Abstract

Quantitative methods have been developed for the analysis of chemical warfare agent degradation products in reaction masses using capillary electrophoresis (CE). This is the first report of a systematic validation of a CE-based method for the analysis of chemical warfare agent degradation products in agent neutralization matrixes (reaction masses). After neutralization with monoethanolamine/water, the nerve agent GB (isopropyl methylphosphonofluoridate, Sarin) gives isopropyl methylphosphonic acid (IMPA) and O-isopropyl O'-(2-amino)ethyl methylphosphonate (GB-MEA adduct). The nerve agent GD (pinacolyl methylphosphonofluoridate, Soman), [pinacolyl = 2-(3,3-dimethyl)butyl] produces pinacolyl methylphosphonic acid (PMPA) and O-pinacolyl O'-(2-amino)ethyl methylphosphonate (GD-MEA adduct). The samples were prepared by dilution of the reaction masses with deionized water before analysis by CE/indirect UV detection or CE/conductivity detection. Migration time precision was less than 4.0% RSD for IMPA and 5.0 RSD for PMPA on a day-to-day basis. The detection limit for both IMPA and PMPA is 100 micrograms/L; the quantitation limit for both is 500 micrograms/L. For calibration standards, IMPA and PMPA gave a linear response (R2 = 0.9999) over the range 0.5-100 micrograms/mL. The interday precision RSDs were 1.9, 1.0, and 0.7% for IMPA at 7.5, 37.5 and 75.0 micrograms/mL, respectively. Corresponding values for PMPA (again, RSD) were 2.9, 1.1, and 1.0% at 7.5, 37.5 and 87.5 micrograms/mL, respectively, as before. Analysis accuracy was assessed by spiking actual neutralization samples with IMPA or PMPA. For IMPA, the seven spike levels used ranged from 20 to 220% of the IMPA background level, and the incremental change in the found IMPA level ranged from 86 to 99 % of the true spiking increment (R2 = 0.9987 for the linear regression). For PMPA, the five spike levels ranged from 10 to 150% of the matrix background level, and similarly, the accuracy obtained ranged from 95 to 97% of the true incremental value (R2 = 0.9999 for the linear regression).

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