A workflow-optimized protocol for accelerated sample preparation and automated Sr separation from natural waters for 87Sr/86Sr determination†

Abstract

Radiogenic strontium (⁸⁷Sr/⁸⁶Sr) is a powerful tool for characterizing and differentiating water reservoirs (among many other applications). The development and improvement of high-precision analytical platforms (namely MC-ICP-MS) has enhanced throughput for isotope ratio determination. However, analyte purification—needed to remove isobaric interferences—continues to occur largely via conventional manual gravity-driven ion exchange chromatography (hereafter: manual IEC), which generally cannot match instrument throughput. This has created a persistent throughput gap that encumbers use and proliferation, emphasizing the need for rapid separation of Sr, and of comprehensive, end-to-end high throughput workflows and analytical approaches that are fit-for-purpose. Here we have developed a workflow-optimized protocol for sample preparation and separation of Sr from natural water samples using both workflow-optimized manual IEC and automated high pressure ion chromatography (HPIC), for subsequent analysis via MC-ICP-MS. These methods have been designed to seamlessly integrate with common international practice for water sample collection. The automated HPIC technique accommodates introduction of water samples filtered with standard 0.45 μm membranes and acidified with ultra-high purity nitric acid (HNO₃, to pH of 1–2, approximated as 0.09 mol per L HNO₃). Filtered and acidified samples are directly introduced into the HPIC system where Sr is separated from other cations (namely Ca) and collected as an isolate in a specific volume of ultrapure water. Strontium isolates, with no further preparation (e.g. dry-down and reflux), are then directly acidified to 0.5 mol per L HNO₃ and analyzed by MC-ICP-MS. This technique can process 40–50 samples in a 24 hour period with mitigated potential for human error, matching current MC-ICP-MS analytical capacity, and achieving analytical precision sufficient to distinguish the variability observed in natural samples across many applications.