Sequential characterizations method and software for quantifying anode, sulfur, and polysulfide in energy storage devices

Abstract

Methods of assessing an energy storage device include providing material for a metal-chalcogen battery, washing the metal anode in a first organic solvent to form a polychalcogenide free metal anode upon which gas chromatography is performed on the polychalcogenide free metal anode to quantify the amount of M0, soaking the chalcogen cathode and the remainder of the metal- chalcogen battery in the organic solvent used to wash the metal anode to form a first solution comprising methylated polychalcogenide and any soluble chalcogen, removing any remaining chalcogen cathode from the first solution, dissolving the remaining chalcogen cathode in a second organic solvent to form a chalcogen containing second solution, performing high performance liquid chromatography -ultraviolet on the first solution and the second solution, separately, and performing battery failure analysis based thereon. The battery failure analysis may be completed using a high-performance liquid chromatography -ultraviolet spectroscopy and gas chromatography sequential characterization (HUGS) computer implemented method.

Description

SEQUENTIAL CHARACTERIZATIONS METHOD AND SOFTWARE FOR QUANTIFYING ANODE, SULFUR, AND POLYSULFIDE IN ENERGY STORAGE DEVICESRELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 709,904, filed October 21, 2024, the entirety of which is incorporated herein by reference.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under DE-EE0007764 awarded by the Department of Energy. The government has certain rights in the invention.BACKGROUND

[0003] As explained by the inventors in their paper, Quantitative insights for diagnosing performance bottlenecks on lithium-sulfur batteries, published on May 23, 2025, published subsequent to the priority application identified above, herein referred to as “Quantitative Insights May 2025,” lithium-sulfur (Li-S) batteries excel in energy storage due to their impressive theoretical specific capacity of 1675 mAh g-1and over 500 W h kg-1energy density. These attributes make them ideal for aviation, electric vehicles, and marine technologies. 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.

[0004] Despite their potential, Li-S batteries face numerous challenges, such as low sulfur conductivity, the polysulfide shuttle effect, inefficient polysulfide conversion, inactive lithium formation, 10 and lithium metal pulverization. These conditions result in lithium or sulfur inventory loss, leading to poor cycling stability. Researchers have applied various strategies to treat these issues, such as nanostructured sulfur composites, localized high-concentration electrolytes (LHCE), protective anode coatings, and optimized electrode designs. However, with many factors influencing performance and an array of potential remedies, pinpointing the most critical failure mechanism for each specific Li-S battery system is essential to enable targeted and effective solutions.

[0005] The key to understanding capacity fade in Li-S battery systems lies in accurately quantifying the inventories of Li and S and determining how and where specific chemical species store capacity after cycling. Precise analysis of the spatial distribution and relative concentrationsof sulfur, lithium, and lithium polysulfides allows researchers to identify the dominant components and assess their contributions to capacity loss. This knowledge is fundamental to diagnosing and addressing capacity degradation in lithium-sulfur batteries. However, achieving this level of precision presents considerable challenges. Vacuum-based characterization techniques, such as X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and scanning transmission electron microscopy (STEM), often lead to sulfur sublimation, compromising the reliability of the measurements. Spectroscopic methods, including ultraviolet-visible spectroscopy (UV-Vis), Raman spectroscopy, and nuclear magnetic resonance spectroscopy (NMR), struggle to distinguish between polysulfides due to their similar functional groups and overlapping spectral features. Furthermore, the dynamic chemical equilibria among polysulfides complicates isolation efforts, as disproportionation or comproportionation reactions can alter their concentrations during separation. These limitations highlight the pressing need for advanced diagnostic techniques to unravel the mechanisms of capacity fade and pave the way for developing more robust and stable lithium-sulfur batteries.

[0006] A promising method for (semi-)quantifying sulfur or polysulfides is chemical modification combined with high-performance liquid chromatography (HPLC), which offers improved detection limits and feasibility for testing in more realistic battery systems. This approach stabilizes polysulfides by converting their reactive sulfur sites into chemically stable derivatives, such as methyl or methylbenzene compounds, effectively quenching their equilibrium transitions. Methyl trifluoromethanesulfonate (MeOTf) is particularly efficient, exhibiting reaction kinetics that is 104times faster than common methylation agents, enabling the effective stabilization of poly sulfides.40 Following derivatization, HPLC separates these sulfur species by retention time, and ultraviolet detection is used to estimate their relative concentrations. However, in previous attempts to quantify lithium-sulfur batteries using HPLC-based methods, conventional chromatographic columns have struggled with insufficient resolution, making it challenging to separate long-chain sulfur species effectively, thereby impacting the quantification of these components. Additionally, toluene-based derivatization methods, which rely on slower reaction kinetics, often cause shifts in the equilibrium of lithium polysulfides during the quenching process, leading to incomplete stabilization.

[0007] Furthermore, the lack of standard reference materials in most studies has prevented absolute quantification of sulfur species, limiting the analysis to semi-quantitative measurements based on peak area changes. This inability to precisely quantify sulfur content hampers efforts to correlate these changes with battery capacity. Finally, many studies modify the battery setup, using flow cells instead of standard coin or pouch cells for sampling, significantly restricting the applicability of these methods to practical battery systems.SUMMARY

[0008] In some example embodiments, there may be provided systems, methods, and articles of manufacture for quantifying anode, sulfur, and / or poly sulfide in an energy storage device.

[0009] In a first aspect, a sequential characterization method combining high-performance liquid chromatography with ultraviolet detection (HPLC-UV) and titration gas chromatography (TGC), referred to as “HUGS” herein, to diagnose failure mechanisms in metal - chalcogen batteries, such as lithium-sulfur (Li-S) batteries, with high precision is disclosed and described. This method accurately quantifies nine distinct sulfur and polysulfide species at concentrations as low as 40 ppb. HUGS has been successfully applied to practical coin and pouch cells without requiring cell modifications. By integrating analytical chemical data with battery cycling performance, HUGS provides critical insights into the underlying causes of degradation of such batteries. Once a failure point is identified, changes to the battery composition and or construction can be introduced to improve battery performance.

[0010] In a second aspect of the disclosed embodiments, a method for analyzing an energy storage device can be provided. The method can comprise providing material for a metal-chalcogen battery including a metal anode, a chalcogen cathode, and a remainder of the metal-chalcogen battery or disassembling an energy storage device to obtain a plurality of components. The remainder of the metal-chalcogen battery comprises one or more of a separator, a case, a spring, and a spacer. The method can comprise preparing a first sample from a first component of the plurality of components. The method can comprise preparing a second sample from a second component of the plurality of components. The method can comprise preparing a third sample from a third component of the plurality of components. The method can comprise analyzing the first sample using titration gas chromatography to quantify lithium content. The method can comprise analyzing the second sample using high-performance liquid chromatography with ultraviolet detection to quantify soluble sulfur species. The method can comprise analyzing the third sample using high-performance liquid chromatography with ultraviolet detection to quantify residual sulfur content. The method can comprise correlating results from the analyzing steps to determine one or more failure mechanisms in the energy storage device. The concentration of an electrolyte in the energy storage device can be increased or decreased and the method repeated. In one embodiment, a secondary energy storage device is assembled with a different electrolyte and the method is repeated.

[0011] The method includes washing the metal anode in a solution comprising a derivatization agent in a first solvent to form a polychalcogenide free metal anode, and performing gas chromatography on the polychalcogenide free metal anode, thereby quantifying the amount of M° in the metal anode. The method includes soaking the chalcogen cathode and the remainder ofthe metal-chalcogen battery in the organic solvent used to wash the metal anode to form a first solution comprising methylated polychalcogenide and any soluble chalcogen, removing any remaining chalcogen cathode from the first solution, and dissolving the remaining chalcogen cathode in a second solvent to form a chalcogen containing second solution. The method includes performing high performance liquid chromatography -ultraviolet on the first solution, thereby quantifying the amount of Mnchalcogenxand chalcogen(i), and performing high performance liquid chromatography-ultraviolet on the second solution, thereby quantifying the amount of chalcogen(s) present. For the Mnchalcogenxn is typically 1 to 3 and x is 3<x<8. Lastly, the method includes performing a battery failure analysis using the data collected and the properties of the battery and its cycling. The metal M can be one or more of Li+, Na+, K+, Rb+, Cs+, Ca2+, Mg2+, and Zn2+. The chalcogen can be one or more of sulfur (S), selenium (Se), tellurium (Te), S / Se hybrid cathodes, and thio- / seleno-organics.

[0012] The method can be repeated any number of times for an energy storage device at each of a plurality of preselected operating cycles. The plurality of preselected operating cycles comprise two or more of (i) a 24 hour initial rest period, (ii) a period of two initial formation cycles (0.05C), (iii) a period of fast capacity decay cycles (0.1 C), (iv) stable cycles (0.1C), and (v) an end of life (0.1 C). In one embodiment, the plurality of preselected operating cycles includes each of (i) to (v).

[0013] In any or all embodiments, the derivatization agent comprises methyl trifluoro methanesulfonate (MeOTf) and the first solvent comprises dimethoxy ethane.

[0014] The energy storage device is a rechargeable energy storage device. The energy storage device is a solid-state battery or an all solid-state battery. The energy storage device is a solid-state battery comprising a liquid electrolyte or a liquified gas electrolyte.

[0015] Performing the battery failure analysis includes calculating three vectors:a accumulated capacity loss due to Mnchalcogenx;P: a difference between total M° loss and a; andy: a difference between a battery charge capacity and stored M° capacity, and can include calculating and displaying a charge capacity storage plot. The method can calso include determining a capacity storage plot.

[0016] In various embodiments of this second aspect, the energy storage device can be a lithium-sulfur battery. The first component can be an anode of the lithium-sulfur battery. The second component can be an electrolyte of the lithium-sulfur battery. The third component can be a cathode of the lithium-sulfur battery. Preparing the first sample can comprise washing the anode with a solvent. Preparing the second sample can comprise extracting the electrolyte with a methylation solution. Preparing the third sample can comprise extracting the cathode withdimethyl ether. The analyzing using high-performance liquid chromatography can comprise using a semi -preparative column. The analyzing using high-performance liquid chromatography can comprise detecting at a wavelength of 230 nanometers. The quantifying of soluble sulfur species can comprise identifying nine distinct sulfur and polysulfide species. The method can further comprise generating a capacity retention analysis based on correlating the results. The method can further comprise identifying specific degradation pathways based on the failure mechanisms. The analyzing can be performed without modifying the energy storage device prior to disassembly.

[0017] In a third aspect of the disclosed embodiments, a computer-implemented method for battery failure analysis is disclosed and described. The method includes receiving gas chromatography and high-performance liquid chromatography-ultraviolet spectroscopy files for a metal-chalcogen battery, wherein the gas chromatography and high-performance liquid chromatography-ultraviolet spectroscopy are sequential characterizations of the metal chalcogen battery, receiving inputs of the metal-chalcogen battery parameters, generating a charge capacity vector and / or storage plot; and determining battery failure points or mechanisms. The gas chromatography and high-performance liquid chromatography-ultraviolet spectroscopy files are generated according to the method of claim 1. The method includes one or more calibration curves for titration gas chromatography and HPLC-UV input or saved in a nontransitory memory of the computing device.

[0018] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and, together with the description, help explain some of the principles associated with the disclosed implementations.

[0020] FIG. 1 depicts a schematic example of the sequential characterization method and system disclosed herein and referred to as the HUGS computer implement software and method.

[0021] FIG. 2 shows an example of information output from one embodiment of HUGS.

[0022] FIG. 3 depicts an example of a HUGS capacity storage plot.

[0023] FIG. 4 depicts an example of a HUGS vector plot and the definition of each vector.

[0024] FIG. 5 depicts an example of three capacity loss mechanism scenarios in Li-S battery derived from HUGS.

[0025] FIG. 6 depicts an example workflow of the Dr. HUGS software for automated dataprocessing of data gathered by the HUGS method and system producing a failure analysis.

[0026] FIG. 7 is an example display screen for the computer interface for a user to input data for the HUGS computer implemented analysis.

[0027] FIG. 8 depicts the definition of cycling regions in one embodiment of a Li-S battery.

[0028] FIG. 9 shows one example of a HUGS vector plot and capacity storage plots for each cycling region of a battery as output by the Dr. HUGS software.

[0029] FIG. 10 shows a mechanism for cycling behavior of Li-S batteries with CS cathodes based on HUGS analysis.

[0009] FIG. 11 shows a second example of a HUGS vector plot and capacity storage plots for each cycling region as output by the Dr. HUGS software.

[0010] FIG. 12 depicts a mechanism for cycling behavior of Li-S batteries with SPAN cathodes based on HUGS analysis.

[0011] FIG. 13 depicts an example schematic of the dimensions of the pouch cell components and its stacking sequence.

[0012] FIG. 14 depicts an example schematic of the sequence of punched portions from the cathode and anode with respect to the tab as A, B and C to perform the HUGS characterization.

[0013] FIG. 15 depicts an example schematic of fixed pressure (30 psi) setup and fixed gap (initial 30 psi pressure) setup.

[0014] FIG. 16 depicts examples of HUGS vector plots and HUGS capacity storage plots for a pouch cell with fixed pressure (Left) and fixed gap (Right) at A, B and C positions.

[0015] FIG. 17 is a graph of charge discharge battery curves for a Na-SPAN battery.

[0016] FIG. 18 is a graph of cycling performance for the Na-SPAN battery.

[0017] FIG. 19 is a HPLC-UV chromatogram for the Na-SPAN battery.

[0018] FIG. 20 is an output of sulfur and polysulfide capacity retention data for the Na-SPAN battery.

[0019] FIG. 21 is a schematic of a computing system for assessing battery performance via the computer implemented HUGS method.DETAILED DESCRIPTION

[0020] In some embodiments, there is provided sequential characterization methods, systems, and articles of manufacture (also referred to herein as “HUGS”) to differentiate and quantify key sulfur species, including polysulfides (Sx2‘), dissolved sulfur (Si), solid sulfur (Ss), metal ions (Mn+), and metallic metals (M°), across all states of charge or discharge in rechargeable metal-sulfur batteries (e.g., Li, Na, Mg) and other sulfur-based energy storage systems. This method addresses the challenge of identifying and quantifying these species, which is critical for understanding sulfur-metal interactions, such as solid-electrolyte interface (SEI) formation andsulfur dissolution, which contribute to capacity loss and low Coulombic efficiency in these systems. While sulfur is used in the examples herein, the methods are not limited to sulfur.Rather, any S-like chalcogen can be used in the battery. For example, other heavier group 16 elements that undergo similar redox behavior can be present in the battery and are each equally evaluatable by the methods and software disclosed herein, including but not limited to Selenium (Se), Tellurium (Te), S / Se hybrid cathodes, and thio- / seleno-organics.

[0021] In some implementations, HUGS may be particularly useful in analyzing metal-sulfur batteries, wherein metal anodes tend to form solid-electrolyte interface layers containing Mn+ions during cycling. The cycling process also results in metal polysulfides (M2 / nSx), which degrade performance. By enabling the differentiation and quantification of sulfur species and metal ions, HUGS may provide insights into the failure mechanisms that hinder the development of high-energy-density rechargeable batteries for next-generation electric vehicles.

[0022] For example, there may be provide an application (referred to as the “the Dr. HUGS software”) to automate data processing from High Performance Liquid Chromatography-Ultraviolet (HPLC-UV), TGC, and battery cycling systems. The Dr. HUGS software application may integrate, normalize, and visualize raw data, and generate key figures, such as chromatograms, peak fits, and diagnostic indicators. The software application may also calculate mass and vector outputs, providing detailed insights into the battery’s failure mechanisms and overall performance. By automating these complex tasks, Dr. HUGS software may enhance the accuracy of the HUGS method, accelerating the development of advanced battery technologies.

[0023] In some embodiments, the HUGS method is unique in its ability to quantitatively measure polysulfides (Sx2‘), dissolved sulfur (S(i)), solid sulfur (S(s)), metal ions (M11), and metallic metals (M°) with precision down to 0.03 mg. By combining these measurements, the method may for example allows for a detailed analysis of the contributions and losses in electrode capacity during each cycle of a Metal-S battery. This may provide insights into the factors limiting battery performance and may help identify strategies for improving the efficiency of Metal-S batteries.

[0024] The sequential characterization of these sulfur and metal species in the specific order achieved by, for example HUGS, including the HUGS method, represents a significant advancement over existing technologies, such as normal HPLC and UV-Vis, which can only show the qualitative results of the poly sulfides. Furthermore, the Dr. HUGS software tool automates the data processing for HPLC-UV, GC, and electrochemical data, generating comprehensive reports and figures in seconds. This may further reduce the time required for data analysis, which typically takes hours with current methods, thereby improving efficiency and accuracy in battery research and development.

[0025] In Metal-Sulfur batteries, sulfur tends to be highly reactive with a metal anode and forms polysulfide species, which may tend to be soluble or non-soluble in the electrolyte.Meanwhile, along with the metal anode getting consumed or corroded, SEI tends to form on the metal-electrolyte interface. The Coulomb of charge entering the battery can be associated with deposited reversible M°, non-reactive dead or inactive M°, Mn+in form of poly sulfides (M2 / nSx) and Mn+lost as SEI. SEI consists of organic and inorganic compounds such as, metal (per)oxides, metal halide, metal carbonate, which do not react with any protic solvent (water or ethanol) to generate EE gas.

[0026] In all embodiments, the metal of the metal-S-like chalcogen batteries, i.e., Mn+, may be selected from the group consisting of Li+, Na+, K+, Rb+, Cs+, Ca2+, Mg2+, Zn2+, and combinations thereof. The batteries can include a variety of electrolytes therein, such as ether-based electrolytes, ether-carbonate mixed electrolytes, ionic liquid-based electrolytes, solid and gel polymer electrolytes, and functional additive-containing electrolytes. Some nonlimiting examples of ether-based electrolytes include solvent based electrolytes such as 1,2-dimethoxy ethane (DME), diethylene glycol dimethyl ether (DEGDME), tetraethylene glycol dimethyl ether (TEGME), and 1,3-dioxolane (DOL); lithium salts, such as lithium bis(trifluoromethanesulfonyl)imide (LiTFSI; IM is common), lithium bis(fluorosulfonyl)imide (LiFSI), and lithium tritiate (LiCFsSCh); and additive, such as lithium nitrate (LiNCh). Some nonlimiting examples of ether-carbonate mixed electrolytes include ethylene carbonate, Diethyl carbonate (DEC), propylene carbonate. Some nonlimiting examples of ionic liquid-based electrolytes include pyrrolidinium salts, such as N-propyl-N-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide (PYR13TFSI), 1 -butyl- 1-methylpyrrolidinium bis (trifluoromethylsulfonyl)imide (PYRMTFSI); and imidazolium salts, such as l-ethyl-3-methylimidazolium Bis(trifluoromethanesulfonyl)imide (EMIMTFSI). Some nonlimiting examples of solid and gel polymer electrolytes include polyethylene oxide-based (PEO-LiTFSI) systems; poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) with DOL / DME uptake; sulfide-based solid electrolytes such as LiwGeP2Si2 (LGPS), and LiePSsCl; and oxide based electrolytes such as LivLasZ O (garnet-type LLZO). Some nonlimiting examples of functional additive-containing electrolytes include redox mediators, such as Lil, Na2Se, organoselenides; polysulfide trapping agents such as metal salts, and nitrogen-containing organics; SEI-forming agents such as fluoroethylene carbonate (FEC), and 1,3,2-dioxathiolane-2,2-dioxide (DTD).

[0027] FIG. 1 depicts a schematic example of the HUGS method, which may be applied to analyze metal-sulfur energy storage devices across various configurations and chemistries. As shown in FIG. 1, the method may involve sequential sample preparation and analysis proceduresthat enable comprehensive characterization of sulfur species distribution. The sample preparation process may begin with disassembly of the energy storage device under controlled atmospheric conditions to prevent oxidation or contamination of reactive species.

[0028] Sample A preparation may involve washing the metal anode 102 with a first organic solvent to remove polysulfide species and form a polysulfide-free metal anode. The washing process may utilize methyltrifluoromethanesulfonate in dimethoxy ethane (MeOTf-DME) as the first organic solvent. The poly sulfide-free metal anode may then be subjected to titration gas chromatography (TGC) analysis to quantify the amount of metallic metal (M0°) present in the anode structure.

[0029] Sample B preparation may involve soaking the sulfur cathode 104 and remainder components of the energy storage device in the organic solvent used for anode washing. The remainder components may include one or more of a separator 106, a case, a spring, and a spacer. This soaking process may form a solution comprising methylated polysulfides and any soluble sulfur (Ss). The cathode material may then be removed from the solution to isolate the dissolved species for subsequent analysis.

[0030] Sample C preparation may involve dissolving any remaining cathode material, which may comprise solid sulfur, in a second organic solvent to form a sulfur-containing solution. The second organic solvent may be dimethoxyethane (DME), which may effectively dissolve solid sulfur species while maintaining chemical stability of the dissolved compounds.

[0031] The analytical procedures may utilize high performance liquid chromatography with ultraviolet detection (HPLC-UV) to quantify specific sulfur species. Sample B analysis may enable quantification of methylated poly sulfides (MnSx) and soluble sulfur (S(i)) present in the electrolyte and on component surfaces. Sample C analysis may enable quantification of solid sulfur (S(s)) that remained bound to the cathode structure after cycling.

[0032] The method may further involve calculating three diagnostic vectors that characterize different capacity loss mechanisms. The a vector may represent accumulated capacity loss due to formation of metal poly sulfides (MnSx), where n may range from 1 to 3 and x may range from 3 to 8. The P vector may represent the difference between total metallic metal loss and the a vector, accounting for capacity losses from sulfide-containing solid electrolyte interphase formation. The y vector may represent the difference between battery charge capacity and stored metallic metal capacity, reflecting lithium inventory changes and discrepancies in charge-discharge behavior.

[0033] In some implementations, the HUGS method involves three samples that are derived from Metal-Sulfur batteries. Here, consider a Li anode and CS cathode-based Li-S battery as an example to understand the samples preparation as seen in FIG. 1 :1. Sample A: Metal anode is first washed with a MeOTf-DME solution to removepolysulfides, resulting in a polysulfide-free metal anode. The anode is then submerged in ethanol to obtain Sample A.2. Sample B: The MeOTf-DME solution used to wash the Li anode is subsequently used to soak the remaining parts of the coin cells, including the cathode, separator, cases, spring, and spacers. This solution, containing all the methylated polysulfides (Sx2‘, 3<x<8) and soluble Ss (S(i)), is defined as Sample B.3. Sample C: An excessive amount of DME is then applied to dissolve all residual Ss (S(s)) left in the cathode with mechanical shear force. This solution is defined as Sample C.

[0034] After obtaining the three samples, gas chromatography (GC) is performed on Sample A to quantify the amount of Li° in the anode by measuring the EL generated from the reaction of Li metal with ethanol. HPLC-UV is performed on Sample B to determine the concentrations of Li2Sxand S(l). Finally, HPLC-UV is performed on Sample C to quantify the amount of S(s) in the cathode. Based on the amounts of Li°, Li2Sx, S(i), and S(s), the capacity loss / retention in Li-S batteries is quantified.

[0035] Based on the HUGS method, a series of results and vectors are generated. With reference to FIG. 2, the capacity analysis framework may provide quantitative assessment of theoretical capacity distribution. The stored metallic metal capacity may be determined through TGC analysis of Sample A, while the sulfur capacity distribution may be determined through HPLC-UV analysis of Samples B and C. The charge capacity may be measured through electrochemical testing protocols applied to the energy storage device during operation. As shown in FIG. 2, an example of information output from the computer-implemented HUGS method, Sample A (TGC) represents the Li° inventory, while Samples B and C (HPLC-UV) show the amounts of Li2Sx, S(i), and S(s) species. These species' amounts / concentrations can be converted into stored / lost capacity in the Li-S battery and can be output from the computer-implemented HIGS method as a capacity storage plot as seen in FIG. 3. FIG. 3 shows the capacity retention analysis in pie chart format, illustrating the relative contributions of different sulfur species to overall capacity retention. The pie chart segments may represent solid sulfur (S(s)), soluble species including polysulfides and dissolved sulfur (S(i)), lithium sulfide and lithium disulfide (for example, Li2S, Li2S2), and solid electrolyte interphase (SEI) components. The relative sizes of these segments may indicate the dominant mechanisms affecting capacity retention in the specific energy storage device configuration. By comparing these values with those obtained from the HUGS method, charged capacity, and theoretical capacity; three vectors are defined and presented as a HUGS vector plot, an example thereof being presented in FIG. 4. The three vectors (a, 0, y) and are as follows:a Accumulated capacity loss due to the contribution from Li2Sx(3<x<8) 0: The difference between total Li loss and accumulated capacity loss due to the contribution from Li2Sx(3<x<8)y: The difference between battery charge capacity and stored Li capacity, i.e., a measure of lithium inventory changed.

[0036] A high concentration of Li2Sxin the electrolyte can result in a large a value, indicating incomplete sulfur conversion and the shuttle effect. Since the total amount of 0 + y represents the Li inventory loss, after excluding the value of 0 (capacity loss due to Li2Sx), y indicates the capacity loss from the non-sulfide solid-electrolyte interface (SEI), Li2S, and Li2S2. The y vector represents the Li° inventory behavior.

[0037] FIG. 5 depicts an example of three capacity loss mechanism scenarios in Li-S battery derived from the computer-implemented HUGS method. By comparing the differences among these vectors, three typical cases are demonstrated in FIG. 5:Case I: y > 0: In this situation, a portion of Li metal does not contribute to electrochemical capacity. The formation of SEI isolates several Li particles, resulting in Li deactivation (sometimes termed ‘inactive Li’ or ‘dead Li’). The formation of inactive Li dominates the battery's capacity loss.Case II: y ~ 0: In this situation, Li2S / Li2S2 is the dominant capacity loss species in the charged state, as both Li and sulfur inventory can be completely lost without capacity contribution. In this case, the shuttling effect dominates the capacity loss.Case III: y < 0: When 0 » a, y 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.

[0038] HUGS (including the Dr. HUGS software and the computer implemented method carried out thereby) may be used to create a detailed report and figures for a metal sulfur battery that includes the noted values and cases describing modes of failure of battery and quantitative assessment of inventories. FIG. 6 depicts an example implementation of the Dr. HUGS software for automated data processing of HUGS characterizations including a workflow of the automated data analysis using for example the Dr. HUGS software. The automated analysis system shown in Figure 6 may integrate multiple data sources including electrochemical (ECHEM), gas chromatography (GC-A), and high performance liquid chromatography (HPLC-E, HPLC-C) data files. The Dr. HUGS software platform may process these raw data files automatically to generate failure analysis results. The software may correlate analytical measurements with the diagnostic scenarios to identify performance constraints and degradation mechanisms.

[0039] Comparisons for cross-validation were performed (Hugs data output vs. manual calculations for the capacity of various species and Li mass quantified by TGC) to validate the outputs and show the accuracy and reliability of thereof. The capacity measurements for various polysulfide species (Li2Ss, Li2S4, Li2Ss, Li2Se, Li2S?, Li2Ss) and sulfur species (Ss(L), Ss(s)) may show excellent agreement between manual and automated approaches. The lithium mass quantification through TGC may also show consistent results between the two methods, validating the reliability of the automated software platform. Based on these cases, the experimental results can be analyzed using the Dr. HUGS software for automated data processing. As shown in FIG. 6 for example, by directly inputting raw data files from battery cycling, HPLC-UV, and GC, the HUGS results are automatically generated in the format of FIG.3 and 4. The results produced by Dr. HUGS had a difference of less than 2 mAh g1for both sulfur / sulfide and lithium species as compared to the manual evaluation. This demonstrates that Dr. HUGS is a reliable tool for analyzing HUGS data. Moreover, manual processing takes over an hour whereas the Dr. HUGS computer implemented method completes the analysis in about one to two minutes, and under one minute depending on the amount of data used for the inputs.

[0040] FIGS. 8-10 relate to HUGS analysis of Li-S Batteries with sulfur cathodes at different cycling regions. The cycling performance analysis shown in FIG. 8 may illustrate capacity and coulombic efficiency evolution across different cycling regions. Region IA may correspond to initial formation cycles, Region IB may correspond to early capacity decay, Region II may correspond to stable cycling, and Region III may correspond to end-of-life conditions. The charge and discharge capacity curves may show distinct behavior patterns in each region, enabling correlation with HUGS analytical results. More specifically, FIG. 8 depicts the definition of cycling regions in a Li-S battery (sulfur loading: 3.6 mg cm2; Electrolyte: Baseline Electrolyte (IM Lithium bis(trifluoromethane)sulfonimide (LiTFSI) in 1,3-dioxolane (DOL): DME (1:1 v / v) + 2 wt. % LiNCh), Rate: 24 hours resting, 2 cycles at 0.05C, followed by 0.1C).

[0041] FIG. 9 may present the comprehensive HUGS analysis results across cycling regions, showing both capacity loss vectors and capacity retention pie charts. The a, P, and y vectors may evolve differently across cycling regions, indicating changes in the dominant degradation mechanisms. The pie charts may show corresponding changes in the distribution of sulfur species, with solid sulfur, soluble species, lithium sulfides, and SEI components varying in relative abundance. More specifically, FIG. 9 shows a HUGS vector plots and capacity storage plots for each cycling region, the results are generated by the Dr. HUGS software.

[0042] The mechanistic interpretation shown in Figure 10 may illustrate the evolution of anode and cathode interfaces across cycling regions. Region 0 may show self-discharge effects with polysulfide dissolution from the carbon host. Region IA may show incomplete sulfur utilizationand formation of inactive lithium. Region IB may show activation of previously inactive species. Region II may show anode passivation due to sulfide-rich SEI formation. Region III may show thick sulfide-dominated SEI formation leading to capacity fade.

[0043] In the example used to generate FIG. 10, the cycling behavior was of Li-S batteries with CS cathodes. Li-S batteries were taken as for example a case study. The batteries comprising a CS cathode, a ‘baseline electrolyte’ (IM Lithium bi s(trifluorom ethane) sulfonimide (LiTFSI) in I,3-dioxolane (DOL): DME (1:1 v / v) + 2 wt. %LiNC>3), and Lithium metal. The electrolyte to sulfur (E / S) ratio is 10 ul mg'1. As demonstrated in FIG. 8 for example, 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). As shown in FIG. 9 for example, HUGS analysis was conducted on these batteries to elucidate further the dominant capacity failure factors. All the results are generated by the Dr. HUGS software, which reduces the time required for the analysis procedure. The HUGS results showed that in Regions IA and IB, the behavior aligns with Case I in FIG. 5, while Regions 2 and 3 correspond to Case II mainly. This indicates that inactive lithium formation and inefficient sulfur and polysulfide conversion primarily impacts early cycles in a liquid sulfur-redox system. A decreasing y vector and increasing 0 vector from Region 1 A to IB suggest ‘reactivation’ of inactive lithium and Sulfur, possibly converting into polysulfides. In contrast, anode passivation due to sulfide-rich SEI formation / growth dominates the stable cycling in Region II. At Region III, end-of-life, due to the LiNCh depleting, thick sulfides SEI growth terminates the battery cycling. Based on the HUGS analysis, a proposed working mechanism is demonstrated in FIG. 10.

[0044] Additional testing via Raman spectroscopy of the cathode, cryo-SEMEDX mapping, and SEM and XPS analyses of the anode as ex situ characterization at the fully charged state for the Li-S coin cell comprising a CS cathode discussed above provide various qualitative insights. Raman spectroscopy of the cathode showed declining S-S bond intensities, particularly from region 0 to IA, suggesting much of the adsorbed sulfur remains inactive early in cycling. Cryo-SEM-EDX mapping revealed changes in the S : C and O : C ratios, indicating sulfur redistribution and the formation of an oxygen-rich cathode interface. These changes highlight the growth of cathode-electrolyte interface (CEI) layers and the shuttle effect, as soluble polysulfides react to form inactive species like Li2S andLi2SOx. On the anode, SEM and XPS analyses showed the progression of SEI. Cross-sectional SEM images reveal moderate SEI growth from Region 0 to II, followed by a sharp increase in thickness in Region III, attributed to LiNOi depletion. XPS spectra confirm the presence of Li2S and other sulfides in the SEI, which isolate Li° and hinder reactivity. Li2SOxspecies, formed via LiNOs-mediated reactions, are observed on the surfacebefore milling and decrease after etching, consistent with previous studies suggesting that the higher lithium gradient in the anode bulk drives the final conversion of all sulfides into I 2S. Data for the above testing is presented in Quantitative Insights May 2025, the entirety of which is incorporated herein by reference. Overall, these results demonstrate sulfur species depletion on the cathode and significant SEI thickening on the anode due to LiNOs consumption. However, qualitative characterizations leave key capacity loss mechanisms unresolved, underscoring the need for quantitative analysis via the HUGS method.

[0045] FIGS. 11-12 related to the HUGS analysis of Li-S batteries with sulfurized polyacrylonitrile cathodes at different cycling regions. FIG. 11 may compare HUGS results between baseline electrolyte and localized high-concentration electrolyte (LDME) systems. The capacity loss vectors and retention pie charts may show different patterns between the two electrolyte systems, with LDME potentially reducing certain degradation mechanisms while maintaining similar overall capacity retention characteristics. FIG. 11 shows, for example, a vector plot and capacity storage plot for each cycling region, the results were generated by Dr. HUGS software and the respective computer-implemented method. Two electrolytes: Base, and LDME (2 M Lithium bis(fluorosulfonyl)imide (LiFSI) in 1,2-Dimethoxy ethane / Bis(2,2,2-trifluoroethyl) ether (DME / BTFE) (1:4 by weight)) are applied separately. The SPAN cathode analysis shown in Figure 12 may illustrate different degradation mechanisms compared to carbon-stabilized sulfur systems. Region 0 may show redistribution of non-covalently bonded sulfur species. Region I may show irreversible structural conversion in the cathode with nonsulfide dominated SEI formation. Regions II and III may show progressive non-sulfide SEI growth and lithium pulverization as the dominant capacity loss mechanisms. In addition to Li-S batteries with physically adsorbed elemental sulfur cathodes, HUGS can also be applied to other types of sulfur cathodes, such as those with covalently bonded sulfur. As shown in FIGS. 11-12, coin cells with sulfurized polyacrylonitrile (SPAN) and different electrolytes (baseline or localized high-concentration electrolyte, LDME) were analyzed using HUGS. The resulting HUGS plots are presented in FIG. 11, and the proposed working mechanisms in FIG. 12. Both SPAN Li-S batteries with baseline and LDME electrolytes exhibit different cycling behaviors compared to the sulfur cathodes shown in FIG. 8. These devices follow the cycling behavior of Case III in FIG. 5, where lithium pulverization and non-sulfide SEI growth are identified as the primary limitations. Although LDME helps mitigate these issues, they persist in the Li-S batteries with SPAN cathode.

[0046] FIGS. 13-16 relate to a pouch cell assembly, setups, and HUGS analysis of Li-S batteries with CS cathodes at different positions in the pouch cell. FIG. 13 depicts an example schematic of the dimensions of the pouch cell components and corresponding stacking sequence.FIG. 14 depicts an example schematic of the sequence of punched portions from the cathode and anode with respect to the tab as A, B and C to perform the HUGS characterization. FIG. 15 depicts an example schematic of fixed pressure (30 psi) setup and fixed gap (initial 30 psi pressure) setup. FIG. 16 depicts examples of HUGS vector plot and HUGS capacity storage plots for a pouch cell with fixed pressure (Left) and fixed gap (Right) at A, B and C positions (the results are generated by Dr. HUGS software, for example).

[0047] In addition to applying HUGS analysis to coin cells, we extended the methodology to pouch cells to examine the impact of different pouch cell setups. The dimensions of the pouch cells are shown in FIG 13, while FIG. 14 highlights three regions selected for HUGS analysis on the cathode / anode: A (near the current collector tab), B (center), and C (comer away from the tab). The testing setups are illustrated in FIG. 15, more specifically a fix or constant pressure setup and a fixed or constant gap setup, and the corresponding HUGS plots are presented in FIG.16. In both configurations shown in FIG. 15, an initial pressure of 30 psi was applied. Based on the HUGS analysis, all cases fall under Case I behavior; however, the constant pressure setup exhibits significantly reduced spatial variation in the HUGS results. Spatial homogeneity in pouch cells is typically correlated with cycling stability, and the HUGS results clearly demonstrate how to quantitatively assess spatial uniformity in pouch cells.

[0048] With reference to FIG. 16, 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. 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.

[0049] 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 reflected in the differences in the P and y vectors, aligning with our previous findings. 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. While constant pressure has a limited influence on the uniform distribution of sulfur across the x-y plane, it enhances the homogeneity of lithiumdeposition, 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.

[0050] 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. The relatively uniform sulfur distribution observed in FIG. 16 likely arises from sampling methodology differences: while solid-phase components (e.g., lithium residues) are extracted from spatially defined regions, dissolved sulfur species are recovered from the bulk electrolyte, representing an averaged chemical environment rather than fixed x-y positions. Nevertheless, in multilayer pouch cells, where each layer can be isolated, the current HUGS platform remains compatible with layer-resolved quantification of both solid and dissolved sulfur species.

[0051] Herein there is provided an example of a functional working prototype. For HUGS. Moreover, the HUGS method has been developed, validated, and applied to various configurations of Li-S batteries, including coin cells and pouch cells. Experimental results have been generated using HPLC-UV, GC, and battery cycling data, and the Dr. HUGS software application has been developed to automate data analysis. Cross-validation with manual calculations has confirmed the accuracy of the software, with less than a 2 mAh g1difference. The method has also been successfully used to analyze both elemental sulfur, and sulfurized polyacrylonitrile (SPAN) batteries and to assess spatial homogeneity in pouch cells. Thus, the invention is fully operational and ready for further testing and optimization.

[0052] Li-S batteries are among the most promising candidates to achieve energy densities of 500 Wh kg1for vehicle and aviation applications. Since the amount of polysulfides is a crucial parameter for evaluating Li-S battery performance, previously difficult to quantify before this invention, the HUGS computer-implemented method will be valuable to research groups in universities, R&D labs, and industrial companies working on Li-S batteries. It provides a reliable way to detect and quantify polysulfides, a key metric for assessing battery performance, and can be applied to various studies involving electrolytes, separators, current collectors, pressure, temperature, and current density.

[0053] The HUGS method may be used to evaluate metal-sulfur batteries, and the HUGS computer-implemented software and method may reduce the time required for complex data analysis, making the process faster and more efficient for all users. It can be utilized for failureanalysis and performance evaluation in various battery formats, including coin cells and pouch cells.

[0054] The MeOTf noted above may be used with a variety of metal ions, such as Na+, K+, Ca2+, etc. The HUGS method may also be used for a metal sulfur battery (e.g., not before battery is made to simply check the polysulfide amounts in any given solution within quantifiable range(s)). The HUGS method may be used as a battery performance indicator. With respect to MeOTf / EtOTf / Propyl Tritiate, any Tritiate anion-based molecule (Methyl Tritiate, Ethyl Tritiate, Benzyl Tritiate) may be useful for the derivatization process. With respect to DME / DOL, any aprotic organic solvent compatible with Li metal. Moreover, different aprotic organic solvent(s) may be used other than DME (Dioxolane, Toluene, etc.) which has higher solubility for Sulfur and no reactivity with Li metal.EXAMPLE 1 : HUGS method sample information

[0055] For a CS battery, the Li anode chip was washed with 2 mL DME + 25 pL MeOTf. The Li chip is then taken to do TGC (sample A). 1 mL DME is then used to wash the Li metal chip to remove all derivatized species remaining on it. The entire solution (3.025 mL) containing derivatized species from the anode is then used to collect all cell components to prepare sample B. Sulfur cathode is then washed with 10 mL DME using magnetic stirring for 15 minutes to prepare sample C.Sample A: Li metal chip titrated in 250 mL Erlenmeyer flasks with 2 mL ethanol.Sample B: 575 pL solution from 3.025 mL of derivatized species + 275 pL DME + 25 pL benzene (internal standard).Sample C: 575 pL solution from 10 mL of DME solution containing residual sulfur (S(s)) + 275 DME + 25 pL benzene.

[0056] For the CS pouch cell, the Li foil was washed with 30 mL DME + 250 pL MeOTf. The punched portion of Li was then taken to do TGC (sample A). The entire solution (30.25 mL) containing derivatized species from the anode is then used to collect all cell components to prepare sample B. Punched sulfur cathode is then washed with 10 mL DME using magnetic stirring for 15 minutes to prepare sample C.

[0057] For the SPAN battery, except for 2 mL DME + 25 pL MeOTf solution), we used 1 mL DME + 25 pL MeOTf as the concentration of polysulfide was lower. So, the overall dilution was reduced by preparing only 2.025 mL of the solution for sample B. No sample C was prepared for SPAN; soluble species (including S(s)) were very small.

[0058] Sample B and C, 600 pL each, had fixed concentrations of benzene, which was used as the internal standard in both calibration curves and the samples to remove instrumental errors. EXAMPLE 2: Na-SPAN battery

[0059] The HUGS computer-implemented method was demonstrated for a sodium-SPAN battery. ANa-SPAN battery was made using Na metal as the anode and Sulfurized polyacrylonitrile (SPAN) cathode with 100 pL of 2M NaFSI in DME:BTFE (1 :4 ratio by weight). A CELGARD brand separator (on the anode side) and a glass fiber separator were used. For HPLC sample preparation, dissembled cells were reacted with 25 pL of methyl tritiate in 2 mL of DME solution turning sodium poly sulfides into the methylated species.

[0060] The HUGS method was demonstrated for other metal-sulfur batteries (Na, K, etc.) using the case of Na-SPAN battery, See FIGS. 17 and 18. Previous Na-SPAN battery research work show signs of the presence of sodium polysulfides. These poly sulfides are shown to be present in the batteries by a methylation step (FIG. 19). The amounts of these species were low as expected for SPAN batteries (FIG. 20). TGC has also been demonstrated previously to work for sodium metal batteries, which can be combined to have a complete HUGS framework for any metal-sulfur batteries.

[0061] Turning now to FIG. 21, the computing system 500 can include a user interface 402, a processor 510, a memory 520, including a nontransitory memory, a storage device 530, and input / output device 540. The processor 510, the memory 520, the storage device 530, and the input / output device 540 can be interconnected via a system bus 550. The processor 510 is capable of processing instructions (and is configured to implement aspects of the HUGS method of analysis and other aspects disclosed herein) for execution within the computing system 500. Such executed instructions can implement one or more components of, for example, the database execution engine. In some implementations of the current subject matter, the processor 510 can be a single-threaded processor. Alternately, the processor 510 can be a multi -threaded processor. The processor 510 is capable of processing instructions stored in the memory 520, on the storage device 530, from a server, the cloud, the Internet of Things, or other connected electronic storage or computing devices. The computing system 500 is configured to display graphical information for and from a user interface 502 and / or a display 504 provided via the input / output device 540. Inputs entered at the user interface 502 and outputs 542 from the input / output device 540 are displayable upon display 504. One example output 504 is shown in FIG. 21, a HUGS vector and capacity storage plot 544.

[0062] The memory 520 is a computer readable medium, such as volatile or non-volatile computer readable medium, that stores information within the computing system 500. The memory 520 can store data structures representing configuration object databases, for example. The storage device 530 is configured to provide persistent storage for the computing system 500. The storage device 530 can be a floppy disk device, a hard disk device, an optical disk device, or a tape device, or other suitable persistent storage means. The input / output device 540 providesinput / output operations for the computing system 500. In some implementations of the current subject matter, the input / output device 540 includes a keyboard and / or pointing device. In various implementations, the input / output device 540 includes the display 504 unit for displaying graphical user interfaces. According to some implementations of the current subject matter, the input / output device 540 can provide input / output operations for a network device. For example, the input / output device 540 can include Ethernet ports or other networking ports to communicate with one or more wired and / or wireless networks (e.g., a local area network (LAN), a wide area network (WAN), or the Internet). In some implementations of the current subject matter, the computing system 500 can be used to execute various interactive computer software applications that can be used for organization, analysis and / or storage of data in various formats.Alternatively, the computing system 500 can be used to execute any type of software applications. These applications can be used to perform various functionalities, e.g., planning functionalities, computing functionalities, communications functionalities, etc. The applications can include various add-in functionalities or can be standalone computing products and / or functionalities. Upon activation within the applications, the functionalities can be used to generate the user interface provided via the input / output device 540. The user interface can be generated and presented to a user by the computing system 500 (e.g., on a computer screen monitor, etc.).

[0063] The computing system 500 includes TGC calibration curve(s), polysulfides calibration curve(s) or polychalcogenide curve(s), Me2Ss or Mexchalcogenycalibration curves stored in the memory therein. The TGC calibration curves can be prepared by the following method demonstrated with Li, but equally applicable for any metal used in a metal-chalcogen battery. Five pieces of Li metal chips weighing 0.28 mg, 1.59 mg, 3.73 mg, 4.96 mg, and 8.61 mg were taken in 250 mL Erlenmeyer flasks and sealed within an Argon environment with a rubber stopper. Later, they were titrated with ethanol to form hydrogen gas. The intensity of the hydrogen gas peak observed in GC was correlated with the Li masses to get the calibration curve.

[0064] The polysulfide calibration curve can be prepared by the following method demonstrated with lithium sulfide, but equally application for any metal-chalcogenide system. A polysulfide mixture was made by targeting the stoichiometry of 16 mM Li2S6by dissolving 12.8 mg sulfur and 3.676 mg Li2S in 5 mL of DME. The chemical reaction is as follows: Li2S + (5 / 8) S8= Li2S6. Five different concentrations of poly sulfide mixtures were prepared by derivatizing 25 pL, 50 pL, 100 pL, 200 pL, and 250 pL of Li2S6with 25 pL of methyl tritiate (MeOTf). 25 pL of benzene was added as an internal standard in all HPLC samples, including these standard solutions. Then, these solutions were diluted by adding DME to prepare 600 pL HPLC samples. Me2Sx(4 < x < 8) species were then separated by semi-preparative HPLC-UV to prepare thefractions. The peak positions were confirmed using HPLC-APCI-MS. These samples were digested. Inductively coupled plasma-mass spectroscopy (ICP-MS) - O2reaction mode can quantify sulfur as [SO] in each sample. Based on Beer-Lambert's Law, the concentration of poly sulfides (or sulfur) should be proportional to the area of the poly sulfide peak in the HPLC-UV chromatogram. A linear relation with A2> 0.96 was achieved for all. The detection limits for each species are up to 40 ppm. The precision and accuracy were validated by taking a known sample of sulfur in DME, repeating it four times with HPLC-UV, and quantifying sulfur using the calibration curve, with an error <5%.

[0065] The Me2S3calibration curve can be prepared by the following method, but is equally applicable to other chalcogens. 1 pL of Me2S3was transferred to 5 mL of DME through Hamilton syringe to make 1.904 mM of Me2S3, which was further diluted into one mM, 0.5 mM, 0.1 mM, and 0.01 mM. Chromatograms and calibration curves were prepared.

[0066] The data for each of the three calibration curves were input into the computing system for storage in the memory and usage in the battery failure analysis. For the HPLC-UV, a Thermo Scientific Vanquish quaternary pump F (VF-P20-A) coupled with a Thermo Scientific Split Sampler FT (VF-A10-A) autosampler were utilized to deliver a mobile phase through a ZORBAX Extend-C18 Column (from Agilent, 80 A, 4.6 x 50 mm, 5 pm) at a flow rate of 0.70 mL min '. The sample injection volume was 5 pL. A binary gradient mobile phase was employed with the following gradient profile: 0 min, 25% methanol (75% water); 20 min, 100% methanol; 25 min, 100% methanol; 26 min, 25% methanol; 30 min, 25% methanol. UV absorbance data was collected at 210 nm (LiNO3) and 230 nm (polysulfides and sulfur) wavelengths and analyzed using the Thermo Scientific Chromeleon Chromatography Data System Software. APCLMS data was recorded for m / z = 50 to 600 with Orbitrap Elite and analyzed using Xcalibur. For semi-preparative high-performance liquid chromatography -ultra violet spectroscopy (semi-preparative HPLC-UV), a Thermo Scientific Vanquish quaternary pump F (VF-P20-A) coupled with a Thermo Scientific Split Sampler FT (VF-A10-A) autosampler was utilized to deliver a mobile phase through a Luna Cl 8(2) (from Phenomenex, 100 A, 10 x 250 mm, 10 pm) at a flow rate of 5 mL min '. The sample injection volume was 100 pL. A binary gradient mobile phase was employed with the following gradient profile: 0 min, 25% methanol (75% water); 20 min, 100% methanol; 25 min, 100% methanol; 26 min, 25% methanol; 30 min, 25% methanol. UV absorbance data was collected at 230 nm (polysulfides and sulfur) wavelength and analyzed using the Thermo Scientific Chromeleon Chromatography Data System Software.

[0067] Samples used to prepare calibration curves for derivatized polysulfide species were prepared by collecting fractions (around 2 mL) and digesting them individually at 180 °C for 3hours in a highly acidic 4 mL HNO3 + H2O2 (1 [thin space (l / 6-em)]:[thin space (l / 6-em)]l v / v) solution. An open system was considered for the removal of methanol. Inductively coupled mass spectroscopy (ICP) matrix solution (1% nitric acid) was added continuously to compensate for losses due to evaporation. 200 pL scandium (200 ppb) was used as the internal standard in 10 mL ICP samples. O2 or oxygen gas reaction mode was used to convert S into [SO] molecule, and its intensities were used for ICP analysis.

[0068] One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs, field programmable gate arrays (FPGAs) computer hardware, firmware, software, and / or combinations thereof.These various aspects or features can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0069] Referring now to FIG. 7, which is a basic representation of a user interface for the computing system 500 in one exemplary screen display. The computer implemented method of high-performance liquid chromatography-ultraviolet spectroscopy and gas chromatography sequential characterization (HUGS) includes the methods described above to collect TGC and HPLC-UV data for various aspects of a solid-state battery, inputting such information related to the battery into the computing system 500, submitting the data, and reviewing, saving, and / or printing the outputs. Inputting the information includes inputting basic information related to the battery being evaluated, such as (i) the cell identification number, cycle number, disassembly at “charge” or “discharge,” S in cathode (mg), Pristine metal mass (mg) (including clicking the “upload electrochemical data” button after filling in the respective boxes); (ii) inputting the TGC experiment information, such as flask volume (mL), the number of pieces of Metal Chip, and uploading TGC files; (iii) inputting the HPLC-UV experimental information including derivatization volume (mL), the amount of polychalcogenide solution (pL), and the amount of chalcogen solution (pL), and uploading the corresponding HPLC-UV files. Once all the above inputs have been entered and uploaded, the user clicks “submit” and the software processes the inputs to generate one or more outputs.

[0070] In one example, a cell id of QI 27 was entered along with 1 for the cycle number, chargefor “disassembly at”, 3.90 mg for the sulfur in the cathode, and 20.85 mg for the pristine lithium mass. Next, the TGC flask volume of 125 ml was entered along with 3 as the number of pieces of lithium chips. At least three TGC files were uploaded, once for each of the lithium chips. Once the above data was confirmed by the user, the HPLC-UV data was entered. The derivatization volume was 3.035 mL, the amount of polysulfide solution was 575 microliters and the amount of sulfur solution was 575 microliters. Next, at least one HPLC-UV chromatogram was uploaded for the soluble species and one for the dissolved solid sulfur. Then, the user clicked submit.

[0071] Thereafter, the HUGS computer-implement method generated a plurality of outputs, such as Theoretical Capacity (alpha, bet, gamma vectors), storage plots, other graphs including the raw TGC or HPLC-UV data, fitted TGC or HPLC-UV data, and the fitting parameters, including on a peak-by-peak basis, and an overall system report. The user can toggle between these various outputs and save the same individually or collectively to their computer, cloud, or other storage or computing device, etc. as various file types including but not limited to png files, pdf files, and excel files.

[0072] These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and / or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example, as would a processor cache or other random-access memory associated with one or more physical processor cores.

[0073] To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer.Other kinds of devices can be used to provide interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including acoustic, speech, or tactile input. Other possible input devices include touch screens or other touch-sensitive devices such as single or multi-point resistive or capacitive track pads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like.

[0074] The HUGS computer implemented method for battery failure analysis is advantageous because it efficiently handles multiple raw data files, completing complex analyses in just minutes. The below observations are based on the experimental data, which is believed to be equally applicable to other S-like chalcogens. In the carbon-stabilized sulfur (CS) system, where sulfur is physically adsorbed, we observed that the dominant factors contributing to capacity fade evolved with cycling. Initially, self-discharge played a major role, gradually transitioning to inactive lithium formation and, eventually, to the accumulation of I 2S on the anode. The HUGS computer implemented method further revealed how sulfur species distribution in CS-based pouch cells is influenced by different testing configurations, highlighting the importance of setup parameters on the behavior of physically adsorbed sulfur. In contrast, the sulfurized polyacrylonitrile (SPAN) system, where sulfur is covalently bonded, exhibited distinct degradation mechanisms. During the early cycling stages, SPAN cathodes demonstrated the ability to recover dissolved polysulfides and sulfur species from the electrolyte, suggesting that sulfur immobilization in SPAN is not exclusively governed by covalent bonding, as traditionally assumed. As cycling progressed, the capacity fade was primarily attributed to lithium inventory loss at the anode. Importantly, replacing conventional ether-based electrolytes with localized high-concentration electrolytes (LHCE) significantly reduced lithium inventory loss, offering a promising strategy to enhance the stability of SPAN systems and improve long-term cycling performance. The HUGS computer implemented method is unparalleled in its capability to analyze the depth and breadth of chalcogen-related degradation in metal-chalcogen batteries. By enabling precise quantification of the chalcogen species across different chalcogen chemical compositions, cell configurations, and testing conditions, HUGS provides a comprehensive platform for diagnosing failure mechanisms. This versatility paves the way for targeted solutions that address various challenges, accelerating the development of next-generation batteries with improved performance and stability

[0075] In the descriptions above and in the claims, phrases such as “at least one of’ or “one or more of’ may occur followed by a conjunctive list of elements or features. The term “and / or” may also occur in a list of two or more elements or features. Unless otherwise implicitly orexplicitly contradicted by the context in which it used such a phrase is intended to mean any of the listed elements or features individually or any of the recited elements or features in combination with any of the other recited elements or features. For example, the phrases “at least one of A and B;” “one or more of A and B;” and “A and / or B” are each intended to mean “A alone, B alone, or A and B together.” A similar interpretation is also intended for lists including three or more items. For example, the phrases “at least one of A, B, and C;” “one or more of A, B, and C;” and “A, B, and / or C” are each intended to mean “A alone, B alone, C alone, A and B together, A and C together, B and C together, or A and B and C together.” Use of the term “based on,” above and in the claims is intended to mean, “based at least in part on,” such that an unrecited feature or element is also permissible.

[0076] The subject matter described herein can be embodied in systems, apparatus, methods, and / or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and / or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and subcombinations of the disclosed features and / or combinations and subcombinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and / or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. For example, the logic flows may include different and / or additional operations than shown without departing from the scope of the present disclosure. One or more operations of the logic flows may be repeated and / or omitted without departing from the scope of the present disclosure. Other implementations may be within the scope of the following claims.

Claims

What is claimed:

1. A method of assessing an energy storage device, the method comprising:providing material for a metal-chalcogen battery including a metal anode, a chalcogen cathode, and a remainder of the metal-chalcogen battery;washing the metal anode in a solution comprising a derivatization agent in a first solvent to form a polychalcogenide free metal anode;performing gas chromatography on the polychalcogenide free metal anode, thereby quantifying the amount of M° in the metal anode;soaking the chalcogen cathode and the remainder of the metal-chalcogen battery in the organic solvent used to wash the metal anode to form a first solution comprising methylated polychalcogenide and any soluble chalcogen;removing any remaining chalcogen cathode from the first solution; anddissolving the remaining chalcogen cathode in a second solvent to form a chalcogen containing second solution;performing high performance liquid chromatography-ultraviolet on the first solution, thereby quantifying the amount of Mnchalcogenxand chalcogen®; andperforming high performance liquid chromatography-ultraviolet on the second solution, thereby quantifying the amount of chalcogen(s) present; andperforming a battery failure analysis;wherein n is 1 to 3 and x is 3<x<8.

2. The method of claim 1, wherein performing the battery failure analysis comprises calculating three vectors:a: accumulated capacity loss due to Mnchalcogenx;P: a difference between total M° loss and a; andy: a difference between a battery charge capacity and stored M° capacity.

3. The method of claim 1, wherein performing the battery failure analysis comprises calculating and displaying a charge capacity storage plot.

4. The method of claim 1, wherein M is selected from the group consisting of Li+, Na+, K+, Rb+, Cs+, Ca2+, Mg2+, Zn2+, and combinations thereof.

5. The method of claim 1, wherein the chalcogen is selected from the group consisting of sulfur (S), selenium (Se), tellurium (Te), S / Se hybrid cathodes, thio- / seleno-organics, and combinations thereof.

6. The method of claim 1, wherein the derivatization agent is methyltrifluoromethanesulfonate (MeOTf).

7. The method of claim 1, wherein the first solvent is dimethoxy ethane.

8. The method of claim 1, wherein the remainder of the metal-chalcogen battery comprises one or more of a separator, a case, a spring, and a spacer.

9. The method of claim 1, further comprising determining a capacity storage plot.

10. The method of claim 1, further comprising repeating the method for an energy storage device at each of a plurality of preselected operating cycles.

11. The method of claim 10, wherein the plurality of preselected operating cycles comprise two or more of (i) a 24 hour initial rest period, (ii) a period of two initial formation cycles (0.05C), (iii) a period of fast capacity decay cycles (0.1 C), (iv) stable cycles (0.1C), and (v) an end of life (0.1 C).

12. The method of claim 11, wherein the plurality of preselected operating cycles includes each of (i) to (v).

13. The method of claim 10, wherein a concentration of an electrolyte in the energy storage device is increased or decreased and the method is repeated.

14. The method of claim 10, wherein a secondary energy storage device is assembled with a different electrolyte and the method is repeated.

15. The method of claim 1, wherein the energy storage device is a rechargeable energy storage device.

16. The method of claim 1, wherein the energy storage device is a solid-state battery or an all solid-state battery.

17. The method of claim 16, wherein the energy storage device is a solid-state battery comprising a liquid electrolyte or a liquified gas electrolyte.

18. A computer-implemented method for battery failure analysis comprising:receiving gas chromatography and high-performance liquid chromatography-ultraviolet spectroscopy files for a metal-chalcogen battery, wherein the gas chromatography and high-performance liquid chromatography-ultraviolet spectroscopy are sequential characterizations of the metal chalcogen battery;receiving inputs of the metal-chalcogen battery parameters;generating a charge capacity vector and / or storage plot; anddetermining battery failure points or mechanisms.

19. The computer-implemented method of claim 1, wherein the gas chromatography and high-performance liquid chromatography -ultraviolet spectroscopy files are generated according to the method of claim 1.

20. The computer-implemented method of claim 18, further comprising one or more calibration curves for titration gas chromatography and HPLC-UV.