DHS Research Experience Summary

摘要

I learned a great deal during my summer internship at Lawrence Livermore National Laboratory (LLNL). I plan to continue a career in research, and I feel that my experience at LLNL has been formative. I was exposed to a new area of research, as part of the Single Particle Aerosol Mass Spectrometry (SPAMS) group, and I had the opportunity to work on projects that I would not have been able to work on anywhere else. The projects both involved the use of a novel mass spectrometer that was developed at LLNL, so I would not have been able to do this research at any other facility. The first project that Zachary and I worked on involved using SPAMS to detect pesticides. The ability to rapidly detect pesticides in a variety of matrices is applicable to many fields including public health, homeland security, and environmental protection. Real-time, or near real-time, detection of potentially harmful or toxic chemical agents can offer significant advantages in the protection of public health from accidental or intentional releases of harmful pesticides, and can help to monitor the environmental effects of controlled releases of pesticides for pest control purposes. The use of organophosphate neurotoxins by terrorists is a possibility that has been described; this is a legitimate threat, considering the ease of access, toxicity, and relatively low cost of these substances. Single Particle Aerosol Mass Spectrometry (SPAMS) has successfully been used to identify a wide array of chemical compounds, including drugs, high explosives, biological materials, and chemical warfare agent simulants. Much of this groundbreaking work was carried out by our group at LLNL. In our work, we had the chance to show that SPAMS fulfills a demonstrated need for a method of carrying out real-time pesticide detection with minimal sample preparation. We did this by using a single particle aerosol mass spectrometer to obtain spectra of five different pesticides. Pesticide samples were chosen to represent four common classes of pesticides that are currently used in the US. Permethrin (a pyrethrin insecticide), dichlorvos and malathion (organophosphates), imidacloprid (a chloronicotinyl pesticide), and carbaryl (a carbamate) were selected for analysis. Samples were aerosolized either in water (using a plastic nebulizer) or in ethanol (using a glass nebulizer), and the particles entered the SPAMS instrument through a focusing lens stack. The particles then passed through a stage with three tracking lasers that were used to determine each particle's velocity. This velocity was used to calculate when to fire a desorption/ionization (D/I) laser in order to fragment the particle for analysis in a dual polarity time of flight mass spectrometer. Signals were digitized, and then analyzed using LLNL-developed software. We obtained chemical mass spectral signatures for each pesticide, and assigned peaks to the mass spectra based on our knowledge of the pesticides chemical structures. We then proved the robustness of our detection method by identifying the presence of pesticides in two real-world matrices: Raid{trademark} Ant Spray and a flea collar. To sample these, we simply needed to direct aerosolized particles into the SPAMS instrument. The minimal sample preparation required makes SPAMS very attractive as a detector. Essentially, we were able to show that SPAMS is a reliable and effective method for detecting pesticides at extremely low concentrations in a variety of matrices and physical states. The other project that I had the opportunity to be a part of did not involve data collection in the lab; it consisted of analyzing a large amount of data that had already been collected. We got to look at data collected over the course of about two months, when the SPAMS instrument was deployed to a public place. The machine sampled the air and collected spectra for over two months, storing all the spectra and associated data; we then looked at an approximately two-month subset of this data to search for patterns in the types of particles being detected. Essentially, we were able to identify particle types among all the spectra collected by clustering the spectra into groups of similar spectra. This was done using software that had been previously developed by our group (Dr. Paul Steele, a former group member, was instrumental in helping us learn how to use the software). Once we had found particles that seemed to recur, we faced the task of trying to figure out what these particles were. To do this, we compared the average spectra for each major cluster to those of several common compounds. We were able to tentatively identify at least one compound this way. We also looked at patterns in the appearance of different compounds. For instance, there were some compounds that only appeared at certain times of the day.