Sudhir Dahal

The efficiency of renewable energy technologies, such as solar and wind power, has drastically increased over the last decade, whereas the cost of owning these technologies has decreased significantly. As a result, these changes have created a huge demand for energy storage devices, including batteries. Of particular interest is the development and improvement of portable lithium-ion (Li-ion) batteries because of their advantages over traditional battery technologies, including having higher energy density compared to other existing rechargeable batteries and faster charging. Researchers are constantly investigating ways to make Li-ion batteries more efficient and safer. Currently, the Li-ion battery technology is far from perfect and there have been several high-profile reports of failures, including explosions and fires caused during charging, use, and storage. In this regard, Raman spectroscopy has proven to be a powerful tool for research and quality control of Li-ion batteries. In this paper, we present Raman as a technique of choice for quality control of manufacturing processes of Li-ion batteries. We highlight two cases of bulk analysis of lithium compounds using Raman spectroscopy during the quality control procedure of raw materials, and one case of analysis for better manufacturing using Raman imaging microscopy.

Sir C.V. Raman contemplated the color of the sea on a sea trip returning back to India from England in 1921. These contemplations led him to conduct a series of research experiments on the scattering of light in then British Calcutta. In 1928, he discovered that when a beam of colored light entered a liquid, a fraction of the light scattered by that liquid was a different color (1–3). Raman showed that the nature of this scattered light was dependent on the type of sample present. Other scientists understood the significance of this phenomenon as an analytical and research tool, and called it the Raman effect. This method became even more valuable with the advent of modern lasers, optics, and computers, with current uses ranging from the nondestructive identification of minerals to the early detection of life-threatening diseases. Raman was awarded the Nobel Prize in physics in 1930 for his discovery (4).

Today, Raman spectroscopy is utilized by researchers and quality control analysts as a tool to analyze cells, tissues, minerals, pharmaceutical compounds, and nanomaterials (5), and it plays an important role in developing new materials as well as in the large-scale manufacturing of such materials.

Over the last decade, with the increased use of renewables and the need for energy storage, researchers have been investigating new generations of materials for rechargeable lithium ion (Li-ion) batteries. Raman spectroscopy plays an important role in this research phase for characterizing novel anode and cathode materials (6–8). Raman spectroscopy has many advantages, but the most important one for battery applications involves the detection and measurement of subtle changes in molecular structure or local chemical environments because the Raman spectral results can easily be correlated with the electrochemical performance.

Raman spectroscopy has been used to analyze many different types of battery components, including cathode materials, anode materials, and electrolytes (6–9). During the manufacturing of Li-ion batteries, there are several challenges and bottlenecks in the analysis workflows. Raman spectroscopy and Raman microscopy can overcome these challenges, especially for analyzing lithium compounds, anode, and cathode slurries, and then utilizing Raman imaging for the defect analysis of anode and cathode films during production. This article focuses on examples of how Raman spectroscopy has been used for the analysis of lithium compounds and how Raman imaging can help solve complex problems arising in the production of anode and cathode films.

Analysis of Raw Materials and Various Battery Components

Li-ion battery manufacturing involves active anode and cathode materials, solvents, and separator films. It is mandatory to control the chemical properties of the materials from suppliers of the components and bulk lithium compounds. A variety of analytical techniques are employed, ranging from bulk sample measurement to surface analytical techniques at various production stages. Techniques such as inductively coupled plasma (ICP)-mass spectrometry (MS) and ICP–optical emission spectroscopy (OES) are widely used in analyzing impurities. At manufacturing plants where they develop their own standards (and also rely on the certificate of analysis provided by the suppliers of raw materials), bulk analysis of samples is carried out using vibrational spectroscopy techniques because of the nondestructive and rapid analysis those techniques provide.

Raman spectroscopy is well suited for bulk analysis of polyatomic lithium salts because the technique is sensitive to hydrated forms of salts. Typical analysis can be carried out without sample preparation and typical analysis time is in minutes.

Lithium hydroxide (and its hydrated forms) and lithium carbonate are used as raw materials for battery manufacturing. It is mandatory to check the chemical identity of these materials and to check whether carbonate is present in lithium hydroxide salts before using them in production. Raman spectroscopy is well suited for this task; typical wet chemistry analytical tests, in contrast, take longer and are destructive.

A Thermo Fisher Scientific DXR3 SmartRaman spectrometer was used to acquire the spectrum of the battery grade lithium hydroxide anhydrous (A) and the spectrum was compared with the lithium carbonate and library reference spectrum of lithium hydroxide monohydrate. The DXR3 SmartRaman is a versatile instrument with sampling flexibility and ease of use. Its universal plate holder enables analysis of solids and liquids with ease. Additionally, the fiber optic probe can enable in situ measurements or can be located in the glove box for analyzing environmentally sensitive samples, such as battery electrolyte solutions.

The shaded areas in Figure 1 show the peaks related to the carbonates, and it can be seen that the battery-grade lithium hydroxide is free from the contaminants. Tests carried out indicated the suitability of lithium hydroxide for cathode manufacturing.

FIGURE 1: Raman spectra of: (a) lithium hydroxide anhydrous, (b) lithium hydroxide monohydrate, and (c) lithium carbonate.

In other tests, several batches of battery-grade lithium hydroxides were compared to check the presence of carbonates. As seen in Figure 2, the Raman spectrum in red shows no contaminants, and the spectra in violet, green, and blue shows the presence of carbonates as contaminants (regions below 2000 cm

FIGURE 2: Raman spectra of battery-grade lithium hydroxide.

S) is another active cathode material used in high-performance solid-state batteries. The presence of carbonates were found easily with bulk sample analysis carried out with Raman analysis.

Figure 3 shows the Raman spectrum of lithium sulfide in red and lithium carbonate in violet. As seen from the shaded area, there are carbonate impurities present in the battery-grade lithium sulfide.

FIGURE 3: Raman spectra of lithium sulfide (red) compared with lithium carbonate (violet).

The primary source of waste in battery manufacturing is coating defects. Currently, electrodes with defective coatings are discarded regardless of the type of defect, which greatly increases the cost of production as the raw materials for electrode manufacturing are very expensive. The quality of the electrode coating depends on uniform thickness, porosity, material distribution, and adhesion to the current collector. Any inhomogeneity of these properties results in defects that lead to local aging of the electrode and a loss in capacity and cycle life.

There are several tests that manufacturers carry out to determine the distribution of organic compounds on the anode and cathode surfaces, to trace the migration of binder on carbon-based anodes, to check for contamination on formed anode and cathode films, and to investigate the thin layer solid electrolyte interface.

After the batteries are assembled and final tests are completed, investigations are carried out on failed batteries to determine the root cause of failure. For such analysis, Raman imaging can be a great tool to complement X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM) analysis. Next, we present few examples of such analysis.

Raman Chemical Imaging of Pristine and Cycled Anodes

Raman spectra of the baseline anode and the cycled anode are shown in Figures 4 and 5. Raman spectral acquisition and imaging were carried out using a Thermo Fisher Scientific DXR3xi Raman imaging microscope. All the samples showed distinct D, G, and 2D bands, characteristic of graphitic carbon. The uncycled, pristine baseline sample had the lowest fluorescence background. The cycled anodes showed varying degrees of fluorescence, indicative of the amount of impurities. For example, anode 5 has the least amount of impurities, thus showing the least fluorescence compared to the others. Fluorescence in Raman spectra of cycled electrodes is common and can originate from multiple sources, such as SEI, residual electrolyte, or transition metal complexes.

FIGURE 4: Raman spectra of pristine cathodes: pristine anode 1 (blue), pristine anode 2 (violet), pristine anode 3 (green), pristine anode 4 (red).

FIGURE 5: Raman spectra of different cycled cathodes.

The Raman spectra of the anodes show contributions from three major components: graphite, carbon black, and fluorescence.

The Raman spectra of the cycled anode exhibits two peaks at 1346 and 1582 cm

, corresponding to the D and G bands, respectively. The D band corresponds to the presence of defects, and the G band indicates ideal graphitic sp

 carbons. The ratio of the D and G band intensities (

) is considered to indicate the degree of disorder in a carbon structure.

During Raman imaging, spectral data collection is synchronized with sample stage movement so that a complete Raman spectrum at each point in the sample (or an area of interest) is acquired. This used to be called Raman mapping; however, with the increased sensitivity and speed of the modern-day instruments, the data can be collected in minutes rather than days, so the term Raman imaging is more commonly used.

The areas in Figure 6 shaded in red mark the G band with higher width and in green correspond to the lower width of the G band, which shows smaller crystals, and therefore a lower order, going more amorphous.

FIGURE 6: Image of the cycled cathode overlay with Raman chemical image. The entire picture (visible image) is approximately 100 μm x 100 μm, and the Raman image (overlay image) is approximately 50 μm x 50 μm in spatial dimensions.

Anode and cathode cross sections are often created for XPS and SEM analysis for morphology and particle distribution analysis, and these cross-sections can often be analyzed using a Raman imaging system. Figure 6 shows such cross-section analysis of an anode.

The micrograph in Figure 7 shows white material; that is, the residual separator material on the electrode after it is removed from the battery and rinsed. The dark area is the anode surface.

FIGURE 7: Chemical image with multivariate curve resolution (MCR). The entire picture (visible image) is approximately 200 μm x 150 μm, and the Raman image (overlay image) is approximately 50 μm x 50 μm in spatial dimensions.

Superimposed in the red square is a chemical image generated from an multivariate curve resolution (MCR) analysis of the Raman data collected over a 50 μm square area of the anode. Red and blue dyed areas represent intensity of the red and blue Raman spectra shown in inset. The red spectrum corresponds to carbon black; blue corresponds to graphite.

MCR reveals the differences in the image so analysts can quickly identify characteristics of the sample, such as inhomogeneous coatings, particle distribution, and physical and chemical differences.

In Figure 7, the image shows just two of the primary components, carbon black and graphite. There are some unique spectral features in the upper left corner of the image. Chemical image contrast (green dye) is generated from peak area of the Raman band at 1117 cm

 (green inset Raman spectrum). This spectrum also has a unique band at 1504 cm

For these peaks, 2-ethylimidazole is a good match. This type of compound is sometimes used in batteries as a safety additive. It decomposes at a high temperature, releasing a gas that increases the cell pressure activating a breaking valve or other safety mechanism.

As the need for more efficient batteries continues to grow, Raman spectroscopy has proven to be an effective tool to study Li-ion batteries and their components. It has many advantages, but the most important ones for battery applications are those that involve subtle changes in molecular structure or local chemical environments, with both in situ and ex situ analysis capabilities. These properties allow Raman analysis to be used in quality control of raw materials, and in the research and analysis of finished materials. There have been significant improvements in commercial Raman instrumentation over the last several years. Important advances in both hardware and software have made modern Raman instruments more user friendly and removed many of the obstacles that, in the past, made routine use of Raman spectroscopy arduous for users with limited expertise.

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(4) Sir Venkata Raman—Facts. NobelPrize.org. Nobel Prize Outreach AB 2022. 22 April 2022. https://www.nobelprize.org/prizes/phys- ics/1930/raman/facts/

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 is with Thermo Fisher Scientific in Les Ulis, France. 

 is with Thermo Fisher Scientific in Hemel Hempstead in the United Kingdom. 

 is with Thermo Fisher Scientific in Madison, Wisconsin. Direct correspondence to: 

Articles

Raman spectroscopy is a valuable tool for research and quality control of lithium-ion (Li-ion) batteries, which are a critical aspect of renewable energy technologies. We highlight two cases of bulk analysis of lithium compounds using Raman spectroscopy.

Lithium-Ion Battery Manufacturing and Quality Control: Raman Spectroscopy, an Analytical Technique of Choice

Micro- and nanoplastics enter the environment as manufactured products in cosmetics and other industrial products, and as breakdown products of plastics of larger size (macroplastics). Recent estimates suggest that the mass of microplastics in aquatic habitats will surpass that of fish by 2050; therefore, plastic pollution is a major environmental issue faced at global scale. Marine scientists have been working in this field for over a decade, and more recently the study of microplastics has captured the attention of other scientists in the environmental field. Despite efforts focused on identifying, quantifying, and assessing the size of microplastics in the environment, standardized methods are lacking. As a result, studies cannot be compared or reproduced. Infrared (IR) microscopy has become an essential technique in the toolbox for the analysis of microplastics in environmental samples. In this work, we conducted a methodical evaluation of significant operational parameters of IR microscopy for accurately determining the size of microplastics, with the overarching goal of outlining performance parameters that will help in the standardization of microplastics analysis.

Plastic pollution is a major environmental issue faced at global scale. Micro- and nanoplastics enter the environment as manufactured products in cosmetics and other industrial products, and as breakdown products of plastics of larger size (macroplastics). According to a recent perspective paper (1), the most pessimistic estimates suggest that the mass of microplastics in aquatic habitats will surpass that of fish by 2050. Interest in the scientific community and public has exponentially increased in the past few years, resulting in a significant increase in the number of publications focused on this topic (2). Marine scientists have been working on this field for a decade, and more recently the study of microplastics has captured the attention of other scientists in the environmental field. Despite efforts focused on identifying, quantifying, and assessing the size of microplastics in the environment, standardized methods and agreement of reporting parameters are lacking (3). As a result, studies cannot be compared or reproduced. Common requests from scientists engaged in the field include the need for reference materials, proficiency tests and interlaboratory studies, and detailed descriptions of instrumental and spectral treatment parameters (2). Multiple international groups (2,3), scientific organizations (such as the Society of Environmental Toxicology and Chemistry), and standardization agencies (such as the American Society for Testing and Materials [ASTM] and the International Organization for Standardization [ISO]) are currently engaging in efforts to fulfill these requests.

 (4) provides an excellent summary of various techniques suitable of the identification of microplastics and their scope. Infrared (IR) microscopy has become an essential technique in the toolbox for the analysis of microplastics in environmental samples. IR microscopy is often used for two purposes: the identification and sizing of microplastics present in the sample. While lack of appropriate standard spectra in the libraries could cause challenges in proper identification, especially for weathered plastics, this problem can be overcome with custom-created libraries and analyzing specific regions of the spectra (unique peaks, valleys, and intensity ratios, to name a few). Therefore, the real challenge is to understand the capability of IR microscopy for microplastics analysis, mainly in terms of smallest measurable microplastic particle size.

The objective of the work presented here was to collect data to support current standardization efforts for the analysis of microplastics in environmental samples by IR microscopy. For this purpose, we conducted a methodical evaluation of significant operational parameters of IR microscopy for accurately determining the size of microplastics, including optical mode, number of scans, and background contribution from filters.

Microplastic standards were purchased from Sigma-Aldrich. They were polymer based micro particles with various diameters: 100 µm polystyrene (P/N 59336), 50 µm polymethacrylate (P/N 74161), 50 µm polystyrene (P/N 74491) and 10 µm polystyrene (P/N 72986).

Three types of filters commonly used to hold the microplastics samples for Fourier transform infrared (FT-IR) spectroscopy studies were evaluated: a stainless-steel mesh filter (Utah Biodiesel Supply, 5 μm pore size), a cellulose ester filter (Whatman, 0.6 μm pore size) and a glass fiber filter (Whatman, 934-AH grade, 1.5 μm pore size).

The measurements were carried out with a Shimadzu AIM-9000 FT-IR microscope in combination with an IRTracer-100 FT-IR spectrophotometer. Instrumental control and data acquisition were done automatically by the AIMSolution software. Data were collected in reflection mode and attenuated total reflectance (ATR) mode. When measuring in the reflection mode, background was acquired using a silver mirror. Experiments conducted in ATR mode were conducted with the Shimadzu ATR attachment with a Ge objective on the FT-IR microscope. The only post-processing steps performed were baseline correction of the data whose signal to noise ratio were calculated, and Kubelka-Munk transformation of the spectrum of the cellulose ester filter. The instrumental parameters are listed in Table I.

All samples were diluted first to 1 mL of Deionized water, except for the 100 μm polystyrene based microplastics sample. For reflection mode measurements, the samples were dispensed onto the filters and dried under ambient conditions. For ATR mode studies, 1–2 drops of the diluted samples or the 100 μm polystyrene-based stock sample were dispensed on a low-E microscope slide, and dried under ambient conditions.

To get a better understanding of types and distribution of the particles on the filters, infrared mapping measurements were carried out using the reflection mode on the FT-IR microscope. The information obtained from mapping is often qualitative; however, some quantitative information can also be obtained with further data analysis. In-depth measurements using more sensitive setup and parameters were carried out on areas of interest determined by mapping.

Samples containing microplastics of decreasing particle size (described in the section 

) were analyzed in reflection mode with the conditions listed in Table I. These experiments were repeated using three filter types (stainless-steel mesh filter, cellulose ester filter, and glass fiber filter) to assess the impact of the background spectra on the detection threshold of the particle sizes.

The reflection mode measurement of the samples was obtained as-is in the filter, and although this measurement mode allows for faster data acquisition with the ability to analyze multiple particles or an entire area automatically, it is susceptible to background noise from the filter element, surrounding particles, and matrix. Therefore, once the particles were identified using the reflection method, a more targeted measurement with better signal-to-noise ratio (S/N) using the ATR attachment on single particles was carried out. Samples containing microplastics of decreasing particle size were analyzed in ATR mode with the conditions listed in Table I. Because ATR is a contact-based measurement for surface measurement (no more than 2–5 µm into the surface), only particles pressed against the ATR crystal are measured, ensuring
higher accuracy.

Evaluation of S/N and Threshold of Detection Based on Sample Size

An experiment was conducted for IR spectral acquisition (reflection mode) on the 100 μm polystyrene-based sample with varying aperture sizes. The aperture sizes started from 5x5 μm, then increased to 10x10 μm. The aperture sizes were gradually increased from 10x10 μm to 100x100 μm, with an interval of 10 μm (Figure 1). This change in aperture size can be directly correlated to the difference in sample size. In addition, and more importantly, using the same particle but with different aperture sizes also ensured that any other experimental variations (inter-sample variation and instrument conditions) were eliminated. In a second experiment, the impact of the number of scans in the S/N of the measurement at each aperture size was evaluated.

Spectral Search Using the FT-IR Polymer Library

Spectral searches for all spectra were performed using polymer libraries provided by the microscope software. The Shimadzu AIM-9000 FT-IR microscope adopts a score system from 0 to 1000 to report search results. In this study, the differential derivative algorithm was used, which calculates the primary differential of each data and compares the differential factor at each point with fitting of whole curves.

Reflection Mode Mapping Measurement Results

Microplastics samples dispensed on filters were scanned automatically using the mapping function of the AIMSolution Software. Aperture sizes were adjusted according to the dimension of the microplastics particles. The ratio of the signature peak intensity over the baseline intensity could be mapped to reflect the specific microplastics abundance at the locale. Figure 2 shows the mapping area settings and the resulting chemical mapping of microplastics samples of four different sizes on a stainless-steel mesh filter.

In Figure 2, it can be observed that mapping measurement in the reflection mode can provide relatively quick qualitative analysis of the microplastics samples in a potentially large area. When measuring particles with a size of 10x10 μm, it became challenging to capture focused images of the samples because the unevenness and the structure of the filters made it almost impossible for the microplastics particles to lie within the focal plane. As a result, the FT-IR spectra acquired for the 10 μm samples tended to reflect signals contributed by a subset of particles rather than that of an exact individual particle.

Similar reflection mapping measurements were obtained for microplastics samples held on a cellulose ester filter and a glass fiber filter. These filters were selected because they are commonly used in environmental laboratories for sample filtration. When those filters were used, signal interferences from the filters were evident (Figure 3). The cellulose ester filter showed IR peaks across the whole measured region (Figure 3e), whereas the glass fiber had IR absorption mainly in the low wavenumber region (Figure 3j). The stainless-steel mesh filter is highly reflective in the IR and can be considered to share similar features to that of the metal mirror used for background measurement; therefore, the spectra were not included in Figure 3. Figure 3 also demonstrates qualitatively how the background spectra negatively affects the detection threshold of the particle size. Overall, using the stainless-steel filter had the advantage of suppressing the signal contributions of the filters.

When measuring in the reflection mode, IR signals from the microplastics samples may be unable to be determined because of unwanted interference, like neighboring contaminants or substance attaching to the samples. It is desirable to measure in the ATR mode because it greatly assists in getting signal only from the targeted area. With a tip diameter of only 12.5 μm, the Shimadzu ATR attachment for AIM-9000 can acquire IR spectra from particles with high specificity. Figure 4 illustrates the microplastic samples and the FT-IR spectra acquired using ATR mode. However, samples were analyzed in triplicates; only one spectrum per particle size is included in the figure. The pictures on the left side are visible images captured for each of the samples analyzed. The FT-IR spectra are shown on the right side, and they illustrate the characteristic absorption bands of the microplastics. Spectral search was performed using polymer libraries provided by the microscope software; search results showed ≥75% match for the four samples analyzed, independently from the size of the particle. Qualitatively, the results demonstrate that FT-IR spectra of microplastics as small as 10 µm can be detected in ATR mode.

Evaluation of S/N and Detection Threshold

Measurements made in reflection mode are known to be more affected by the presence of interferences and background than ATR measurements; therefore, it can be assumed that the threshold of detection for determining the size of microplastics will be higher in reflection mode than ATR mode. To quantitatively determine the threshold of detection under the most challenging conditions, a series of experiments was conducted in reflection mode. FT-IR spectra were acquired on a 100 μm polystyrene particle, with variable aperture sizes gradually increasing from 5x5 μm to 100x100 μm (Figure 1); four replicate measurements at each aperture were performed. This change in aperture size can be directly correlated to the difference in sample size (See the Experimental Approach section for more details). Peak-to-peak S/N values at each aperture setting were evaluated; results are shown in Figure 4. Threshold of detection was determined based on measurements that presented a S/N > 3 (5). According to the results included in Figure 5, it can be concluded that when using the typical operational conditions of FT-IR microscopy (included in Table I), the smallest particle size that can reliably measured is 10x10 μm. From Figure 5, it was clear that the greatest increase in S/N took place when the sample (aperture) size was increased from 5x5 μm to 10x10 μm. After the sample (aperture) size was increased to 40x40 μm and higher, the rate of increase of S/N remained constant.

To further confirm the threshold of detection in terms of particle size, another study was carried out with sample sizes of 5x5 μm and 10x10 μm. In this study, the number of scans averaged to obtain a spectrum were gradually increased. The number of scans was set at 32, 64, 128, and 256 scans for the two sample sizes mentioned above. Measurements were replicated five times. Peak-to-peak S/N was evaluated at each measurement. As can be concluded from Figure 6, S/N in the 5x5 μm sample size did not show any substantial improvement with the increased number of scans. The averaged S/N values stayed close to 0.5, suggesting that only noise was observed. When the sample size was changed to 10x10 μm, S/N showed a noticeable increase as the number of scans increased. S/N should increase as more scans are averaged when a real signal is detected, this result further verifies that the signal from the 10x10 μm was real and that from the 5x5 μm sample was primarily noise; thereby, this confirms the threshold of 10x10 μm.

This work demonstrates a series of method and experimental conditions in FT-IR microscopy in both reflectance and ATR mode for identification and accurate determination of the size of microplastics. The results presented in this paper are a subset of data being prepared for standardizing the analysis of microplastics in environmental samples. We have demonstrated that, by using typical operating conditions of FT-IR microscopy, it is possible to identify and distinguish between microplastic particles as small as 10x10 μm in dimension. This threshold value held true for both the reflection measurement method (which is faster, but can experience signal interference from neighboring particles and matrix) and the ATR method, which is specific to a particle to get “cleaner” measurements but is slower because of the setup involved. In the reflection measurement itself, this threshold value was determined and verified by two separate analysis methods, further verifying the capability of the technique.

J.M. Andrade, B. Ferreiro, P. López-Mahía, and S. Muniategui-Lorenzo, 

W. Cowger, A. Booth, B. Hamilton, S. Primpke, et al., 

is a Product Specialist in the Molecular Spectroscopy group at Shimadzu Scientific Instruments in Columbia, Maryland. 

is the Product Manager of the Molecular Spectroscopy group at Shimadzu Scientific instruments in Columbia, Maryland. 

 is the Environmental Market Manager at Shimadzu Scientific Instruments, in Columbia, Maryland. Direct correspondence to: 

Here we conduct an evaluation of significant operational parameters of IR microscopy for accurately determining the size of microplastics, with the overarching goal of outlining performance parameters that will help in the standardization of microplastics analysis.

Sudhir Dahal

Articles

Lithium-Ion Battery Manufacturing and Quality Control: Raman Spectroscopy, an Analytical Technique of Choice

Insights Toward Standardization of the Analysis of Microplastics by Infrared Microscopy