Quantitative and Qualitative Analyses of Mass Spectra of OEL Materials by Artificial Neural Network and Interface Evaluation: Results from a VAMAS Interlaboratory Study.
Quantitative and Qualitative Analyses of Mass Spectra of OEL Materials by Artificial Neural Network and Interface Evaluation: Results from a VAMAS Interlaboratory Study.
复制标题
通过人工神经网络和界面评估对 OEL 材料的质谱进行定量和定性分析：VAMAS 实验室间研究的结果。
DOI：
10.1021/acs.analchem.3c03173
复制
发表时间：
2023
影响因子：
7.4
通讯作者：
Aoyagi S
中科院分区：
文献类型：
--
作者：
Aoyagi S
基于凡尔赛先进材料和标准实验室间研究(TW2 A31)的结果，尝试使用人工神经网络(ANN)系统对三(2-苯基吡啶)Ir(III)(Ir(Ppy)3)和三(8-羟基喹啉)铝(Alq3)的二元混合物进行定量分析，以评估基质效应校正和研究界面确定。用飞行时间二次离子质谱仪(ToF-SIMS)、OrbiSIMS(OrbiSIMS)、激光解吸电离(LDI)、中性团簇诱导解吸/电离(DINeC)和X射线光电子能谱(XPS)测量了具有不同Ir(Ppy)3比例(0、0.25、0.50、0.75和1.00)的二元混合物以及含有这些混合物和纯样品的多层膜。用一个隐含层的简单人工神经网络对其进行了分析。用简单的人工神经网络系统预测了未知样品和多层膜界面的Ir(Ppy)3比值，尽管二元混合物的质谱图显示了基质效应。简单人工神经网络显示的界面Ir(Ppy)_3比值与XPS结果和ToF-SIMS深度分布相一致。简单的人工神经网络系统不仅提供了未知样品的定量信息，而且还显示了与样品中每个分子相关的重要质量峰，而没有优先信息。简单人工神经网络显示的重要质量峰与电离过程有关。通过较软的电离方法，如LDI和DINeC获得的光谱集的简单人工神经网络结果表明，大离子如三聚体。从建立评价受基质效应影响的混合样品的人工神经网络模型的第一步开始，表明简单的人工神经网络方法对于获得用于鉴定的候选质量峰和假设有助于进一步分析的混合条件是有用的。
Quantitative analysis of binary mixtures of tris(2-phenylpyridinato)iridium(III) (Ir(ppy)3) and tris(8-hydroxyquinolinato)aluminum (Alq3) by using an artificial neural network (ANN) system to mass spectra was attempted based on the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study (TW2 A31) to evaluate matrix-effect correction and to investigate interface determination. Monolayers of binary mixtures having different Ir(ppy)3ratios (0, 0.25, 0.50, 0.75, and 1.00), and the multilayers containing these mixtures and pure samples were measured using time-of-flight secondary ion mass spectrometry (ToF-SIMS) with different primary ion beams, OrbiSIMS (SIMS with both Orbitrap and ToF mass spectrometers), laser desorption ionization (LDI), desorption/ionization induced by neutral clusters (DINeC), and X-ray photoelectron spectroscopy (XPS). The mass spectra were analyzed using a simple ANN with one hidden layer. The Ir(ppy)3ratios of the unknown samples and the interfaces of the multilayers were predicted using the simple ANN system, even though the mass spectra of binary mixtures exhibited matrix effects. The Ir(ppy)3ratios at the interfaces indicated by the simple ANN were consistent with the XPS results and the ToF-SIMS depth profiles. The simple ANN system not only provided quantitative information on unknown samples, but also indicated important mass peaks related to each molecule in the samples withouta prioriinformation. The important mass peaks indicated by the simple ANN depended on the ionization process. The simple ANN results of the spectra sets obtained by a softer ionization method, such as LDI and DINeC, suggested large ions such as trimers. From the first step of the investigation to build an ANN model for evaluating mixture samples influenced by matrix effects, it was indicated that the simple ANN method is useful for obtaining candidate mass peaks for identification and for assuming mixture conditions that are helpful for further analysis.
登录
查看更多内容
Ar 簇离子深度剖面中混合有机层的 TOF-SIMS 矩阵效应
影响因子：
1.7
作者：
Kazuma Takahashi;S. Aoyagi;Tomoko Kawashima
通讯作者：
Tomoko Kawashima
影响因子：
2.1
作者：
Portz, Andre;Bomhardt, Karolin;Duerr, Michael
通讯作者：
Duerr, Michael
影响因子：
7.4
作者：
Rabbani, Sadia Sheraz Nee;Razo, Irma Berrueta;Vickerman, John C.
通讯作者：
Vickerman, John C.
影响因子：
1.7
作者：
Lee, J. L. S.;Gilmore, I. S.;Seah, M. P.
通讯作者：
Seah, M. P.
影响因子：
1.7
作者：
Matjacic, Lidija;Seah, Martin P.;Gilmore, Ian S.
通讯作者：
Gilmore, Ian S.