3 1.60 1.55 3.03 2.29 1.25 28.00 24.00 SiO2 3.34 3.50 6.26 4.86 3.78 -11.64 7.41 S 18.43 33.80 0.00 27.75 33.80 - - K2O 0.306 0.433 0.387 0.484 0.320 -4.38 35.31 CaO 2.05 2.02 2.61 3.27 1.96 4.59 3.06 Fe 18.22 28.58 27.45 30.84 29.60 -38.45 -3.45 Cu 15.50 28.00 30.72 28.42 24.20 -35.95 15.70 Zn 0.025 0.057 0.049 0.055 0.057 -56.14 0.00 C * 0.138 △ - - - - LOI * △ 27.04 - - - - 注：“*”表示XRF不能直接分析该参数，无数据；“△”表示在LOI或C其中一项参数有测量结果时，另一项结果不参与校正计算；“-”表示未定值或未统计计算。 据表 4可知，GBW07166以氧化物模式或添加LOI校正计算结果后，测试结果较认定值误差较大，当添加全硫、全碳校正计算结果后，其多个元素的平均相对误差由27.2%降至9.5%，准确度大幅提高。此外，在没有条件测定全硫、全碳元素时，选用SQX软件中Sulfide校正模式重新平衡计算，其结果也与认定值较为相近。
3. 应用实例
选取3件不同类型的未知样品，应用XRF半定量程序分析，根据XRF半定量初步分析结果，计算对照结果见表 5。未知样品1、2在添加烧失量(LOI)校正计算后半定量分析结果与化学法分析结果比较，多个元素的平均相对误差分别由46.2%降至18.0%和37.6%降至7.1%。未知样品3添加总硫、总碳校正计算结果后，其多个元素的平均相对误差由28.1%降至10%，准确度得到了明显提高。若与DZ/T 130—2006《地质矿产实验室测试质量管理规范》要求定量分析规范中误差允许限(Yc)相比较，除少部分项目能满足规范要求外，大部分项目还是达不到定量分析要求。但是如铝土矿中的Al2O3，碳酸盐矿物中的CaO、MgO，硫化物多金属矿中Fe、Zn、Cu、Pb等元素的相对误差均在5%以内，与DZ/T 130—2006要求较为接近。
表 5 某未知样品各种校正模式的计算值与化学分析值对比Table 5. Calculated values and chemical analysis values of various correction modes for the unknown sample样品编号 分析元素 氧化物模式测试
结果(%)平衡校准计算
结果(%)化学法测定值
(%)氧化物模式测试
结果相对误差(%)平衡校准计算结果
相对误差(%)允许限Yc
(%)Al2O3 86.97 76.11 78.01 11.49 -2.44 0.63 SiO2 2.94 1.82 1.31 124.43 38.93 4.17 Fe2O3 3.26 2.54 2.55 24.84 -0.46 5.11 TiO2 4.29 3.4 3.10 38.38 9.78 4.80 未知样品1 K2O 0.19 0.17 0.16 18.75 6.25 10.45 CaO 0.33 0.31 0.31 6.45 0.00 9.00 MgO 0.29 0.25 0.20 45.00 25.00 9.95 P2O5 0.28 0.22 0.14 100.00 61.37 10.76 LOI * 14.6 14.6 - - 2.58 Na2O 0.76 0.71 0.781 -2.59 -8.72 7.17 MgO 0.29 0.24 0.21 39.14 14.95 9.84 Al2O3 0.48 0.39 0.31 54.13 26.16 9.00 SiO2 1.15 0.93 0.83 38.66 12.45 7.05 P2O5 1.24 1.00 0.97 28.34 2.88 6.77 Fe2O3 1.70 1.21 1.18 44.18 2.13 6.41 未知样品2 S 4.72 △ 3.21 47.04 - 4.74 CaO 1.13 0.81 0.83 36.64 -2.40 7.05 Cr 18.49 13.09 12.89 43.46 1.53 2.75 Ni 22.81 16.18 16 42.55 1.12 2.47 Cu 14.53 10.31 10.33 40.62 -0.23 3.04 Zn 3.04 2.16 2.02 43.24 5.94 5.49 LOI * 37.00 37.00 - - - MgO 0.21 0.200 0.185 14.49 8.11 10.12 Al2O3 0.53 0.472 0.427 23.87 10.61 8.34 SiO2 2.18 1.818 1.650 32.00 10.15 5.83 P2O5 0.02 0.024 0.028 -34.29 -13.21 14.91 S 18.84 34.02 34.02 - - - K2O 0.05 0.048 0.042 15.44 13.31 13.77 未知样品3 CaO 0.15 0.127 0.137 7.72 -7.50 10.81 TiO2 0.03 0.031 0.026 30.98 20.78 15.18 Fe 4.52 6.942 6.720 -32.81 3.31 3.64 Cu 0.68 1.057 1.218 -44.37 -13.18 6.36 Zn 32.24 50.517 48.250 -33.18 4.70 1.15 Pb 1.58 2.505 2.646 -40.46 -5.33 5.05 C * 1.21 1.21 - - - 注：“*”表示XRF不能直接分析该参数，无数据；“△”表示在LOI有测量结果时，该项结果不参与校正计算；“-”表示未定值或未统计计算。 4. 结论
实验证明采用本文提出的校正模式进行校正，分析铝土矿、碳酸盐矿物和硫化物多金属矿中多元素的平均准确度提高了2.6~4.5倍，半定量分析结果准确度大幅提高。其中，铝土矿中的Al2O3，碳酸盐矿物中的CaO、MgO，硫化物矿物中Fe、Zn、Cu、Pb等主量元素的相对误差均在5%以内，与化学法分析结果较为相近。本方法可快速、较为准确地测定铝土矿、碳酸盐矿物和硫化物矿物中多元素的含量。
这种化学法与半定量分析软件相结合的半定量校正模式，不仅可用于铝土矿、碳酸盐矿物和硫化物矿物，还适用于烧失量较高的锰矿、磷矿等矿物的压片半定量分析[29-30]。对于硫化物矿物等多金属矿的定量全分析，因为这类矿物容易腐蚀铂坩埚而很少采用熔片制样XRF分析[31]，通常采用化学分析法，但流程繁琐，本文研究方法可作为一种有效的矿石全分析的补充手段。
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图 1 两种模式下干扰元素镍对钌测定的影响
Figure 1. Effect of interference element Ni on the determination of Ru in two modes
表 1 ICP-MS仪器工作参数
Table 1 Working parameters of ICP-MS instrument
工作参数 设定值 工作参数 设定值 射频功率 1550W 冷却气流速 15L/min 采样深度 8mm 载气流速 1L/min 雾化室温度 2℃ 辅助气流速 1L/min 提取透镜电压 −165V 碰撞气(He)流速 3.6L/min 
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表 2 镍锍试金配料组成
Table 2 Composition of nickel sulfide fire assay ingredients
样品类型 称样量
(g)硫
(g)羰基镍
(g)羰基铁
(g)硼砂
(g)碳酸钠
(g)二氧化硅
(g)面粉
(g)土壤 20 2 1.6 4 25 20 5 1 水系沉积物 20 2 1.6 4 25 20 6 1
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表 3 不同盐酸对应的流程空白
Table 3 Blank values corresponding to different classes of hydrochloric acid
试剂 Ru
(ng/g)Rh
(ng/g)Pd
(ng/g)Os
(ng/g)Ir
(ng/g)Pt
(ng/g)Ⅰ-分析纯 0.0350 0.0003 0.0023 0.0006 ＜0.0001 0.0019 Ⅰ-优级纯 0.0346 0.0004 0.0013 0.0005 ＜0.0001 0.0015 Ⅱ-分析纯 0.0018 0.0001 0.0016 0.0002 ＜0.0001 0.0012 Ⅱ-优级纯 0.0015 0.0002 0.0013 0.0002 ＜0.0001 0.0011
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表 4 不同熔剂用量对应的流程空白
Table 4 Blank values corresponding to different amounts of flux
熔剂用量
(g)Ru
(ng/g)Rh
(ng/g)Pd
(ng/g)Os
(ng/g)Ir
(ng/g)Pt
(ng/g)20 0.390 0.051 0.340 0.010 0.014 0.141 40 0.511 0.076 0.366 0.008 0.008 0.126 60 0.303 0.071 0.361 0.009 0.009 0.125 80 0.414 0.064 0.349 0.010 0.009 0.128
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表 5 不同镍基体的铂族元素含量
Table 5 PGE contents in different Ni matrices
试剂 Ru
(ng/g)Rh
(ng/g)Pd
(ng/g)Os
(ng/g)Ir
(ng/g)Pt
(ng/g)镍粉 0.469 0.095 0.509 4.977 1.962 0.590 硝酸镍 0.779 0.169 0.918 1.107 0.573 0.753 氧化镍 0.560 0.159 0.452 0.480 0.744 0.597 Ⅴ-羰基镍 0.175 0.109 0.096 0.050 0.006 0.120 Ⅵ-羰基镍 0.012 0.010 0.091 0.009 0.009 0.125
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表 6 镍元素对钌元素测定结果的影响
Table 6 Influence of Ni on the determination results of Ru
样品编号 Ru的加入量
(ng/mL)Ni的加入量
(ng/mL)Ru的测定值
(ng/mL)Ru的回收率
(%)1 0.04 500 0.042 105 2 0.04 1000 0.047 116 3 0.04 5000 0.053 133 4 0.04 10000 0.060 150 5 0.04 50000 0.086 214 6 0.04 100000 0.122 304 7 0.04 500000 0.209 521
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表 7 动能歧视模式下铂族元素检出限和测定下限
Table 7 Detection limits and quantification limits for PGEs in kinetic energy discrimination model.
方法参数 Ru
(ng/g)Rh
(ng/g)Pd
(ng/g)Os
(ng/g)Ir
(ng/g)Pt
(ng/g)空白平均值 0.013 0.008 0.155 0.015 0.011 0.112 检出限(3s) 0.005 0.008 0.050 0.012 0.007 0.058 测定下限(10s) 0.015 0.024 0.150 0.036 0.021 0.174
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表 8 标准物质分析结果
Table 8 Analytical results of PGEs in national reference materials
标准物质编号 元素 测定值
(ng/g)RSD
(%)标准值
(ng/g)相对误差
(%)加标量
(ng/g)测定总值
(ng/g)回收率
(%)GBW07288
(土壤)Ru 0.053 7.21 0.05 6.00 0.05 0.101 94 Rh 0.019 7.31 0.017 11.8 0.05 0.068 98 Pd 0.290 9.23 0.26 11.5 0.5 0.750 92 Os 0.055 8.05 0.05 10.0 0.05 0.109 108 Ir 0.035 8.55 0.032 9.38 0.05 0.081 92 Pt 0.280 6.93 0.26 7.69 0.5 0.790 102 GBW07289
(水系沉积物)Ru 0.110 5.38 0.1 10.0 0.1 0.220 110 Rh 0.103 7.08 0.095 8.42 0.1 0.198 95 Pd 2.200 7.66 2.3 −4.35 5 7.660 109 Os 0.066 7.96 0.06 10.0 0.05 0.120 108 Ir 0.054 7.16 0.05 8.00 0.05 0.103 98 Pt 1.700 5.33 1.6 6.25 1 2.790 109 GBW07294
(土壤)Ru 0.588 6.28 0.66 −10.9 0.5 1.051 93 Rh 1.080 5.88 1.1 −1.82 1 2.010 93 Pd 15.100 3.85 15.2 −0.66 10 25.600 105 Os 0.650 9.37 0.64 1.56 0.5 1.170 104 Ir 1.110 3.88 1.2 −7.50 1 2.131 102 Pt 14.100 3.99 14.7 −4.08 10 23.500 94
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期刊类型引用(5)
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