High-Resolution Atomic Absorption Spectrometry Combined with Machine Learning Data Processing for Isotope Amount Ratio Analysis of Lithium

<a>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 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 multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). The calibration ML model was validated with two certified reference materials (LSVEC and IRMM-016). The procedure was applied to the isotope amount ratio determination of a set of stock chemicals (Li₂CO₃, LiNO₃, LiCl, and LiOH) and a BAM candidate reference material, that is, LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) 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 </a><a>precision obtained ranged from 1.9‰ to 6.2‰</a>. 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 molecular absorption spectrometry with and without matrix purification. The results obtained were metrologically compatible with each other.

High-Resolution Atomic Absorption Spectrometry Combined with Machine Learning Data Processing for Isotope Amount Ratio Analysis of Lithium

<a>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 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 multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). The calibration ML model was validated with two certified reference materials (LSVEC and IRMM-016). The procedure was applied to the isotope amount ratio determination of a set of stock chemicals (Li₂CO₃, LiNO₃, LiCl, and LiOH) and a BAM candidate reference material, that is, LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) 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 </a><a>precision obtained ranged from 1.9‰ to 6.2‰</a>. 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 molecular absorption spectrometry with and without matrix purification. The results obtained were metrologically compatible with each other.

Development and characterisation of new glycine certified reference materials for SI-traceable13C/12C isotope amount ratio measurements

Variations in the stable isotopic composition of carbon are used in diverse fields, including biology, environmental sciences, food and drug authentication and forensic applications.

A critical review on isotopic fractionation correction methods for accurate isotope amount ratio measurements by MC-ICP-MS

Proper correction of mass-dependent and mass-independent isotopic fractionation is crucial to obtain accurate isotope amount ratios by multicollector inductively coupled plasma mass spectrometry (MC-ICP-MS).