Quantitative Investigation of the Size-dependent Aggregation of Nanoplastics
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摘要:
由于塑料制品大量使用和不当处置，环境中微塑料(尤其是纳米塑料)的地球化学行为已成为全球关注的热点问题。团聚效应是控制纳米塑料地球化学行为的重要因素。自然界中纳米塑料大小不一，然而已有的研究结果对于纳米塑料尺寸与团聚效应的关联性还存在一定矛盾。为揭示不同尺寸纳米塑料的团聚行为及影响作用机制，本文以50nm、100nm、200nm聚苯乙烯纳米塑料(PS50、PS100、PS200)为研究对象，利用动态光散射技术实时监测不同pH(3.0～10.0)及NaCl溶液(浓度0～800mmol/L)中纳米塑料的Zeta电位(ζ 电位)和水动力直径，并通过理论计算得到三种粒径纳米塑料的临界团聚浓度(CCC)和总相互作用能。PS50、PS100和PS200去离子水中的初始ζ电位分别为−35.2mV、−35.1mV和−38.2mV，高的表面负电荷使其在水中保持分散。离子强度增加引起的电荷屏蔽效应促进了纳米塑料的团聚，PS50、PS100、PS200在NaCl溶液中CCC值分别为325mmol/L、296mmol/L、264mmol/L，表明初始ζ电位值接近时，粒径越小的纳米塑料越稳定，能够在环境中较长时间地迁移。随着pH从酸性增加至碱性，纳米塑料表面酸性官能团发生去质子化，负电荷增多，导致其团聚行为受到抑制。当pH=7时，即使是在较高离子强度下(400mmol/L NaCl)，PS100 和 PS200基本恢复稳定，但 PS50 仍发生快速团聚，可能因为在此条 件下 PS50 的 ζ 电位仍较小(−19.3mV)。通过回归分析可知，三种尺寸纳米塑料的团聚行为与ζ电位密切相关(r2为0.70～0.88)。因此在实际应用中，需要综合考虑溶液pH、离子强度以及纳米塑料自身尺寸等容易影响ζ电位的因素，以更精准地预测和评估纳米塑料在自然环境中的地球化学行为。
要点（1）尺寸越小的纳米塑料(NPs)具有更高的临界团聚浓度(CCC)，因此可以迁移更远的距离。
（2）离子强度和pH是影响NPs聚集的关键因素。
（3）NPs的团聚受带电界面电荷状态的控制。
HIGHLIGHTS(1) Smaller NPs have higher CCC value than larger NPs and thus may transport longer distance.
(2) Ionic strength and pH are the key factors to affect the aggregation of NPs.
(3) The aggregation of NPs is controlled by their electrical state of charged interfaces.
Abstract:The geochemical behavior of microplastics (MPs) and nanoplastics (NPs) in the environment has become a global hot topic. Aggregation effect is an important factor controlling the geochemical behavior of NPs, yet there is conflicting evidence regarding the dependence of aggregation on NPs size. Investigating the general patterns and dominant mechanisms governing the aggregation behavior of different-sized NPs under various environmental conditions, will provide help in understanding and predicting the fate of NPs with different sizes. The study has shown that NPs with the same chemical composition but different sizes have different stability and mobility under the same conditions. The critical coagulation concentration (CCC) for NPs increases with the decrease in particle size at a fixed surface ζ potential (CCC=325mmol/L, 296mmol/L, 264mmol/L for 50nm, 100nm, and 200nm, respectively); indicating smaller NPs may transport longer distances. As the pH increased from 5.5 to 7, the negative surface charge of 100 and 200nm NPs allowed them to remain stable even at higher ionic strength. However, 50 nm NPs underwent rapid aggregation because of its smaller ζ potential. Therefore, the effects of pH, ionic strength and NPs sizes should be considered comprehensively in predicting and evaluating the geochemical behavior of NPs in the natural environment. The BRIEF REPORT is available for this paper at http://www.ykcs.ac.cn/en/article/doi/10.15898/j.ykcs.202305020058.
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Keywords:
- nanoplastics /
- aggregation /
- dynamic light scattering /
- critical coagulation concentrations /
- size-dependent effect
BRIEF REPORTSignificance: Microplastics (MPs) are defined as plastic fragments or particles with the size of <5mm[3], which are directly manufactured by industry or derived from weathering of large-sized plastic[4]. These tiny plastics are easily ingested by organisms of various trophic levels and have toxic effects on organisms[5-6]. Clarifying the geochemical transport behavior and fate of MPs is a crucial prerequisite for assessing their environmental impacts throughout the entire life cycle, making it a hot topic in environmental research[7-8]. Among all the MPs, those with a size <1μm are defined as nanoplastics (NPs)[9-10]. NPs exhibit stronger interactions with other pollutants and more adverse eco-impacts on living things than MPs[11-12]. Furthermore, the quantitative analysis of NPs is more challenging than that of MPs. The smaller size, stronger Brownian motion, and higher specific surface area of NPs result in significantly different environmental behaviors from MPs[13]. Therefore, it is necessary to further study the transport and fate of NPs.
The aggregation behavior of NPs in aquatic environments is an important factor influencing its transport and fate[14]. When the aggregation rate is slow, NPs can remain suspended and transport long distances with river currents. Conversely, rapid aggregation leads to a substantial increase in the size of NPs aggregates, making them more prone to settling at the water bottom[15]. In recent years, scholars have investigated the influence of environmental factors such as pH value and ion strength on NPs aggregation by using dynamic light scattering (DLS) technique[18-19]. These studies have not taken into account the effects of NPs size, while the NPs exist as various sizes in the environment. Size, as a paramount property of NPs is likely to significantly impact their aggregation behavior. Currently, there is limited research on how changes in NPs size affect aggregation behavior. Therefore, it is imperative to explore the combined effects of particle size, ion strength, pH value, and other factors on NPs aggregation behavior and elucidate the primary mechanisms involved.
Investigating the general patterns and dominant mechanisms governing the aggregation behavior of different-sized NPs under various environmental conditions, will provide help in understanding and predicting the fate of NPs with different sizes. The study has shown that NPs with the same chemical composition but different sizes have different stability and mobility under the same chemical solution conditions. The critical coagulation concentration (CCC) for NPs increases with the decrease in particle size at a fixed surface ζ potential, thus the smaller NPs may transport longer distances. Therefore, the effects of solution pH, ionic strength and size of NPs should be considered comprehensively in predicting and evaluating the geochemical behavior of NPs in the natural environment.
Methods: The dynamic light scattering (DLS) technique was used to quantitively measure the aggregation kinetics of three typical polystyrene NPs (PSNPs) with the size of 50nm (PS50), 100nm (PS100) and 200nm (PS200), respectively, under various environmental conditions. The aggregation kinetics experiments of PSNPs were conducted at room temperature (25℃). In brief, samples containing 1.25mL of PSNPs suspension (20mg/L) were prepared in a sample cuvette. Subsequently, 1.25mL of NaCl electrolyte solution was added to the samples. After 1 second of rapid mixing, the sample cuvette was immediately transferred to the sample chamber of a nanoparticle size/zeta potential analyzer to measure the hydrodynamic diameters (Dh) of PSNPs.
To clarify the size-dependent aggregation of NPs, the ζ potentials and Dh of PS50, PS100 and PS200 were measured in the presence of NaCl (0-800mmol/L). The initial pH value was not adjusted, and the pH of the samples was finally stabilized at 5.5±0.3 after testing. Furthermore, the ζ potentials and hydrodynamic diameters of PS50, PS100, PS200 were obtained at the range of pH (3.0-10.0) in a 400mmol/L NaCl solution. The initial pH of the solution was adjusted using 0.1mol/L sodium hydroxide and 0.1mol/L hydrochloric acid. For each set of solution conditions, the experiments were repeated twice. Importantly, the critical coagulation concentration (CCC) and interaction potential energy were calculated.
Data and Results: (1) DLS technique combined with Derjaguin-Landau–Verwey–Overbeek theory (DLVO) was used to investigate the mechanism of the size-dependent aggregation of NPs. The morphology of PS50, PS100 and PS200 were all spherical and had good dispersity in deionized water, as shown in Fig.1. With the increase of NaCl concentration, the aggregation rate of the three NPs gradually increased at 200-400mmol/L. When the NaCl concentration was above 400mmol/L, the aggregation rate of NPs reached the maximum and no longer increased, as shown in Fig.2. To quantify the dispersion stability of different-sized NPs under various solution conditions, the attachment efficiencies (α) of PS50, PS100, and PS200 as a function of solution electrolyte concentration were obtained by normalizing the initial aggregation rate of NPs according to equation (3). The CCC values of PS50, PS100 and PS200 in NaCl solution were 325mmol/L, 296mmol/L and 264mmol/L by fitting the stability profile with equation (4). The results show that larger PSNPs were more likely to aggregate.
Fig.5 shows that the stability profile was in good agreement with DLVO theoretical calculations. For instance, the energy barrier among NPs decreased with increasing NaCl concentration resulting in a higher tendency for NPs to aggregate. As the electrolyte concentration exceeded the CCC, the energy barrier was eliminated, thus the van der Waals attraction forces dominated the particle interactions. However, the CCC values of PS50, PS100 and PS200 obtained by the aggregation kinetics experiment deviated from DLVO theory. The reason for such a deviation may be due to the application of the superposition principle in evaluating the electrical interaction energy[38]. Taking into account the case that the thickness of the electrical double layer isn’t necessarily much smaller than the linear size of the particle, it can be speculated that the smaller NPs had the thicker double electric layer, accordingly, a higher electrolyte concentration is required to completely compress the double electric layer.
(2) ζ potential serves as a crucial parameter for quantitative assessment of NPs stability. Under all experimental conditions, a significant correlation was observed between the attachment efficiency and ζ potential (r2=0.70-0.88, p<0.05), as shown in Fig.6. This indicates that environmental factors such as ion strength and pH values affected the dispersion stability of NPs by altering electrostatic interaction. Data obtained by Lee et al.[19] for PSNPs in natural river water and seawater, as well as their ζ potentials, also supported this finding. The PSNPs had ζ potentials in the range of −30mV to −24mV in river water at different temperatures, indicating PSNPs exhibited a relatively stable condition. In seawater, the ζ potentials of PSNPs were in the range of −15mV to −5mV, and rapid aggregation occurred. Therefore, ζ potentials can be used to preliminarily assess the stability of NPs.
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中国的铍资源主要来自花岗伟晶岩型铍矿床，据2014年底统计，中国有91处铍矿区，总储量为57.1万吨，预计到2030年铍需求量将达到250吨。然而，中国铍矿品位普遍较低(低于0.25%)，98%的矿山因成本过高无法开采，国内铍资源产量无法满足自身需求，对外依存度高达87%［1-2］。因此，铍金属已成为“被卡脖子的金属”。此外，由于铍矿具有军工属性，中国在海外获取铍资源的途径得不到保障，资源安全形势不容乐观。因此，针对铍矿床中Be的赋存形式进行深入研究，尤其是对含Be矿物中Be的含量进行定量研究，显得尤为重要，这对于提高Be矿床的价值和利用效率具有重要意义。
对于含Be矿物中原位Be的精准定量测量，前人使用二次离子质谱(SIMS)、激光剥蚀电感耦合等离子体质谱(LA-ICP-MS)等仪器分析Be元素含量［3-8］，但这些方法主要用于分析矿物中的微量Be含量，且对矿物均有损伤。电子探针(EPMA)高分辨率、高灵敏度，可测定Be～U元素含量，具有矿物微区无损定量分析的优势。利用电子探针对含Be矿物中Be元素进行精确定量测量，对含Be矿物的形成机制、元素类质同象替代特征、矿物晶体化学结构［9-12］、铍矿床的成矿机制等科学研究方面具有重大意义。同时，采用电子探针对关键金属Be进行分析，对于了解铍的赋存状态和地球化学性质非常重要，也为铍矿床的成矿理论研究、勘查和指导找矿提供了重要的技术支持。
Be是电子探针能够测量的最轻元素，由于其特征X射线的Kα线系能量极低(小于1 keV)，以及绿柱石、硅铍石或羟硅铍石等常见铍矿物中Si元素的高级次阶峰对Be Kα线产生干扰，使得测量难度极大，同时也受限于仪器所配备的分光晶体的研发。因此，Be的精确定量分析一直是困扰电子探针实验人员多年的难题。尽管如此，电子探针对Be矿物中的Be还是进行了大量的定量测量研究。2001年，Dyar等［13］使用CAMECA SX-50电子探针测得硅铍石和硼铍石中Be含量，并给出Be的校正标样是Be金属，这是电子探针对含Be矿物中Be测量方面的开创性工作。2006年张文兰等［14］使用JXA-8800电子探针定量分析绿柱石中Be，标样为金属Be，并对分析条件和分析过程中遇到的问题进行讨论。2017年Khiller［9］使用CAMECA SX-100电子探针对马林斯基矿(Mariinskite)中的Be元素进行定量分析。2020年赵同新等［15］使用EPMA-1720电子探针对新疆绿柱石进行定量分析；吴润秋等［16］使用EPMA-1720H电子探针对绿柱石、硅铍石、羟硅铍石进行定量分析；张文兰等［17］使用JXA-8100电子探针重新对绿柱石进行定量分析；2022年张文兰等［11］使用JXA-8100电子探针对除了绿柱石之外的金绿宝石、硅铍石、硼铍石和锌日光榴石开展Be元素最佳定量分析方法研究；Wu等［18］使用EPMA-1720H电子探针对绿柱石、硅铍石、羟硅铍石、日光榴石、香花石、金绿宝石、镁塔菲石、孟宪民石、闽江石、磷钙铍石、磷锶铍石进行定量分析研究。
除了前人研究的绿柱石、硅铍石、羟硅铍石之外［19-21］，还有需要进一步研究的磷铍钙石、磷钙铍石、磷铍钠石等铍磷酸盐矿物［22-25］。在对铍磷酸盐矿物使用配备LSA300分光晶体的电子探针进行定量分析过程中，发现P的高级次阶峰会提升Be Kα峰的背景值，这表明LSA300分光晶体在分析铍磷酸盐矿物方面存在一定的局限性［18］。因此，本文采用配备LDE3H分光晶体的电子探针，对磷铍钙石中Be元素在不同加速电压、分析束流下的特征X射线的峰背比、磷铍钙石全元素定性分析等方面进行了一系列研究，获得一套准确定量分析磷铍钙石元素含量的方法。拟为电子探针用户提供更精确的铍磷酸盐矿物定量分析方法，并为开展铍资源的研究及找矿勘查提供更有力的技术支撑。
1. 实验样品制备与分析条件设定
1.1 实验样品选择与前处理
磷铍钙石一般呈无色、灰色、浅黄等颜色产在富磷花岗岩或者花岗伟晶岩中，拉曼特征峰值为516、529、584、594、983、1005、1128、1140cm−1等。本次分析测试的磷铍钙石样品采自赣西北狮子岭Li-Ta矿床中的锂(白)云母花岗岩。对锂(白)云母花岗岩中磷铍钙石进行激光拉曼光谱分析，与RUFF拉曼数据库中磷铍钙石(RRUFF R100064)进行对比，确定该矿物为磷铍钙石(图1)。
在磷铍钙石定量分析过程中，首先将含有磷铍钙石的锂(白)云母花岗岩加工成宽25mm、长35mm的探针片，并进行抛光和镀碳。本实验使用日本电子IB-29510VET高真空蒸镀仪对样品进行镀膜，确保碳膜厚度达到15nm，从而使样品与标样的碳膜厚度保持一致，避免因碳膜厚度不同而导致的误差。
1.2 分析条件的选择
前人已经开展了一系列关于使用电子探针对含Be矿物中Be进行定量测量的研究，这些研究的具体测试条件见表1。然而，Be作为一种超轻元素，其产生的X射线稀疏且量少，因此其本身的计数率和峰背比相对较低。在进行Be元素分析时，首先需要考虑的是如何提高Be的计数率和峰背比。为了提高Be的计数率与峰背比，在设置分析条件时，要重点考虑加速电压与分析束流的大小［17］。参考张文兰等［17］对绿柱石中Be元素的波谱扫描方式，本次实验使用配有LDE3H分光晶体的电子探针对磷铍钙石进行Be元素波谱扫描。首先固定分析束流大小为100nA，分别使用不同的加速电压10kV、12kV、15kV对磷铍钙石中Be进行波谱扫描，扫描条件：步长为50μm；驻留时间为1000ms；L值的范围为140～220mm，该范围覆盖了Be元素的整个峰型。将扫描结果进行相关处理(图2a)。图2a显示在加速电压为10kV、12kV、15kV中，加速电压为10kV时，Be元素的峰背比最高(表2)。因此，选取10kV为磷铍钙石进行原位分析时的加速电压。
表 1 铍矿物电子探针定量分析条件及标准样品的选择Table 1. Analytical conditions and selection of standard samples for Be-bearing minerals determined by EPMA.电子探针型号 分光晶体 加速电压
(kV)探针电流（nA） 标准样品 含Be矿物 参考文献 CAMECA SX-50 PC3 10 40 金属Be 硅铍石、硼铍石 Dyar等［13］ 日本电子JXA-8800 LDEB 10 20 金属Be 绿柱石 张文兰等［14］ CAMECA SX-100 PC3 10 100～150 金绿宝石 马林斯基矿 Khiller等［9］ 岛津EPMA-1720 LSA200 10 20 绿柱石 绿柱石 赵同新等［15］ 岛津EPMA-1720H LSA300 12 50～200 硅铍石、绿柱石 绿柱石、硅铍石、羟硅铍石 吴润秋等［16］ 日本电子JXA-8100 LDE3H 10 20 金属Be 绿柱石 张文兰等［17］ 日本电子JXA-8100 LDE3H 10(锌日光
榴石15)20 硅铍石、金属Be 金绿宝石、硅铍石、硼铍石、锌日光榴石 张文兰等［11］ 岛津 EPMA-1720H LSA300 10 50～100 硅铍石、绿柱石、日光
榴石、硅铍铝钠石(SPI)、合成闽江石绿柱石、硅铍石、羟硅铍石、日光榴石、
香花石、金绿宝石、镁塔菲石、孟宪民石、闽江石、磷钙铍石、磷锶铍石Wu等［18］ ![]()
图 2 不同条件下磷铍钙石Be元素的波谱扫描图：(a)束流100nA，不同加速电压条件下磷铍钙石波谱扫描图；(b)加速电压10kV，不同束流条件下磷铍钙石波谱扫描图Figure 2. Be element spectral scanning results of herderite under different conditions. (a) Spectral scanning results of herderite under different accelerating voltages at the probe current of 100nA; (b) Spectral scanning results of herderite under different probe currents at the accelerating voltage of 10kV表 2 不同加速电压与束流下，磷铍钙石Be元素波谱扫描获得的峰背比值Table 2. Peak to background ratio of herderite by element spectral scanning under different accelerating voltages and probe currents参数 实验条件：分析束流100nA 实验条件：加速电压10kV 15kV 12kV 10kV 100nA 50nA 20nA 10nA 峰位计数(cps) 1609 1718 1831 1831 700 352 188 背景计数(cps) 1107 1071 917 917 443 188 116 峰背比 2.91 3.21 3.99 3.99 3.16 3.74 3.24 当加速电压确定为10kV后，分别选取不同的分析束流(10、20、50、100nA)，对磷铍钙石进行Be元素波谱扫描，扫描条件同上。分析结果(图2b)显示Be的特征X射线强度会随着分析束流的增大而增大。但在电子探针测量过程中，含Be矿物都会发生Be峰位漂移且漂移的大小与分析束流大小相关［4，16-17］，对磷铍钙石进行Be元素波谱扫描时，同样也发现了Be峰位漂移现象(图2b)。由于Be峰位漂移会导致Be的最大计数位置与最小计数位置发生变化，峰背比也会随之发生变化。因此，本文将不同分析束流下的Be的最大计数位置为峰位，最小计数位置为上下背景，各自测试60s，然后将不同分析束流下的峰位计数、背景计数进行相关计算，获取峰背比(表2)。
使用大的分析束流分析样品时，存在着样品和碳膜被损坏的风险，尤其是对于含水和含挥发组分F的矿物［11］。磷铍钙石晶体结构式为CaBePO4[F0.75(OH)0.25]，是一种既含H2O也含F的矿物。在本次测试中，加速电压为10kV，分析束流分别为100、50、20、10nA进行测量，发现电流越大，对磷铍钙石的损坏程度越大(图3)。考虑到Be的峰背比以及分析束流对磷铍钙石的损伤，所以磷铍钙石中Be元素最佳测量条件为加速电压10kV，分析束流20nA。
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图 3 磷铍钙石图像：(a)分析前磷铍钙石BSE(背散射电子)图像；(b)分析后磷铍钙石BSE图像；(c)分析前磷铍钙石SE(二次电子)图像；(d)分析后磷铍钙石SE图像Figure 3. Images of herderite: (a) BSE image of herderite before analysis; (b) BSE image of herderite after analysis; (c) SE image of herderite before analysis; (d) SE image of herderite after analysis2. 磷铍钙石的定量分析结果
本次研究在定量分析前，首先使用配有LDE3H分光晶体的电子探针对磷铍钙石采用加速电压为10kV、分析束流为20nA进行全元素定性分析(测试条件见表3)。从结果来看，该磷铍钙石主要由Ca、P、Be、F元素组成，不含Fe、Mn等原子序数大的元素(图4)，避免了Fe、Mn等原子序数大的元素对Be元素的质量吸收效应［17］；也不含有Si元素，避免了Si元素的高级次阶峰(L2，3和M1)对Be背景值的干扰［15］。
表 3 磷铍钙石全元素测量条件Table 3. Measurement conditions of total elements in herderite分光晶体 L值范围
(mm)驻留时间
(ms)步长
(μm)涉及的元素种类 LDE3H 140～220 1000 50 Be LDE1 75～95 500 50 F、Fe、Mn TAP 72.314～135 500 50 Na、Mg、Al、Si等 LiF 120～160 500 50 Fe、Mn等 PETH 90～210 500 50 P、Ca、K、Cl等 ![]()
图 4 磷铍钙石定性分析图像：(a)全元素分析；(b) 图a中紫框放大Figure 4. Qualitative analysis of herderite: (a) Total element analysis; (b) The purple box in Figure a is enlarged.对磷铍钙石进行定性分析后，接下来对磷铍钙石开展元素定量分析工作。根据上述对于实验条件的探索，加速电压10kV、分析束流20nA不仅可以得到磷铍钙石中Be的合适计数率与峰背比，还满足磷铍钙石中的其他元素(如Ca、F、P等)的测量。此外，加速电压10kV、分析束流20nA对磷铍钙石的损伤程度相对较小。由于磷铍钙石中Be含量相对较低，因此采用延长峰位和背景的测试时间，用来提高测量Be元素的准确性。峰位与背景测量时间分别为60s和30s，校正方法采样ZAF法(测试条件见表4)，分析结果见表5。
表 4 磷铍钙石定量分析条件Table 4. Quantitative analysis conditions of herderite元素 分光
晶体束斑
(μm)窗口
(V)偏压
(V)增益 计数管高压
(V)峰位
(mm)上背景
(mm)下背景
(mm)峰位计数
时间
(s)背景计数
时间
(s)寻峰
次数脉冲高度分析
模式设定
(Diff/Int)标准样品 F LDE1 10 9.3 0.7 64 1632 84.51 5 5 10 5 1 Dif 黄玉 Ca PETH 10 0 0.7 32 1676 107.561 5 5 10 5 1 Int 磷灰石 Be LDE3H 10 9.3 0.7 64 1736 171.777 11.5 27.5 60 30 1 Dif 金属铍 P PETH 10 0 0.7 32 1688 197.141 5 5 10 5 1 Int 五磷酸镧 表 5 磷铍钙石定量分析结果Table 5. Quantitative analysis results of herderite元素 元素含量(%) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 P2O5 43.08 44.39 42.38 44.70 43.28 42.16 42.77 43.58 44.35 42.49 42.72 43.01 41.48 43.47 44.64 BeO 14.51 15.56 15.78 15.56 15.43 15.16 15.67 15.43 15.50 15.14 15.76 15.39 15.46 15.44 15.12 CaO 33.70 33.84 33.91 33.67 33.91 33.83 34.14 33.86 33.57 34.14 34.12 33.88 33.89 33.84 34.16 F 7.53 8.61 8.01 8.60 8.15 7.67 7.03 8.17 8.14 7.69 8.03 7.45 8.17 8.17 7.69 H2O* 1.90 1.55 1.58 1.60 1.63 1.72 2.10 1.66 1.77 1.75 1.61 1.93 1.39 1.64 2.02 O=F 3.16 3.62 3.36 3.61 3.42 3.22 2.95 3.43 3.42 3.23 3.37 3.13 3.43 3.43 3.23 Total 97.69 100.34 98.57 100.87 98.97 98.52 99.42 99.60 100.63 98.59 99.05 98.77 97.69 99.13 100.56 元素 以P=1为基础 P 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Be 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Ca 0.990 0.965 1.013 0.953 0.992 1.016 1.010 0.983 0.958 1.017 1.011 0.997 1.034 0.980 0.968 F 0.653 0.725 0.706 0.718 0.703 0.679 0.614 0.700 0.685 0.676 0.702 0.647 0.736 0.698 0.644 OH* 0.347 0.275 0.294 0.282 0.297 0.321 0.386 0.300 0.315 0.324 0.298 0.353 0.264 0.302 0.356 元素 磷铍钙石中BeO分析结果 S.D 2.85 2.64 2.49 2.64 2.90 2.94 2.18 3.59 2.49 2.49 3.14 2.18 2.75 3.14 2.93 D.L 589 669 619 669 750 752 553 625 783 619 759 553 706 759 560 宜春雅山岩体中(羟)磷铍钙石 元素 黄小龙等［24，26］ 车旭东等［25］ 点号1 点号2 点号3 点号4 点号5 点号1 点号2 点号3 点号4 点号5 P2O5 44.61 42.98 42.52 42.35 41.64 43.60 44.40 43.42 43.10 42.84 BeO 15.76 15.27 15.05 14.96 14.78 15.45 15.65 15.30 15.21 15.37 CaO 33.34 32.17 32.50 32.58 32.30 33.88 32.68 33.33 33.96 33.24 F 5.31 7.03 5.91 7.28 5.19 4.70 4.96 5.05 5.48 5.10 H2O* 3.14 2.12 2.59 1.92 2.82 3.31 3.28 3.12 2.87 3.02 O=F 2.23 2.95 2.48 3.06 2.18 1.97 2.08 2.12 2.30 2.14 Total 99.93 96.62 96.09 96.04 94.55 98.96 98.89 98.10 98.32 97.43 元素 以P=1为基础 P 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Be 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Ca 0.946 0.947 0.967 0.974 0.966 0.983 0.931 0.971 0.997 0.996 F 0.445 0.611 0.519 0.642 0.445 0.403 0.417 0.434 0.475 0.445 OH* 0.555 0.389 0.481 0.358 0.555 0.597 0.583 0.566 0.525 0.555 注：“*”为计算值；S.D为标准偏差(%)；D.L为最低探测极限(μg/g)。 表5中对赣西北狮子岭岩体中的磷铍钙石进行了15个点的测试分析。磷铍钙石中P2O5含量为41.48%～44.7%（平均值43.23%）；BeO含量14.51%～15.78%（平均值15.39%）；CaO含量33.57%～34.16%（平均值33.89%）；F含量7.03%～8.61%（平均7.94%），与雅山岩体中的(羟)磷铍钙石相比（P2O5含量平均43.15%，BeO平均15.28%，CaO平均33.00%，F平均5.60%），P2O5、BeO、CaO含量相近，但F含量相差较大。经研究，由于F与OH可以替换，雅山岩体中部分磷铍钙石被热液蚀变形成为羟磷铍钙石。表5给出每个点BeO的X射线强度计数的标准偏差（S.D），15个测试点BeO的S.D值为2.18%～3.59%，平均2.75%；标准误差为0.3wt%。对于Be元素来说，获得这样的S.D值是相当不错的结果。通过将狮子岭岩体中的磷铍钙石成分与雅山岩体中的(羟)磷铍钙石成分进行对比，并将其与BeO的S.D值进行了分析，本文发现狮子岭岩体的分析结果无论是从总量（平均值99.22%）还是从BeO含量（平均含量15.28%）来看，都属于很理想的数据。
3. 讨论
3.1 元素Si、P高级次阶峰对Be Kα峰的干扰问题
前人使用EPMA-1720（分光晶体：LSA300）在测量铍硅酸盐矿物时，发现Si L2，3、M1峰对Be Kα峰存在干扰现象，并使用PHA对干扰峰进行过滤［15-16］。而使用JXA-8100（分光晶体LDE3H）、CAMECA SX-100（分光晶体PC3）对铍硅酸盐矿物进行测量时，没有出现Si L2，3、M1峰对Be Kα峰的干扰现象，Si L2，3、M1峰只是抬高了Be Kα峰的背景，在测量时设置上下背景，略过Si的高级次阶峰即可［9，11，17］。本文使用配有LDE3H分光晶体的电子探针对磷铍钙石进行全元素定性分析，发现磷铍钙石不存在Si元素。因此，无需考虑Si L2，3、M1峰对Be Kα峰存在干扰现象。
Wu等［18］使用EPMA-1720H（分光晶体LSA300）分析闽江石、磷锶铍石、磷钙铍石，发现P的L谱线的高级次阶峰接近Be Kα峰，导致Be的高背景(-)，并在铍磷酸盐矿物的分析过程中，由于分光晶体的局限性，不能在分析程序中设置Be的背景来解决P的L谱线的高级次阶峰对Be Kα峰的干扰。本文使用配有LDE3H分光晶体的电子探针对磷铍钙石定性分析时，发现P Kβ峰对Be Kα峰没有干扰现象，只是对Be的背景值存在干扰现象(图4)。参考前人解决Si L2，3、M1峰对Be背景的干扰现象［11，17］，本次研究通过设置上下背景(图4b)，绕过P Kβ峰，即可获得满意的结果。
综上所述，对铍磷酸矿物进行测量时，无需考虑Si L2，3、M1峰对Be Kα峰干扰的问题。但是，P的高级次阶峰对Be背景值的干扰较大。通过使用配有LDE3H分光晶体的电子探针，并设置合适的Be的上下背景，可以有效地降低P的高级次阶峰对Be背景值的干扰。
3.2 EPMA测量Be元素的难点
3.2.1 电子构型和矿物晶体结构对Be特征X射线的影响
特征X射线是一种高能电磁辐射，具有很强穿透力，其产生是基于原子的电子结构和能级跃迁原理。元素的化学性质、结构特征等控制特征X射线的波长、波形、相对强度等［16，27］。Be属于超轻元素，它的电子构型为1s22s2（K、L层分别有2个电子），当高速电子轰击Be元素时，1s轨道（K层）上的低能量的电子获得能量跃迁到2p（L层）轨道，形成K激发态，由于激发态不稳定，所以2p轨道的电子又返回1s轨道上，从而释放出特征X射线［18］。由于Be的2p轨道上没有电子，需要通过高能电子轰击，使部分电子处于2p轨道上，再跃迁到K层，释放特征X射线［23］。Be由于原子半径和离子半径特别小，电负性又相对较高，所以在含Be矿物中，Be与O以共价键的方式形成BeO化合物，原来应处于激发态2p轨道上的两个电子，回不到原始的2s轨道，而是以Be-O原子轨道杂化的方式形成一种轨道能量级介于2s、2p之间的新分子轨道。相比于Be金属中的电子跃迁，成键后的Be-O中电子跃迁所需要的能量更小。根据波长λ=hc/E，能量减小，波长增大，就会导致含Be矿物的Be特征X射线峰位发生右移［11，16-17］。因此，Be的特征X射线峰形平坦、会出现不同程度的右移现象。此外，Be的特征X射线还具有波长长(＞11.4nm)、能量低(＜110eV)、原始辐射的衰减很大、峰背比较低等特征［28］。
Wu等［18］对铍氧化物、铍硅酸盐、铍磷酸盐等矿物进行测量发现，这些矿物Be Kα峰相似，而对孟宪民石与金属Be进行测量发现，其Be的Kα峰位均不同于氧化物、硅酸盐、磷酸盐的Be Kα峰。这个现象是因为铍氧化物、铍硅酸盐、铍磷酸盐均具有[BeO4]结构，孟宪民石晶体结构中存在[BO3]结构，金属Be中Be原子以立方或者六角形排序。对具有相似的ABe2(PO4)化学式的闽江石与磷锶铍石进行EPMA分析发现，由于两者P、Be在矿物晶体结构的分布状态不同［29-30］，两者Be峰位也发生变化［18］。
3.2.2 矿物化学成分对Be特征X射线的影响
除了晶体结构外，矿物本身的化学成分对Be的测量也有影响。Be的原子序数为4，对电子探针分析来讲，属于原子序数最小的一个元素。原子序数越高的元素，质量吸收效应越低；相反，原子序数越低的元素，质量吸收效应越高，例如Fe的Kα质量吸收系数为100，最大为700，而Be的质量吸收系数最大可达125300。自然界中常见的含Be矿物为绿柱石(含Fe、Mg、Cr等成分)，日光榴石(含Fe、Mn、As、S等成分)，硅铍石(含Si)等。含Be矿物本身拥有的其他元素均比Be的原子序数要大，在电子探针测量过程中，含Be矿物自身含有元素(Fe、Mn、Si、Al等)对Be的特征X射线产生强烈的质量吸收效应，Be的大部分计数被这些元素所吸收。同时，Be具有特殊的电子结构(1s22s2)，这种结构导致Be对氧原子有很高的亲和力。Be与O结合形成氧化物，甚至形成含羟基(—OH)矿物如羟硅铍石、硼铍石、羟磷铍钙石等。O原子或OH根对Be特征X射线的产生也有干扰［16］。
综上所述，Be的精确定量分析除了与Be的特征X射线本身的特征(峰形平坦、峰位向右漂移、峰背比较低)有关之外，还与含Be矿物的晶体结构和化学成分有关。
4. 结论
使用配有LDE3H分光晶体电子探针的仪器，对以磷铍钙石为代表的铍磷酸盐矿物，开展了最佳定量分析条件的探索。探讨了不同加速电压和分析束流下，Be峰背比值和磷铍钙石的损失情况。实验结果表明加速电压10kV以及分析束流20nA为磷铍钙石的最佳分析条件。在保证数据可靠性的前提下，该分析条件对磷铍钙石的损伤也相对较小。配有LDE3H分光晶体的电子探针对含Be矿物进行定量分析时，可以通过设置合适的Be背景值，降低其他元素(例如P、Si等)的高级次阶峰对Be背景值的干扰。含Be矿物的测量难点与Be特征X射线本身的特征、含Be矿物的晶体结构和化学成分均有关。
本文运用配有LDE3H分光晶体的电子探针，建立了磷铍钙石的定量分析方法，解决了电子探针对铍磷酸盐矿物定量不准确的难题，也为解决铍资源的赋存状态、成矿机制、找矿勘探提供了技术支持。在自然界中存在多种铍磷酸盐矿物，如磷钠铍石、红磷锰铍石、磷铍钡石等，本文仅对磷铍钙石进行了定量分析，今后有必要加强铍磷酸盐矿物的定量分析研究，进一步探索铍磷酸盐矿物的电子探针精准的定量分析方法。
致谢： 实验过程中，与捷欧路(北京)科贸有限公司胡晋生老师进行了有意义的讨论。论文撰写过程中，得到高杰硕士在拉曼光谱分析方面的帮助，承蒙匿名专家对论文提出了突出的建设性修改意见。在此表示感谢！
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图 1 不同尺寸PSNPs的表征：a～c分别为PS50、PS100和PS200的TEM图像；d～f分别为与之对应DLS测量的平均水动力直径；g～i为三种PSNPs的 ζ 电位值随pH值的变化情况
Figure 1. Characterization of the size-dependent PSNPs: a-c represent the TEM images of PS50, PS100 and PS200, respectively;d-f are the corresponding intensity-weighted hydrodynamic diameter distribution (Dh) of 10mg/L PSNPs determined by DLS;g-i show the ζ potentials as a function of pH in deionized water. The morphology of three PSNPs is all sphericity and has good dispersity. All the data present a good consistency.
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图 2 PS50 (a)、PS100 (b)、PS200 (c)在不同浓度氯化钠溶液中(0～800mmol/L)水动力直径(Dh)随时间的变化
Figure 2. Hydrodynamic diameter (Dh) changes of PS50 (a), PS100 (b) and PS200 (c) over 15min in the presence of NaCl(0-800mmol/L) at pH=5.5±0.3. The aggregation cannot happen while NaCl concentration below 200mmol/L;gradually aggregate at 200-400mmol/L; remain the fast aggregation while NaCl concentration above 400mmol/L.
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图 3 PS50、PS100 和 PS200在不同浓度氯化钠溶液中(0～800mmol/L)的附着效率(a)及ζ电位(b)
Figure 3. Attachment efficiency (a) and ζ potentials (b) as a function of the concentrations of NaCl (0−800mmol/L) for PS50, PS100 and PS200. The CCC values for PS50, PS100 and PS200 were 325mmol/L, 296mmol/L and 264mmol/L, indicating that larger PSNPs are easier to aggregate.
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图 4 在不同pH条件下(pH=3、7、10)PS50 (a，d)、PS100 (b，e)、PS200 (c，f)在400mmol/L 氯化钠溶液中的团聚动力学和ζ电位值
Figure 4. Effects of pH on aggregation of PS50 (a, d), PS100 (b, e), PS200 (c, f): Hydrodynamic diameter (a-c) and ζ potentials (d-f) (PSNPs concentration was 10mg/L; NaCl concentration was 400mmol/L exceeding the CCC values for the three PSNPs; pH=3, 7, and 10). The aggregation rate decreased with the increase of pH due to the deprotonation.
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图 5 DLVO理论拟合获得PS50 (a)、PS100 (b)、PS200 (c)在氯化钠溶液中的相互作用能
Figure 5. Total energy profiles obtained by DLVO theory of PS50(a), PS100(b) and PS200(c) in solutions with varied NaCl concentrations. The dominant interaction forces between PSNPs was electrostatic repulsion when the total interaction energy exhibited negativity, while van der Waals attractive forces was responsible to the positive total interaction energies. The energy barrier for limiting PSNPs aggregation vanished when ionic strength was higher than the CCC value.
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图 6 在所有实验条件下PS50 (a)、PS100 (b)、PS200 (c)附着效率与ζ电位之间的线性回归模型 (红色区域和蓝色区域分别代表95%置信区间和预测区间)
Figure 6. The linear regression models between attachment efficiency and ζ potential of PS50(a), PS100(b) and PS200(c) under all experimental conditions. The red line zone and blue zone represent the 95% confidence interval and prediction interval，respectively. The correlation coefficients (r2) of PS50, PS100 and PS200 are 0.88, 0.77 and 0.70, respectively, indicating that ζ potential is more favorable for predicting the aggregation behavior of smaller-size PSNPs.
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