We present a combined theoretical and experimental study of the stability of ions in a linear ion trap under the application of one or two auxiliary radiofrequency (RF) fields, in order to perform simultaneous resonant excitation/ejection of several different ions. The influence of the amplitude and frequency of the auxiliary field is addressed through the construction of experimental and theoretical stability diagrams. Theoretical diagrams are constructed using the method developed by Konenkov et al. [J. Am. Soc. Mass Spectrom. 13, 597 (2002)]. We propose a new representation of stability diagrams more adapted to the study of auxiliary excitations than the canonical one. Stability regions are represented as a function of the fundamental RF amplitude and of the relative intensity of the excitation. This representation facilitates the monitoring of the evolution of the mass-selectivity of first- and higher-order resonant excitations in the trap, for which an empirical law is derived. We also show that the relative phase shift between the excitation field and the main driving field has a strong influence on the shape of the diagrams.

Download full-text PDF

Source
http://dx.doi.org/10.1255/ejms.1227DOI Listing

Publication Analysis

Top Keywords

representation stability
8
stability diagrams
8
stability
5
alternative representation
4
stability diagram
4
diagram quadrupole
4
quadrupole ion
4
ion traps
4
traps additional
4
additional quadrupolar
4

Similar Publications

How SNARE proteins generate force to fuse membranes.

Biophys J

January 2025

Department of Chemical Engineering, Columbia University, New York, NY 10027. Electronic address:

Membrane fusion is central to fundamental cellular processes such as exocytosis, when an intracellular machinery fuses membrane-enclosed vesicles to the plasma membrane for contents release. The core machinery components are the SNARE proteins. SNARE complexation pulls the membranes together, but the fusion mechanism remains unclear.

View Article and Find Full Text PDF

Nonlinear homogenised finite element (hFE) models can accurately predict stiffness and strength of ultra-distal sections of the radius and tibia using in vivo HR-pQCT images. Recent findings showed good stiffness prediction at these distal sections but a limited ability to reproduce experimental strain localisation. The coarseness of voxel-based meshes reduces the computational effort at the cost of heavily simplifying the underlying geometry of the cortex, the gradient of material properties, and the resulting strain distribution.

View Article and Find Full Text PDF

With advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart-Table (C-T) methods are straightforward and linear but inadequate for robots with flexible joints and linkages. To overcome this limitation, we propose a Flexible MPC (FMPC) framework that incorporates joint dynamics modeling and emphasizes bounded gait control to enable humanoid robots to achieve stable motion in various conditions.

View Article and Find Full Text PDF

Multi-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative hierarchical graph attention actor-critic reinforcement learning method. This method naturally models the interactions within a multi-agent system as a graph, employing hierarchical graph attention to capture the complex cooperative and competitive relationships among agents, thereby enhancing their adaptability to dynamic environments.

View Article and Find Full Text PDF

Atrial fibrillation (AF) is the most common persistent arrhythmia, and it is crucial to develop generalizable automatic AF detection methods. However, supervised AF detection is often limited in performance due to the difficulty in obtaining labeled data. To address the gap between limited labeled data and the requirements for model robustness and generalization in single-lead ECG AF detection, we proposed a semi-supervised contrastive learning method named MLMCL for AF detection.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!