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Digital quadrupoles are driven with rectangular RF waveforms and, through duty cycle control, can access higher-order Mathieu space stability zones (HZs) using comparatively low voltages. Analytically, accessing these zones remains attractive as HZs exhibit higher resolving powers ((m/z)/(Δm/z)) compared to the conventional quadrupole operation in zone (1,1). Presented here is a modification of a commercial quadrupole time-of-flight (Q-TOF) equipped with a low-voltage digital waveform driver to navigate the filtering quadrupole in the HZs. We demonstrate that a digital quadrupole operating in zones (3,1) and (3,2) achieves a narrowed isolation window for analytes up to 690 and 1543 m/z, respectively, compared to conventional operational modes. Experimental trends suggest this performance to continue to approximately 900 m/z in zone (3,1) and 2500 m/z in zone (3,2). These narrowed isolation windows were utilized to generate MS/MS data from a mixture of m-hydroxybenzoylecgonine (306 m/z) and cocaine-d3 (307 m/z), which exhibited minimal chimeric nature without the use of deconvolution software. The development of hybrid systems capable of both sine and digital operation provides a mechanism to maximize selectivity of the HZs while maintaining the benefits of traditional modes of operation. Minimizing chimeric interferences is of particular importance, and the narrow isolation widths afforded by HZs are readily accessible using tandem instruments for analytes <2500 m/z. 

Abstract Motivation Tandem mass spectrometry is an essential technology for characterizing chemical compounds at high sensitivity and throughput, and is commonly adopted in many fields. However, computational methods for automated compound identification from their MS/MS spectra are still limited, especially for novel compounds that have not been previously characterized. In recent years, in silico methods were proposed to predict the MS/MS spectra of compounds, which can then be used to expand the reference spectral libraries for compound identification. However, these methods did not consider the compounds’ 3D conformations, and thus neglected critical structural information. Results We present the 3D Molecular Network for Mass Spectra Prediction (3DMolMS), a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. We evaluated the model on the experimental spectra collected in several spectral libraries. The results showed that 3DMolMS predicted the spectra with the average cosine similarity of 0.691 and 0.478 with the experimental MS/MS spectra acquired in positive and negative ion modes, respectively. Furthermore, 3DMolMS model can be generalized to the prediction of MS/MS spectra acquired by different labs on different instruments through minor fine-tuning on a small set of spectra. Finally, we demonstrate that the molecular representation learned by 3DMolMS from MS/MS spectra prediction can be adapted to enhance the prediction of chemical properties such as the elution time in the liquid chromatography and the collisional cross section measured by ion mobility spectrometry, both of which are often used to improve compound identification. Availability and implementation The codes of 3DMolMS are available at https://github.com/JosieHong/3DMolMS and the web service is at https://spectrumprediction.gnps2.org.