In the lower troposphere, rapid collisions between ions and trace gases result in the transfer of positive charge to the highest proton affinity species and negative charge to the lowest proton affinity species. Measurements of the chemical composition of ambient ions thus provide direct insight into the most acidic and basic trace gases and their ion–molecule clusters – compounds thought to be important for new particle formation and growth. We deployed an atmospheric pressure interface time-of-flight mass spectrometer (APi-ToF) to measure ambient ion chemical composition during the 2016 Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) campaign at the United States Department of Energy Atmospheric Radiation Measurement facility in the Southern Great Plains (SGP), an agricultural region. Cations and anions were measured for alternating periods of ∼ 24 h over 1 month. We use binned positive matrix factorization (binPMF) and generalized Kendrick analysis (GKA) to obtain information about the chemical formulas and temporal variation in ionic composition without the need for averaging over a long timescale or a priori high-resolution peak fitting. Negative ions consist of strong acids including sulfuric and nitric acid, organosulfates, and clusters of NO3- with highly oxygenated organic molecules (HOMs) derived from monoterpene (MT) and sesquiterpene (SQT) oxidation. Organonitrates derived from SQTs account for most of the HOM signal. Combined with the diel profiles and back trajectory analysis, these results suggest that NO3 radical chemistry is active at this site. SQT oxidation products likely contribute to particle growth at the SGP site. The positive ions consist of bases including alkyl pyridines and amines and a series of high-mass species. Nearly all the positive ions contained only one nitrogen atom and in general support ammonia and amines as being the dominant bases that could participate in new particle formation. Overall, this work demonstrates how APi-ToF measurements combined with binPMF analysis can provide insight into the temporal evolution of compounds important for new particle formation and growth.

The COVID-19 pandemic has posed a challenge for maintaining an engaging learning environment while using remote laboratory formats. In this work, we describe a Student Choice Project (SCP) in an undergraduate instrumental analysis course that was adapted for remote learning without sacrificing research-based learning goals. We discuss the implementation and assessment of this SCP, selectedstudent results, and student feedback. Students were provided handheld carbondioxide monitors and charged with designing and implementing an investigationcentered on COVID-19 airborne transmission. The real-time monitors providedexperience with a new analytical tool that demanded considerations and analysis notcommon to other methods discussed in the course. Students were motivated by the ability to design their own projects and by the real-world implications of their findings. They performed well for all assessments, reported a positive experience, and recommended these monitors be added to the typical repertoire of instrumentation for the course

Chemical identification of new particle formation and growth precursors through positive matrix factorization of ambient ion measurements S1 Fitting of binPMF peaksIn order to evaluate the error introduced by the binPMF and peak-fitting process, synthetic peaks were generated and analyzed.First, Gaussian peaks at selected positions between m/z 200 and 550 were generated in time-of-flight (ToF) space.Peak widths were equal to real peaks observed at the selected m/z.The peaks were sampled in ToF space at the same interval as the APi-ToF data acquisition.The ToF space to m/z space transformation was calculated as:where p1 and p2 are the fit parameters selected for the simulation and ToF is the time of flight (ns).This function was selected for the simulated data because it was found to the best fit function for real data and was used to fit both positive and negative mode data throughout the campaign."True" values for p1 and p2 were selected for the simulation.To simulate an upper limit estimate on error in the mass calibration and its impact on the binPMF results, pairs of p1 and p2 values were randomly selected from the set of p1 and p2 values fit from the ambient data using Tofware.Because p1 and p2 do not vary independently, each pair of values consisted of parameters calculated for the same time point in the calibration.This is an upper estimate of our error because it assumes that all shifts in mass calibration contribute error, but there are real shifts in p1 and p2 that result from temperature changes, drift within the instrument, and other factors.Following the ToF to m/z space transformation, the synthetic peaks in m/z space were binned using the same bins and bin widths used for binPMF and fit with a Gaussian to determine the peak center.Error introduced by the m/z calibration was determined using a Monte Carlo method to randomly select many sets of p1 and p2.Root mean squared errors introduced by this method were approximately 50 ppm for both positive and negative mode data.The simulation was also repeated using only the "true" fit parameters to determine whether error in the peak positions originated from simulated error in the mass calibration or from the binning and fitting procedure.Error was negligible (<<1 ppm) when using the "true" fit parameters, suggesting that most error is from the mass calibration and not the fitting procedure.Peak broadening was also evaluated.Peaks may be broadened both by the procedure of binning and fitting peaks to bins and by shifts in the mass calibration throughout the campaign.Figure S1 shows the comparison between the peak in a 15-minute average mass spectrum at m/z 487 and the Gaussian peak fit to the bins at that mass.Minimal broadening is observed.It should also be noted that peak widths have no direct implications for the conclusions of this work.Peak shape was also investigated.Figure S2 compares the high-resolution peak shape calculated in Tofware and a Gaussian

The bark of Prunus africana may contain atranorin, atraric acid, beta-sitosterol and its esters, ferulic acid and its esters, and N-butylbenzene sulfonamide, compounds that have been shown to improve the conditions of benign prostatic hyperplasia, enlarged prostate. An analytical scheme, involving liquid-solid extractions, saponifications, and LC-APCI-MS (triple quadrupole) analysis, was developed, optimized, and validated to determine the compounds at mu g/g levels. Limits of quantification were in the low ng/mL range except for beta-sitosterol. All of the compounds plus two internal standards eluted in under 10 min on a phenyl-hexyl column with gradient elution involving water-methanol and acetonitrile. The mass fraction of the compounds in Prunus africana bark (four samples) and commercial pygeum products (seven samples), derived from bark, were compared. Bark and pygeum were similar in their content of atranorin and atraric acid, found at low mu g/g levels, and in the fact that ferulic acid was almost totally (>90%) in the form of esters. In contrast, the total amount of ferulic acid was on average four times higher in bark (450 mu g/g) than in pygeum while the opposite was true for total beta-sitosterol. Some pygeum samples had levels of total beta-sitosterol above 10,000 mu g/g while the compound in bark was relatively invariant at about 680 mu g/g. The fraction of free beta-sitosterol varied significantly between bark (33%) and pygeum (nearly all). In pygeum, the measured total beta-sitosterol concentration generally followed the labeled values for phytosterol content. No N-butylbenzene sulfonamide was found in any of the bark and pygeum samples. (C) 2018 Elsevier By. All rights reserved.