Ionization and Matrix Effects

Characterize and optimize ionization chemistry

The vision of this thrust area is to fully understand all ionization processes for any ionization mode. The aim is to integrate the empirical knowledge with computational approaches toward solving diverse analysis challenges—both quantitative and qualitative.

Progress on this technical area will enable innovative analyte-specific detection capabilities with exceptional sensitivity through manipulated gas-phase ionization chemistry. Reference-free identification and quantitation are additional desired goals. Improvements in tailored ionization will drive performance enhancements toward unparalleled sensitivity and selective ionization for new applications in mass spectrometry.

Scientific Focus

• Prediction of ionization propensity based on chemical structure
• Tailored ionization
  • Selective ionization by way of competitive charge affinity
  • Formation of unique product ions from adduct formation
  • Stabilization of labile ionization products
• Reference-free quantification
• Reaction kinetics.

Separations and Detection 

Understand and exploit ion manipulations and detection that impact data generation

The vision of this focus area is to enable lossless gas-phase ion confinement and separation by a precise understanding of the electric field effects on the ions. Outcomes will have bearing in three areas:

1. Confident identification of unknown compounds through sensitive, precise, and predictive ion mobility mass spectrometric measurements. New insights into the subtle structural differences between molecules will open the door to understanding how ions are being detected and their utility on determining the precise role these molecules play.
2. A new ion trajectory simulations capability that will enable entire system modeling. The new capability will model realistic effects of excessive charges and electric at interfaces on ion motion. Understanding the effects of excessive charge will enable the design of new ultra-sensitive mass spectrometers that take advantage of the ability to generate intense ion beams. This new capability will also enable the development of new types of mass spectrometers and gas-phase separation techniques that will probe molecular properties with great detail.
3. Working with the ionization and matrix effects enabling technology. New capabilities for sensitive detection of small molecules at atmospheric pressure will be developed to enable ion confinement at high pressure.

Scientific Focus

• Precise gas-phase separations
• Elevated pressure ion confinement, separation, and detection
• Excessive charge effects
• Manipulation of ion beams
• Development of new ion simulation capability.

Molecular Modeling

Prediction of molecular behaviors relevant to mass spectrometry from first principles

The goal of this focus area is to elucidate physical-chemical properties of small analytes in the gas phase to elucidate the fundamental principles that regulate their structure and reactivity under mass spectrometry conditions. This will be achieved by adopting a hierarchical set of tools from modern computational chemistry to calculate thermodynamic quantities, while considering the complexity of the possible microenvironments of analytes from complex mixtures under mass spectrometry conditions. 

Understanding fundamental principles underlying reactivity of analytes in the gas phase will help develop quantitative predictive models to inform experimental interpretation.

 Scientific Focus

• Thermodynamic and kinetic properties (e.g., fragmentation patterns, adduct ion affinity)
• Gas-phase structure predictions
• Effects of competitive ionization
• Interspecies interaction and matric effects
• Validation of models with experimental data.

Statistics and Machine Learning

Algorithmic characterization of small molecules and uncertainty assessment

The goal of this focus area is to provide fundamental applications of statistics and machine learning, as well as algorithmic developments as needed, in a data-driven manner focused on the complete, accurate, and statistically rigorous characterization of small molecule identification and quantification.

These methods will use advanced statistical and machine learning methods in combination with mass spectrometry expertise to predict and model fundamental mass spectrometry processes that enable accurate library identification and reference-free identification to characterize known and novel small molecules. This domain-aware machine learning strategy will influence the full mass spectrometry process from the point of data measurement to accurate characterization of the small molecule content of a sample.

Scientific Focus

• Domain-informed machine learning to enable automated compound identification, quantification, and confidence metrics
• Prediction of fundamental mass spectrometry properties, such as fragmentation ion spectra
• Quantification of uncertainty in the identification of small molecules
• Automated quality assessment and quality control to make sure high-quality data generation
• Statistical requirements for long-term studies that would allow for reference-free batch correction.