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Glow discharge optical emission spectroscopy (GD-OES) is a technique for the analysis of solids such as metals, semiconductors, and ceramics. A low-pressure glow discharge plasma is applied in this system, which ‘sputters’ and promotes the sample atoms to a higher energy state. When the atoms return to their ground state, they emit light with characteristic wavelengths, which a spectrometer can detect. Thus, GD-OES combines the advantages of ICP-OES with solid sampling techniques, which enables it to determine the bulk elemental composition and depth profiles. However, direct solid sampling methods such as glow-discharge spectroscopy require reference materials for calibration due to the strong matrix effect.
Reference materials are essential when the accuracy and reliability of measurement results need to be guaranteed to generate confidence in the analysis. These materials are frequently used to determine measurement uncertainty, validate methods, suitability testing, and quality assurance. In addition, they guarantee that measurement results can be compared to recognized reference values. Unfortunately, the availability of certified reference materials suited to calibrate all elements in different matrix materials is limited. Therefore various calibration strategies and the preparation of traceable matrix-matched calibration standards will be discussed.
Machine learning is an essential component of the growing field of data science. Through statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. Therefore, it was tried in our work to combine GD-OES with machine learning strategies to establish a new and robust calibration model, which can be used to identify the elemental composition and concentration of metals from a single spectrum. For this purpose, copper reference materials from different manufacturers, which contain various impurity elements, were investigated using GD-OES. The obtained spectra information are evaluated with different algorithms (e.g., gradient boosting and artificial neural networks), and the results are compared and discussed in detail.
What does GD-OES reveal about the aging and manufacturing processes of lithium-ion batteries?
(2024)
Glow-Discharge Optical Emission Spectroscopy (GD-OES), a powerful analytical technique, sheds light on the two critical aspects of lithium-ion batteries (LIBs): manufacturing and aging 1, 2. We optimized cell production in manufacturing by adjusting parameters, including cathode doping, electrolyte concentration, and pressing force. GD-OES provided in-depth elemental composition and homogeneity analysis, which is crucial for identifying optimal manufacturing conditions. These findings were validated by electrochemical impedance spectroscopy, confirming the quality of the manufactured batteries.
Shifting the focus to aging, we use GD-OES for fluorine depth profiling, a key element in understanding polymer and electrolyte degradation. However, fluorine presents analytical challenges. We addressed this by substituting argon with a neon:argon mixture, which significantly enhanced fluorine detection sensitivity. This advancement not only improves accuracy but also holds the potential to guide sustainable and cost-efficient manufacturing strategies.
Through its versatility, GD-OES has proven to be a powerful tool for not only optimizing LIB manufacturing processes but also gaining deeper insights into their aging mechanisms. This research extends beyond academic interest, offering tangible benefits for the industry by translating into improved battery quality, extended lifespan, and overall performance.
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.
Neues Energielabel
(2018)
Resource efficiency is a much discussed topic in terms of improving
the sustainability of energy related and energy non-related
products. Resource efficiency aspects such as the availability
of spare parts, the ability to dismantle, etc. have been included
in draft working documents in the revision of several already
existing Ecodesign regulations as a first step. However, often
these aspects are not consistent with the current technology and
design of these products. A possible reason could be a lack of
sufficient consultation or of a methodology which is sufficiently
tailored for this topic. The established strategies and tools, used
by policymakers, such as the Methodology for the Ecodesign
of Energy-related Products (MEErP), do not seem to deal with
these aspects appropriately. Draft requirements need to be very
well developed before being discussed with member states and
other related stakeholders, because including resource efficiency
parameters could lead to additional, very wide-ranging effects
on society. This topic cannot be covered well with legislative
tools developed primarily for energy aspects. In this paper, a
method is presented which can be used to combine products’
properties with crucial resource efficiency indicators. The method
can be used to develop a set of draft legislative requirements
and to pre-evaluate these requirements by target groups which
would be affected by additional legal requirements. These include:
market surveillance authorities, standardization organizations,
manufacturers and their associations, environmental
organizations and research facilities. The method incorporates
stakeholders’ feedback to identify potential resource efficiency
measures for materials and/or products, their impact on the
European ecology, economy and society. Based on this it would
help to develop legislative requirements which are feasible and
desirable. The results can then be fed into the formal legislative
process, probably speeding it up.