Quantitative and Qualitative Analyses of Mass Spectra of OEL Materials by Artificial Neural Network and Interface Evaluation: Results from a VAMAS Interlaboratory Study

Abstract

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)₃ ratios (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)₃ ratios 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)₃ ratios 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 without a priori information. 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.

Figure 1

Concept of the simple ANN quantitative analysis system. Sizes of the hidden layer and labels (ratios) were 7 and 5, respectively. Numbers of the samples and the peaks (variables) are s and v, respectively. W1₁ and W2₁ were weights for the first sample highlighted in light blue.

Figure 2

Calibration curves of ion intensities: (a) circle, m/z 655 for Ir(ppy)₃, and triangle, m/z 460 for Alq₃ obtained with TOF-SIMS using Bi₃²⁺; (b) m/z 654 for Ir(ppy)₃ and 460 for Alq₃ obtained with J105 using H₂O cluster ions; (c) m/z = 655 for Ir(ppy)₃ and m/z = 459 for Alq₃ obtained with OrbiSIMS using Ar₁₄₀₀⁺; (d) m/z 655 for Ir(ppy)₃ and m/z 459 for Alq₃ obtained with LDI-TOF; (e) m/z 656 for Ir(ppy)₃ and m/z 460 for Alq₃ obtained with DINeC.

Figure 3

Concentration dependence of the mass peaks. The peaks m/z 577.11 and 442.06 were suggested by simple ANN quantitative analysis.

Figure 4

Analysis of the multilayer sample (a) by XPS (b) and ToF-SIMS (c). The mass peaks at m/z 26.99 and 501.1 are related to Alq₃ and Ir(ppy)₃, respectively.

Figure 5

Calibration curves before and after the matrix effect correction by the relative sensitivity factor (RSF).

Figure 6

Depth profiles of the multilayer sample. Closed circle: mass peak m/z 501 intensity related to Ir(ppy)₃ ratio, ×: Ir(ppy)₃ ratio predicted by the simple ANN, open triangle: Ir(ppy)₃ ratio predicted by matrix-effect correction based on RSF, and closed triangle: Alq₃ ratio predicted by matrix-effect correction based on RSF.