Analytical Instrument Documents

We have used our newly-developed AI model to create a database of predicted EI mass spectra for around 100 million compounds. In this work, we introduce a polymer materials application that uses msFineAnalysis AI for structural analysis.

We have used our newly-developed AI model to create a database of predicted EI mass spectra for around 100 million compounds. In this work, we introduce a polymer materials application that uses msFineAnalysis AI for structural analysis.

We have used our newly-developed AI model to create a database of predicted EI mass spectra for around 100 million compounds. In this work, we introduce a polymer materials application that uses msFineAnalysis AI for structural analysis.

We have used our newly-developed AI model to create a database of predicted EI mass spectra for around 100 million compounds. In this work, we introduce AI structure analysis function in automatic structure analysis software msFineAnalysis AI.

JEOL’s GC-high resolution mass spectrometer (GC-HRMS), JMS-T2000GC, is capable of: 1) high precision mass analysis; 2) detection of molecular and fragment ions through electron ionization (EI) and soft ionization (SI); and 3) auto analysis of acquired data by msFineAnalysis AI to determine chemical formulas and predict chemical structures. In this work, we identified the chemical components contained in an herbal medicine using a JMS-T2000GC and msFineAnalysis AI.

JEOL’s latest analytical software, msFineAnalysis AI, is designed to provide speedy analysis of GC-HRMS data acquired by both EI and SI, determine chemical formulas, and predict chemical structures. In this work, we used msFineAnalysis AI to identify impurities in 2-methoxy-1-methylethyl acetate (PGMEA), a cleaning solution for wafer surfaces.

Currently, JASON SMILEQ supports the generation of two types of analytical reports based on quantitative analysis results. These reports offer comprehensive insights into the interpretation of quantitative data. This application note focuses on the impact of standard sample uncertainty, a key factor, and presents the results of a more detailed analysis of uncertainty factors conducted using the findings obtained in Part 1 and Part 2.

Currently, JASON SMILEQ supports the generation of two types of analytical reports based on quantitative analysis results. These reports offer comprehensive insights into the interpretation of quantitative data. This application note covers the following: Building on the findings from Part 1. Evaluation of Uncertainty Factors, it expands into variance analysis to provide a more detailed examination of uncertainty factors and their contributions. Furthermore, Part 3 leverages the insights from both Part 1 and Part 2 to present a deeper analysis of uncertainty factors.

Currently, JASON SMILEQ supports the generation of two types of analytical reports based on quantitative analysis results. These reports offer comprehensive insights into the interpretation of quantitative data. As part of the background, this application note provides an overview of the factors contributing to uncertainty and their significance in quantitative NMR analysis. The main focus lies in presenting an overview of SMILEQ reports and offering an in-depth explanation of the 'Uncertainty Report.' Parts 2 and 3 delve deeper into the findings presented in the Uncertainty Report, transitioning into variance analysis to examine uncertainties arising from factors not covered in the report. Additionally, these sections showcase examples illustrating the application of variance analysis and the evaluation of uncertainty factors.

Headspace solid-phase microextraction (SPME) was used to trap and concentrate trace volatiles from two polymer samples for analysis by combined gas chromatography/mass spectrometry (GC-MS) using the HTA 2800T combination autosampler and the JEOL UltraQuad™ SQ-Zeta single quadrupole GC-MS system.