Graphormer-IR: Graph Transformers Predict Experimental IR Spectra Using Highly Specialized Attention.

Infrared (IR) spectroscopy is an important analytical tool in various chemical and forensic domains and a great deal of effort has gone into developing in silico methods for predicting experimental spectra. A key challenge in this regard is generating highly accurate spectra quickly to enable real-time feedback between computation and experiment. Here, we employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra using only simplified molecular-input line-entry system (SMILES) strings. Our data set includes 53,528 high-quality spectra, measured in five different experimental media (i.e., phases), for molecules containing the elements H, C, N, O, F, Si, S, P, Cl, Br, and I. When using only atomic numbers for node encodings, Graphormer-IR achieved a mean test spectral information similarity (SISµ) value of 0.8449 ± 0.0012 (n = 5), which surpasses that the current state-of-the-art model Chemprop-IR (SISµ = 0.8409 ± 0.0014, n = 5) with only 36% of the encoded information. Augmenting node embeddings with additional node-level descriptors in learned embeddings generated through a multilayer perceptron improves scores to SISµ = 0.8523 ± 0.0006, a total improvement of 19.7σ (t = 19). These improved scores show how Graphormer-IR excels in capturing long-range interactions like hydrogen bonding, anharmonic peak positions in experimental spectra, and stretching frequencies of uncommon functional groups. Scaling our architecture to 210 attention heads demonstrates specialist-like behavior for distinct IR frequencies that improves model performance. Our model utilizes novel architectures, including a global node for phase encoding, learned node feature embeddings, and a one-dimensional (1D) smoothing convolutional neural network (CNN). Graphormer-IR's innovations underscore its value over traditional message-passing neural networks (MPNNs) due to its expressive embeddings and ability to capture long-range intramolecular relationships.

Fluorinated Propionic Acids Unmasked: Puzzling Fragmentation Phenomena of the Deprotonated Species.

Environmental contamination by per- and polyfluorinated substances (PFAS) is an emerging concern for the public. In this study, short-chain PFAS such as deprotonated per- and polyfluorinated propionic acids are investigated using a combination of infrared multiple-photon dissociation (IRMPD) spectroscopy, collision-induced dissociation (CID), and density functional theory calculations. IRMPD and CID proceed via multiple competing pathways: (1) production of fluoroformate (FCO2-) and the associated ethylene derivative, (2) production of HF and the associated carbanion, or (3) loss of CO2 and the associated carbanion. Fluorinated propionic acids with at least one fluorine atom bound to the terminal carbon yield FCO2-, whereas loss of HF is observed in polyfluorinated species with at least one fluorine atom bound to the α-carbon. To explore the reaction pathways of the various fluorinated propionic acids, the nudged elastic band method is employed. The relative energy of the four-membered ring transition state leading to FCO2- dictates which product channel is observed in dissociation.

Unsymmetrical Imidazopyrimidine-Based Ligand and Bimetallic Complexes.

With bimetallic catalysts becoming increasingly important, the development of electronically and structurally diverse binucleating ligands is desired. This work describes the synthesis of unsymmetric ligand 2,7-di(pyridin-2-yl)imidazo[1,2-a]pyrimidine (dpip) that is achieved in four steps on a multigram scale in an overall 54% yield. The ability of dpip to act as a scaffold for the formation of bimetallic complexes is demonstrated with the one-step syntheses of the dicopper complex [Cu2(dpip)(µ-OH)(CF3COO)3] (4), the dipalladium complex [Pd2(dpip)(µ-OH)(CF3COO)2](CF3COO)·CF3COOH (5), and the dimeric dinickel complex [Ni4(dpip)2(µ-Cl)4Cl2MeOH6][2Cl] (6) in good yields (79-92%). All bimetallic complexes were characterized by spectroscopic methods and X-ray crystallography, which revealed metal-metal distances between 3.4821(9) and 4.106(2) Å. Additionally, quantum chemical calculations were conducted on complex 4 and an analogous 1,8-naphthyridine-based dicopper complex to investigate the differences between the imidazopyrimidine motif reported here and the widely used 1,8-naphthyridine motif. Natural bonding orbital (NBO) and Mayer bond order (MBO) analyses validated the ability of dpip to coordinate metals more strongly. Finally, NBO calculations quantified the differences in the binding energy between the two pockets of the unsymmetrical dpip ligand.

Protomers of the green and cyan fluorescent protein chromophores investigated using action spectroscopy.

The photophysics of biochromophore ions often depends on the isomeric or protomeric distribution, yet this distribution, and the individual isomer contributions to an action spectrum, can be difficult to quantify. Here, we use two separate photodissociation action spectroscopy instruments to record electronic spectra for protonated forms of the green (pHBDI+) and cyan (Cyan+) fluorescent protein chromophores. One instrument allows for cryogenic (T = 40 ± 10 K) cooling of the ions, while the other offers the ability to perform protomer-selective photodissociation spectroscopy. We show that both chromophores are generated as two protomers when using electrospray ionisation, and that the protomers have partially overlapping absorption profiles associated with the S1 ← S0 transition. The action spectra for both species span the 340-460 nm range, although the spectral onset for the pHBDI+ protomer with the proton residing on the carbonyl oxygen is red-shifted by ≈40 nm relative to the lower-energy imine protomer. Similarly, the imine and carbonyl protomers are the lowest energy forms of Cyan+, with the main band for the carbonyl protomer red-shifted by ≈60 nm relative to the lower-energy imine protomer. The present strategy for investigating protomers can be applied to a wide range of other biochromophore ions.

Bridging the Gap between Differential Mobility, Log S, and Log P Using Machine Learning and SHAP Analysis.

Aqueous solubility, log S, and the water-octanol partition coefficient, log P, are physicochemical properties that are used to screen the viability of drug candidates and to estimate mass transport in the environment. In this work, differential mobility spectrometry (DMS) experiments performed in microsolvating environments are used to train machine learning (ML) frameworks that predict the log S and log P of various molecule classes. In lieu of a consistent source of experimentally measured log S and log P values, the OPERA package was used to evaluate the aqueous solubility and hydrophobicity of 333 analytes. With ion mobility/DMS data (e.g., CCS, dispersion curves) as input, we used ML regressors and ensemble stacking to derive relationships with a high degree of explainability, as assessed via SHapley Additive exPlanations (SHAP) analysis. The DMS-based regression models returned scores of R2 = 0.67 and RMSE = 1.03 ± 0.10 for log S predictions and R2 = 0.67 and RMSE = 1.20 ± 0.10 for log P after 5-fold random cross-validation. SHAP analysis reveals that the regressors strongly weighted gas-phase clustering in log P correlations. The addition of structural descriptors (e.g., # of aromatic carbons) improved log S predictions to yield RMSE = 0.84 ± 0.07 and R2 = 0.78. Similarly, log P predictions using the same data resulted in an RMSE of 0.83 ± 0.04 and R2 = 0.84. The SHAP analysis of log P models highlights the need for additional experimental parameters describing hydrophobic interactions. These results were achieved with a smaller dataset (333 instances) and minimal structural correlation compared to purely structure-based models, underscoring the value of employing DMS data in predictive models.

MobCal-MPI 2.0: an accurate and parallelized package for calculating field-dependent collision cross sections and ion mobilities.

Ion mobility spectrometry (IMS), which can be employed as either a stand-alone instrument or coupled to mass spectrometry, has become an important tool for analytical chemistry. Because of the direct relation between an ion's mobility and its structure, which is intrinsically related to its collision cross section (CCS), IMS techniques can be used in tandem with computational tools to elucidate ion geometric structure. Here, we present MobCal-MPI 2.0, a software package that demonstrates excellent accuracy (RMSE 2.16%) and efficiency in calculating low-field CCSs via the trajectory method (≤30 minutes on 8 cores for ions with ≤70 atoms). MobCal-MPI 2.0 expands on its predecessor by enabling the calculation of high-field mobilities through the implementation of the 2nd order approximation to two-temperature theory (2TT). By further introducing an empirical correction to account for deviations between 2TT and experiment, MobCal-MPI 2.0 can compute accurate high-field mobilities that exhibit a mean deviation of <4% from experimentally measured values. Moreover, the velocities used to sample ion-neutral collisions were updated from a weighted to a linear grid, enabling the near-instantaneous evaluation of mobility/CCS at any effective temperature from a single set of N2 scattering trajectories. Several enhancements made to the code are also discussed, including updates to the statistical analysis of collision event sampling and benchmarking of overall performance.

Chemical Transformations Can Occur during DMS Separations: Lessons Learned from Beer's Bittering Compounds.

While developing a DMS-based separation method for beer's bittering compounds, we observed that the argentinated forms of humulone tautomers (i.e., [Hum + Ag]+) were partially resolvable in a N2 environment seeded with 1.5 mol % of isopropyl alcohol (IPA). Attempting to improve the separation by introducing resolving gas unexpectedly caused the peaks for the cis-keto and trans-keto tautomers of [Hum + Ag]+ to coalesce. To understand why resolution loss occurred, we first confirmed that each of the tautomeric forms (i.e., dienol, cis-keto, and trans-keto) responsible for the three peaks in the [Hum + Ag]+ ionogram were assigned to the correct species by employing collision-induced dissociation, UV photodissociation spectroscopy, and hydrogen-deuterium exchange (HDX). The observation of HDX indicated that proton transfer was stimulated by dynamic clustering processes between IPA and [Hum + Ag]+ during DMS transit. Because IPA accretion preferentially occurs at Ag+, which can form pseudocovalent bonds with a suitable electron donor, solvent clustering also facilitated the formation of exceptionally stable microsolvated ions. The exceptional stability of these microsolvated configurations disproportionately impacted the compensation voltage (CV) required to elute each tautomer when the temperature within the DMS cell was varied. The disparity in CV response caused the peaks for the cis- and trans-keto species to merge when a temperature gradient was induced by the resolving gas. Moreover, simulations showed that microsolvation with IPA mediates dienol to trans-keto tautomerization during DMS transit, which, to the best of our knowledge, is the first observation of keto/enol tautomerization occurring within an ion-mobility device.

First-Principles Modeling of Preferential Solvation in Mixed-Modifier Differential Mobility Spectrometry.

Differential mobility spectrometry (DMS) separates ions based on mobility differences between high and low electric field conditions. To enhance resolution, solvents such as water and acetonitrile are often used to modify the collision environment and take advantage of differing dynamic clustering behavior between analytes that coelute in hard-sphere environments (e.g., N2). When binary solvent mixtures are used to modify the DMS environment, one solvent can have a dominant influence over the other with respect to ion trajectories. For example, for quinoline derivatives, a 9:1 water:acetonitrile solvent mixture exhibited identical behavior to an environment containing only acetonitrile as a modifier. It was hypothesized that this effect arises due to the significantly different binding strengths of the two solvents. Here, we utilize a first-principles model of DMS to study analytes in single and binary solvent mixtures and explore the effects governing the dominance of one solvent over the other. Computed DMS dispersion curves of quinoline derivatives are in excellent agreement with those measured experimentally. For mixed-modifier environments, the predicted cluster populations show a clear preferential solvation of ions with the stronger binding solvent. The influence of ion-solvent binding energies, solvent concentration, and solvent molecule size is discussed in the context of the observed DMS behavior. This work can guide the usage of binary solvent mixtures for improving ion separations in cases where compounds coelute in pure N2 and in single-solvent modifier environments. Moreover, our results indicate that binary solvent mixtures can be used to create a relative scale for solvent binding energies.

Argentination: A Silver Bullet for Cannabinoid Separation by Differential Mobility Spectrometry.

As the legality of cannabis continues to evolve globally, there is a growing demand for methods that can accurately quantitate cannabinoids found in commercial products. However, the isobaric nature of many cannabinoids, along with variations in extraction methods and product formulations, makes cannabinoid quantitation by mass spectrometry (MS) challenging. Here, we demonstrate that differential mobility spectrometry (DMS) and tandem-MS can distinguish a set of seven cannabinoids, five of which are isobaric: Δ9-tetrahydrocannabinol (Δ9-THC), Δ8-THC, exo-THC, cannabidiol, cannabichromene, cannabinol, and cannabigerol. Analytes were detected as argentinated species ([M + Ag]+), which, when subjected to collision-induced dissociation, led to the unexpected discovery that argentination promotes distinct fragmentation patterns for each cannabinoid. The unique fragment ions formed were rationalized by discerning fragmentation mechanisms that follow each cannabinoid's MS3 behavior. The differing fragmentation behaviors between species suggest that argentination can distinguish cannabinoids by tandem-MS, although not quantitatively as some cannabinoids produce small amounts of a fragment ion that is isobaric with the major fragment generated by another cannabinoid. By adding DMS to the tandem-MS workflow, it becomes possible to resolve each cannabinoid in a pure N2 environment by deconvoluting the contribution of each cannabinoid to a specific fragmentation channel. To this end, we used DMS in conjunction with a multiple reaction monitoring workflow to assess cannabinoid levels in two cannabis extracts. Our methodology exhibited excellent accuracy, limits of detection (10-20 ppb depending on the cannabinoid), and linearity during quantitation by standard addition (R2 > 0.99).

Validation of Field-Dependent Ion-Solvent Cluster Modeling via Direct Measurement of Cluster Size Distributions.

Ion mobility spectrometry is widely used in analytical chemistry, either as a stand-alone technique or coupled to mass spectrometry. Ions in the gas phase tend to form loosely bound clusters with surrounding solvent vapors, artificially increasing the collisional cross section and the mass of the ion. This, in turn, affects ion mobility and influences separation. Further, ion-solvent clusters play an important role in most ionization mechanisms occurring in the gas phase. Consequently, a deeper understanding of ion-solvent cluster association and dissociation processes is desirable to guide experimental design and interpretation. A few computational models exist, which aim to describe the amount of clustering as a function of the reduced electric field strength, bath gas pressure and temperature, and the chemical species probed. It is especially challenging to model ion mobility under high reduced electrical field strengths due to the nonthermal conditions created by the field. In this work, we aim to validate a recently proposed first-principles model by comparing its predictions with direct measurements of cluster size distributions over a range of 20-120 Td as observed using a High Kinetic Energy Ion Mobility Spectrometer coupled to a mass spectrometer (HiKE-IMS-MS). By studying H+(H2O)n, [MeOH + H + n(H2O)]+, [ACE + H + n(H2O)]+, and [PhNH2 + H + n(H2O)]+ as test systems, we find very good agreement between model and experiment, supporting the validity of the computational workflow. Further, the detailed information gained from the modeling yields important insights into the cluster dynamics within the HiKE-IMS, allowing for better interpretation of the measured ion mobility spectra.

Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time.

During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R2 = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.

Age- and sex-related differences in baboon (Papio anubis) gray matter covariation.

Age-related changes in cognition, brain morphology, and behavior are exhibited in several primate species. Baboons, like humans, naturally develop Alzheimer's disease-like pathology and cognitive declines with age and are an underutilized model for studies of aging. To determine age-related differences in gray matter covariation of 89 olive baboons (Papio anubis), we used source-based morphometry (SBM) to analyze data from magnetic resonance images. We hypothesized that we would find significant age effects in one or more SBM components, particularly those which include regions influenced by age in humans and other nonhuman primates (NHPs). A multivariate analysis of variance revealed that individual weighted gray matter covariation scores differed across the age classes. Elderly baboons contributed significantly less to gray matter covariation components including the brainstem, superior parietal cortex, thalamus, and pallidum compared to juveniles, and middle and superior frontal cortex compared to juveniles and young adults (p < 0.05). Future studies should examine the relationship between the changes in gray matter covariation reported here and age-related cognitive decline.

Kinetics in DMS: Modeling Clustering and Declustering Reactions.

Differential mobility spectrometry (DMS) uses high-frequency oscillating electrical fields to harness the differential mobility of ions for separating complex sample mixtures prior to detection. To increase the resolving power, a dynamic microsolvation environment is often created by introducing solvent vapors. Here, relatively large clusters are formed at low-field conditions which then evaporate to form smaller clusters at high-field conditions. The kinetics of these processes as the electrical field strength oscillates are not well studied. Here, we develop a computational framework to investigate how the different reactions (cluster association, cluster dissociation, and fast conformational changes) behave at different field strengths. We aim to better understand these processes, their effect on experimental outcomes, and whether DMS model accuracy is improved via incorporating their description. We find that cluster association and dissociation reactions for typical ion-solvent pairs are fast compared to the time scale of the varying separation fields usually used. However, low solvent concentration, small dipole moments, and strong ion-solvent binding can result in reaction rates small enough that a lag is observed in the ion's DMS response. This can yield differences of several volts in the compensation voltages required to correct ion trajectories for optimal transmission. We also find that the proposed kinetic approach yields generally better agreement with experiment than using a modified Boltzmann weighting scheme. Thus, this work provides insights into the chemical dynamics occurring within the DMS cell while also increasing the accuracy of dispersion plot predictions.

σ-Holes in Iodonium Ylides: Halogen-Bond Activation of Carboxylic Acids, Phenols and Thiophenols May Enable Their X-H Insertion Reactions.

The first systematic evaluation of the electrostatic potential energy maps of iodonium ylides was conducted. We determined that they possess two σ-holes of differing electron deficiencies, with the more electropositive σ-hole located opposite the dative I-C bond to the ß-dicarbonyl motif, and the lesser electropositive σ-hole located opposite the iodoarene C-I bond. We also conducted the first systematic evaluation of carboxylic acids, phenols and thiophenols in the O/S-alkylation reaction of iodonium ylides. While carboxylic acids and thiophenols were found to be generally viable, only phenols possessing electron-withdrawing substituents were effective. This high-yielding and highly chemoselective reaction is believed to involve halogen-bond activation of heteroatoms, and nicely complements existing diazo-based methods for alkylation of acidic functional groups.

Excited-State Barrier Controls E → Z Photoisomerization in p-Hydroxycinnamate Biochromophores.

Molecules based on the deprotonated p-hydroxycinnamate moiety are widespread in nature, including serving as UV filters in the leaves of plants and as the biochromophore in photoactive yellow protein. The photophysical behavior of these chromophores is centered around a rapid E → Z photoisomerization by passage through a conical intersection seam. Here, we use photoisomerization and photodissociation action spectroscopies with deprotonated 4-hydroxybenzal acetone (pCK-) to characterize a wavelength-dependent bifurcation between electron autodetachment (spontaneous ejection of an electron from the S1 state because it is situated in the detachment continuum) and E → Z photoisomerization. While autodetachment occurs across the entire S1(ππ*) band (370-480 nm), E → Z photoisomerization occurs only over a blue portion of the band (370-430 nm). No E → Z photoisomerization is observed when the ketone functional group in pCK- is replaced with an ester or carboxylic acid. The wavelength-dependent bifurcation is consistent with potential energy surface calculations showing that a barrier separates the Franck-Condon region from the E → Z isomerizing conical intersection. The barrier height, which is substantially higher in the gas phase than in solution, depends on the functional group and governs whether E → Z photoisomerization occurs more rapidly than autodetachment.

Evidence of Gas-Phase Attachment of Molecular Oxygen to Deprotonated Hydroquinone During Ion-Mobility Mass Spectrometry.

Gas-phase addition of dioxygen to certain ions is a well-known phenomenon in mass spectrometry. For this reaction to occur, the presence of a distonic radical site on the precursor ion is thought to be a prerequisite. Herein, we report that oxygen adduct formation can take place also with deprotonated hydroquinone, which in fact is an even-electron species without a radical site. When the product-ion spectrum of the m/z 109 ion, generated by electrospray ionization from a solution of hydroquinone in acetonitrile, was recorded under ion-mobility conditions, a new peak was observed at m/z 141. However, an analogous peak was not visible in the spectrum acquired under nonmobility conditions (i.e., without any gas introduced to the mobility cell). Presumably, traces of oxygen present in the collision gas instigate an ion-molecule reaction to produce an adduct of m/z 141, which upon activation results in CO and H2O loss to form a product ion of m/z 95. Isotope-labeling studies confirmed that one of the hydrogen atoms from the hydroxy group and another from the aromatic ring contribute to the water loss instigated from the m/z 141 adduct. Furthermore, computational methods indicated the three-dimensional structure of the ground-state deprotonated hydroquinone to be distinctly different from those of its 1,2- and 1,3-isomers. Calculations predicted that all atoms in the two m/z 109 ions generated from catechol and resorcinol lie on one plane. In contrast, the structure of the m/z 109 ion from hydroquinone was significantly different. Computations predicted that the hydrogen atom on the intact hydroxyl group of deprotonated hydroquinone protrudes out of plane from rest of the atoms. Consequently, the exposed OH group can interact with an incoming dioxygen molecule. Computations conducted at the CAM-B3LYP/6-311++g(2d,2p) level of theory detected a minimum energy crossing point (MECP) at -4.3 kJ mol-1 below the separated O2 + deprotonated hydroquinone triplet threshold. In contrast, similar calculations conducted for catechol and resorcinol yielded MECPs of +116.9 and +69.1 kJ mol-1, respectively, above the associated triplet thresholds. These results indicated that the curve crossing required to form singlet products upon reaction with triplet O2 is favorable in the case of hydroquinone and unfavorable in the cases of catechol and resorcinol. In practical terms, the selective oxygen addition appears to be a diagnostically useful reaction to differentiate hydroquinone from its ring isomers.

The hitchhiker's guide to dynamic ion-solvent clustering: applications in differential ion mobility spectrometry.

This article highlights the fundamentals of ion-solvent clustering processes that are pertinent to understanding an ion's behaviour during differential mobility spectrometry (DMS) experiments. We contrast DMS with static-field ion mobility, where separation is affected by mobility differences under the high-field and low-field conditions of an asymmetric oscillating electric field. Although commonly used in mass spectrometric (MS) workflows to enhance signal-to-noise ratios and remove isobaric contaminants, the chemistry and physics that underpins the phenomenon of differential mobility has yet to be fully fleshed out. Moreover, we are just now making progress towards understanding how the DMS separation waveform creates a dynamic clustering environment when the carrier gas is seeded with the vapour of a volatile solvent molecule (e.g., methanol). Interestingly, one can correlate the dynamic clustering behaviour observed in DMS experiments with gas-phase and solution-phase molecular properties such as hydrophobicity, acidity, and solubility. However, to create a generalized, global model for property determination using DMS data one must employ machine learning. In this article, we provide a first-principles description of differential ion mobility in a dynamic clustering environment. We then discuss the correlation between dynamic clustering propensity and analyte physicochemical properties and demonstrate that analytes exhibiting similar ion-solvent interactions (e.g., charge-dipole) follow well-defined trends with respect to DMS clustering behaviour. Finally, we describe how supervised machine learning can be used to create predictive models of molecular properties using DMS data. We additionally highlight open questions in the field and provide our perspective on future directions that can be explored.

Phenotypic and genetic associations between gray matter covariation and tool use skill in chimpanzees (Pan troglodytes): Repeatability in two genetically isolated populations.

Humans and chimpanzees both exhibit a diverse set of tool use skills which suggests selection for tool manufacture and use occurred in the common ancestors of the two species. Our group has previously reported phenotypic and genetic associations between tool use skill and gray matter covariation, as quantified by source-based morphometry (SBM), in chimpanzees. As a follow up study, here we evaluated repeatability in heritability in SBM components and their phenotypic association with tool use skill in two genetically independent chimpanzee cohorts. Within the two independent cohorts of chimpanzees, we identified 8 and 16 SBM components, respectively. Significant heritability was evident for multiple SBM components within both cohorts. Further, phenotypic associations between tool use performance and the SBM components were largely consistent between the two cohorts; the most consistent finding being an association between tool use performance and an SBM component including the posterior superior temporal sulcus (STS) and superior temporal gyrus (STG), and the interior and superior parietal regions (p < 0.05). These findings indicate that the STS, STG, and parietal cortices are phenotypically and genetically implicated in chimpanzee tool use abilities.

Rapid separation of cannabinoid isomer sets using differential mobility spectrometry and mass spectrometry.

With legalization and decriminalization of cannabis in many parts of the world comes the need for rapid separation and quantitation of the psychoactive ingredients. Here, we demonstrate the use of differential mobility spectrometry (DMS) mass spectrometry for the analysis of five cannabinoid molecules: the isomer set of Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabichromine (CBC), and the (-)-tetrahydrocannabinolic acid (THCA) and cannabidiolic acid (CBDA) isomer pair. Analytes were investigated under a variety of gas-phase environments to identify optimal separation conditions based on ion differential mobilities. Separation of the isomers was complicated by the formation of ion-solvent adducts during electrospray ionization (ESI). The observation of ion-solvent adducts correlated with calculated intermolecular binding energies. Introducing 1.5% (v/v) isopropyl alcohol into the N2 carrier gas resulted in strong clustering with the cannabinoid isomers, displacing ESI solvent from the adducts and enabling separation and quantitation of the cannabinoid isomer sets within seconds. Quantification of the carboxylated isomers in marijuana flower was performed to demonstrate analysis of cannabis samples.

Improved First-Principles Model of Differential Mobility Using Higher Order Two-Temperature Theory.

Differential mobility spectrometry is a separation technique that may be applied to a variety of analytes ranging from small molecule drugs to peptides and proteins. Although rudimentary theoretical models of differential mobility exist, these models are often only applied to small molecules and atomic ions without considering the effects of dynamic microsolvation. Here, we advance our theoretical description of differential ion mobility in pure N2 and microsolvating environments by incorporating higher order corrections to two-temperature theory (2TT) and a pseudoequilibrium approach to describe ion-neutral interactions. When comparing theoretical predictions to experimentally measured dispersion plots of over 300 different compounds, we find that higher order corrections to 2TT reduce errors by roughly a factor of 2 when compared to first order. Model predictions are accurate especially for pure N2 environments (mean absolute error of 4 V at SV = 4000 V). For strongly clustering environments, accurate thermochemical corrections for ion-solvent clustering are likely required to reliably predict differential ion mobility behavior. Within our model, general trends associated with clustering strength, solvent vapor concentration, and background gas temperature are well reproduced, and fine structure visible in some dispersion plots is captured. These results provide insight into the dynamic ion-solvent clustering process that underpins the phenomenon of differential ion mobility.