In a recent study published in the Journal of Chromatography A, led by scientists from the University of Orleans in Orléans, France, Shimadzu France, and Shimadzu Europa Gmbh, supercritical fluid extraction (SFE) was combined with supercritical fluid chromatography (SFC) hyphenated to mass spectrometry (MS) to analyze plastic additives that could be transferred into the environment from laboratory gloves (1).
Composed of organic polymers and small-molecule additives to obtain specific physico-chemical properties in the final product, each type of plastic composition has different properties that are suitable for different applications. One such type of application is in the hospital sector, with plastics being used in medical devices like infusion tubing, blood bags, syringes, and medical gloves. Introducing additives is vital during plastic formulation, as it can help control material properties, like flexibility or color, while potentially increasing a product’s lifetime by enhancing resistance to oxidation, thermal stability, and aging degradation. However, with plastic additives, there is always a risk for potential migration, which can lead to their transfer to blood, nutritive liquid, water, or human skin, among others.
For this study, the scientists combined supercritical fluid extraction SFE-SFC-MS in an online system in hopes of characterizing plastic additives in laboratory gloves, which were taken as samples of medical devices. A set of target compounds, consisting of 18 plasticizers, 4 antioxidants, and 2 lubricants, was defined, with their detectability with MS also being examined. Electrospray ionization (ESI) provided better detectability than atmospheric pressure chemical ionization (APCI). Afterwords, potential stationary phases were examined using the Derringer desirability function as a means of assistance. This technique is typically used to select the “best” values from a set and comparing unrelated features between different objects; in fact, when first described by the creators, it was used to select polymeric materials based on various properties (2).After examination, an isocratic chromatographic method (CO2: methanol 95:5) was developed. The extraction method was examined with a 3-level full factorial design of experiments, which would optimize the extraction temperature (40 ºC) and pressure (200 bar).
The online SFE-SFC-MS method was then compared to offline methods where samples were extracted with liquid solvents at atmospheric pressure or high pressure before being analyzed using SFC-MS. In all cases, the offline methods showed significant contaminants being issued from laboratory plastic materials, such as nitrogen drying stations, syringes, and filters. Meanwhile, the online method, in addition to allowing complete elimination of laboratory contaminations, managed to save time, solvents, and laboratory consumables. The scientists were also able to transfer a compressible fluid from a loading loop, which is favorable to high efficiency, since the resulting chromatographic peaks are much thinner than when transferring a liquid. When compared to injecting liquid heptane, the efficiency increase was 3.4-fold, with injecting liquid methanol, which is a common SFC practice, leading to a 13-fold efficiency increase.
With these findings, an online SFE-SFC-MS method shows great potential for characterizing plastic additives from small amounts of plastic samples. In future works, quantitation and method validation will be addressed to help better understand variability issues. As such, this method will be applied to diverse medical devices.
(1) Caux, B.; Jores, C. D. S.; Abou-Naccoul, R.; Horie, S.; West, C. Advantages of Online Supercritical Fluid Extraction and Chromatography Hyphenated to Mass Spectrometry to Analyse Plastic Additives in Laboratory Gloves. J. Chromatogr. A 2024, 17353, 465323. DOI: 10.1016/j.chroma.2024.465323
(2) West, C. Statistics for Analysts Who Hate Statistics, Part VI: Derringer Desirability Functions. LCGC International 2017. https://www.chromatographyonline.com/view/statistics-analysts-who-hate-statistics-part-vi-derringer-desirability-functions (accessed 2024-9-27)