Distinguishing Engineered TiO2 Nanomaterials from Natural Ti Nanomaterials in Soil Using spICP-TOFMS and Machine Learning
Distinguishing Engineered TiO2 Nanomaterials from Natural Ti Nanomaterials in Soil Using spICP-TOFMS and Machine Learning
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DOI：
10.1021/acs.est.1c02950
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发表时间：
2022-03-01
影响因子：
11.4
通讯作者：
Lowry, Gregory, V
中科院分区：
文献类型：
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作者：
Bland, Garret D.;Battifarano, Matthew;Lowry, Gregory, V
识别由土壤中地球丰富的元素制成的工程纳米材料（ENM）是困难的，因为土壤也含有含有类似元素的天然纳米材料（NNM）。在这里，机器学习模型使用元素指纹和质量分布的三个TiO 2 ENMs和钛基NNMs恢复从三个自然土壤测量单粒子电感耦合等离子体飞行时间质谱（spICP-TOFMS）被用来识别土壤中的TiO 2 ENMs。合成的TiO 2 ENM与其他元素无关（>98%），而40%的从废水污泥中回收的Ti基ENM颗粒具有可区分的元素缔合。尽管土壤来自不同地区，但所有从土壤中提取的Ti基NNM具有相似的化学指纹，并且>60%的含Ti NNM没有可测量的缔合元素。当钛质量分布存在差异时，机器学习模型可以最好地区分NNM和ENM。经过训练的LR模型可以对浓度为150 mg/kg或更高的100 nm TiO 2 ENM进行分类。对于大多数ENM-土壤组合，可以使用这种方法确认土壤中TiO 2 ENM的存在，但是在大部分TiO 2 ENM和Ti-NNM中缺乏独特的化学指纹，这增加了模型的不确定性，并阻碍了准确的定量。
Identifying engineered nanomaterials (ENMs) made from earth-abundant elements in soils is difficult because soil also contains natural nanomaterials (NNMs) containing similar elements. Here, machine learning models using elemental fingerprints and mass distributions of three TiO2 ENMs and Ti-based NNMs recovered from three natural soils measured by single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) was used to identify TiO2 ENMs in soil. Synthesized TiO2 ENMs were unassociated with other elements (>98%), while 40% of Ti-based ENM particles recovered from wastewater sludge had distinguishable elemental associations. All Ti-based NNMs extracted from soil had a similar chemical fingerprint despite the soils being from different regions, and >60% of Ti-containing NNMs had no measurable associated elements. A machine learning model best distinguished NNMs and ENMs when differences in Ti-mass distribution existed between them. A trained LR model could classify 100 nm TiO2 ENMs at concentrations of 150 mg kg(-1) or greater. The presence of TiO2 ENMs in soil could be confirmed using this approach for most ENM-soil combinations, but the absence of a unique chemical fingerprint in a large fraction of both TiO2 ENMs and Ti-NNMs increases model uncertainty and hinders accurate quantification.