High-field Asymmetric-wave Ion Mobility Spectrometry (FAIMS) is an analytical technique for separating and identifying ions based on their differential mobility under the influence of an asymmetric electric field [1], [2], [3]. FAIMS, functioning under atmospheric pressure and characterized by its capacity for continuous ion detection and rich spectral information, is extensively utilized in a variety of fields including public safety [4], explosives detection [5,6], drug discovery [7], tracing of volatile organic compounds [5,8], and healthcare industry [9], [10], [11], [12], [13].

As depicted in Fig. 1, a FAIMS analyzer consists of three principal zones: ionization, screening, and detection. Within the ionization zone, the sample is ionized into ions under the influence of the ion source. These ions undergo separation in the screening zone. Ion separation is achieved by alternately exposing ions to strong and weak electric fields perpendicular to the carrier gas flow. This process utilizes a high-frequency asymmetric waveform which is called the dispersion voltage (DV), leading to different ion mobilities due to the energy change in collisions with bath gas molecules under varying electric fields. A compensation voltage (CV) is applied to prevent ions from colliding with the electrodes, enabling selective ion transport [2]. The resultant FAIMS spectrum is characterized by the ion current intensity at different CV levels, where the peak positions reveal the types of substances and the peak heights indicate their concentrations.

FAIMS presents both qualitative and quantitative information of the analyzed samples through its spectral data. However, the spectral data of FAIMS are influenced by various factors in both field applications and laboratory testing. Previous experimental studies demonstrated that the carrier gas [14], [15], [16], separation voltage [17], [18], [19], sample humidity [20,21], and ambient temperature [22,23] can affect the peak position, width, and signal intensity. In theoretical research, Shvartsburg [24] initially developed a comprehensive theoretical model that includes various FAIMS parameters, aimed at calculating the system's sensitivity and resolution. Building upon this foundation, Krylov [25] significantly enhanced the model by extending its parameters to encompass carrier gas flow rates, amplitude and waveform of the separation electric field, and the geometrical characteristics of the separation electrodes. The integration of comprehensive ion characteristics, such as ion diffusion, mobility, and the interplay between ion mobility and the electric field, substantially improved the model. This refinement enabled a more accurate adaptation to the empirical data from ion mobility spectrometry, elucidating most of the phenomena observed in experiments and successfully predicting the effects of high-field ion focusing. Subsequently, Chen [18] incorporation of ion trajectory and loss altitude into the model further refined its capacity to qualitatively depict variations in peak profiles. The existing experimental and theoretical studies both adopt symmetric triangular wave or Gaussian-type functions to fit the FAIMS spectra, and all ignore the impact of CV scanning speed on the FAIMS spectra.

The scanning speed is a crucial parameter for various sensors and analytical equipment. In other detection instruments, such as mass spectrometry and chromatography, it has been observed that the scanning speed influences the spectral data. Banner [26], Butterworth [27], and McWilliam [28] observed distortion of the mass spectral peaks and developed models to describe this distortion. These studies delve into the impact of various scanning settings on the features of peaks in the instruments, providing insights into the relationship between scanning conditions and spectral data quality. In our previous work [29], we discovered that the scanning speed of compensation voltage (CV) affects peak position and proposed a method for peak position calculation to mitigate the impact of scanning speed. However, the impact of the CV scanning speed on FAIMS spectral data has not yet been fully understood.

In this study, we explored the effect of CV scanning speed on spectral data and proposed a function to describe such a relationship. The characteristics of the model were explored. A series of spectra were obtained by changing the CV scanning speed and sample type to verify the applicability of the theory under different conditions.