Wildfire smoke destroys stratospheric ozone.

Large wildfires inject smoke and biomass-burning products into the mid-latitude stratosphere, where they destroy ozone, which protects us from ultraviolet radiation. The infrared spectrometer on the Atmospheric Chemistry Experiment satellite measured the spectra of smoke particles from the "Black Summer" fires in Australia in late 2019 and early 2020, revealing that they contain oxygenated organic functional groups and water adsorption on the surfaces. These injected smoke particles have produced unexpected and extreme perturbations in stratospheric gases beyond any seen in the previous 15 years of measurements, including increases in formaldehyde, chlorine nitrate, chlorine monoxide, and hypochlorous acid and decreases in ozone, nitrogen dioxide, and hydrochloric acid. These perturbations in stratospheric composition have the potential to affect ozone chemistry in unexpected ways.

Determining Collision Cross Sections from Differential Ion Mobility Spectrometry.

The experimental determination of ion-neutral collision cross sections (CCSs) is generally confined to ion mobility spectrometry (IMS) technologies that operate under the so-called low-field limit or those that enable empirical calibration strategies (e.g., traveling wave IMS; TWIMS). Correlation of ion trajectories to CCS in other non-linear IMS techniques that employ dynamic electric fields, such as differential mobility spectrometry (DMS), has remained a challenge since its inception. Here, we describe how an ion's CCS can be measured from DMS experiments using a machine learning (ML)-based calibration. The differential mobility of 409 molecular cations (m/z: 86-683 Da and CCS 110-236 Å2) was measured in a N2 environment to train the ML framework. Several open-source ML routines were tested and trained using DMS-MS data in the form of the parent ion's m/z and the compensation voltage required for elution at specific separation voltages between 1500 and 4000 V. The best performing ML model, random forest regression, predicted CCSs with a mean absolute percent error of 2.6 ± 0.4% for analytes excluded from the training set (i.e., out-of-the-bag external validation). This accuracy approaches the inherent statistical error of ∼2.2% for the MobCal-MPI CCS calculations employed for training purposes and the <2% threshold for matching literature CCSs with those obtained on a TWIMS platform.

Ligand specificity and affinity in the sulforhodamine B binding RNA aptamer.

Binding affinity and selectivity are critical properties of aptamers that must be optimized for any application. The sulforhodamine B binding RNA aptamer (SRB-2) is a somewhat promiscuous aptamer that can bind ligands that vary markedly in shape, size and charge. Here we categorize potential ligands based on their binding mode and structural characteristics required for high affinity and selectivity. Several known and potential ligands of SRB-2 were screened for binding affinity using LSPR, ITC and NMR spectroscopy. The study shows that rhodamine B has the ideal structural and electrostatic properties for selective and high-affinity binding of the SRB-2 aptamer.

Understanding Nontraditional Differential Mobility Behavior: A Case Study of the Tricarbastannatrane Cation, N(CH2CH2CH2)3Sn.

The effect of strong ion-solvent interactions on the differential mobility behavior of the tricarbastannatrane cation, N(CH2CH2CH2)3Sn+, has been investigated. Exotic "type D" dispersion behavior, which is intermediate to the more common types C and A behavior, is observed for gaseous N2 environments that are seeded with acetone and acetonitrile vapor. Quantum chemical calculations and first-principles modeling show that under low-field conditions [N(CH2CH2CH2)3Sn + solvent]+ complexes containing a single solvent molecule survive the entire separation waveform duty cycle and interact weakly with the chemically modified environment. However, at high separation voltages, the ion-solvent bond dissociates and dynamic clustering ensues.

A First Principle Model of Differential Ion Mobility: the Effect of Ion-Solvent Clustering.

The use of differential mobility spectrometry (DMS) as a separation tool prior to mass analysis has increased in popularity over the years. However, the fundamental principles behind the difference between high- and low-field mobility is still a matter of debate-especially regarding the strong impact of solvent molecules added to the gas phase in chemically modified DMS environments. In this contribution, we aim to present a thorough model for the determination of the ion mobility over a wide range of field strengths and subsequent calculation of DMS dispersion plots. Our model relies on first principle calculations only, incorporating the modeling of the "hard-sphere" mobility, the change in CCS with field strength, and the degree of clustering of solvent molecules to the ion. We show that all three factors have to be taken into account to qualitatively predict dispersion plots. In particular, type A behavior (i.e., strong clustering) in DMS can only be explained by a significant change of the mean cluster size with field strengths. The fact that our model correctly predicts trends between differently strong binding solvents, as well as the solvent concentration and the background gas temperature, highlights the importance of clustering for differential mobility.

A parallelized molecular collision cross section package with optimized accuracy and efficiency.

Ion mobility-based separation prior to mass spectrometry has become an invaluable tool in the structural elucidation of gas-phase ions and in the characterization of complex mixtures. Application of ion mobility to structural studies requires an accurate methodology to bridge theoretical modelling of chemical structure with experimental determination of an ion's collision cross section (CCS). Herein, we present a refined methodology for calculating ion CCS using parallel computing architectures that makes use of atom specific parameters, which we have called MobCal-MPI. Tuning of ion-nitrogen van der Waals potentials on a diverse calibration set of 162 molecules returned a RMSE of 2.60% in CCS calculations of molecules containing the elements C, H, O, N, F, P, S, Cl, Br, and I. External validation of the ion-nitrogen potential was performed on an additional 50 compounds not present in the validation set, returning a RMSE of 2.31% for the CCSs of these compounds. Owing to the use of parameters from the MMFF94 forcefield, the calibration of the van der Waals potential can be extended to additional atoms defined in the MMFF94 forcefield (i.e., Li, Na, K, Si, Mg, Ca, Fe, Cu, Zn). We expect that the work presented here will serve as a foundation for facile determination of molecular CCSs, as MobCal-MPI boasts up to 64-fold speedups over traditional calculation packages.

Determining molecular properties with differential mobility spectrometry and machine learning.

The fast and accurate determination of molecular properties is highly desirable for many facets of chemical research, particularly in drug discovery where pre-clinical assays play an important role in paring down large sets of drug candidates. Here, we present the use of supervised machine learning to treat differential mobility spectrometry - mass spectrometry data for ten topological classes of drug candidates. We demonstrate that the gas-phase clustering behavior probed in our experiments can be used to predict the candidates' condensed phase molecular properties, such as cell permeability, solubility, polar surface area, and water/octanol distribution coefficient. All of these measurements are performed in minutes and require mere nanograms of each drug examined. Moreover, by tuning gas temperature within the differential mobility spectrometer, one can fine tune the extent of ion-solvent clustering to separate subtly different molecular geometries and to discriminate molecules of very similar physicochemical properties.