Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Metrology and Measurement Systems

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Improving the efficiency of soft electron ionization by using the oscillatory movement of electrons

Huang Ze-Jian, Jiang You, Dai Xin-Hua, Zhou Ming-Fei, Fang Xiang

Metrology and Measurement Systems

2024

Vol. 31, nr 2

353--367

In process analytical chemistry, mass spectrometry analysis using a soft electron ionization (EI) source has qualitative advantages. However, the relatively small ionization cross-section of soft EI leads to lower sensitivity. To address this issue, a novel method has been developed to enhance the sensitivity of soft EI by utilizing a dual electron repeller and an ionization chamber to form a U-shaped electric field, causing electrons to oscillate within the field and effectively increasing the electron collision cross-sectional area. By combining with an electron lens, the virtual cathode effect at low electron energy can be reduced or even eliminated, thereby improving ionization efficiency. This method has resulted in a significant increase in signal intensity for m/z 18(H2O), with a factor of 4.2 at an electron energy of 25 eV and a factor of 3.75 at 20 eV, compared to the electron receiving mode. Additionally, it reduces the required emission current, which is beneficial for prolonging the life of the filament. The proposed technique is expected to expand the application of soft EI, particularly for rapid online analysis in process analytical chemistry such as catalyst research and chemical reaction process monitoring.

Quantitative analysis of reaction gases or exhaust using an online process mass spectrometer

Wei Fu-Qiang, Huang Ze-Jian, Jian You, Dai Xin-Hua, Fang Xiang, Jin Shang-Zhong

Metrology and Measurement Systems

2023

Vol. 30, nr 2

337--351

Online quantitative analysis of reaction gases or exhaust in industrial production is of great significance to improve the production capacity and process. A novel method is developed for the online quantitative analysis of reaction gases or exhaust using quantitative mathematical models combined with the linear regression algorithm of machine learning. After accurately estimating the component gases and their contents in the reaction gases or exhaust, a ratio matrix is constructed to separate the relevant overlapping peaks. The ratio and calibration standard gases are detected, filtered, normalized, and linearly regressed with an online process mass spectrometer to correct the ratio matrices and obtain the relative sensitivity matrices. A quantitative mathematical model can be established to obtain the content of each component of the reaction gases or exhaust in real time. The maximum quantification error and relative standard deviation of the method are within 0.3% and 1%, after online quantification of the representative yeast fermenter tail gas.