Automated Diagnosis of Performance Bottolenecks in Lithium-Sulfur Batteries

Automated Diagnosis of Performance Bottolenecks in Lithium-Sulfur Batteries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Automated Diagnosis of Performance Bottolenecks in Lithium-Sulfur Batteries Saurabh Parab, Jonathan Lee, Matthew Miyagishima, Qiushi Miao, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5456378/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lithium-sulfur (Li-S) batteries offer high energy density and low cost, making them ideal for electric vehicles and aviation. However, the many factors affecting Li-S battery cycling performance complicate researchers' efforts to pinpoint the bottleneck. To address this, a toolkit called High-performance liquid chromatography, Ultraviolet spectroscopy, and Gas chromatography Sequential characterizations (HUGS) was developed using sequential analytical chemistry. Along with this, data analysis software Dr. HUGS © was created to automate the ‘diagnoses’ of the key degradation mechanisms, similar to a doctor assessing a patient. Our analysis reveals that carbon sulfur cathodes suffer capacity loss due to lithium sulfide buildup on the anode. HUGS shows that constant pressure setups in Li-S pouch cells improve compositional uniformity over constant gap setups. Conversely, sulfurized polyacrylonitrile batteries experience non-sulfide solid-electrolyte-interface formation and lithium pulverization—issues mitigated by localized high-concentration electrolytes. This work demonstrates how analytical chemistry techniques and automated data analysis can accelerate the diagnosis of the complexities of electrochemical systems, advancing next-generation, high-performance Li-S batteries. Materials Chemistry Analytical Chemistry battery lithium sulfur electrolyte sulfurized polyacrylonitrile characterization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lithium-sulfur (Li-S) batteries excel in energy storage due to their impressive theoretical specific capacity of 1675 mAh g -1 and over 500Wh/kg energy density 1–5 . These attributes make them ideal for aviation, electric vehicles, and marine technologies. Chemically, sulfur as a cathode material has distinct advantages over traditional transition metal-based systems. Its abundance and ability to undergo multi-electron redox reactions significantly boost energy storage potential. Moreover, sulfur is widely accessible and often sourced as a byproduct of petrochemical processes. This technology reduces reliance on limited resources. With their unique electrochemical properties and sustainable material base, Li-S batteries represent a groundbreaking approach to the future of energy storage 6–8 . Despite their potential, Li-S batteries face numerous challenges, such as low sulfur conductivity, the polysulfide shuttle effect 9 , inefficient polysulfide conversion, inactive lithium formation 10 , and lithium metal pulverization 11 . These conditions result in lithium or sulfur inventory loss, leading to poor cycling stability 12 . Researchers have applied various therapies to treat these issues, such as nanostructured sulfur composites 13–15 , localized high-concentration electrolytes (LHCE), protective anode coatings, and optimized electrode designs 16 . However, with so many symptoms impacting performance and numerous treatment plans available, it is crucial to identify the most critical issue to diagnose and address for each specific Li-S battery 17,18 . Diagnosing the failure parameters in Li-S batteries remains incomplete, especially while quantifying sulfur/lithium species, which can directly correlate with the battery capacity. This makes it difficult to understand the root causes of performance degradation fully. Therefore, it hinders the development of effective strategies to enhance Li-S battery performance. Conventional characterization tools offer only limited or qualitative insights 19–32 . Furthermore, multiple chemical equilibria among S 8 and various polysulfides can alter their concentrations during characterization 23,26,33 . These diagnostic limitations hinder the ability to correlate sulfur species with battery capacity behavior accurately. A promising method for (semi-) quantifying sulfur/polysulfides is chemical modification combined with high-performance liquid chromatography-ultraviolet-visible spectroscopy (HPLC-UV) 33–38 . This approach "freezes" polysulfides by converting their active sulfur sites into stable sulfurized groups, like methyl or methylbenzene derivatives, quenching their equilibrium transitions. Methyl Trifluoromethanesulfonate (MeOTf) is particularly efficient, acting 10⁴ times faster than common methylation agents to stabilize polysulfides 39 . Post-derivatization, HPLC separates these species by retention time, allowing UV detection to estimate their relative concentrations. However, the absence of derivative standards, limited detection range, low temporal resolution, and small analytical HPLC column injection volume restrict absolute quantification and the ability to distinguish dissolved sulfur from Li 2 S 8 . On the other hand, Titration gas chromatography (TGC) can quantify the lithium inventory loss for the anode side 40–46 . However, the lack of detailed polysulfide concentration information complicates establishing a relationship between lithium inventory loss and sulfur inventory loss, both could impact Li-S battery performance. In this work, we developed a HPLC-UV and TGC sequential characterization method (HUGS) to diagnose the failure mechanisms in Li-S batteries with high precision. The technique can quantify polysulfides and sulfur down to 40 ppb and lithium metal at 10 ppb . Like a doctor diagnosing a patient, HUGS compares these analytical chemical data with the battery's cycling results to identify degradation causes. Automated data analysis with Dr. HUGS © software makes this diagnostic process streamlined and completed in seconds. Users input raw data, and the software automatically pinpoints key issues within the battery. Using Dr. HUGS © methodology, we identified distinct degradation pathways for several systems. In carbon-stabilized sulfur (CS), the cathode exhibits liquid-dominated sulfur redox reactions, while the anode forms inactive lithium early on and later develops a sulfide-dominated solid-electrolyte interface (SEI). Applying HUGS to Li-S pouch cells revealed that constant pressure setups result in better compositional homogeneity than constant gap setups. In sulfurized polyacrylonitrile (SPAN), despite minimal polysulfide shuttling at the cathode, the anode experiences non-sulfide SEI growth and lithium pulverization, partially mitigated by localized high-concentration electrolytes (LHCE). This study demonstrates that sulfur in Li-S batteries follows different degradation pathways depending on its composition, setup, and testing conditions. Ultimately, by leveraging advanced diagnostic tools rooted in analytical chemistry, we deepen our understanding of battery degradations and pave the way for targeted solutions that can significantly accelerate advancements in Li-S battery technology. HUGS results interpretation and automation in data processing The sulfur and lithium inventory loss for this study is based on an in-house developed method, HUGS, as demonstrated in Figure 1a. The method involves three samples derived from disassembled Li-S coin cells, prepared sequentially: 1. Sample A : The Li anode is washed with a MeOTf-DME solution to remove polysulfides, then titrated with ethanol to obtain Sample A. 2. Sample B : The wash solution is used to soak the remaining cell components (cathode, separator, cases, spring, spacers), creating a solution with methylated Li 2 S x (3≤x≤8) and soluble S 8 (S (L) ), defined as Sample B. 3. Sample C : Excess DME dissolves residual S 8 (S (S) ) in the cathode under mechanical shear force due, forming Sample C. After obtaining the samples, Gas Chromatography (GC) is performed on Sample A to quantify Li 0 in the anode by measuring H 2 generated from the Li-ethanol titration reaction. HPLC-UV is conducted on Sample B to determine Li 2 S x and S (L) concentrations and Sample C to quantify S (S) in the cathode. These measurements of Li 0 , Li 2 S x , S (L) , and S (S) allow for quantifying capacity retention/loss in Li-S batteries. The HUGS method is validated, as detailed in the Methods, Supplementary Information, under the ‘HUGS Method Validation’ session and Figures S1-4. Based on the HUGS method, a series of results are obtained. Figure 1b shows Sample A (TGC) representing the Li₀ inventory, while Samples B and C (HPLC-UV) display the amounts of Li₂S x , S (L) , and S (S) , which are converted into the theoretical capacity of the Li-S battery (Figure 1c). Given that most polysulfides are soluble, the HUGS storage plot reflects the cathode’s sulfur trapping ability. Using the sulfur theoretical capacity, lost capacity from Li₂S x , Li⁺, and charge capacity, three vectors are defined as follows (Figure 1d): a: Capacity loss due to Li 2 S x (3≤x≤8); b: Difference between stored Li capacity and Li₂S x (3≤x≤8) capacity loss; and g: Difference between charge capacity and stored Li capacity. Its capacity loss related with inactive Li. The relative changes in the three vectors can be classified into three typical cases, as shown in Figure 1e: Case I: γ > 0: A portion of Li and S did not contribute to electrochemical capacity. The formation of SEI isolates several Li particles, resulting in Li deactivation (inactive or dead Li 10,40 ). Case II: γ ≈ 0, a + b ≈ Li inventory loss: Sulfide SEI, such as Li 2 S, is the dominant capacity loss species in the charged state, as both Li and sulfur inventory can be completely lost without capacity contribution. Case III: γ >α, γ becomes negative, indicating Li inventory loss is greater than the capacity loss. In this case, non-sulfide SEI formation and Li pulverization dominate the capacity loss. These cases demonstrate that HUGS vectors reveal the dominant factors in Li-S battery capacity loss, allowing clear identification of key degradation symptoms. The HUGS method effectively identifies dominant failure factors in Li-S batteries. However, the data analysis is time-consuming: Conventional HUGS analysis for a single Li-S battery requires over one hour (Figure 2a). This duration is attributed to processing four raw data files (battery cycling curves, GC, and two HPLC files) and quantifying over nine species, including Li, S (L) , S (S) , and polysulfides (Li 2 S x , 3≤x≤8). Therefore, we developed Dr. HUGS © , an automated software for efficient HUGS data analysis. Users can obtain results within seconds by uploading the raw experimental data, with the entire process taking less than two minutes. We cross-verified Dr. HUGS © ' results to validate accuracy against conventional manual processing, as shown in Figure 2b. The discrepancies were minimal, demonstrating the reliability of the automated analysis. Moreover, Dr. HUGS © can automatically match results to the three cases presented in Figure 1e, facilitating the diagnosis of performance constraints in Li-S batteries. A video demonstration of the automated diagnosis is available in the Supporting Video ‘Dr. HUGS © Demo’ with additional software details in Supplementary Information under ‘Dr. HUGS © Software’ Session. This automation significantly reduces processing time while maintaining consistency and accuracy in diagnosing failure mechanisms in Li-S batteries, establishing Dr. HUGS © as a powerful tool for advancing HUGS-based research. Quantifying factors affecting Li-S battery cycle Life with CS cathodes in coin cells The first case study examines the ‘classic’ Li-S coin cells comprising a CS cathode, a ‘baseline electrolyte’ (1M Lithium bis(trifluoromethane)sulfonimide (LiTFSI) in 1,3-dioxolane (DOL): DME (1:1 v/v) + 2 wt. % LiNO 3 ), and Lithium metal 6 . The electrolyte to sulfur (E/S) ratio is 10 ml mg -1 . As demonstrated in Figure 3a, the batteries' cycling was categorized into five regions to analyze their cycling behavior: 0 – rested for 24 hours, IA – two initial formation cycles (0.05 C), IB – fast capacity decay cycles (0.1 C), II – stable cycles (0.1 C), and III – end of life (0.1 C). Based on these regions, we first conducted a series of conventional characterizations, with the battery curves and results thoroughly discussed and presented in the Supplementary Information under the ‘CS Cathode’ session and Figures S5-8. The results show that sulfide species decrease on the cathode during CS Li-S battery cycling, while SEI thickness on the anode sharply increases due to LiNO 3 depletion (Figure S9). The SEI comprises (poly)sulfides, Li 2 SO 4 , and inactive Li 0 . However, the key factors driving capacity failure in specific cycling regions remain unclear without quantitative analysis. HUGS analysis (results proceeded with Dr. HUGS © ) was conducted on these batteries to diagnose the dominant capacity failure factors (Figure 3b). For these cells, Samples B and C results revealed the capacities stored in sulfur and polysulfides across each cycling region (Figures 3b and S10). Initially, a 24-hour rested battery showed a capacity loss of over 200 mAh g -1 due to self-discharge and sulfur dissolution from the cathode. This is because of DME's high solubility to polysulfides and elemental sulfur 47 . Despite some solid-state S (S) increase in later cycles, soluble sulfur/sulfide species remained dominant in stored capacity, with almost no S (S) remaining in Region III due to LiNO 3 depletion 48 . Thus, the CS Li-S battery operates as a liquid sulfur-redox-dominated system. The HUGS vector plot (Figure 3b) showed that in Regions IA and IB, the behavior aligns with Case I in Figure 1e, while Regions 2 and 3 correspond to Case II mainly. This indicates that inactive lithium formation and inefficient Sulfur and polysulfide conversion primarily impact early cycles in a liquid sulfur-redox system. This inefficiency of Sulfur conversion is also seen in 1 st discharge HUGS in Figure S10. A decreasing g vector and increasing b vector from Region 1A to 1B suggest ‘reactivation’ of inactive lithium and Sulfur, possibly converting into polysulfides, consistent with previous study 10 . In contrast, anode passivation due to sulfide-rich SEI formation/growth dominates the stable cycling in Region II. Due to the LiNO3 depletion at Region III, thick sulfides SEI growth (Figure S6c and d) terminates the battery cycling. More cases are shown in Supplementary Information under ‘Special Cases in CS batteries’ Session and Figure S12. Overall, in the liquid sulfur-redox-dominated coin cell system, the main capacity failure factors are self-discharge (Region 0), inactive lithium formation (Region 1), sulfide-rich passivating SEI (Region II), and thick sulfide-dominated SEI growth (Region III), as summarized in Figure 3c. Spatial Component Variations in Li-S Pouch Cells with CS Cathodes under Different Setup Configurations In addition to coin cells, HUGS was applied to CS pouch cells to quantitatively analyze the lateral distribution of lithium, sulfur, and sulfides, as shown in Figure 4. Single-layer pouch cells were assembled using a CS cathode with similar areal loading and baseline electrolyte as previously discussed, with a cathode dimension of 3×3 cm² (Figure 4a). Compared to coin cells, the larger area of pouch cells leads to more complex pressure and electric field distributions, significantly affecting the compositional homogeneity during cycling 49 . We selected three positions (A, B, and C) along the diagonal from the cathode's current collector tab to investigate the compositional homogeneity, as shown in Figure 4a. Samples for HUGS analysis were taken from both the cathode and anode at these positions. Electrolyte samples were directly extracted from the pouch cells for analysis. Further details regarding the pouch cell setup can be found in the Supplementary Information under the ‘CS Pouch Cells’ session. Figure 4b presents two testing configurations for the pouch cells: constant gap and constant pressure setups. In both configurations, an initial pressure of 30 psi was applied. The results of the HUGS analysis are shown in Figure 4c, with the corresponding discharge-charge curves, cycling performance, and sulfur quantification analysis provided in Supplementary Figures S13 and S14. From Figure 4c, sulfur quantification across positions A, B, and C shows minimal variation for both testing configurations. This behavior may be attributed to the nature of the CS-baseline electrolyte system, which is driven by solid-liquid-solid reactions. Polysulfides can readily diffuse within the x-y plane in the liquid phase, resulting in uniform deposition and minimal compositional differences across positions. However, a higher solid sulfur (S (S) ) content is observed in the constant gap setup compared to constant pressure. This is likely due to increased internal pressure caused by volume expansion during lithiation, which restricts the transition of sulfur from solid to liquid 50 . In contrast, the constant pressure setup releases this internal pressure, resulting in a greater presence of polysulfides in the electrolyte. Nevertheless, the effect of the two configurations on sulfur distribution is relatively minor. For lithium, significant differences are observed between the configurations. The constant gap setup has a pronounced variation in lithium inventory from positions A to C, whereas the constant pressure configuration shows a more uniform lithium distribution. This is clearly reflected in the differences in the b and g vectors, aligning with our previous findings 51,52 . The more even pressure distribution in the constant pressure configuration facilitates uniform lithium deposition, which may explain why constant pressure setups are often associated with improved cycling performance in Li-S batteries 53 . While constant pressure has a limited influence on the uniform distribution of sulfur across the x-y plane, it enhances the homogeneity of lithium deposition, thereby contributing to longer battery life. Furthermore, lithium inventory loss in both setups increases with distance from the current collector tab. This is likely caused by an uneven cell gap introduced by the tab as the cell size increases, leading to more complete reactions near the tab due to improved current collection efficiency and slightly higher pressure in that area. The HUGS analysis of Li-S pouch cells effectively distinguishes lateral compositional variations between the cathode and anode across diverse testing configurations. Notably, while the observed compositional distributions are influenced by parameters such as initial pressure settings, cells with a constant pressure setup exhibit more uniform compositional distribution, suggesting improved performance. Quantifying factors affecting Li-S battery cycle Life with SPAN cathodes and different electrolytes Using HUGS, we discussed the cycling behavior of Li-S batteries with CS cathodes and an electrolyte with higher polysulfide solubility, where physical adsorption predominantly stabilizes the elemental sulfur within the cathode. We then analyzed the cathode, where the sulfur is covalently bonded within a polymer. A typical material in Li-S batteries we selected for this is SPAN 54 . Due to covalently bonded sulfur, SPAN reduces polysulfide dissolution and the shuttle effect, improving the battery's cycle life and efficiency 55–57 . A SPAN structure is proposed with polymer and adsorbed short-chain sulfur species. These short-chain sulfur can be re-organized in DME, as shown in Figure 5a. A more detailed discussion of experimental evidence for this proposed structure is shown in Supplementary Information, ‘SPAN structure reconstruction’ session, and Figure S15. Furthermore, in Figure 5b, we defined the cycling regions for SPAN batteries, like the CS system. These regions are: 0 – rested for 24 hours, I – two initial formation cycles (0.05 C), II – stable cycles (0.1 C for baseline and 0.2 C for LDME electrolyte), III – end of life (0.1 C for baseline and 0.2 C for LDME electrolyte). Ex-situ characterizations were conducted to study the cycling behavior of the SPAN battery with a baseline electrolyte, detailed in the Supplementary Information, under the ‘Characterization of SPAN’ (Figure S16 to S20) session. These analyses showed better sulfur retention and minimal morphological change in the SPAN cathode compared to the CS cathode. The anode contained minimal sulfur species until the end of life, unlike in the CS battery. Overall, the shuttle effect is limited in Li-S batteries with SPAN cathodes and baseline electrolytes, enabling stable cycling until LiNO 3 depletion. HUGS analysis was performed consequentially (Figures 5c and S20). Unlike the CS system, Figure 5c HUGS capacity storage plots reveal significantly lower storage capacity as polysulfides and sulfur in the electrolyte due to most of the sulfur in SPAN being stabilized by covalent bonds, which limits bond cleavage and dissolution. From Region 0 to Region I, even less sulfur capacity is found in the electrolyte, suggesting that dissolved/reconstructed S 8 is "re-captured" by the cathode after cycling. By the end of cycling, the electrolyte shows less than 100 mAh g -1 capacity is stored in soluble species. The HUGS vector plot (Figure 5c) shows that the a vector remains negligible, indicating that the shuttling effect does not drive the capacity loss in SPAN. Case III in Figure 1e is consistently dominant from Region I to III, which suggests that non-sulfide SEI formation and Li pulverization are the main contributors to capacity loss. Cross-section FIB-SEM images of the Li-SPAN anode (Figure S19) confirm SEI growth and Li pulverization after cycling. Since the SEI primarily consists of organic-rich composites, its growth can lead to detached pulverized Li, increasing capacity loss as indicated by the g vector. Furthermore, as shown in Figure 5c, we applied LDME electrolytes (2 M Lithium bis(fluorosulfonyl)imide (LiFSI) in 1,2-Dimethoxyethane/ Bis(2,2,2-trifluoroethyl) ether (DME/BTFE) (1:4 by weight)) to the Li-SPAN batteries to investigate its HUGS vector plot further. The LDME electrolyte, a localized high-concentration ether-based electrolyte, can stable cycle Li-SPAN batteries over hundreds of cycles (Figure 5b) 58 . Compared to the baseline electrolyte, as shown in Figure 5c, it can further reduce the shuttling effect of the batteries. More importantly, in the Figure 5c HUGS vector plot, with LDME electrolyte, all b vectors are further decreased, demonstrating improved cycling stability. The capacity loss mechanism in this system still aligns with Case III, where non-sulfide SEI growth and Li pulverization dominate, leading to capacity loss and eventual failure. While LDME cannot fully prevent these issues, it can mitigate them. A proposed mechanism for Li-SPAN battery cycling behavior is demonstrated in Figure 5d. A small portion of short-chain sulfur redistribution and growth in the SPAN cathode, such as S 8 in the electrolyte, is observed during the battery resting. Once the cycle begins, the SPAN battery operates with significantly less shuttle effect-induced capacity loss, with non-sulfide SEI formation and Li pulverization being the dominant capacity loss mechanisms (Case III). Though the non-sulfide SEI growth and Li pulverization persist in the LDME system, a stable SEI in the LDME electrolyte prolongs cycling life, reduces anode SEI growth, and mitigates Li pulverization. Conclusion In this study, we introduced the HUGS analytical toolkit and Dr. HUGS© software, enabling automated quantification of capacity loss factors in the cathode, electrolyte, and anode of Li-S batteries. This innovation reduces analysis time from over an hour to under two minutes and with the same precision of diagnosis. We applied HUGS to investigate CS cathode coin cells, spatial variations in pouch cells, and SPAN coin cells with different electrolytes, showcasing its versatility in diagnosing performance-limiting factors. For CS coin cells, HUGS vector plots revealed early capacity loss from inactive lithium formation, transitioning to sulfide SEI growth in later cycles. In CS pouch cells, sulfur distribution remained uniform, while lithium varied significantly, with better uniformity under constant pressure. SPAN cells showed solid-state cathode reactions, reducing polysulfide dissolution and shuttling. HUGS analysis indicated non-sulfide SEI growth and lithium pulverization as the main degradation mechanisms, and LDME electrolytes further reduced these effects, improving cycling stability. HUGS plays a crucial role in diagnosing Li-S battery “illnesses” by leveraging analytical chemistry for sequential, quantitative analysis. This approach highlights how sulfur stabilization, electrolyte selection, and testing configurations contribute to capacity degradation and lithium loss. By effectively identifying core issues such as polysulifdes shuttling, SEI growth, and lithium pulverization, the HUGS method guides targeted strategies to improve cycling performance. It provides a quantitative framework for evaluating treatment efficacy in Li-S batteries. Declarations Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgments: This work was supported by the Office of Vehicle Technologies of the US Department of Energy through the Advanced Battery Materials Research (BMR) Program (Battery500 Consortium) under contract DE-EE0007764. Arbin Battery Testing Facility and Shimadzu Gas Chromatography machine from UCSD were used. FIB and TEM characterizations were performed at the San Diego Nanotechnology Infrastructure (SDNI) of UCSD, a member of the National Nanotechnology Coordinated Infrastructure supported by the National Science Foundation (Grant ECCS1542148). NSF supported using the Raman facility through the UC San Diego Materials Research Science and Engineering Center (UCSD MRSEC), grant #DMR-201192. The authors acknowledge using facilities and instrumentation at the UC Irvine Materials Research Institute (IMRI), which the National Science Foundation partly supports through the UC Irvine Materials Research Science and Engineering Center (DMR-2011967). XPS experiments were performed using instrumentation funded in part by the National Science Foundation Major Research Instrumentation Program under grant no.CHE-1338173. This work made use of the Keck-II facility of Northwestern University’s NUANCE Center, which has received support from the SHyNE Resource (NSF ECCS-2025633), the IIN, and Northwestern's MRSEC program (NSF DMR-2308691). ICP-MS (Thermo iCAP RQ single-quadrupole ICP-MS system), HPLC-UV, and HPLC-APCI-MS measurements were taken at the Environmental and Complex Analysis Laboratory (ECAL) at the University of California, San Diego. S.P. thanks Gita Singh and Neal Arakawa for their suggestions on the manuscript. S.P. thanks S.W. for helping with figure designs. Author contributions: S.P., S.W., and Y.S.M. conceived the project. S.P. designed and implemented the HUGS method, led HPLC, TGC, Cryo-FIB–SEM, and Raman experiments, and performed data analysis with S.W.. S.P., J.L., M.M., Q.M., and B.B. fabricated cells and electrolytes. S.P. and M.M. developed the Dr. HUGS © software. S.P., M.M. and B.B. made pouch cells. A.L., L.A., and B.S. supported cryo-FIB setup, and B.B. assisted with GC calibration. K.R. performed ToF-SIMS, with analysis by S.W., who also conducted XPS and data analysis. Q.M., S.W., and P.L. provided SPAN electrodes, and R.S., F.D., and M.C. contributed GM cathodes. S.P. and S.W. co-wrote the manuscript, with all authors discussing and approving the final version. Corresponding authors: Correspondence to Shen Wang, Ying Shirley Meng Competing interests: A patent disclosure and a copyright is filed with University of California San Diego’s Office of Innovation and Commercialization. Additional information: The data that support the findings of this study are available from the corresponding authors on reasonable request. References Chung, S.-H. & Manthiram, A. Current Status and Future Prospects of Metal–Sulfur Batteries. Adv. Mater. 31 , 1901125 (2019). Bhargav, A., He, J., Gupta, A. & Manthiram, A. Lithium-Sulfur Batteries: Attaining the Critical Metrics. Joule 4 , 285–291 (2020). Bruce, P. G., Freunberger, S. A., Hardwick, L. J. & Tarascon, J.-M. Li–O2 and Li–S batteries with high energy storage. Nat. Mater. 11 , 19–29 (2012). Chung, S.-H., Chang, C.-H. & Manthiram, A. Progress on the Critical Parameters for Lithium–Sulfur Batteries to be Practically Viable. Adv. Funct. Mater. 28 , 1801188 (2018). Nakamura, N., Ahn, S., Momma, T. & Osaka, T. Future potential for lithium-sulfur batteries. J. Power Sources 558 , 232566 (2023). Ji, X., Lee, K. T. & Nazar, L. F. A highly ordered nanostructured carbon–sulphur cathode for lithium–sulphur batteries. Nat. Mater. 8 , 500–506 (2009). Liu, R. et al. Establishing reaction networks in the 16-electron sulfur reduction reaction. Nature 626 , 98–104 (2024). Wang, D. et al. Realizing high-capacity all-solid-state lithium-sulfur batteries using a low-density inorganic solid-state electrolyte. Nat. Commun. 14 , 1895 (2023). Akridge, J. Li/S fundamental chemistry and application to high-performance rechargeable batteries. Solid State Ion. 175 , 243–245 (2004). Weret, M. A. et al. Reviving Inactive Lithium and Stabilizing Lithium Deposition for Improving the Performance of Anode-Free Lithium–Sulfur Batteries. ACS Energy Lett. 8 , 2817–2823 (2023). Hong, H., Che Mohamad, N. A. R., Chae, K., Marques Mota, F. & Kim, D. H. The lithium metal anode in Li–S batteries: challenges and recent progress. J. Mater. Chem. A 9 , 10012–10038 (2021). Su, Y.-S., Fu, Y., Cochell, T. & Manthiram, A. A strategic approach to recharging lithium-sulphur batteries for long cycle life. Nat. Commun. 4 , 2985 (2013). Liang, X. et al. A highly efficient polysulfide mediator for lithium–sulfur batteries. Nat. Commun. 6 , 5682 (2015). Yang, Y. et al. High-Capacity Micrometer-Sized Li 2 S Particles as Cathode Materials for Advanced Rechargeable Lithium-Ion Batteries. J. Am. Chem. Soc. 134 , 15387–15394 (2012). Vinayan, B. P. et al. Nitrogen Rich Hierarchically Organized Porous Carbon/Sulfur Composite Cathode Electrode for High Performance Li/S Battery: A Mechanistic Investigation by Operando Spectroscopic Studies. Adv. Mater. Interfaces 3 , 1600372 (2016). Wang, S. et al. Structural Transformation in a Sulfurized Polymer Cathode to Enable Long-Life Rechargeable Lithium–Sulfur Batteries. J. Am. Chem. Soc. 145 , 9624–9633 (2023). Yu, X. & Manthiram, A. A Progress Report on Metal–Sulfur Batteries. Adv. Funct. Mater. 30 , 2004084 (2020). Yao, W., Liao, K., Lai, T., Sul, H. & Manthiram, A. Rechargeable Metal-Sulfur Batteries: Key Materials to Mechanisms. Chem. Rev. 124 , 4935–5118 (2024). Patel, M. U. M. et al. Li-S Battery Analyzed by UV/Vis in Operando Mode. ChemSusChem 6 , 1177–1181 (2013). Patel, M. U. M. & Dominko, R. Application of In Operando UV/Vis Spectroscopy in Lithium–Sulfur Batteries. ChemSusChem 7 , 2167–2175 (2014). Zou, Q. & Lu, Y.-C. Solvent-Dictated Lithium Sulfur Redox Reactions: An Operando UV–vis Spectroscopic Study. J. Phys. Chem. Lett. 7 , 1518–1525 (2016). He, Q., Freiberg, A. T. S., Patel, M. U. M., Qian, S. & Gasteiger, H. A. Operando Identification of Liquid Intermediates in Lithium–Sulfur Batteries via Transmission UV–vis Spectroscopy. J. Electrochem. Soc. 167 , 080508 (2020). Barchasz, C. et al. Lithium/Sulfur Cell Discharge Mechanism: An Original Approach for Intermediate Species Identification. Anal. Chem. 84 , 3973–3980 (2012). Nelson, J. et al. In Operando X-ray Diffraction and Transmission X-ray Microscopy of Lithium Sulfur Batteries. J. Am. Chem. Soc. 134 , 6337–6343 (2012). Lowe, M. A., Gao, J. & Abruña, H. D. Mechanistic insights into operational lithium–sulfur batteries by in situ X-ray diffraction and absorption spectroscopy. RSC Adv. 4 , 18347 (2014). Wang, Q. et al. Direct Observation of Sulfur Radicals as Reaction Media in Lithium Sulfur Batteries. J. Electrochem. Soc. 162 , A474–A478 (2015). Helen, M. et al. Single step transformation of sulphur to Li2S2/Li2S in Li-S batteries. Sci. Rep. 5 , 12146 (2015). Conder, J. et al. Direct observation of lithium polysulfides in lithium–sulfur batteries using operando X-ray diffraction. Nat. Energy 2 , 1–7 (2017). Patel, M. U. M. et al. X-ray Absorption Near-Edge Structure and Nuclear Magnetic Resonance Study of the Lithium–Sulfur Battery and its Components. ChemPhysChem 15 , 894–904 (2014). Wang, H. et al. In Situ NMR Observation of the Temporal Speciation of Lithium Sulfur Batteries during Electrochemical Cycling. J. Phys. Chem. C 121 , 6011–6017 (2017). Levin, B. D. A. et al. Characterization of Sulfur and Nanostructured Sulfur Battery Cathodes in Electron Microscopy Without Sublimation Artifacts. Microsc. Microanal. 23 , 155–162 (2017). Diao, Y., Xie, K., Xiong, S. & Hong, X. Analysis of Polysulfide Dissolved in Electrolyte in Discharge-Charge Process of Li-S Battery. J. Electrochem. Soc. 159 , A421 (2012). Kamyshny, A., Goifman, A., Gun, J., Rizkov, D. & Lev, O. Equilibrium Distribution of Polysulfide Ions in Aqueous Solutions at 25 °C: A New Approach for the Study of Polysulfides’ Equilibria. Environ. Sci. Technol. 38 , 6633–6644 (2004). Kawase, A., Shirai, S., Yamoto, Y., Arakawa, R. & Takata, T. Electrochemical reactions of lithium–sulfur batteries: an analytical study using the organic conversion technique. Phys. Chem. Chem. Phys. 16 , 9344–9350 (2014). Zheng, D. et al. Quantitative Chromatographic Determination of Dissolved Elemental Sulfur in the Non-Aqueous Electrolyte for Lithium-Sulfur Batteries. J. Electrochem. Soc. 162 , A203–A206 (2015). Zheng, D., Yang, X.-Q. & Qu, D. Reaction between Lithium Anode and Polysulfide Ions in a Lithium–Sulfur Battery. ChemSusChem 9 , 2348–2350 (2016). Zheng, D. et al. Reduction mechanism of sulfur in lithium–sulfur battery: From elemental sulfur to polysulfide. J. Power Sources 301 , 312–316 (2016). Zheng, D. et al. Investigation of the Li–S Battery Mechanism by Real-Time Monitoring of the Changes of Sulfur and Polysulfide Species during the Discharge and Charge. ACS Appl. Mater. Interfaces 9 , 4326–4332 (2017). Stang, P. J., Hanack, M. & Subramanian, L. R. Perfluoroalkanesulfonic Esters: Methods of Preparation and Applications in Organic Chemistry. Synthesis 1982 , 85–126 (2002). Fang, C. et al. Quantifying inactive lithium in lithium metal batteries. Nature 572 , 511–515 (2019). Bao, W. et al. Quantifying lithium loss in amorphous silicon thin-film anodes via titration-gas chromatography. Cell Rep. Phys. Sci. 2 , (2021). Sreenarayanan, B. et al. Recycling silicon scrap for spherical Si–C composite as high-performance lithium-ion battery anodes. J. Power Sources 578 , 233245 (2023). Sreenarayanan, B. et al. Quantification of lithium inventory loss in micro silicon anode via titration-gas chromatography. J. Power Sources 531 , 231327 (2022). Sayahpour, B. et al. Quantitative analysis of sodium metal deposition and interphase in Na metal batteries. Energy Environ. Sci. 17 , 1216–1228 (2024). Lu, B. et al. Suppressing Chemical Corrosions of Lithium Metal Anodes. Adv. Energy Mater. 12 , 2202012 (2022). Tan, D. H. S. et al. Carbon-free high-loading silicon anodes enabled by sulfide solid electrolytes. Science 373 , 1494–1499 (2021). Adeoye, H. A., Dent, M., Watts, J. F., Tennison, S. & Lekakou, C. Solubility and dissolution kinetics of sulfur and sulfides in electrolyte solvents for lithium–sulfur and sodium–sulfur batteries. J. Chem. Phys. 158 , 064702 (2023). Zhang, S. S. A new finding on the role of LiNO3 in lithium-sulfur battery. J. Power Sources 322 , 99–105 (2016). Huang, X. et al. Comprehensive evaluation of safety performance and failure mechanism analysis for lithium sulfur pouch cells. Energy Storage Mater. 30 , 87–97 (2020). Shi, L. et al. Reaction heterogeneity in practical high-energy lithium–sulfur pouch cells. Energy Environ. Sci. 13 , 3620–3632 (2020). Chen, Y.-T. et al. Enabling Uniform and Accurate Control of Cycling Pressure for All-Solid-State Batteries. Adv. Energy Mater. 14 , 2304327 (2024). Fang, C. et al. Pressure-tailored lithium deposition and dissolution in lithium metal batteries. Nat. Energy 6 , 987–994 (2021). Schmidt, F. et al. Influence of external stack pressure on the performance of Li-S pouch cell. J. Phys. Energy 4 , 014004 (2022). Wang, J., Yang, J., Xie, J. & Xu, N. A Novel Conductive Polymer–Sulfur Composite Cathode Material for Rechargeable Lithium Batteries. Adv. Mater. 14 , 963–965 (2002). Kuai, D. et al. Interfacial Electrochemical Lithiation and Dissolution Mechanisms at a Sulfurized Polyacrylonitrile Cathode Surface. ACS Energy Lett. 9 , 810–818 (2024). Tan, S. et al. Structural and Interphasial Stabilities of Sulfurized Polyacrylonitrile (SPAN) Cathode. ACS Energy Lett. 8 , 2496–2504 (2023). Wang, W. et al. Recognizing the Mechanism of Sulfurized Polyacrylonitrile Cathode Materials for Li–S Batteries and beyond in Al–S Batteries. ACS Energy Lett. 3 , 2899–2907 (2018). Liu, H. et al. Ultrahigh coulombic efficiency electrolyte enables Li||SPAN batteries with superior cycling performance. Mater. Today 42 , 17–28 (2021). Supplementary Information Supplementary Information is not available with this version Additional Declarations The authors declare potential competing interests as follows: A provisional patent application (US Provisional Application serial number 63709904) for this work has been filed by Y.S.M., S.P., S.W., and M.M. through UC San Diego’s Office of Innovation and Commercialization. This patent application contains claims related to HUGS methodology and Dr. HUGS automation software for Metal-Sulfur batteries’ analysis. The remaining authors declare no competing interests. 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Diego","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Lee","suffix":""},{"id":378314654,"identity":"b598486a-4bab-401c-ae99-be0ae89a62e5","order_by":2,"name":"Matthew Miyagishima","email":"","orcid":"","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Miyagishima","suffix":""},{"id":378314655,"identity":"08376e1b-9220-4eb3-8661-26617f3729c3","order_by":3,"name":"Qiushi Miao","email":"","orcid":"","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Qiushi","middleName":"","lastName":"Miao","suffix":""},{"id":378314656,"identity":"08aa003f-d3c7-4a57-a920-759f4b51f4c9","order_by":4,"name":"Bhargav Bhamwala","email":"","orcid":"","institution":"University of California San 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21:15:03","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5456378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5456378/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69362183,"identity":"f4a138a9-ced3-499f-ab01-901203f9107d","added_by":"auto","created_at":"2024-11-19 14:28:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3973793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh-performance liquid chromatography - Ultraviolet spectroscopy and titration-Gas chromatography Sequential characterizations (HUGS) method sample preparation and results interpretation. a. \u003c/strong\u003eSchematic of HUGS; \u003cstrong\u003eb\u003c/strong\u003e. Information output based on HUGS; \u003cstrong\u003ec\u003c/strong\u003e. HUGS capacity storage plot. \u003cstrong\u003ed.\u003c/strong\u003e HUGS vector plot and the definition of each vector; \u003cstrong\u003ee\u003c/strong\u003e. Three capacity loss mechanism scenarios in Li-S battery derived from HUGS.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5456378/v1/80b41732117a9c46dfa1e2b7.png"},{"id":69360528,"identity":"14282d74-6799-4d9c-a1eb-37df954f7b22","added_by":"auto","created_at":"2024-11-19 14:20:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5159002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDr. HUGS\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e©\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e software for automated data processing of HUGS characterization. a.\u003c/strong\u003e workflow and estimated time of conventional data processing and automated data analysis using Dr. HUGS\u003csup\u003e©\u003c/sup\u003e software; \u003cstrong\u003eb.\u003c/strong\u003e Cross-validation of HUGS data between conventional processing and Dr. HUGS\u003csup\u003e©\u003c/sup\u003e automated analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5456378/v1/4040fee9bf8e8e569e7a6177.png"},{"id":69360526,"identity":"61ba855a-c462-48a9-9f48-69b16684dddc","added_by":"auto","created_at":"2024-11-19 14:20:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":941416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLi-S battery cycling, HUGS analysis, and proposed cycling behavior of Li-S batteries with CS cathodes in different cycling regions of a coin cell. a\u003c/strong\u003e. Defining cycling regions in CS battery (Sulfur loading: 3.6 mg cm\u003csup\u003e-2\u003c/sup\u003e with 10 wt. % carboxymethyl cellulose (CMC) and Super-P (10 wt. %), Electrolyte: 1M Lithium bis(trifluoromethane)sulfonimide (LiTFSI) in \u0026nbsp;1,3-dioxolane (DOL): DME (1:1 v/v) + 2 wt. % LiNO\u003csub\u003e3\u003c/sub\u003e, E:S = 10 mL mg\u003csup\u003e-1\u003c/sup\u003e, anode Li: 250 mm, rate: 24 hours resting, 2 cycles at 0.05 C, then 0.1 C).\u003cstrong\u003e b.\u003c/strong\u003e HUGS analysis results, \u003cstrong\u003ec.\u003c/strong\u003e proposed mechanism of cycling behavior with CS cathodes and baseline electrolyte.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5456378/v1/5229a761dd5b71b829dfff04.png"},{"id":69360529,"identity":"1cbf70ab-c278-4f58-b929-555565e36084","added_by":"auto","created_at":"2024-11-19 14:20:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2993218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHUGS analysis of Li-S pouch cells with different testing configurations.\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003e Dimensions of pouch cell components and regions (position A, B, and C.) selected for HUGS analysis. \u003cstrong\u003eb.\u003c/strong\u003e Schematics of testing configurations for the pouch cells: fixed gap (initial pressure 30 psi) and fixed pressure (30 psi). \u003cstrong\u003ec.\u003c/strong\u003e HUGS vector and capacity storage plots for a pouch cell under fixed pressure (left) and fixed gap (right) conditions at positions A, B, and C.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5456378/v1/6e2082c482a2a03af9d0bc1f.png"},{"id":69362182,"identity":"e056c155-0206-4737-acb4-e0442687f4ab","added_by":"auto","created_at":"2024-11-19 14:28:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5975440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSPAN structural analysis, cycling performance, and HUGS analysis and mechanism of Li-SPAN battery at different cycling regions in a coin cell. a\u003c/strong\u003e. Proposed SPAN structure and Sulfur reorganization during the resting; \u003cstrong\u003eb\u003c/strong\u003e. Defining the cycling regions in Li-SPAN battery (SPAN loading: 5 mg cm\u003csup\u003e-2\u003c/sup\u003e with CMC (10 wt. %) and Super-P (10 wt. %), Baseline: 1M LiTFSI in DME: DOL (1:1 v/v) + 2 wt. % LiNO\u003csub\u003e3\u003c/sub\u003e with E:S = 10 mL mg\u003csup\u003e-1\u003c/sup\u003e, LDME: 2 M Lithium bis(fluorosulfonyl)imide (LiFSI) in 1,2-Dimethoxyethane/ Bis(2,2,2-trifluoroethyl) ether (DME/BTFE) (1:4 by weight) with E= 30 mL, anode Li: 250 mm, rate: 24 hours resting, 2 cycles at 0.05 C, then 0.1 C for baseline and 0.2 C for LDME.\u003cstrong\u003e c.\u003c/strong\u003e HUGS vector plot and HUGS capacity storage plots for each cycling region. \u003cstrong\u003ed.\u003c/strong\u003e The proposed mechanism of cycling behavior with SPAN cathodes and baseline electrolytes.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5456378/v1/ed7369d0507628e02170b9aa.png"},{"id":69362661,"identity":"920c95be-ab1a-4066-b1d6-62455214a55d","added_by":"auto","created_at":"2024-11-19 14:36:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27985781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5456378/v1/f0bc1011-d27c-4669-aa1c-5153623a4c1d.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: A provisional patent application (US Provisional Application serial number 63709904) for this work has been filed by Y.S.M., S.P., S.W., and M.M. through UC San Diego’s Office of Innovation and Commercialization. This patent application contains claims related to HUGS methodology and Dr. HUGS automation software for Metal-Sulfur batteries’ analysis. The remaining authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAutomated Diagnosis of Performance Bottolenecks in Lithium-Sulfur Batteries\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLithium-sulfur (Li-S) batteries excel in energy storage due to their impressive theoretical specific capacity of 1675 mAh g\u003csup\u003e-1\u003c/sup\u003e and over 500Wh/kg energy density\u003csup\u003e1–5\u003c/sup\u003e. These attributes make them ideal for aviation, electric vehicles, and marine technologies. Chemically, sulfur as a cathode material has distinct advantages over traditional transition metal-based systems. Its abundance and ability to undergo multi-electron redox reactions significantly boost energy storage potential. Moreover, sulfur is widely accessible and often sourced as a byproduct of petrochemical processes. This technology reduces reliance on limited resources. With their unique electrochemical properties and sustainable material base, Li-S batteries represent a groundbreaking approach to the future of energy storage\u003csup\u003e6–8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite their potential, Li-S batteries face numerous challenges, such as low sulfur conductivity, the polysulfide shuttle effect\u003csup\u003e9\u003c/sup\u003e, inefficient polysulfide conversion, inactive lithium formation\u003csup\u003e10\u003c/sup\u003e, and lithium metal pulverization\u003csup\u003e11\u003c/sup\u003e. These conditions result in lithium or sulfur inventory loss, leading to poor cycling stability\u0026nbsp;\u003csup\u003e12\u003c/sup\u003e. Researchers have applied various therapies to treat these issues, such as nanostructured sulfur composites\u003csup\u003e13–15\u003c/sup\u003e, localized high-concentration electrolytes (LHCE), protective anode coatings, and optimized electrode designs\u003csup\u003e16\u003c/sup\u003e. However, with so many symptoms impacting performance and numerous treatment plans available, it is crucial to identify the most critical issue to diagnose and address for each specific Li-S battery\u003csup\u003e17,18\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDiagnosing the failure parameters in Li-S batteries remains incomplete, especially while quantifying sulfur/lithium species, which can directly correlate with the battery capacity. This makes it difficult to understand the root causes of performance degradation fully. Therefore, it hinders the development of effective strategies to enhance Li-S battery performance. Conventional characterization tools offer only limited or qualitative insights\u003csup\u003e19–32\u003c/sup\u003e. Furthermore, multiple chemical equilibria among S\u003csub\u003e8\u003c/sub\u003e and various polysulfides can alter their concentrations during characterization\u003csup\u003e23,26,33\u003c/sup\u003e. These diagnostic limitations hinder the ability to correlate sulfur species with battery capacity behavior accurately.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA promising method for (semi-) quantifying sulfur/polysulfides is chemical modification combined with high-performance liquid chromatography-ultraviolet-visible spectroscopy (HPLC-UV)\u0026nbsp;\u003csup\u003e33–38\u003c/sup\u003e. This approach \"freezes\" polysulfides by converting their active sulfur sites into stable sulfurized groups, like methyl or methylbenzene derivatives, quenching their equilibrium transitions. Methyl Trifluoromethanesulfonate (MeOTf) is particularly efficient, acting 10⁴ times faster than common methylation agents to stabilize polysulfides\u003csup\u003e39\u003c/sup\u003e. Post-derivatization, HPLC separates these species by retention time, allowing UV detection to estimate their relative concentrations. However, the absence of derivative standards, limited detection range, low temporal resolution, and small analytical HPLC column injection volume restrict absolute quantification and the ability to distinguish dissolved sulfur from Li\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003e8\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eOn the other hand, Titration gas chromatography (TGC) can quantify the lithium inventory loss for the anode side\u003csup\u003e40–46\u003c/sup\u003e. However, the lack of detailed polysulfide concentration information complicates establishing a relationship between lithium inventory loss and sulfur inventory loss, both could impact Li-S battery performance.\u003c/p\u003e\n\u003cp\u003eIn this work, we developed a HPLC-UV and TGC sequential characterization method (HUGS) to diagnose the failure mechanisms in Li-S batteries with high precision. The technique can quantify polysulfides and sulfur down to 40 \u003cem\u003eppb\u0026nbsp;\u003c/em\u003eand lithium metal at 10 \u003cem\u003eppb\u003c/em\u003e. Like a doctor diagnosing a patient, HUGS compares these analytical chemical data with the battery's cycling results to identify degradation causes. Automated data analysis with Dr. HUGS\u003csup\u003e©\u003c/sup\u003e software makes this diagnostic process streamlined and completed in seconds. Users input raw data, and the software automatically pinpoints key issues within the battery.\u003c/p\u003e\n\u003cp\u003eUsing Dr. HUGS\u003csup\u003e©\u003c/sup\u003e methodology, we identified distinct degradation pathways for several systems. In carbon-stabilized sulfur (CS), the cathode exhibits liquid-dominated sulfur redox reactions, while the anode forms inactive lithium early on and later develops a sulfide-dominated solid-electrolyte interface (SEI). Applying HUGS to Li-S pouch cells revealed that constant pressure setups result in better compositional homogeneity than constant gap setups. In sulfurized polyacrylonitrile (SPAN), despite minimal polysulfide shuttling at the cathode, the anode experiences non-sulfide SEI growth and lithium pulverization, partially mitigated by localized high-concentration electrolytes (LHCE). This study demonstrates that sulfur in Li-S batteries follows different degradation pathways depending on its composition, setup, and testing conditions. Ultimately, by leveraging advanced diagnostic tools rooted in analytical chemistry, we deepen our understanding of battery degradations and pave the way for targeted solutions that can significantly accelerate advancements in Li-S battery technology.\u0026nbsp;\u003c/p\u003e"},{"header":"HUGS results interpretation and automation in data processing","content":"\u003cp\u003eThe sulfur and lithium inventory loss for this study is based on an in-house developed method, HUGS, as demonstrated in Figure 1a. The method involves three samples derived from disassembled Li-S coin cells, prepared sequentially:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; \u003cstrong\u003eSample A\u003c/strong\u003e: The Li anode is washed with a MeOTf-DME solution to remove polysulfides, then titrated with ethanol to obtain Sample A.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; \u003cstrong\u003eSample B\u003c/strong\u003e: The wash solution is used to soak the remaining cell components (cathode, separator, cases, spring, spacers), creating a solution with methylated Li\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003ex\u003c/sub\u003e (3\u0026le;x\u0026le;8) and soluble S\u003csub\u003e8\u003c/sub\u003e (S\u003csub\u003e(L)\u003c/sub\u003e), defined as Sample B.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; \u003cstrong\u003eSample C\u003c/strong\u003e: Excess DME dissolves residual S\u003csub\u003e8\u003c/sub\u003e (S\u003csub\u003e(S)\u003c/sub\u003e) in the cathode under mechanical shear force due, forming Sample C.\u003c/p\u003e\n\u003cp\u003eAfter obtaining the samples, Gas Chromatography (GC) is performed on Sample A to quantify Li\u003csup\u003e0\u003c/sup\u003e in the anode by measuring H\u003csub\u003e2\u003c/sub\u003e generated from the Li-ethanol titration reaction. HPLC-UV is conducted on Sample B to determine Li\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003ex\u003c/sub\u003e and S\u003csub\u003e(L)\u003c/sub\u003e concentrations and Sample C to quantify S\u003csub\u003e(S)\u003c/sub\u003e in the cathode. These measurements of Li\u003csup\u003e0\u003c/sup\u003e, Li\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003ex\u003c/sub\u003e, S\u003csub\u003e(L)\u003c/sub\u003e, and S\u003csub\u003e(S)\u003c/sub\u003e allow for quantifying capacity retention/loss in Li-S batteries. The HUGS method is validated, as detailed in the Methods, Supplementary Information, under the \u0026lsquo;HUGS Method Validation\u0026rsquo; session and Figures S1-4.\u003c/p\u003e\n\u003cp\u003eBased on the HUGS method, a series of results are obtained. Figure 1b shows Sample A (TGC) representing the Li₀\u0026nbsp;inventory, while Samples B and C (HPLC-UV) display the amounts of Li₂S\u003csub\u003ex\u003c/sub\u003e, S\u003csub\u003e(L)\u003c/sub\u003e, and S\u003csub\u003e(S)\u003c/sub\u003e, which are converted into the theoretical capacity of the Li-S battery (Figure 1c). Given that most polysulfides are soluble, the HUGS storage plot reflects the cathode\u0026rsquo;s sulfur trapping ability. Using the sulfur theoretical capacity, lost capacity from Li₂S\u003csub\u003ex\u003c/sub\u003e, Li⁺, and charge capacity, three vectors are defined as follows (Figure 1d):\u0026nbsp;a: Capacity loss due to Li\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003ex\u003c/sub\u003e (3\u0026le;x\u0026le;8); b: Difference between stored Li capacity and Li₂S\u003csub\u003ex\u003c/sub\u003e (3\u0026le;x\u0026le;8) capacity loss; and g: Difference between charge capacity and stored Li capacity. Its capacity loss related with inactive Li.\u003c/p\u003e\n\u003cp\u003eThe relative changes in the three vectors can be classified into three typical cases, as shown in Figure 1e: Case I: \u0026gamma; \u0026gt; 0: A portion of Li and S did not contribute to electrochemical capacity. The formation of SEI isolates several Li particles, resulting in Li deactivation (inactive or dead Li\u003csup\u003e10,40\u003c/sup\u003e). Case II: \u0026gamma; \u0026asymp; 0, a + b \u0026asymp; Li inventory loss: Sulfide SEI, such as Li\u003csub\u003e2\u003c/sub\u003eS, is the dominant capacity loss species in the charged state, as both Li and sulfur inventory can be completely lost without capacity contribution. Case III: \u0026gamma; \u0026lt; 0: When \u0026beta; \u0026gt;\u0026gt;\u0026alpha;, \u0026gamma; becomes negative, indicating Li inventory loss is greater than the capacity loss. In this case, non-sulfide SEI formation and Li pulverization dominate the capacity loss.\u003c/p\u003e\n\u003cp\u003eThese cases demonstrate that HUGS vectors reveal the dominant factors in Li-S battery capacity loss, allowing clear identification of key degradation symptoms.\u003c/p\u003e\n\u003cp\u003eThe HUGS method effectively identifies dominant failure factors in Li-S batteries. However, the data analysis is time-consuming: Conventional HUGS analysis for a single Li-S battery requires over one hour (Figure 2a). This duration is attributed to processing four raw data files (battery cycling curves, GC, and two HPLC files) and quantifying over nine species, including Li, S\u003csub\u003e(L)\u003c/sub\u003e, S\u003csub\u003e(S)\u003c/sub\u003e, and polysulfides (Li\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003ex\u003c/sub\u003e, 3\u0026le;x\u0026le;8). Therefore, we developed Dr. HUGS\u003csup\u003e\u0026copy;\u003c/sup\u003e, an automated software for efficient HUGS data analysis. Users can obtain results within seconds by uploading the raw experimental data, with the entire process taking less than two minutes. We cross-verified Dr. HUGS\u003csup\u003e\u0026copy;\u003c/sup\u003e\u0026apos; results to validate accuracy against conventional manual processing, as shown in Figure 2b. The discrepancies were minimal, demonstrating the reliability of the automated analysis.\u003c/p\u003e\n\u003cp\u003eMoreover, Dr. HUGS\u003csup\u003e\u0026copy;\u003c/sup\u003e can automatically match results to the three cases presented in Figure 1e, facilitating the diagnosis of performance constraints in Li-S batteries. A video demonstration of the automated diagnosis is available in the Supporting Video \u0026lsquo;Dr. HUGS\u003csup\u003e\u0026copy;\u003c/sup\u003e Demo\u0026rsquo; with additional software details in\u0026nbsp;Supplementary Information under \u0026lsquo;Dr. HUGS\u003csup\u003e\u0026copy;\u003c/sup\u003e Software\u0026rsquo; Session. This automation significantly reduces processing time while maintaining consistency and accuracy in diagnosing failure mechanisms in Li-S batteries, establishing Dr. HUGS\u003csup\u003e\u0026copy;\u003c/sup\u003e as a powerful tool for advancing HUGS-based research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eQuantifying factors affecting Li-S battery cycle Life\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewith\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;CS cathodes in coin cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first case study examines the \u0026lsquo;classic\u0026rsquo; Li-S coin cells comprising a CS cathode, a \u0026lsquo;baseline electrolyte\u0026rsquo; (1M Lithium bis(trifluoromethane)sulfonimide (LiTFSI) in 1,3-dioxolane (DOL): DME (1:1 v/v) + 2 wt. % LiNO\u003csub\u003e3\u003c/sub\u003e), and Lithium metal\u003csup\u003e6\u003c/sup\u003e. The electrolyte to sulfur (E/S) ratio is 10\u0026nbsp;ml mg\u003csup\u003e-1\u003c/sup\u003e. As demonstrated in Figure 3a, the batteries\u0026apos; cycling was categorized into five regions to analyze their cycling behavior: 0 \u0026ndash; rested for 24 hours, IA \u0026ndash; two initial formation cycles (0.05 C), IB \u0026ndash; fast capacity decay cycles (0.1 C), II \u0026ndash; stable cycles (0.1 C), and III \u0026ndash; end of life (0.1 C).\u0026nbsp;Based on these regions, we first conducted a series of conventional characterizations, with the battery curves and results thoroughly discussed and presented in the Supplementary Information under the \u0026lsquo;CS Cathode\u0026rsquo; session and Figures S5-8. The results show that sulfide species decrease on the cathode during CS Li-S battery cycling, while SEI thickness on the anode sharply increases due to LiNO\u003csub\u003e3\u003c/sub\u003e depletion (Figure S9). The SEI comprises (poly)sulfides, Li\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, and inactive Li\u003csup\u003e0\u003c/sup\u003e. However, the key factors driving capacity failure in specific cycling regions remain unclear without quantitative analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHUGS analysis (results proceeded with Dr. HUGS\u003csup\u003e\u0026copy;\u003c/sup\u003e) was conducted on these batteries to diagnose the dominant capacity failure factors (Figure 3b). For these cells, Samples B and C results revealed the capacities stored in sulfur and polysulfides across each cycling region (Figures 3b and S10). Initially, a 24-hour rested battery showed a capacity loss of over 200 mAh g\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003edue to self-discharge and sulfur dissolution from the cathode. This is because of DME\u0026apos;s high solubility to polysulfides and elemental sulfur\u003csup\u003e47\u003c/sup\u003e. Despite some solid-state S\u003csub\u003e(S)\u003c/sub\u003e increase in later cycles, soluble sulfur/sulfide species remained dominant in stored capacity, with almost no S\u003csub\u003e(S)\u003c/sub\u003e remaining in Region III due to LiNO\u003csub\u003e3\u003c/sub\u003e depletion\u003csup\u003e48\u003c/sup\u003e. Thus, the CS Li-S battery operates as a liquid sulfur-redox-dominated system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe HUGS vector plot (Figure 3b) showed that in Regions IA and IB, the behavior aligns with Case I in Figure 1e, while Regions 2 and 3 correspond to Case II mainly. This indicates that inactive lithium formation and inefficient Sulfur and polysulfide conversion primarily impact early cycles in a liquid sulfur-redox system. This inefficiency of Sulfur conversion is also seen in 1\u003csup\u003est\u003c/sup\u003e discharge HUGS in Figure S10. A decreasing g vector and increasing b vector from Region 1A to 1B suggest \u0026lsquo;reactivation\u0026rsquo; of inactive lithium and Sulfur, possibly converting into polysulfides, consistent with previous study\u003csup\u003e10\u003c/sup\u003e. In contrast, anode passivation due to sulfide-rich SEI formation/growth dominates the stable cycling in Region II. Due to the LiNO3 depletion at Region III, thick sulfides SEI growth (Figure S6c and d) terminates the battery cycling. More cases are shown in\u0026nbsp;Supplementary Information under \u0026lsquo;Special Cases in CS batteries\u0026rsquo; Session and Figure S12.\u003c/p\u003e\n\u003cp\u003eOverall, in the liquid sulfur-redox-dominated coin cell system, the main capacity failure factors are self-discharge (Region 0), inactive lithium formation (Region 1), sulfide-rich passivating SEI (Region II), and thick sulfide-dominated SEI growth (Region III), as summarized in Figure 3c.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Component Variations in Li-S Pouch Cells with CS Cathodes under Different Setup Configurations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to coin cells, HUGS was applied to CS pouch cells to quantitatively analyze the lateral distribution of lithium, sulfur, and sulfides, as shown in Figure 4. Single-layer pouch cells were assembled using a CS cathode with similar areal loading and baseline electrolyte as previously discussed, with a cathode dimension of 3\u0026times;3 cm\u0026sup2; (Figure 4a). Compared to coin cells, the larger area of pouch cells leads to more complex pressure and electric field distributions, significantly affecting the compositional homogeneity during cycling\u003csup\u003e49\u003c/sup\u003e. We selected three positions (A, B, and C) along the diagonal from the cathode\u0026apos;s current collector tab to investigate the compositional homogeneity, as shown in Figure 4a. Samples for HUGS analysis were taken from both the cathode and anode at these positions. Electrolyte samples were directly extracted from the pouch cells for analysis. Further details regarding the pouch cell setup can be found in the Supplementary Information under the \u0026lsquo;CS Pouch Cells\u0026rsquo; session.\u003c/p\u003e\n\u003cp\u003eFigure 4b presents two testing configurations for the pouch cells: constant gap and constant pressure setups. In both configurations, an initial pressure of 30 psi was applied. The results of the HUGS analysis are shown in Figure 4c, with the corresponding discharge-charge curves, cycling performance, and sulfur quantification analysis provided in Supplementary Figures S13 and S14.\u003c/p\u003e\n\u003cp\u003eFrom Figure 4c, sulfur quantification across positions A, B, and C shows minimal variation for both testing configurations. This behavior may be attributed to the nature of the CS-baseline electrolyte system, which is driven by solid-liquid-solid reactions. Polysulfides can readily diffuse within the x-y plane in the liquid phase, resulting in uniform deposition and minimal compositional differences across positions. However, a higher solid sulfur (S\u003csub\u003e(S)\u003c/sub\u003e) content is observed in the constant gap setup compared to constant pressure. This is likely due to increased internal pressure caused by volume expansion during lithiation, which restricts the transition of sulfur from solid to liquid\u003csup\u003e50\u003c/sup\u003e. In contrast, the constant pressure setup releases this internal pressure, resulting in a greater presence of polysulfides in the electrolyte. Nevertheless, the effect of the two configurations on sulfur distribution is relatively minor.\u003c/p\u003e\n\u003cp\u003eFor lithium, significant differences are observed between the configurations. The constant gap setup has a pronounced variation in lithium inventory from positions A to C, whereas the constant pressure configuration shows a more uniform lithium distribution. This is clearly reflected in the differences in the\u0026nbsp;b\u0026nbsp;and\u0026nbsp;g\u0026nbsp;vectors, aligning with our previous findings\u003csup\u003e51,52\u003c/sup\u003e. The more even pressure distribution in the constant pressure configuration facilitates uniform lithium deposition, which may explain why constant pressure setups are often associated with improved cycling performance in Li-S batteries\u003csup\u003e53\u003c/sup\u003e. While constant pressure has a limited influence on the uniform distribution of sulfur across the x-y plane, it enhances the homogeneity of lithium deposition, thereby contributing to longer battery life. Furthermore, lithium inventory loss in both setups increases with distance from the current collector tab. This is likely caused by an uneven cell gap introduced by the tab as the cell size increases, leading to more complete reactions near the tab due to improved current collection efficiency and slightly higher pressure in that area.\u003c/p\u003e\n\u003cp\u003eThe HUGS analysis of Li-S pouch cells effectively distinguishes lateral compositional variations between the cathode and anode across diverse testing configurations. Notably, while the observed compositional distributions are influenced by parameters such as initial pressure settings, cells with a constant pressure setup exhibit more uniform compositional distribution, suggesting improved performance.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eQuantifying factors affecting Li-S battery cycle Life\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ewith\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SPAN cathodes and different electrolytes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing HUGS, we discussed the cycling behavior of Li-S batteries with CS cathodes and an electrolyte with higher polysulfide solubility, where physical adsorption predominantly stabilizes the elemental sulfur within the cathode. We then analyzed the cathode, where the sulfur is covalently bonded within a polymer. A typical material in Li-S batteries we selected for this is SPAN\u003csup\u003e54\u003c/sup\u003e. Due to covalently bonded sulfur, SPAN reduces polysulfide dissolution and the shuttle effect, improving the battery\u0026apos;s cycle life and efficiency\u003csup\u003e55\u0026ndash;57\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA SPAN structure is proposed with polymer and adsorbed short-chain sulfur species. These short-chain sulfur can be re-organized in DME, as shown in Figure 5a. A more detailed discussion of experimental evidence for this proposed structure is shown in Supplementary Information, \u0026lsquo;SPAN structure reconstruction\u0026rsquo; session, and Figure S15.\u003c/p\u003e\n\u003cp\u003eFurthermore, in Figure 5b, we defined the cycling regions for SPAN batteries, like the CS system. These regions are: 0 \u0026ndash; rested for 24 hours, I \u0026ndash; two initial formation cycles (0.05 C), II \u0026ndash; stable cycles (0.1 C for baseline and 0.2 C for LDME electrolyte), III \u0026ndash; end of life (0.1 C for baseline and 0.2 C for LDME electrolyte).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEx-situ characterizations were conducted to study the cycling behavior of the SPAN battery with a baseline electrolyte, detailed in the Supplementary Information, under the \u0026lsquo;Characterization of SPAN\u0026rsquo; (Figure S16 to S20) session. These analyses showed better sulfur retention and minimal morphological change in the SPAN cathode compared to the CS cathode. The anode contained minimal sulfur species until the end of life, unlike in the CS battery. Overall, the shuttle effect is limited in Li-S batteries with SPAN cathodes and baseline electrolytes, enabling stable cycling until LiNO\u003csub\u003e3\u003c/sub\u003e depletion.\u003c/p\u003e\n\u003cp\u003eHUGS analysis was performed consequentially (Figures 5c and S20). Unlike the CS system, Figure 5c HUGS capacity storage plots reveal significantly lower storage capacity as polysulfides and sulfur in the electrolyte due to most of the sulfur in SPAN being stabilized by covalent bonds, which limits bond cleavage and dissolution. From Region 0 to Region I, even less sulfur capacity is found in the electrolyte, suggesting that dissolved/reconstructed S\u003csub\u003e8\u003c/sub\u003e is \u0026quot;re-captured\u0026quot; by the cathode after cycling. By the end of cycling, the electrolyte shows less than 100 mAh g\u003csup\u003e-1\u003c/sup\u003e capacity is stored in soluble species.\u0026nbsp;The HUGS vector plot (Figure 5c) shows that the\u0026nbsp;a\u0026nbsp;vector remains negligible,\u0026nbsp;indicating that the shuttling effect does not drive the capacity loss in SPAN.\u0026nbsp;Case III in Figure 1e is consistently dominant from Region I to III, which suggests that non-sulfide SEI formation and Li pulverization are the main contributors to capacity loss. Cross-section FIB-SEM images of the Li-SPAN anode (Figure S19) confirm SEI growth and Li pulverization after cycling. Since the SEI primarily consists of organic-rich composites, its growth can lead to detached pulverized Li, increasing capacity loss as indicated by the\u0026nbsp;g\u0026nbsp;vector.\u003c/p\u003e\n\u003cp\u003eFurthermore, as shown in Figure 5c, we applied LDME electrolytes (2 M Lithium bis(fluorosulfonyl)imide (LiFSI) in 1,2-Dimethoxyethane/ Bis(2,2,2-trifluoroethyl) ether (DME/BTFE) (1:4 by weight)) to the Li-SPAN batteries to investigate its HUGS vector plot further. The LDME electrolyte, a localized high-concentration ether-based electrolyte, can stable cycle Li-SPAN batteries over hundreds of cycles (Figure 5b)\u003csup\u003e58\u003c/sup\u003e. Compared to the baseline electrolyte, as shown in Figure 5c, it can further reduce the shuttling effect of the batteries. More importantly, in the Figure 5c HUGS vector plot, with LDME electrolyte, all\u0026nbsp;b\u0026nbsp;vectors are further decreased, demonstrating improved cycling stability. The capacity loss mechanism in this system still aligns with Case III, where non-sulfide SEI growth and Li pulverization dominate, leading to capacity loss and eventual failure. While LDME cannot fully prevent these issues, it can mitigate them.\u003c/p\u003e\n\u003cp\u003eA proposed mechanism for Li-SPAN battery cycling behavior is demonstrated in Figure 5d. A small portion of short-chain sulfur redistribution and growth in the SPAN cathode, such as S\u003csub\u003e8\u003c/sub\u003e in the electrolyte, is observed during the battery resting. Once the cycle begins, the SPAN battery operates with significantly less shuttle effect-induced capacity loss, with non-sulfide SEI formation and Li pulverization being the dominant capacity loss mechanisms (Case III). Though the non-sulfide SEI growth and Li pulverization persist in the LDME system, a stable SEI in the LDME electrolyte prolongs cycling life, reduces anode SEI growth, and mitigates Li pulverization.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we introduced the HUGS analytical toolkit and Dr. HUGS\u0026copy; software, enabling automated quantification of capacity loss factors in the cathode, electrolyte, and anode of Li-S batteries. This innovation reduces analysis time from over an hour to under two minutes and with the same precision of diagnosis. We applied HUGS to investigate CS cathode coin cells, spatial variations in pouch cells, and SPAN coin cells with different electrolytes, showcasing its versatility in diagnosing performance-limiting factors.\u003c/p\u003e \u003cp\u003eFor CS coin cells, HUGS vector plots revealed early capacity loss from inactive lithium formation, transitioning to sulfide SEI growth in later cycles. In CS pouch cells, sulfur distribution remained uniform, while lithium varied significantly, with better uniformity under constant pressure. SPAN cells showed solid-state cathode reactions, reducing polysulfide dissolution and shuttling. HUGS analysis indicated non-sulfide SEI growth and lithium pulverization as the main degradation mechanisms, and LDME electrolytes further reduced these effects, improving cycling stability.\u003c/p\u003e \u003cp\u003eHUGS plays a crucial role in diagnosing Li-S battery \u0026ldquo;illnesses\u0026rdquo; by leveraging analytical chemistry for sequential, quantitative analysis. This approach highlights how sulfur stabilization, electrolyte selection, and testing configurations contribute to capacity degradation and lithium loss. By effectively identifying core issues such as polysulifdes shuttling, SEI growth, and lithium pulverization, the HUGS method guides targeted strategies to improve cycling performance. It provides a quantitative framework for evaluating treatment efficacy in Li-S batteries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e This work was supported by the Office of Vehicle Technologies of the US Department of Energy through the Advanced Battery Materials Research (BMR) Program (Battery500 Consortium) under contract DE-EE0007764. Arbin Battery Testing Facility and Shimadzu Gas Chromatography machine from UCSD were used. FIB and TEM characterizations were performed at the San Diego Nanotechnology Infrastructure (SDNI) of UCSD, a member of the National Nanotechnology Coordinated Infrastructure supported by the National Science Foundation (Grant ECCS1542148). NSF supported using the Raman facility through the UC San Diego Materials Research Science and Engineering Center (UCSD MRSEC), grant #DMR-201192. The authors acknowledge using facilities and instrumentation at the UC Irvine Materials Research Institute (IMRI), which the National Science Foundation partly supports through the UC Irvine Materials Research Science and Engineering Center (DMR-2011967). XPS experiments were performed using instrumentation funded in part by the National Science Foundation Major Research Instrumentation Program under grant no.CHE-1338173. This work made use of the Keck-II facility of Northwestern University’s NUANCE Center, which has received support from the SHyNE Resource (NSF ECCS-2025633), the IIN, and Northwestern's MRSEC program (NSF DMR-2308691). ICP-MS (Thermo iCAP RQ single-quadrupole ICP-MS system), HPLC-UV, and HPLC-APCI-MS measurements were taken at the Environmental and Complex Analysis Laboratory (ECAL) at the University of California, San Diego. S.P. thanks Gita Singh and Neal Arakawa for their suggestions on the manuscript. S.P. thanks S.W. for helping with figure designs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e S.P., S.W., and Y.S.M. conceived the project. S.P. designed and implemented the HUGS method, led HPLC, TGC, Cryo-FIB–SEM, and Raman experiments, and performed data analysis with S.W.. S.P., J.L., M.M., Q.M., and B.B. fabricated cells and electrolytes. S.P. and M.M. developed the Dr. HUGS\u003csup\u003e©\u003c/sup\u003e software. S.P., M.M. and B.B. made pouch cells. A.L., L.A., and B.S. supported cryo-FIB setup, and B.B. assisted with GC calibration. K.R. performed ToF-SIMS, with analysis by S.W., who also conducted XPS and data analysis. Q.M., S.W., and P.L. provided SPAN electrodes, and R.S., F.D., and M.C. contributed GM cathodes. S.P. and S.W. co-wrote the manuscript, with all authors discussing and approving the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors:\u0026nbsp;\u003c/strong\u003eCorrespondence to Shen Wang, Ying Shirley Meng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e A patent disclosure and a copyright is filed with University of California San Diego’s Office of Innovation and Commercialization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding authors on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eChung, S.-H. \u0026amp; Manthiram, A. Current Status and Future Prospects of Metal\u0026ndash;Sulfur Batteries. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1901125 (2019).\u003c/li\u003e\n \u003cli\u003eBhargav, A., He, J., Gupta, A. \u0026amp; Manthiram, A. Lithium-Sulfur Batteries: Attaining the Critical Metrics. \u003cem\u003eJoule\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 285\u0026ndash;291 (2020).\u003c/li\u003e\n \u003cli\u003eBruce, P. G., Freunberger, S. A., Hardwick, L. J. \u0026amp; Tarascon, J.-M. Li\u0026ndash;O2 and Li\u0026ndash;S batteries with high energy storage. \u003cem\u003eNat. Mater.\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 19\u0026ndash;29 (2012).\u003c/li\u003e\n \u003cli\u003eChung, S.-H., Chang, C.-H. \u0026amp; Manthiram, A. Progress on the Critical Parameters for Lithium\u0026ndash;Sulfur Batteries to be Practically Viable. \u003cem\u003eAdv. Funct. Mater.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 1801188 (2018).\u003c/li\u003e\n \u003cli\u003eNakamura, N., Ahn, S., Momma, T. \u0026amp; Osaka, T. Future potential for lithium-sulfur batteries. \u003cem\u003eJ. Power Sources\u003c/em\u003e \u003cstrong\u003e558\u003c/strong\u003e, 232566 (2023).\u003c/li\u003e\n \u003cli\u003eJi, X., Lee, K. T. \u0026amp; Nazar, L. F. A highly ordered nanostructured carbon\u0026ndash;sulphur cathode for lithium\u0026ndash;sulphur batteries. \u003cem\u003eNat. Mater.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 500\u0026ndash;506 (2009).\u003c/li\u003e\n \u003cli\u003eLiu, R. \u003cem\u003eet al.\u003c/em\u003e Establishing reaction networks in the 16-electron sulfur reduction reaction. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e626\u003c/strong\u003e, 98\u0026ndash;104 (2024).\u003c/li\u003e\n \u003cli\u003eWang, D. \u003cem\u003eet al.\u003c/em\u003e Realizing high-capacity all-solid-state lithium-sulfur batteries using a low-density inorganic solid-state electrolyte. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1895 (2023).\u003c/li\u003e\n \u003cli\u003eAkridge, J. Li/S fundamental chemistry and application to high-performance rechargeable batteries. \u003cem\u003eSolid State Ion.\u003c/em\u003e \u003cstrong\u003e175\u003c/strong\u003e, 243\u0026ndash;245 (2004).\u003c/li\u003e\n \u003cli\u003eWeret, M. A. \u003cem\u003eet al.\u003c/em\u003e Reviving Inactive Lithium and Stabilizing Lithium Deposition for Improving the Performance of Anode-Free Lithium\u0026ndash;Sulfur Batteries. \u003cem\u003eACS Energy Lett.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 2817\u0026ndash;2823 (2023).\u003c/li\u003e\n \u003cli\u003eHong, H., Che Mohamad, N. A. R., Chae, K., Marques Mota, F. \u0026amp; Kim, D. H. The lithium metal anode in Li\u0026ndash;S batteries: challenges and recent progress. \u003cem\u003eJ. Mater. Chem. A\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 10012\u0026ndash;10038 (2021).\u003c/li\u003e\n \u003cli\u003eSu, Y.-S., Fu, Y., Cochell, T. \u0026amp; Manthiram, A. A strategic approach to recharging lithium-sulphur batteries for long cycle life. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 2985 (2013).\u003c/li\u003e\n \u003cli\u003eLiang, X. \u003cem\u003eet al.\u003c/em\u003e A highly efficient polysulfide mediator for lithium\u0026ndash;sulfur batteries. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 5682 (2015).\u003c/li\u003e\n \u003cli\u003eYang, Y. \u003cem\u003eet al.\u003c/em\u003e High-Capacity Micrometer-Sized Li \u003csub\u003e2\u003c/sub\u003e S Particles as Cathode Materials for Advanced Rechargeable Lithium-Ion Batteries. \u003cem\u003eJ. Am. Chem. Soc.\u003c/em\u003e \u003cstrong\u003e134\u003c/strong\u003e, 15387\u0026ndash;15394 (2012).\u003c/li\u003e\n \u003cli\u003eVinayan, B. P. \u003cem\u003eet al.\u003c/em\u003e Nitrogen Rich Hierarchically Organized Porous Carbon/Sulfur Composite Cathode Electrode for High Performance Li/S Battery: A Mechanistic Investigation by Operando Spectroscopic Studies. \u003cem\u003eAdv. Mater. Interfaces\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 1600372 (2016).\u003c/li\u003e\n \u003cli\u003eWang, S. \u003cem\u003eet al.\u003c/em\u003e Structural Transformation in a Sulfurized Polymer Cathode to Enable Long-Life Rechargeable Lithium\u0026ndash;Sulfur Batteries. \u003cem\u003eJ. Am. Chem. Soc.\u003c/em\u003e \u003cstrong\u003e145\u003c/strong\u003e, 9624\u0026ndash;9633 (2023).\u003c/li\u003e\n \u003cli\u003eYu, X. \u0026amp; Manthiram, A. A Progress Report on Metal\u0026ndash;Sulfur Batteries. \u003cem\u003eAdv. Funct. Mater.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 2004084 (2020).\u003c/li\u003e\n \u003cli\u003eYao, W., Liao, K., Lai, T., Sul, H. \u0026amp; Manthiram, A. Rechargeable Metal-Sulfur Batteries: Key Materials to Mechanisms. \u003cem\u003eChem. Rev.\u003c/em\u003e \u003cstrong\u003e124\u003c/strong\u003e, 4935\u0026ndash;5118 (2024).\u003c/li\u003e\n \u003cli\u003ePatel, M. U. M. \u003cem\u003eet al.\u003c/em\u003e Li-S Battery Analyzed by UV/Vis in Operando Mode. \u003cem\u003eChemSusChem\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1177\u0026ndash;1181 (2013).\u003c/li\u003e\n \u003cli\u003ePatel, M. U. M. \u0026amp; Dominko, R. Application of In Operando UV/Vis Spectroscopy in Lithium\u0026ndash;Sulfur Batteries. \u003cem\u003eChemSusChem\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 2167\u0026ndash;2175 (2014).\u003c/li\u003e\n \u003cli\u003eZou, Q. \u0026amp; Lu, Y.-C. Solvent-Dictated Lithium Sulfur Redox Reactions: An Operando UV\u0026ndash;vis Spectroscopic Study. \u003cem\u003eJ. Phys. Chem. Lett.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 1518\u0026ndash;1525 (2016).\u003c/li\u003e\n \u003cli\u003eHe, Q., Freiberg, A. T. S., Patel, M. U. M., Qian, S. \u0026amp; Gasteiger, H. A. Operando Identification of Liquid Intermediates in Lithium\u0026ndash;Sulfur Batteries via Transmission UV\u0026ndash;vis Spectroscopy. \u003cem\u003eJ. Electrochem. Soc.\u003c/em\u003e \u003cstrong\u003e167\u003c/strong\u003e, 080508 (2020).\u003c/li\u003e\n \u003cli\u003eBarchasz, C. \u003cem\u003eet al.\u003c/em\u003e Lithium/Sulfur Cell Discharge Mechanism: An Original Approach for Intermediate Species Identification. \u003cem\u003eAnal. Chem.\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 3973\u0026ndash;3980 (2012).\u003c/li\u003e\n \u003cli\u003eNelson, J. \u003cem\u003eet al.\u003c/em\u003e In Operando X-ray Diffraction and Transmission X-ray Microscopy of Lithium Sulfur Batteries. \u003cem\u003eJ. Am. Chem. Soc.\u003c/em\u003e \u003cstrong\u003e134\u003c/strong\u003e, 6337\u0026ndash;6343 (2012).\u003c/li\u003e\n \u003cli\u003eLowe, M. A., Gao, J. \u0026amp; Abru\u0026ntilde;a, H. D. Mechanistic insights into operational lithium\u0026ndash;sulfur batteries by in situ X-ray diffraction and absorption spectroscopy. \u003cem\u003eRSC Adv.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 18347 (2014).\u003c/li\u003e\n \u003cli\u003eWang, Q. \u003cem\u003eet al.\u003c/em\u003e Direct Observation of Sulfur Radicals as Reaction Media in Lithium Sulfur Batteries. \u003cem\u003eJ. Electrochem. Soc.\u003c/em\u003e \u003cstrong\u003e162\u003c/strong\u003e, A474\u0026ndash;A478 (2015).\u003c/li\u003e\n \u003cli\u003eHelen, M. \u003cem\u003eet al.\u003c/em\u003e Single step transformation of sulphur to Li2S2/Li2S in Li-S batteries. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 12146 (2015).\u003c/li\u003e\n \u003cli\u003eConder, J. \u003cem\u003eet al.\u003c/em\u003e Direct observation of lithium polysulfides in lithium\u0026ndash;sulfur batteries using operando X-ray diffraction. \u003cem\u003eNat. Energy\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 1\u0026ndash;7 (2017).\u003c/li\u003e\n \u003cli\u003ePatel, M. U. M. \u003cem\u003eet al.\u003c/em\u003e X-ray Absorption Near-Edge Structure and Nuclear Magnetic Resonance Study of the Lithium\u0026ndash;Sulfur Battery and its Components. \u003cem\u003eChemPhysChem\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 894\u0026ndash;904 (2014).\u003c/li\u003e\n \u003cli\u003eWang, H. \u003cem\u003eet al.\u003c/em\u003e In Situ NMR Observation of the Temporal Speciation of Lithium Sulfur Batteries during Electrochemical Cycling. \u003cem\u003eJ. Phys. Chem. C\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e, 6011\u0026ndash;6017 (2017).\u003c/li\u003e\n \u003cli\u003eLevin, B. D. A. \u003cem\u003eet al.\u003c/em\u003e Characterization of Sulfur and Nanostructured Sulfur Battery Cathodes in Electron Microscopy Without Sublimation Artifacts. \u003cem\u003eMicrosc. Microanal.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 155\u0026ndash;162 (2017).\u003c/li\u003e\n \u003cli\u003eDiao, Y., Xie, K., Xiong, S. \u0026amp; Hong, X. Analysis of Polysulfide Dissolved in Electrolyte in Discharge-Charge Process of Li-S Battery. \u003cem\u003eJ. Electrochem. Soc.\u003c/em\u003e \u003cstrong\u003e159\u003c/strong\u003e, A421 (2012).\u003c/li\u003e\n \u003cli\u003eKamyshny, A., Goifman, A., Gun, J., Rizkov, D. \u0026amp; Lev, O. Equilibrium Distribution of Polysulfide Ions in Aqueous Solutions at 25 \u0026deg;C: A New Approach for the Study of Polysulfides\u0026rsquo; Equilibria. \u003cem\u003eEnviron. Sci. Technol.\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 6633\u0026ndash;6644 (2004).\u003c/li\u003e\n \u003cli\u003eKawase, A., Shirai, S., Yamoto, Y., Arakawa, R. \u0026amp; Takata, T. Electrochemical reactions of lithium\u0026ndash;sulfur batteries: an analytical study using the organic conversion technique. \u003cem\u003ePhys. Chem. Chem. Phys.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 9344\u0026ndash;9350 (2014).\u003c/li\u003e\n \u003cli\u003eZheng, D. \u003cem\u003eet al.\u003c/em\u003e Quantitative Chromatographic Determination of Dissolved Elemental Sulfur in the Non-Aqueous Electrolyte for Lithium-Sulfur Batteries. \u003cem\u003eJ. Electrochem. Soc.\u003c/em\u003e \u003cstrong\u003e162\u003c/strong\u003e, A203\u0026ndash;A206 (2015).\u003c/li\u003e\n \u003cli\u003eZheng, D., Yang, X.-Q. \u0026amp; Qu, D. Reaction between Lithium Anode and Polysulfide Ions in a Lithium\u0026ndash;Sulfur Battery. \u003cem\u003eChemSusChem\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 2348\u0026ndash;2350 (2016).\u003c/li\u003e\n \u003cli\u003eZheng, D. \u003cem\u003eet al.\u003c/em\u003e Reduction mechanism of sulfur in lithium\u0026ndash;sulfur battery: From elemental sulfur to polysulfide. \u003cem\u003eJ. Power Sources\u003c/em\u003e \u003cstrong\u003e301\u003c/strong\u003e, 312\u0026ndash;316 (2016).\u003c/li\u003e\n \u003cli\u003eZheng, D. \u003cem\u003eet al.\u003c/em\u003e Investigation of the Li\u0026ndash;S Battery Mechanism by Real-Time Monitoring of the Changes of Sulfur and Polysulfide Species during the Discharge and Charge. \u003cem\u003eACS Appl. Mater. Interfaces\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 4326\u0026ndash;4332 (2017).\u003c/li\u003e\n \u003cli\u003eStang, P. J., Hanack, M. \u0026amp; Subramanian, L. R. Perfluoroalkanesulfonic Esters: Methods of Preparation and Applications in Organic Chemistry. \u003cem\u003eSynthesis\u003c/em\u003e \u003cstrong\u003e1982\u003c/strong\u003e, 85\u0026ndash;126 (2002).\u003c/li\u003e\n \u003cli\u003eFang, C. \u003cem\u003eet al.\u003c/em\u003e Quantifying inactive lithium in lithium metal batteries. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e572\u003c/strong\u003e, 511\u0026ndash;515 (2019).\u003c/li\u003e\n \u003cli\u003eBao, W. \u003cem\u003eet al.\u003c/em\u003e Quantifying lithium loss in amorphous silicon thin-film anodes via titration-gas chromatography. \u003cem\u003eCell Rep. Phys. Sci.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, (2021).\u003c/li\u003e\n \u003cli\u003eSreenarayanan, B. \u003cem\u003eet al.\u003c/em\u003e Recycling silicon scrap for spherical Si\u0026ndash;C composite as high-performance lithium-ion battery anodes. \u003cem\u003eJ. Power Sources\u003c/em\u003e \u003cstrong\u003e578\u003c/strong\u003e, 233245 (2023).\u003c/li\u003e\n \u003cli\u003eSreenarayanan, B. \u003cem\u003eet al.\u003c/em\u003e Quantification of lithium inventory loss in micro silicon anode via titration-gas chromatography. \u003cem\u003eJ. Power Sources\u003c/em\u003e \u003cstrong\u003e531\u003c/strong\u003e, 231327 (2022).\u003c/li\u003e\n \u003cli\u003eSayahpour, B. \u003cem\u003eet al.\u003c/em\u003e Quantitative analysis of sodium metal deposition and interphase in Na metal batteries. \u003cem\u003eEnergy Environ. Sci.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 1216\u0026ndash;1228 (2024).\u003c/li\u003e\n \u003cli\u003eLu, B. \u003cem\u003eet al.\u003c/em\u003e Suppressing Chemical Corrosions of Lithium Metal Anodes. \u003cem\u003eAdv. Energy Mater.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 2202012 (2022).\u003c/li\u003e\n \u003cli\u003eTan, D. H. S. \u003cem\u003eet al.\u003c/em\u003e Carbon-free high-loading silicon anodes enabled by sulfide solid electrolytes. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e373\u003c/strong\u003e, 1494\u0026ndash;1499 (2021).\u003c/li\u003e\n \u003cli\u003eAdeoye, H. A., Dent, M., Watts, J. F., Tennison, S. \u0026amp; Lekakou, C. Solubility and dissolution kinetics of sulfur and sulfides in electrolyte solvents for lithium\u0026ndash;sulfur and sodium\u0026ndash;sulfur batteries. \u003cem\u003eJ. Chem. Phys.\u003c/em\u003e \u003cstrong\u003e158\u003c/strong\u003e, 064702 (2023).\u003c/li\u003e\n \u003cli\u003eZhang, S. S. A new finding on the role of LiNO3 in lithium-sulfur battery. \u003cem\u003eJ. Power Sources\u003c/em\u003e \u003cstrong\u003e322\u003c/strong\u003e, 99\u0026ndash;105 (2016).\u003c/li\u003e\n \u003cli\u003eHuang, X. \u003cem\u003eet al.\u003c/em\u003e Comprehensive evaluation of safety performance and failure mechanism analysis for lithium sulfur pouch cells. \u003cem\u003eEnergy Storage Mater.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 87\u0026ndash;97 (2020).\u003c/li\u003e\n \u003cli\u003eShi, L. \u003cem\u003eet al.\u003c/em\u003e Reaction heterogeneity in practical high-energy lithium\u0026ndash;sulfur pouch cells. \u003cem\u003eEnergy Environ. Sci.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 3620\u0026ndash;3632 (2020).\u003c/li\u003e\n \u003cli\u003eChen, Y.-T. \u003cem\u003eet al.\u003c/em\u003e Enabling Uniform and Accurate Control of Cycling Pressure for All-Solid-State Batteries. \u003cem\u003eAdv. Energy Mater.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2304327 (2024).\u003c/li\u003e\n \u003cli\u003eFang, C. \u003cem\u003eet al.\u003c/em\u003e Pressure-tailored lithium deposition and dissolution in lithium metal batteries. \u003cem\u003eNat. Energy\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 987\u0026ndash;994 (2021).\u003c/li\u003e\n \u003cli\u003eSchmidt, F. \u003cem\u003eet al.\u003c/em\u003e Influence of external stack pressure on the performance of Li-S pouch cell. \u003cem\u003eJ. Phys. Energy\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 014004 (2022).\u003c/li\u003e\n \u003cli\u003eWang, J., Yang, J., Xie, J. \u0026amp; Xu, N. A Novel Conductive Polymer\u0026ndash;Sulfur Composite Cathode Material for Rechargeable Lithium Batteries. \u003cem\u003eAdv. Mater.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 963\u0026ndash;965 (2002).\u003c/li\u003e\n \u003cli\u003eKuai, D. \u003cem\u003eet al.\u003c/em\u003e Interfacial Electrochemical Lithiation and Dissolution Mechanisms at a Sulfurized Polyacrylonitrile Cathode Surface. \u003cem\u003eACS Energy Lett.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 810\u0026ndash;818 (2024).\u003c/li\u003e\n \u003cli\u003eTan, S. \u003cem\u003eet al.\u003c/em\u003e Structural and Interphasial Stabilities of Sulfurized Polyacrylonitrile (SPAN) Cathode. \u003cem\u003eACS Energy Lett.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 2496\u0026ndash;2504 (2023).\u003c/li\u003e\n \u003cli\u003eWang, W. \u003cem\u003eet al.\u003c/em\u003e Recognizing the Mechanism of Sulfurized Polyacrylonitrile Cathode Materials for Li\u0026ndash;S Batteries and beyond in Al\u0026ndash;S Batteries. \u003cem\u003eACS Energy Lett.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 2899\u0026ndash;2907 (2018).\u003c/li\u003e\n \u003cli\u003eLiu, H. \u003cem\u003eet al.\u003c/em\u003e Ultrahigh coulombic efficiency electrolyte enables Li||SPAN batteries with superior cycling performance. \u003cem\u003eMater. Today\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 17\u0026ndash;28 (2021).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Information","content":"\u003cp\u003eSupplementary Information is not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Dominican University of California","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"battery, lithium, sulfur, electrolyte, sulfurized polyacrylonitrile, characterization","lastPublishedDoi":"10.21203/rs.3.rs-5456378/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5456378/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLithium-sulfur (Li-S) batteries offer high energy density and low cost, making them ideal for electric vehicles and aviation. However, the many factors affecting Li-S battery cycling performance complicate researchers' efforts to pinpoint the bottleneck. To address this, a toolkit called High-performance liquid chromatography, Ultraviolet spectroscopy, and Gas chromatography Sequential characterizations (HUGS) was developed using sequential analytical chemistry. Along with this, data analysis software Dr. HUGS\u003csup\u003e©\u003c/sup\u003e was created to automate the ‘diagnoses’ of the key degradation mechanisms, similar to a doctor assessing a patient. Our analysis reveals that carbon sulfur cathodes suffer capacity loss due to lithium sulfide buildup on the anode. HUGS shows that constant pressure setups in Li-S pouch cells improve compositional uniformity over constant gap setups. Conversely, sulfurized polyacrylonitrile batteries experience non-sulfide solid-electrolyte-interface formation and lithium pulverization—issues mitigated by localized high-concentration electrolytes. This work demonstrates how analytical chemistry techniques and automated data analysis can accelerate the diagnosis of the complexities of electrochemical systems, advancing next-generation, high-performance Li-S batteries.\u003c/p\u003e","manuscriptTitle":"Automated Diagnosis of Performance Bottolenecks in Lithium-Sulfur Batteries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 14:20:09","doi":"10.21203/rs.3.rs-5456378/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e4724b3-094c-4f09-942b-a5358a7062ea","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40286554,"name":"Materials Chemistry"},{"id":40286555,"name":"Analytical Chemistry"}],"tags":[],"updatedAt":"2024-11-19T14:20:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 14:20:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5456378","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5456378","identity":"rs-5456378","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}