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Acoustically levitated droplets have been suggested as compartmentalized, yet wall-less microreactors for high-throughput reaction optimization purposes. The absence of walls is envisioned to simplify up-scaling of the optimized reaction conditions found in the microliter volumes. A consequent pursuance of high-throughput chemistry calls for a fast, robust and sensitive analysis suited for online interrogation. For reaction optimization, targeted Analysis with relatively low sensitivity suffices, while a fast, robust and automated sampling is paramount. To follow this approach, in this contribution, a direct coupling of levitated droplets to a homebuilt ion mobility spectrometer (IMS) is presented. The sampling, Transfer to the gas phase, as well as the ionization are all performed by a single exposure of the sampling volume to the resonant output of a mid-IR laser. Once formed, the nascent spatially and temporally evolving analyte ion cloud needs to be guided out of the acoustically confined trap into the inlet of the ion mobility spectrometer. Since the IMS is operated at ambient pressure, no fluid dynamic along a pressure Gradient can be employed. Instead, the transfer is achieved by the electrostatic potential gradient inside a dual ring electrode ion optics, guiding the analyte ion cloud into the first stage of the IMS linear drift tube accelerator. The design of the appropriate atmospheric pressure ion optics is based on the original vacuum ion optics design of Wiley and McLaren. The obtained experimental results nicely coincide with ion trajectory calculations based on a collisional model.
An alternative method for lithium isotope amount ratio analysis based on a combination of high-resolution atomic absorption spectrometry and spectral data analysis by machine learning (ML) is proposed herein. It is based on the well-known isotope shift of approximately 15 pm for the electronic transition 22P←22S at around the wavelength of 670.8 nm, which can be measured by the state-of-the-art high-resolution continuum source graphite furnace atomic absorption spectrometry. For isotope amount ratio analysis, a scalable tree boosting ML algorithm (XGBoost) was employed and calibrated using a set of samples with 6Li isotope amount fractions, ranging from 0.06 to 0.99 mol mol–1, previously determined by a multicollector inductively coupled plasma mass spectrometer (MC-ICP-MS). The calibration ML model was validated with two certified reference materials (LSVEC and IRMM-016). The procedure was applied toward the isotope amount ratio determination of a set of stock chemicals (Li2CO3, LiNO3, LiCl, and LiOH) and a BAM candidate reference material NMC111 (LiNi1/3Mn1/3Co1/3O2), a Li-battery cathode material. The results of these determinations were compared with those obtained by MC-ICP-MS and found to be metrologically comparable and compatible. The residual bias was −1.8‰, and the precision obtained ranged from 1.9 to 6.2‰. This precision was sufficient to resolve naturally occurring variations, as demonstrated for samples ranging from approximately −3 to +15‰. To assess its suitability to technical applications, the NMC111 cathode candidate reference material was analyzed using high-resolution continuum source atomic absorption spectrometry with and without matrix purification. The results obtained were metrologically compatible with each other.