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A broad range of inorganic nanoparticles (NPs) and their dissolved ions possess a possible toxicological risk for human health and the environment. Reliable and robust measurements of dissolution effects may be influenced by the sample matrix, which challenges the analytical method of choice. In this study, CuO NPs were investigated in several dissolution experiments. Two analytical techniques (dynamic light scattering (DLS) and inductively-coupled plasma mass spectrometry (ICP-MS)) were used to characterize NPs (size distribution curves) time-dependently in different complex matrices (e.g., artificial lung lining fluids and cell culture media). The advantages and challenges of each analytical approach are evaluated and discussed. Additionally, a direct-injection single particle (DI sp)ICP-MS technique for assessing the size distribution curve of the dissolved particles was developed and evaluated. The DI technique provides a sensitive response even at low concentrations without any dilution of the complex sample matrix. These experiments were further enhanced with an automated data evaluation procedure to objectively distinguish between ionic and NP events.
With this approach, a fast and reproducible determination of inorganic NPs and ionic backgrounds can be achieved. This study can serve as guidance when choosing the optimal analytical method for NP characterization and for the determination of the origin of an adverse effect in NP toxicity.
An alternative method for lithium isotope analysis by using high-resolution atomic absorption spectrometry (HR-CS-AAS) is proposed herein. This method is based on monitoring the 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 HR-CS-AAS. Isotope analysis can be used for (i) the traceable determination of Li concentration and (ii) isotope amount ratio analysis based on a combination of HR-CS-AAS and spectral data analysis by machine learning (ML).
In the first case, the Li spectra are described as the linear superposition of the contributions of the respective isotopes, each consisting of a spin-orbit doublet, which can be expressed as Gaussian components with constant spectral position and width and different relative intensity, reflecting the isotope ratio in the sample. Precision was further improved by using lanthanum as internal spectral standard. The procedure has been validated using human serum-certified reference materials. The results are metrologically comparable and compatible with the certified values.
In the second case, 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. The training ML model was validated with certified reference materials. The procedure was applied to the isotope amount ratio determination of a set of stock chemicals and a BAM candidate reference material NMC111 (LiNi1/3Mn1/3Co1/3O2), a Li-battery cathode material. 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. The NMC111 cathode candidate reference material was analyzed using high-resolution continuum source atomic absorption spectrometry with and without matrix purification to assess its suitability for technical applications. The results obtained were metrologically compatible with each other.
High-resolution absorption isotopic spectrometry as a tool for aging studies of Li-ion batteries
(2022)
Lithium (Li) is the key element in the manufacturing of batteries. Isotopic study of Li may help to identify the causes of battery aging due to isotopic fractionation during charge/discharge cycles.
Isotopic ratio determination is based on monitoring the isotopic components of lithium by their spin-orbit coupling and its isotopic shift of about 15 pm for the 22P←22S electronic transition around 670.788 nm. In this work, we propose improvements to our previous work [1] by using a higher-resolution double echelle modular spectrometer (HR-DEMON II) coupled to a continuum source graphite furnace atomic absorption spectrometer (HR-CS-GF-AAS) for the isotopic analysis of Li.
The data analysis was carried out by using a decision-tree-based ensemble machine learning (ML) algorithm (XGBoost). A set of samples with 6Li isotope amount fractions ranging from 0.0004 to 0.99 mol mol-1 was used for the algorithm's training. Subsequently, the procedure was validated by a set of stock chemicals (Li2CO3, LiNO3, LiCl, and LiOH) and a BAM candidate reference material, a cathode material (NMC111). Finally, the ML model was applied to determine the isotope ratio of geological samples, including anorthosite, granite, soil, rhyolite, nepheline syenite, and basalt and battery samples. These samples were measured as digested without any further purification step.
Improvements in the optical resolution resolve the lithium isotopic components of the atomic spectra. In the studied geological samples, were found δ7Li values between -0.5 and 4.5 ‰ with a precision range of 1 to 2 ‰. In addition, the proposed method was validated with multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), and these results are comparable and compatible.
High-resolution continuum source graphite furnace molecular absorption spectrometry (HR-CS-GF-MAS) was employed for determining adsorbable organic chlorine (AOCl) in water. Organic chlorine was indirectly quantified by monitoring the molecular absorption of the transient aluminum monochloride molecule (AlCl) around a wavelength of 261.42 nm in a graphite furnace. An aluminum solution was used as the molecularforming modifier. A zirconium coated graphite furnace, as well as Sr and Ag solutions were applied as modifiers for a maximal enhancement of the absorption signal. The pyrolysis and vaporization temperatures were 600 °C and 2300 °C, respectively. Non-spectral interferences were observed with F, Br, and I at concentrations higher than 6 mg L-1, 50 mg L-1, and 100 mg L-1, respectively. Calibration curves with NaCl, 4-chlorophenol, and trichlorophenol present the same slope and dynamic range, which indicates the chlorine atom specificity of the method. This method was evaluated and validated using synthetic water samples, following the current standard DIN EN ISO 9562:2004 for the determination of the sum parameter adsorbable organic halides (AOX) for water quality. These samples contain 4-chlorophenol as the chlorinated organic standard in an inorganic chloride matrix. Prior to analysis, organic chlorine was extracted from the inorganic matrix via solid-phase extraction with a recovery rate >95%. There were no statistically significant differences observed between measured and known values and for a t-test a confidence level of 95% was achieved. The limits of detection and characteristic mass were found to be 48 and 22 pg, respectively. The calibration curve was linear in the range 0.1–2.5 ng with a correlation coefficient R2 = 0.9986.
The quantification of the sulphur mass fraction in pure copper and copper alloys by GDMS and LA-ICP-MS revealed a lack of traceability mainly due to a lack of suitable certified reference materials for calibrating the instruments. Within this study GDMS and LA-ICP-MS were applied as routine analytical tools to quantify sulphur in copper samples by applying reference materials as calibrators, which were characterized for their sulphur mass fraction by IDMS beforehand. Different external calibration strategies were applied including a matrix cross type calibration. Both techniques with all calibration strategies were validated by using certified reference materials (others than those used for calibration) and good agreement with the reference values was achieved except for the matrix cross type calibration, for which the agreement was slightly worse. All measurement results were accompanied by an uncertainty statement. For GDMS, the relative expanded (k = 2) measurement uncertainty ranged from 3% to 7%, while for LA-ICP-MS it ranged from 11% to 33% when applying matrix-matched calibration in the sulphur mass fraction range between 25 mg kg-1 and 1300 mg kg-1. For cross-type calibration the relative expanded (k = 2) measurement uncertainty need to be increased to at least 12% for GDMS and to at least 54% for LA-ICP-MS to yield metrological compatibility with the reference values. The so obtained measurement results are traceable to the international system of units (SI) via IDMS reference values, which is clearly illustrated by the unbroken chain of calibrations in the metrological traceability scheme.
In this poster for the SCIX Conference, we apply an analytical procedure based on the monitoring of the lithium isotope through the partially resolved isotope shift in the electronic transition 22P<-22S around wavelength 670.80 nm using high-resolution continuum source atomic absorption spectrometer (HR-CS-AAS) in combination with machine learning (ML) for the determination of Li Isotope ratio analysis.
Chemical and pharmaceutical companies have to find new paths to survive successfully in a changing environment, while also finding more flexible ways of product and process development to bring their products to market more quickly – especially high-quality high-end products like fine chemicals or pharmaceuticals. The potential of digital technologies belongs to these.
A current approach uses flexible and modular chemical production units, which can produce different high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce the time to market of new products. At the same time, we need to move towards knowledge-based production that takes into account all essential equipment, process and control data from plants and laboratories and makes valuable expertise available and transferable. The potential of data from production together with its contextual information is often not yet consistently used today for a comprehensive understanding of production.
By giving examples this paper outlines a possible more holistic approach to digitalisation and the use of machine-based methods in the production of specialty chemicals and pharmaceuticals through the introduction of integrated and networked systems and processes.