Targeting Metal Impurities for the Detection and Quantification of Carbon Black Particles in Water via spICP-MS

Carbon black (CB) is a nanomaterial with numerous industrial applications and high potential for integration into nano-enabled water treatment devices. However, few analytical techniques are capable of measuring CB in water at environmentally relevant concentrations. Therefore, we intended to establish a quantification method for CB with lower detection limits through utilization of trace metal impurities as analytical tracers. Various metal impurities were investigated in six commercial CB materials, and the Monarch 1000 CB was chosen as a model for further testing. The La impurity was chosen as a tracer for spICP-MS analysis based on measured concentration, low detection limits, and lack of polyatomic interferences. CB stability in water and adhesion to the spICP-MS introduction system presented a challenge that was mitigated by the addition of a nonionic surfactant to the matrix. Following optimization, the limit of detection (64 μg/L) and quantification (122 μg/L) for Monarch 1000 CB demonstrated the applicability of this approach to samples expected to contain trace amounts of CB. When compared against gravimetric analysis and UV–visible absorption spectroscopy, spICP-MS quantification exhibited similar sensitivity but with the ability to detect concentrations an order of magnitude lower. Method detection and sensitivity was unaffected when dissolved La was spiked into CB samples at environmentally relevant concentrations. Additionally, a more complex synthetic matrix representative of drinking water caused no appreciable impact to CB quantification. In comparison to existing quantification techniques, this method has achieved competitive sensitivity, a wide working range for quantification, and high selectivity for tracing possible release of CB materials with known metal contents.炭黑（Carbon black, CB）是一类具备诸多工业应用场景的纳米材料，在集成纳米功能水处理装置方面拥有极高应用潜力。然而当前鲜有分析技术可在环境相关浓度水平下对水体中的炭黑进行定量检测。为此，本研究拟通过利用痕量金属杂质作为分析示踪剂，建立一种具有更低检出限的炭黑定量方法。研究人员对6种商用炭黑材料中的多种金属杂质开展了排查，并选取Monarch 1000炭黑作为后续测试的模型样品。基于实测浓度、低检出限特性以及无多原子干扰的优势，研究选择镧（La）杂质作为单颗粒电感耦合等离子体质谱（spICP-MS）分析的示踪剂。水体中炭黑的稳定性问题以及其在spICP-MS进样系统表面的附着问题曾给实验带来挑战，通过在基体中添加非离子表面活性剂，该问题得到有效缓解。经过参数优化后，Monarch 1000炭黑的检出限为64 μg/L、定量限为122 μg/L，证明该方法可适用于含痕量炭黑的样品检测。与重量分析法及紫外-可见吸收光谱法相比，spICP-MS定量法展现出相当的灵敏度，且可检测低一个数量级的浓度。当在环境相关浓度水平下向炭黑样品中加入溶解态镧时，方法的检出性能与灵敏度未受影响。此外，采用模拟饮用水的复杂合成基体开展实验时，并未对炭黑定量结果造成显著干扰。相较于现有定量技术，本方法已实现具备竞争力的灵敏度、宽泛的定量工作范围，以及对含已知金属含量的炭黑材料潜在释放的高选择性示踪能力。