From Bench to Factory: The Role of Chemical Purity and Process Control in Lithium-Ion Battery Innovation

The widespread adoption of lithium-ion batteries has transformed modern energy storage, enabling applications ranging from consumer electronics to electric vehicles and grid-scale systems. However, continued progress depends on addressing key challenges in safety, performance, affordability, and sustainability, particularly as technologies scale from laboratory research to gigafactory production. This article examines the critical role of analytical chemistry techniques and process control across the battery value chain. It highlights how advanced analytical techniques enable mitigation of thermal runaway, reduction of cell-to-cell variability, control of impurities, cost optimization, and improved recyclability. Together, these strategies support safer, higher-performing, more sustainable battery technologies and faster commercialization of next-generation chemistries.

The introduction of the lithium-ion battery in the latter half of the 20^th century revolutionized battery science by delivering improved energy density, battery life, portability, and higher efficiency (Figure 1). This breakthrough led to a dramatic expansion in the range of applications using batteries, from everyday uses such as consumer electronics to electric vehicles, electric grid storage solutions, medical devices, and defense applications.

To further accelerate adoption and broaden application scope,key challenges must be addressed, such as safety, performance, affordability, and sustainability. To deliver on these needs, processes need to be effectively scaled from bench to laboratory to pilot and ultimately to factory scale. Throughout this scaling process, the implementation and maintenance of stringent process controls, including the management of impurities and contaminants, have become increasingly important.

This article delves into the core customer needs of safety, performance, affordability, and sustainability, and provides guidance on some of the more commonly overlooked process controls that are necessary to deliver on these needs.

Safety

Thermal runaway, a critical safety concern in lithium-ion battery technologies, occurs when uncontrolled chemical reactions cause the battery to swell, overheat, and release flammable and toxic gases. If left unchecked, the battery may explode. While many in the industry believe that thermal runaway is an eventuality that can be managed solely by ensuring fires do not propagate to adjacent cells, researchers emphasize that mitigation begins with chemistry. By understanding the composition of battery cell gases and electrolyte degradation products, researchers can gain insight into reaction mechanisms within the cell, thereby supporting component optimization, which can in turn reduce the chances of thermal runaway from occurring.^1–4

Typical analytical techniques used to characterize swell gases, those formed during thermal runaway events, as well as those released during cell manufacturing and cycling, include gas chromatography (GC),⁵ GC–mass spectrometry (GC–MS),³ GC–tandem mass spectrometry (GC–MS/MS), GC–quadrupole time of flight mass spectrometry (GC-QTOF-MS), GC flame ionization detection (GC-FID), GC–mass selective detection (GC-MSD), and micro-GC–MS, as well as Fourier transform infrared spectroscopy (FT-IR). While GC–MS solutions provide quantitative information for specified target analytes, GC-QTOF instrumentation offers valuable qualitative information about all compounds in the gas. Using this technique, accurate mass capabilities help to identify potential “unknown unknowns” in the sample, whose identification can be verified via downstream analysis using GC–MS/MS.⁶

In addition to testing gases generated prior to and during thermal runaway, the accurate characterization of the electrolyte, cathode, anode, separator material, current collector, and solid electrolyte interphase also helps to support improvements in battery safety and performance. Titration techniques can facilitate these types of compositional analyses and often leverage several analytical tools.^7–12

There are also specific battery types, such as all-solid-state batteries (ASSB), which have observed increased interest and investment over the past few years due to their potential to deliver improved safety, weight, size, and performance. Despite their promise, ASSBs face several technical and economic hurdles. These include high production costs (partly due to the use of rare earth elements), high interfacial resistance (resulting from poor contact between solid electrolytes and the electrode), reduced ion transport efficiency, and challenges with scaling. Assuming these shortcomings are addressed, ASSBs are poised to become one of the dominant battery types of the future.

Performance

At the production level, one of the most significant pain points that battery manufacturers face is managing and mitigating cell-to-cell variability. Expressed as differences in energy density, capacitance, lifetime, and impedance, cell-to-cell variability can reduce the yield of high-grade “Class A” batteries and increase the percentage of those classified as Class B or C.^13–15 Batteries in these lower classes have limited applications, and fetch a lower selling price, as many products require high-performing Class A batteries for enhanced safety and performance. When assembled in a pack, cells with poor performance can place strain on others in the configuration, leading to early-life failures. Not only is this undesirable from a customer confidence and retention standpoint, but it can also pose a safety risk, as it increases the chances of thermal runaway, and may result in costly recalls.

Several well-established factors contribute to cell-to-cell variability, including electrode thickness and density, particle size distribution, and the weight fraction of active materials. Beyond these items, leading manufacturers of complete batteries and individual battery components have long recognized the importance of comprehensive chemical characterizations —not only of intermediates and final products, but also of incoming raw and starting materials.

Although raw material suppliers typically provide a certificate of analysis detailing composition and purity, these certificates generally include only analytes of known concern. Unlisted impurities or compounds—despite their potential to positively or negatively influence product performance—often lack defined acceptance criteria. As a result, batch-to-batch variations in these unmonitored constituents are common.

These variations present a significant challenge when scaling technologies from bench to pilot to gigafactory scales, since it’s typical for small-batch chemicals to be used during bench-scale development. In contrast, bulk materials tend to be used for scaled operations. Such variability can be further compounded by changes in suppliers, geographical mine location, or processing methods. By proactively characterizing materials throughout the entire value chain, regardless of production scale, issues can be mitigated, troubleshooting reduced, and scaling processes optimized. Additionally, such monitoring can provide valuable information that can be fed into machine-learning models, supporting faster battery chemistry optimizations.

Another best practice from leading manufacturers is to ensure that intermediate and final products are characterized for both physical and chemical properties, including screening for contaminants introduced during the production cycle. Ultimately, the characterization and monitoring of raw materials, intermediates, and final products for contaminants and impurities have now become a routine part of quality and process control in top-tier commercial production labs. This practice would also benefit research and development (R&D) labs, streamlining scalability and reducing R&D-to-commercialization timelines.

Inductively coupled plasma–optical emissions spectroscopy (ICP-OES), (FT-IR), ultraviolet–visible (UV-vis) spectroscopy, liquid chromatography–mass spectrometry (LC–MS) or liquid chromatography–tandem mass spectrometry (LC–MS/MS), ion chromatography (IC), and nuclear magnetic resonance (NMR) are typically used to characterize impurities and contaminants; however, the latter technology can struggle to analyze the complex matrices common to battery technologies. These instruments are also used to characterize analytes of interest in raw materials, intermediates, and end products. This comprehensive approach allows battery scientists to account for and correct batch variations.

In recent years, there has also been an increased interest in using inductively coupled plasma–mass spectrometry (ICP–MS) as a screening and characterization tool. This has been driven by the development of next-generation batteries, which are safer, better performing, and have less variability than their predecessors, leading to a change in purity demand from the conventional 99.5% metals basis purity to 99.9%.¹⁶ The industry expects this demand for purity to continue evolving as battery technologies become more advanced, and it is expected to reach 99.999% for certain components and applications over the coming years.^17–21

In addition to analyzing impurities, moisture content, crystal structure, and surface chemistry are commonly established initially, and should also be periodically reevaluated during scaling.

Affordability

With up to 50% of the cost of a battery tied to the cathode, depending upon the specific chemistry,^22,23 it’s unsurprising that cost-reduction efforts have primarily focused on this component. While recent advancements in low voltage nickel manganese cobalt (NMC) cells have demonstrated high temperature lifetimes surpassing those of LFP,²⁴ many companies have started to move away from cathodes containing cobalt due to their high cost and associated ethical concerns. Consequently, several mainstream battery technologies, such as lithium iron phosphate (LFP), lithium manganese oxide (LMO), and lithium manganese iron phosphate (LMFP), are seeing increased adoption. In these technologies, considerable research has been conductedto mitigatethe migration of transition metals from the cathode to the anode, a process known as transition metal dissolution (TMD). This process has become more apparent with the migration away from cobalt-based battery technologies towards manganese and other transition metals. This chemistry change results in increased side reactions and losses in electrochemical performance.^25,26 Reducing TMD has motivated research²⁷ into new battery electrolytes that can protect the cathode active materials from dissolution, as well as surface-coating techniques for cathodes, which can suppress TMD while also allowing less cobalt to be used.²⁸

Emerging technologies such as lithium-sulfur (Li-S) batteries are also attracting attention due to their high theoretical energy densities. However, there are still practical obstacles to this battery chemistry, which are related to sulfur dissolution, polysulfide shuttling, and capacity fade.^29–31 These issues primarily stem from the complex electrochemistry of sulfur, and a great deal of research in analytical chemicals is ongoing to address these limitations.^29–31 In parallel, interest in alternative battery chemistries and battery types for specific applications is growing. These include sodium-ion batteries (SIB), which can leverage abundant and low-cost materials and are being explored for stationary battery energy storage applications.

Currently, anodes are estimated to account for approximately 14% of the battery cost.^22,23 There has been increased research interest in anode materials to increase the anode capacity and improve overall performance. One of the materials being explored to improve the performance of anodes while also reducing costs is silicon. Recent studies have found that when silicon is mixed with carbon as a composite, for example, Si-C, challenges associated with pure silicon, such as volume expansion and consequent mechanical degradation, unstable solid electrolyte interphase formation, and rapid capacity fade, are addressed. Additionally, this approach has been found to enhance the ionic conductivity of electrode materials and battery lifetime and has also been found to support faster charging.³²

Sustainability

One of the primary drivers behind recent developments in battery technology has been its potential to significantly lower carbon emissions from the automobile industry. However, according to the World Economic Forum,³³ battery electric vehicles (BEVs) are 1.5 to 2 times more carbon-intensive to produce than internal combustion engine vehicles (ICEs). This is due to the energy-intensive battery production processes from the mining and refining of materials needed to manufacture the various battery components.

Once on the road, however, BEVs use electricity to charge, and their carbon footprint depends primarily on the electricity source. Overall, various publications have indicated that BEVs can demonstrate lower carbon emissions than ICE vehicles over their entire lifespan.³⁴ However, it may take several months to a few years to offset the carbon credit deficit from production.

To close the carbon gap between ICE and BEV vehicles, some companies are sourcing raw materials produced using cleaner energy sources. Other companies are looking to optimize the weight of BEVs (for which the battery weight is a major contributor). This approach provides a means of lowering the rate at which the battery depletes on a single charge, reducing the recharge cadence and its associated carbon footprint.Meanwhile, advancements in longer-lasting batteries are helping to close the gap more rapidly and reduce the rate of battery replacement. Many battery researchers are pursuing multiple strategies simultaneously to reduce costs, carbon footprints, enhance performance, and mitigate supply chain uncertainties.

Some governments have adopted a whole-life-cycle approach to battery technologies to further improve sustainability and offset the high costs associated with raw materials mining. This approach requires R&D scientists and mainstream producers to consider the end-of-life of batteries. Since BEVs typically use Grade A batteries, some automobile manufacturers have begun to consider energy storage solutions as a second-life application of these batteries. However, due to uncertainties about the safety and performance of these materials, much work is still needed to understand the chemical and physical properties of the various components following depletion, ensuring the batteries can be reliably reused.

Conversely, others are focusing on producing batteries that can be more easily recycled and seeking alternatives to per- and poly-fluorinated substances, commonly referred to as PFAS.³⁵ An example of the use of PFAS in batteries includes the use of polyvinylidene fluoride (PVDF) binders in active materials, fluorinated chemicals in electrolyte solutions, and electrolyte additives.^36,37 PFAS have been applied in binders for ceramic coatings used in battery separators or, in some cases, the battery separator itself where PDVF or its co-polymer, poly(vinylidenefluoride-co-hexafluoropropylene (PVDF-HFP), is used in place of polypropylene (PP) or polyethylene (PE).^38–39

Battery recyclers are also innovating to improve the yields of raw materials and reduce laboratory overhead costs—including expenditures on gas, consumables, electricity, water, waste disposal, and personnel. Historically, recycling has relied on bulk chemistries and has not always prioritized sustainability. However, thought leaders in this space now recognize that sustainability goes hand in hand with profitability. Overhead can be significantly reduced, hazardous waste minimized, and additional revenue streams created by extracting useful compounds, materials, and acids from the recycling waste stream. To achieve this efficiently, on-site chemical characterizations are essential and can be enhanced through machine learning to deliver faster optimizations.^40–42

Conclusion

As innovators and mainstream producers investigate the next generation of battery technologies or aim to improve productivity and yields, it’s clear that comprehensive chemical characterization studies are needed. To maintain market leadership, battery manufacturers must focus on the quality of their products, as well as the reliability and efficiency of their operations across all locations. The decision to partner with companies known for providing outstanding analytical solutions, reliable service, and post-sales expertise at all factory locations is not just a matter of operational efficiency—it's a strategic imperative which ensures consistency, scalability, quality, and innovation, and mitigates risk. These factors are essential for achieving long-term success and growth in this competitive and rapidly evolving industry.

References

(1) Metzger, M.; Gasteiger, H. A. Diagnosing Battery Degradation via Gas Analysis. Energy Environ. Mater. 2022, 5 (3), 688−692. DOI: 10.1002/eem2.12326

(2) Zhang, J. The Analysis of Swelling Gas in Lithium-Ion Batteries with an Agilent 990 Micro GC. Agilent Application Note 5994-2321EN, 2020 (accessed 2026-03-04).

(3) Lin, C.; Yan, H.; Qi, C; et al. Research on Thermal Runaway and Gas Generation Characteristics of NCM811 High Energy Density Lithium-Ion Batteries Under Different Triggering Methods. Case Stud. Therm. Eng. 2024, 64, 105417. DOI: 10.1016/j.csite.2024.105417

(4) Eaton, D.; Garretson, L.; Baumann, S. Avoid Thermal Runaway by Monitoring Battery Swell Gas and Electrolyte Degradation. Agilent Application Note 5994-7166EN, 2025 (accessed 2026-03-04).

(5) U.S. Department of Transportation (U.S. DOT). Lithium Battery Thermal Runaway Vent Gas Analysis DOT/FAA/TC-15/59, 2015 (accessed 2026-03-04).

(6) Zou, A.; Zhang, Y. F.; Chevallier, O. Multiplatform Approach for Lithium-Ion Battery Electrolyte Compositional Analysis. Agilent Application Note 5994-7014EN, 2023 (accessed 2026-03-04).

(7) Fang, C.; Li, J.; Zhang, M.; et al. Quantifying Inactive Lithium in Lithium Metal Batteries. Nature 2019, 572, 511−515. DOI: 10.1038/s41586-019-1481-z

(8) Zou, A.; Li, S.; Chun, H. A.; McCurdy, E. Accurate ICP-MS Analysis of Elemental Impurities in Electrolyte Used for Lithium-Ion Batteries. Agilent Application Note 5994-5363EN, 2022 (accessed 2026-03-04).

(9) Alwan, W.; Zieschang, F. Advancing Research of Lithium-Ion Batteries Using the Agilent Cary 630 FTIR Spectrometer. Agilent Application Note 5994-6144EN, 2023 (accessed 2026-03-04).

(10) Shang, H.; Zhang, J. Analysis of Carbonate Esters and Additives in Battery Electrolyte Using Agilent 8860 GC. Agilent Application Note 5994-5888EN, 2023 (accessed 2026-03-04).

(11) Singha, S.; Drvodelic, N. Analysis of Elemental Impurities in Lithium Iron Phosphate Cathode Materials for LIBs by ICP-OES. Agilent Application Note 5994-6736EN, 2023 (accessed 2026-03-04).

(12) Hobold, G. M.; Wang, C.; Steinberg, K.; Li, Y.; Gallant, B. M. High Lithium Oxide Prevalence in the Lithium Solid–Electrolyte Interphase for High Coulombic Efficiency. Nature Energy 2024, 9, 580−591. DOI: 10.1038/s41560-024-01494-x

(13) Xing, J. How to Distinguish Grade A and Grade B LiFePO4 Prismatic Battery Cell? KHZH. 2022. (accessed 2026-03-04).

(14) SUNESS. Home Energy Storage Batteries: Comparative Analysis of A-Grade and B-Grade Lithium Battery Cells2023. (accessed 2026-03-04).

(15) Liu, E. LiFePO4 Battery Grades: Grade A, B, and C Explained. LiFePO4 Battery (accessed 2026-03-04).

(16) Hanton, S. D. The Mining and Refining Challenges to Produce High Purity Lithium. LabManager Magazine 2021 (accessed 2026-03-04).

(17) Zong, L.; Zhu, B.; Lu, Z.; et al. Nanopurification of Silicon from 84% to 99.999% Purity with a Simple and Scalable Process. Proc. Natl. Acad. Sci. U.S.A. 2015, 112 (44), 13473−13477. DOI: 10.1073/pnas.1513012112

(18) Leavy, D. The Contribution of High-Purity Materials to Decarbonisation. Innovation News Network. (accessed 2026-03-04).

(19) REFLEX Advanced Materials. Reflex Achieves 99.999% Purity from Ongoing Collaborative Metallurgical Testing2022 (accessed 2026-03-04).

(20) Sun, K. Exploring High Purity Alumina: The Battery Metal That's Flying Under The Radar. Market Index 2023 (accessed 2026-03-04).

(21) Goodfellow, C. Why 99.999% Pure Aluminium Foil is Essential in Modern Lab Science. Advent Research Materials 2025 (accessed 2026-03-04).

(22) Wentker, M.; Greenwood, M.; Leker, J. A Bottom-Up Approach to Lithium-Ion Battery Cost Modelling with a Focus on Cathode Active Materials. Energies 2019, 12 (3), 504. DOI: 10.3390/en12030504

(23) Bhutada, G. Breaking Down the Cost of an EV Battery Cell. Elements 2022 (accessed 2026-03-04).

(24) Aiken, C. P.; Logan, E. R.; Eldesoky, A.; et al. Li[Ni0.5Mn0.3Co0.2]O2 as a Superior Alternative to LiFePO4 for Long-Lived Low Voltage Li-Ion Cells. J. Electrochem. Soc. 2022, 169 (5), 050512. DOI: 10.1149/1945-7111/ac67b5

(25) Kang, S.; Cho, S.; Kang, Y.-M. Getting to the Bottom of Transition Metal Dissolution. Nat. Nanotechnol. 2023, 18, 700−701. DOI: 10.1038/s41565-023-01396-1

(26) Rynearson, L.; Antolin, C.; Jayawardana, C.; et al. Speciation of Transition Metal Dissolution in Electrolyte from Common Cathode Materials. Angew. Chem., Int. Ed. 2024, 63 (5), e202317109. DOI: 10.1002/anie.202317109

(27) Shu, W.; Li, J.; Zhang, G.; et al. Progress on Transition Metal Ions Dissolution Suppression Strategies in Prussian Blue Analogs for Aqueous Sodium-/Potassium-Ion Batteries. Nano-Micro Lett. 2024, 16, 128. DOI: 10.1007/s40820-024-01355-y

(28) Boyd, D. A.; Quine, C. M.; Pasalic, J.; et al. Suppression of Transition Metal Dissolution in Mn-Rich Layered Oxide Cathodes with Graphene Nanocomposite Dry Coatings. J. Electrochem. Soc. 2024, 171, 100532. DOI: 10.1149/1945-7111/ad867f

(29) Gao, X.; Kim, S. C.; Liao, S.-L.; et al. Solvation-Property Relationship of Lithium-Sulphur Battery Electrolytes. Nat. Commun. 2023, 14, 44527. DOI: 10.1038/s41467-023-44527

(30) Chen, J.; Fu, Y.; Guo, J. Development of Electrolytes Under Lean Condition in Lithium-Sulfur Batteries. Adv. Mater. 2024, 36, 2401263. DOI: 10.1002/adma.202401263

(31) Zhou, J.; Chandrappa, M. L. H.; Tan, S.; et al. Healable and Conductive Sulfur Iodide for Solid-State Li-S Batteries. Nature 2024, 627, 301−305. DOI: 10.1038/s41586-024-07101-z

(32) Jung, R.; Metzger, M.; Hearing, D.; et al. Consumption of Fluoroethylene Carbonate (FEC) on Si-C Composite Electrodes for Li-Ion Batteries. J. Electrochem. Soc. 2016, 163, A1705−A1716. DOI: 10.1149/2.0951608jes

(33) World Economic Forum. Circular Economy Transportation Action Track. 2025 (accessed 2026-03-04).

(34) Moseman, A.; Paltsev, S. Are Electric Vehicles Definitely Better for the Climate than Gas-Powered Cars?MIT Climate Portal 2022 (accessed 2026-03-04).

(35) Savvidou, E. K.; Rensmo, A.; Benskin, J. P.; et al. PFAS-Free Energy Storage: Investigating Alternatives for Lithium Ion Batteries. Environ. Sci. Technol. 2024, 58 (50), 21908−21917. DOI: 10.1021/acs.est.4c06083

(36) Rensmo, A.; Savvidou, E. K.; Cousins, I. T.; et al. Lithium-Ion Battery Recycling: A Source of Per- and Polyfluoroalkyl Substances (PFAS) to the Environment? Environ. Sci.: Processes Impacts 2023, 25 (6), 1015−1030. DOI: 10.1039/D2EM00511E

(37) Ma, P.; Xue, C.; Wang, K.-H.; et al. Molecular Structure Optimization of Fluorinated Ether Electrolyte for All Temperature Fast Charging Lithium-Ion Battery. ACS Energy Lett. 2024, 9 (12), 6144−6152. DOI: 10.1021/acsenergylett.4c01999

(38) Arbizzani, C.; De Giorgio, F.; Mastragostino, M. Characterization Tests for Plug-In Hybrid Electric Vehicle Application of Graphite/LiNi0.4Mn1.6O4 Cells with Two Different Separators and Electrolytes. J. Power Sources 2016, 266, 170−174. DOI: 10.1016/j.jpowsour.2014.05.016

(39) RECHARGE Magazine. Applications for Derogations from PFAS REACH Restriction for Batteries2023 (accessed 2026-03-04).

(40) Ellis, L. D.; Buteau, S.; Hames, S. G.; et al. A New Method for Determining the Concentration of Electrolyte Components in Lithium-Ion Cells, Using Fourier Transform Infrared Spectroscopy and Machine Learning. J. Electrochem. Soc. 2018, 165, A256. DOI: 10.1149/2.0861802jes

(41) Buteau, S.; Lee, E.; Young, S.; et al. User-Friendly Freeware for Determining the Concentration of Electrolyte Components in Lithium Ion Cells Using Fourier Transform Infrared Spectroscopy, Beer's Law, and Machine Learning. J. Electrochem. Soc. 2019, 166, A3102. DOI: 10.1149/2.0151914jes

(42) Carter, J. A.; O’Brien, L. M.; Harville, T.; et al. Machine Learning Tools to Estimate the Severity of Matrix Effects and Predict Analyte Recovery in Inductively Coupled Plasma Optical Emission Spectrometry. Talanta 2021, 223, 121665. DOI: 10.1016/j.talanta.2020.121665

Author Information