Green surfactants powering sustainable batteries: industrial-scale life cycle assessment of Tween and Span surfactants for battery systems

Abstract

Due to their outstanding ability to improve interfacial phenomena and battery performance, non-ionic Tween/Span surfactants have been widely applied in the battery field. However, although Tween/Span have been classified as more environmentally friendly surfactants compared with other surfactants, there remains a lack of quantitative research on its environmental footprint. This study presents a comprehensive life cycle assessment (LCA) of Tween/Span surfactants, utilizing primary industrial data. The results indicated that producing 1000 kg of Tween series surfactants required 16.5–17.1 MWh of energy and emitted 2922–3019 kg CO₂ eq., while the Span series surfactants required 14.5–16.4 MWh t⁻¹ and emitted 2564–2895 kg CO₂ eq. per t. Uncertainty analysis confirmed the robustness of the data. Sensitivity analysis identified surfactant price factors and oleic acid/ethylene oxide characterization factors as key parameters. Reducing the environmental impact of upstream oleic acid/ethylene oxide production helps improve the sustainability of Tween/Span. Moreover, when considering carbon uptake, bio-based raw materials significantly reduced the carbon footprint (44.8%). Crucially, our data reveal that Ecoinvent proxies overestimate the carbon footprint by 14% (Tween) and 92% (Span) and energy demand by 71% for both. A case study on Tween 80-assisted Si/C anode manufacturing demonstrates that, despite added production burdens, surfactant-driven performance enhancements reduce the overall battery life cycle impacts. This work provides validated and high-quality LCI, demonstrates the sustainability of Tween/Span surfactants for battery applications, and offers critical metrics for advancing green chemistry in sustainable energy storage.

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Green foundation

1. Our work is based on a comprehensive LCA of Tween and Span surfactants using industrial-scale raw data, demonstrating the sustainability of Tween/Span surfactants in battery applications and providing key metrics for advancing green chemistry in sustainable energy storage.

2. Our data show that Ecoinvent agents overestimate the carbon footprint by 14% (Tween) and 92% (Span), as well as 71% of energy demand. A case study shows that using Tween-assisted silicon–carbon anodes increases the carbon footprint by 42%, but the improvement in battery performance reduces the carbon footprint by 22% per cycle.

3. Our research indicates that the clean production of oleic acid and sorbitol in the future will help reduce the environmental impact of Tween/Span. Exploring the carbon uptake potential of bio-based raw materials also aids in identifying emission reduction pathways for Tween/Span.

1. Introduction

The global transition toward renewable energy and electrified transportation has intensified the demand for advanced battery systems, including lithium-ion batteries (LIBs), zinc-ion batteries (ZIBs), and others.^1–3 While these systems differ in electrochemical mechanisms, they face universal challenges linked to interfacial phenomena, including uneven charge distribution, dendritic metal growth, and electrolyte degradation, all of which limit energy density and operational longevity.^4–7 In response, surfactant-assisted interfacial engineering has emerged as a pivotal strategy owing to its unique ability to modify interfacial energetics and transport properties.^8–11 These advantages are well demonstrated by Tween and Span surfactants, whose tunable hydrophilic–lipophilic balance enables precise interfacial engineering across battery systems.^12–14 At present, Tween and Span are widely utilized in battery fabrication, management, and recycling processes.^13,15,16 In LIBs, these surfactants aid in the preparation of high-performance silicon-based anodes and lithium-based cathodes.^17,18 For ZIBs, Tween series surfactants containing both zincophilic and hydrophobic units serve as electrolyte additives, significantly enhancing the stability and reversibility of zinc cathodes.^19–21 Moreover, Tween and Span have been employed in the development and optimization of emerging battery systems, including sodium-ion batteries (SIBs), aluminum–air batteries (AABs), zinc–air batteries (ZABs), redox flow batteries (RFBs), and lithium metal batteries (LMBs).^22–26

Although Tween and Span surfactants are increasingly used in battery systems and widely applied across various stages of battery production (e.g., electrode slurry preparation, separator treatment, and electrolyte additive solutions), nearly all studies on battery environmental impact assessments have overlooked the environmental risks associated with surfactants.^27–31 This omission does not stem from rational and rigorous calculations but reflects a limitation of early-stage research, potentially leading to misjudgments in the assessment of battery sustainability.³² To our knowledge, no studies have yet investigated the environmental impact of the large-scale deployment of Tween/Span surfactants. This knowledge gap likely explains why research applying these surfactants in battery technologies has rarely evaluated their comprehensive environmental effects, despite the claims of the environmental friendliness of Tween/Span surfactants.^17,33 Currently, only a handful of studies have assessed the environmental impacts of Tween and Span during their application phases.^34,35 For instance, one existing study evaluated the environmental performance of Tween 80 when used to prepare nanofluids for oil production.³⁴ In that study, the environmental data for Tween 80 were obtained from the non-ionic surfactant production records (under the washing agent category) in the Ecoinvent database, representing a mixture of ethoxylated alcohol surfactants based on dodecanol and fatty acid-derived surfactants.^34,36 However, this oversimplified substitution was merely an expedient solution adopted due to the lack of early LCA data and cannot be applied appropriately to the mass production and development of Tween and Span products.³⁷ Another study on Span 80 application was limited to gate-to-gate processes (excluding upstream and downstream processes) and did not account for the environmental impacts of production.³⁵ Such inadequate assessment approaches may lead to unexpected ecological trade-offs in the pursuit of high-performance batteries.

To address these challenges, this study conducted a comprehensive LCA of Tween and Span surfactants utilizing primary industrial data. The investigated process chain comprised dehydration, esterification, neutralization, decolorization, addition reactions, and quality control. This work establishes the first comprehensive LCI data for Tween and Span surfactants and calculates the corresponding LCA results based on these primary data. The obtained LCA results encompass characterization outcomes for full-spectrum environmental impact categories and cumulative energy demand, along with contribution analysis and Monte Carlo simulations. These analyses collectively elucidate the comprehensive environmental profile of Tween and Span products while evaluating the effects of data uncertainty and thereby provide actionable recommendations for performance enhancement. Furthermore, sensitivity analysis was performed on critical parameters and underlying assumptions to assess their potential influence on LCA. As a practical demonstration, the application of Tween surfactants in electrode manufacturing was examined to evaluate their environmental sustainability claims critically. Collectively, this research advances the implementation of green chemistry principles in the development of sustainable battery technology.

2. Methods

2.1. Process description

The Asia-Pacific region represents the largest and most dynamic global market for surfactants, with China maintaining a dominant position that accounted for approximately 44% of the regional share in 2024.³⁸ Accordingly, this study selected a Chinese manufacturer of Tween and Span surfactants to represent the complete production processes. For Span products, the manufacturing sequence comprises raw material inspection, dehydration, esterification, neutralization and decolorization, granulation (for solid products), and final quality inspection. A portion of the Span products undergoes further processing through addition reactions, followed by neutralization, decolorization, and final inspection to produce Tween products.

The examination procedures involve comprehensive laboratory testing of input materials and output products to verify compliance with physicochemical specifications, such as moisture content, saponification value, and fatty acid composition. The production process begins with mixing sorbitol solution and phosphoric acid in a dehydration reactor. Subsequent etherification–dehydration of sorbitol occurs under negative pressure conditions (−0.095 MPa to −0.08 MPa) at controlled temperatures (140–170 °C) with phosphoric acid catalysis. The resulting sorbitan undergoes esterification with specific fatty acids (stearic, oleic, or lauric acid) under alkaline catalysis to produce the corresponding Span products, with reaction conditions maintained at −0.095 MPa to −0.08 MPa and 180–230 °C (eqn (1)). Liquid Span products (Span 20 and Span 80) undergo neutralization and decolorization using hydrogen peroxide, while solid variants (Span 40 and Span 60) are transferred to granulation towers for further processing before final inspection. Tween production extends this process chain, where selected Span products react with ethylene oxide gas (generated by evaporators) in addition reactors (eqn (2)) under controlled conditions (−0.1 MPa to 0.2 MPa, 110–170 °C). Neutralization and decolorization are then performed using hydrogen peroxide, glacial acetic acid, or phosphoric acid, depending on the purchaser's specifications, followed by a final quality inspection. Table S1 summarizes the required physicochemical properties for typical Tween and Span products from the selected manufacturer.

2.2. Goal and scope definition

This study conducted a cradle-to-gate LCA of Tween and Span surfactants, encompassing all relevant background processes. The resulting LCA data serve as environmental performance benchmarks for applications in batteries and other sectors (e.g., pharmaceuticals, food, and cosmetics). The assessment complies with ISO 14040 and 14044 standards, targeting surfactant manufacturers and research institutions.^39,40 The LCA was performed in China in 2022, establishing the temporal and geographical scope of this study.

The functional unit was defined as either the production of 1000 kg of total products (comprising various Tween and Span surfactants) or 1000 kg of a specified individual product (a particular Tween or Span surfactant). As shown in Fig. 1, the system boundary encompasses direct emissions (wastewater, exhaust gases, and solid waste) from surfactant production and secondary processes (including material and energy production). Impacts from factory construction, equipment manufacturing, and maintenance were excluded, as they become negligible over repeated production cycles. The production facility utilized water recycling systems, where process water from direct water input, dehydration, and esterification reactions is predominantly consumed during recirculation, with the remaining portion being treated at wastewater treatment plants before discharge into rivers. Following the ISO 14044 standard, economic allocation was applied to distribute environmental impacts among co-products when allocation was unavoidable and physical relationships could not be established.^39,41 This approach was selected because system expansion was inapplicable due to the integrated production chain and the lack of representative background data for substitution. Economic allocation avoids the counter-intuitive outcome from mass allocation, which would assign identical burdens to the intermediate (Span) and final (Tween) products. Table S2 provides the manufacturer's guide prices, which form the basis for economic allocation.

Fig. 1 The system boundary diagram of Tween and Span products and water balance.

2.3. Life cycle inventory

A complete and reliable LCI is the foundational requirement for conducting an LCA.⁴² The LCI includes both foreground and background data. Foreground data were obtained from a surfactant manufacturer located in Guangdong Province, China, with an annual production capacity of 30 000 t of Span and 20 000 t of Tween. Due to insufficient information on carbon dioxide and methane emissions from fuel combustion (natural gas and biomass), these emissions were estimated using literature values and the IPCC emission factor database.^43–45 Table S3 summarizes the input and output data for the production processes, representing annual average material and energy consumption as well as pollution emissions. Each data point was rigorously reviewed by LCA specialists, engineers, and industry experts to ensure reliability and accuracy.

For background data, most energy and material inputs were sourced from the Ecoinvent database, prioritizing data representative of the Chinese (CN) market.³⁶ When CN-specific data were unavailable, global average (GLO) or rest of world (ROW) production data were used as substitutes. The electricity profile was modeled based on China's 2022 average grid mix (Fig. S1), where coal-fired power generation constituted the dominant contributor, accounting for approximately 63%. Furthermore, this study reconstructed foreground data for certain products (sorbitol and biomass fuels) that were either missing or outdated in the Ecoinvent database (Tables S4 and S5).

For non-product outputs, distinct treatment and disposal methods were implemented based on the characteristics of the pollutants. Wastewater containing residues from experimental testing was treated as industrial effluent. Inert wastes (heat transfer oil furnace ash and dust removal sediments), domestic waste, and general plastic waste were disposed of in landfills. Hazardous wastes (e.g., acid substance packaging bags) were directed to specialized incineration facilities. Notably, energy recovery during incineration was intentionally excluded from analysis to conservatively emphasize the environmental hazards posed by these materials.

2.4. Life cycle impact assessment

This study employs the Global Warming Potential over a 100-year timeframe (IPCC GWP 100a) and Cumulative Energy Demand (CED) methods to assess climate change impacts and energy requirements of Tween and Span production. For global warming potential (GWP), carbon uptake during biomass growth was explicitly excluded from consideration. This conservative approach is standard in LCA to prevent overestimating the climate benefits of bio-based materials, as it focuses on the immediate emissions from the production system. It also ensures comparability with LCAs of fossil-based products. The influence of including biogenic carbon was subsequently explored in the sensitivity analysis to provide a complete picture. Additionally, the ReCiPe Hierarchist (H) 2016 method was applied to evaluate the full-spectrum environmental footprint of surfactant manufacturing, covering both midpoint impact categories (e.g., fine particulate matter formation) and endpoint indicators (e.g., human health damage).^46,47 During the life cycle impact assessment phase, inventory data were transformed into characterized results for specific impact categories using established characterization factors. The complete set of characterization factors applied in this study is presented in Table S21.

2.5. Monte Carlo analysis and sensitivity analysis

Monte Carlo analysis was employed to evaluate data quality in the life cycle inventory and quantify the reliability of LCA results.⁴⁸ Specifically, this approach characterizes uncertainty by computing probability distributions of potential errors in input variables through stochastic modeling. The simulation utilizes random sampling methods to generate variable-specific outcomes according to predefined probability distributions, revealing their statistical characteristics. In this study, all input parameters were assumed to follow log-normal distributions. Uncertainty coefficients for input parameters were determined through a qualitative/semi-quantitative pedigree matrix approach (Tables S6 and S7). After aggregating all uncertainty coefficients, the geometric standard deviation (SD) was calculated using eqn (3), where U₁, U₂, U₃, U₄, U₅, and U_b represent reliability, completeness, temporal correlation, geographical correlation, technological correlation, and basic uncertainty coefficients, respectively. Following the calculation of SD for all parameters, Monte Carlo simulations with 1000 iterations were performed to characterize the potential probability distribution of LCA results.

Sensitivity analysis was conducted to evaluate model uncertainty by systematically varying input parameters within defined ranges.⁴⁹ This approach quantified output variations to identify critical process parameters and their relative contributions to LCA results. The regional electricity mix considered in this analysis is detailed in Fig. S1. The evolution of China's electricity structure from 2022 to 2050 is shown in Fig. S13.⁵⁰

3. Results

3.1. Global warming potential and energy demand

Carbon emissions serve as a critical indicator of environmental sustainability.⁵¹Fig. 2a presents the GWP and CED results for total product production, both of which are closely related to carbon emissions. The production of 1000 kg of total products required 15.5 MWh of energy and emitted 2735 kg CO₂ eq. of greenhouse gases. Direct greenhouse gas emissions from fuel combustion (natural gas and biomass) during production contributed minimally, accounting for only 181 kg CO₂ eq. The primary drivers of GWP were the use of oleic acid, ethylene oxide, and sorbitol as raw materials, emitting 977 kg, 646 kg, and 576 kg CO₂ eq. respectively. Regarding energy consumption, ethylene oxide, oleic acid, and sorbitol emerged as the most significant contributors, accounting for 33.6%, 29.9%, and 22.2% of the total energy demand, respectively. The energy requirements are distributed across different energy carriers, with non-renewable fossil fuels and renewable biomass fuels constituting the largest shares at 56.6% and 37.2%, respectively (Fig. S2a). Notably, fossil fuels dominate the energy carriers for ethylene oxide (the major energy consumer), while biomass primarily supports oleic acid production (the secondary contributor) (Fig. S2b). This energy structure disparity explains the differing GWP and CED contribution patterns.

Fig. 2 Analysis of global warming potential and cumulative energy demand. (a) The global warming potential and cumulative energy demand for producing 1000 kg of the total products. (b) The allocation percentages of mass and economic allocations. (c) The global warming potential and cumulative energy demand for the production of 1000 kg of the specified products. GWP values were obtained using the IPCC GWP 100a method. The blue and yellow dotted lines represent the global warming potential and cumulative energy demand for the production of 1000 kg of total products, respectively.

The carbon footprint and energy consumption of specific products were determined through allocation based on product mass and market prices across different Tween and Span series (Fig. 2b and c). Among these products, Tween 80 and Span 60 represent the highest production volumes for Tween and Span products, respectively, at this manufacturing facility. As Tween production involves additional processing steps beyond Span manufacturing, these products typically command higher market prices (Table S2). Economic allocation resulted in a slightly increased environmental impact allocation for Tween products compared with mass-based allocation (Fig. 2b). Comparative results from both allocation methods are presented in Fig. 2c. Under mass allocation, the carbon footprint and energy consumption per 1000 kg of specific products remained identical to those of total products (shown as dashed lines). When applying economic allocation, the products are ranked by GWP and CED as follows: Tween 20 > Tween 40 > Tween 60 > Tween 80 > Span 40 > Span 20 > Span 60 > Span 80. Tween products consistently showed higher impacts, with Tween 20 being most significant (17.1 MWh t⁻¹ energy and 3019 kg CO₂ eq. per t). This analysis highlights the significance of allocation method selection in LCAs of complex chemical manufacturing processes and provides quantitative evidence for surfactant product differentiation.

3.2. Full-spectrum and endpoint environmental profiles

Table S8 and Fig. S3 present the characterized, normalized, and weighted full-spectrum environmental impacts for producing 1000 kg of total products. Normalization results were calculated based on 2010 global pollution emissions to reflect the relative importance of different environmental impact categories (Table S10), while weighting results reflect the value choices embedded in the ReCiPe method.³⁹ The above approaches identified five key midpoint impact categories: global warming potential (2798 kg CO₂ eq.), fine particulate matter formation (3.1 kg PM_2.5 eq.), freshwater ecotoxicity (102.4 kg 1,4-DCB), marine ecotoxicity (105.8 kg 1,4-DCB), and human carcinogenic toxicity (90.7 kg 1,4-DCB). As a methodological sensitivity check for the focus on carbon emissions, the GWP derived from the ReCiPe method showed close agreement with the IPCC GWP 100a results, with variations of less than 3% (Table S8 and Fig. 2). Further analysis of environmental hotspots across midpoint indicators (Fig. 3) revealed sorbitol (range: 14–47%; average: 34%), oleic acid (range: 6–76%; average: 29%), and ethylene oxide (range: 1–56%; average: 20%) as the primary contributors to environmental impacts. Notably, the distribution of environmental hotspots varied substantially across different midpoint indicators. For instance, while ethylene oxide contributed to fossil resource scarcity, it accounted for less than 1% of marine eutrophication. Conversely, oleic acid showed an opposite contribution pattern, which can be attributed to differences in energy sources used during production (Fig. S2). The greater use of renewable biomass energy for oleic acid production reduced its burden on fossil resource scarcity, but fertilizer use during biomass cultivation led to higher marine eutrophication impacts.

Fig. 3 Contribution analysis of full-spectrum environmental categories.

The endpoint environmental impact categories, incorporating further grouping and weighting, were analyzed to provide sufficient clarity for decision-making (Fig. 4 and Tables S11 and S12).⁵² The production of Tween and Span products showed the greatest impact on human health (89%), followed by ecosystems (7%) and resources (3%) (Fig. 4a). To elucidate the primary drivers behind the dominant human health impact, the contributing midpoint categories were further analyzed (Fig. S15 and S16). It was found that global warming and fine particulate matter formation are the two most significant contributors, accounting for approximately 49% and 37% of the human health damage, respectively. The prominence of global warming is attributed to the substantial greenhouse gas emissions from the production of key raw materials like oleic acid, ethylene oxide, and sorbitol, which are linked to long-term climate-associated health risks. The significant contribution to fine particulate matter formation originates from airborne emissions released during upstream processes, such as biomass cultivation for bio-based feedstocks and energy combustion, which are directly implicated in causing cardiorespiratory diseases upon inhalation. Regarding total environmental impacts, the top three contributors, in descending order, were oleic acid (30%), sorbitol (27%), and ethylene oxide (25%) (Fig. 4a). These results showed slight variations from the average contribution ranking of midpoint categories, reflecting different valuation considerations for various midpoint environmental impact categories during the weighting process. After applying economic allocation to the total products, Tween 20 exhibited the highest endpoint environmental impacts, with specific values of 72.5 Pt for human health, 5.9 Pt for ecosystems, and 2.8 Pt for resources (Fig. 4b). The analysis demonstrates that while midpoint indicators provide detailed environmental profiles, the weighted endpoint assessment offered a more comprehensive perspective for environmental decision-making by incorporating damage categories and their relative importance.

Fig. 4 Analysis of endpoint environmental categories. (a) The endpoint environmental impacts for the production of 1000 kg of the total products. (b) The endpoint environmental impacts for the production of 1000 kg of the specified products.

3.3. Data quality assessment and uncertainty analysis

An uncertainty analysis was conducted to capture variability in the LCA results, implemented based on a comprehensive data quality assessment of all input and output parameters to determine the reliability of the data. Tables S13 and S14 present the data quality indicators for inventory data used in uncertainty analysis. Most data were classified as verified measurement data validated by external experts, demonstrating high reliability. As these data originated from the studied Chinese enterprise and were collected within a three-year timeframe, they achieved optimal scores for temporal, geographical, and technological representativeness. The completeness score was moderately reduced since the data came from a single representative enterprise. However, in pedigree matrix methodology, data completeness has limited influence on overall uncertainty.⁵³ Consequently, the collected Tween and Span production data meet nearly all defined criteria with high representativeness.

Fig. 5 and S4 display uncertainty distributions for GWP and CED per 1000 kg of both total and specific products. The results indicated a mean greenhouse gas emission of 2726 kg CO₂ eq. for 1000 kg of total products, with a 95% confidence interval ranging from 2140 to 3387 kg CO₂ eq. The mean value showed less than 1% deviation from deterministic LCA results, indicating no significant discrepancy. Tables S15 and S16 summarize key statistical metrics from the uncertainty analysis across all environmental impacts and energy demands, including probabilistic means, standard deviations, coefficients of variation, and 95% confidence interval bounds. Statistical analysis revealed relatively low uncertainty for GWP and CED, with mean coefficients of variation of 12.1% and 10.1%, respectively, indicating limited dispersion. However, certain impact categories like human carcinogenic toxicity and human non-carcinogenic toxicity exhibited higher uncertainty, requiring cautious interpretation and application of these specific indicators.⁵⁴ The systematic quantification of uncertainties enhances the robustness and credibility of LCA findings while identifying priority areas for future data refinement.

Fig. 5 Uncertainty analysis of global warming potential. (a) Relative frequency distribution, (b) cumulative frequency distribution, and (c) box plot distribution for the production of 1000 kg of the total or specified products. Uncertainty distribution via 1000 Monte Carlo simulations. The dotted line indicates the 95% confidence interval. The rectangles within the box in the box plot represent the mean.

3.4. Sensitivity analysis

Sensitivity analysis was performed to evaluate the influence of model parameter variations on LCA. Based on prior assumptions and LCA results, the study assessed the effects of product market price fluctuations, carbon uptake considerations, spatiotemporal variations, and deviations in the environmental performance of key input materials on environmental impact results (Fig. 6).

Fig. 6 Results of the sensitivity analysis. (a) Sensitivity analysis of price coefficient for the production of 1000 kg of the specified products (Tween 80). (b) Global warming potential of including (incl.) and excluding (excl.) carbon uptake for the production of 1000 kg of the total products. (c) The global warming potential and cumulative energy demand trends for the production of 1000 kg of the total products under different national electricity mixes. (d) Impact of changes in the GWP of key materials on the GWP for the production of 1000 kg of the total products. Price coefficient equals the actual price divided by the guideline price. The blue and yellow dotted lines represent the global warming potential and cumulative energy demand for the production of 1000 kg of total products, respectively. Country codes: US (United States), CN (China), UK (United Kingdom), FR (France), DE (Germany), and IN (India). Specifying key material GWP changes of −100% and 100% means that no GWP and twice the GWP are considered, respectively.

Product pricing significantly influences economic allocation shares, with higher-cost products bearing greater environmental burdens. Using Tween 80 as an example, this study examined how price variations affected impact allocation (Fig. 6a). When the price coefficient of Tween 80 increased from 100% to 200% while other product prices remained constant, the carbon footprint and energy demand per 1000 kg of Tween 80 rose by factors of 1.69 and 1.70, respectively. Further price increases resulted in diminishing marginal effects on the escalation of impact. These findings highlight the market-responsive flexibility of economic allocation.⁵⁵ If substantial discrepancies exist between the reference prices provided in this study and actual market values, practitioners should recalibrate allocation ratios using the transparent inventory data to reflect local economic conditions.

The impact of including CO₂ uptake for bio-based inputs (e.g., sorbitol) and biomass fuels on GWP in Tween and Span production was assessed (Fig. 6b and S5). Carbon uptake data for raw materials were sourced from the Ecoinvent database.^36,56 When accounting for carbon uptake, biomass fuels were assumed carbon-neutral (excluding combustion emissions). The results demonstrated a 44.8% reduction in GWP per 1000 kg of total products, primarily driven by CO₂ absorption during biomass growth of sorbitol and oleic acid. This underscores the critical need for explicit methodological disclosure when comparing LCA results that include or exclude carbon uptake.

Geographic location influences the product carbon footprint, with impacts determined by regional electricity mixes.⁵⁷ The study compared carbon footprint and energy consumption differences for Tween and Span production across various countries (Fig. 6c). In addition to self-built inventory data, indirect electricity inputs for key materials (oleic acid, ethylene oxide, and stearic acid) were adjusted. The results demonstrated that countries with cleaner electricity structures generate lower greenhouse gas emissions, though the disparity was not pronounced (Fig. S1). France, with the lowest carbon footprint, exhibited only an 11.5% reduction in GWP per 1000 kg of total product compared with India (the highest). Additionally, the impact of China's power sector evolution from 2022 to 2050 on product carbon footprints was considered. Compared with 2022, the GWP and CED for producing 1000 kg of total product will decrease by 9% and 3%, respectively, by 2050 (Fig. S14). Therefore, neither geographic location nor time is a primary sensitive factor for Tween and Span products.

Background data for most raw materials in this study were sourced from the Ecoinvent database or the literature, which may not fully align with actual factory conditions. Accordingly, the influence of carbon footprint variations in key materials on the GWP of Tween and Span products was evaluated. The findings indicated that a 50% increase in the carbon footprint of sorbitol, oleic acid, ethylene oxide, stearic acid, and lauric acid raises the GWP per 1000 kg of total product by 11%, 18%, 12%, 2%, and 2%, respectively. Oleic acid emerged as the most sensitive parameter for GWP. For CED, ethylene oxide was the most sensitive parameter (Fig. S6).

3.5. Surfactant power sustainable battery applications

Tween and Span products are increasingly used throughout the battery sector, including manufacturing, management, and recycling stages. Table S17 summarizes recent representative studies on the applications of Tween and Span in batteries. While most studies claim that these surfactants are environmentally friendly, the lack of relevant environmental data creates uncertainty regarding their true eco-friendliness. Consequently, this study selected one representative case to analyze the environmental sustainability of surfactants in battery applications. Specifically, Tween 80 was employed to assist in synthesizing silicon–carbon anodes for LIBs (Fig. 7a and S7).¹⁷ A detailed LCA description is provided in Text S1, covering background information, goal and scope definition, and inventory analysis.

Fig. 7 Environmental impact and energy consumption analysis of Tween-assisted battery anode material preparation. (a) Flowchart of Si/C and Si/C-TW80 anode preparation. (b) Results and (c) contribution analysis of the global warming potential of Si/C-TW80 and Si/C in different functional units. (d) Results and (e) contribution analysis of the cumulative energy demand of Si/C-TW80 and Si/C in different functional units. (f) Comparison of full-spectrum environmental impacts of Si/C-TW80 and Si/C in different functional units.

Environmental impacts and energy consumption for Tween 80-assisted battery anode preparation are shown in Fig. 7. When the functional unit is 1 kg of anode material, the Si/C-TW80 anode (with Tween 80) exhibited 42% higher GWP and 16% higher CED than the Si/C anode (without Tween 80) (Fig. 7b and d). For the Si/C-TW80 anode, surfactant addition contributed 14% to the total GWP and 25% to the total CED (Fig. 7c and e). Furthermore, additional direct emissions caused by the surfactant use were another key factor contributing to 25% of the elevated GWP (Fig. 7c). Midpoint and endpoint impact assessments corroborated these findings (Fig. 7f, S8, and S9). Crucially, however, Tween 80 enhances the reversible capacity and electrode lifespan of the battery.¹⁷ When evaluated per functional unit of one charge–discharge cycle, the Si/C-TW80 anode shows 22% lower GWP and 36% lower energy consumption than the Si/C anode (Fig. 7b and d). Additionally, except for a few midpoint impact categories (stratospheric ozone depletion, ozone formation, and land use), all other environmental impact categories demonstrate reduced values (Fig. 7f and S9). Thus, while Tween 80 increases the initial environmental burdens, the extended service life sufficiently offsets these impacts and may reduce the overall environmental harm. These findings provide insights into evaluating the environmental trade-offs associated with surfactants in battery applications.

4. Discussion

This study evaluated the life cycle environmental impacts and energy demands of Tween and Span surfactants based on industrial-scale primary data. The results indicated that producing 1000 kg of Tween series products required 16.5–17.1 MWh of energy and emitted 2922–3019 kg CO₂ eq. of greenhouse gases. For Span series products, energy consumption and GWP ranged from 14.5 to 16.4 MWh t⁻¹ and from 2564 to 2895 kg CO₂ eq. per t, respectively. Uncertainty and sensitivity analyses were integrated into the LCA model to assess the influence of data quality and key assumptions on the results. Overall, Tween and Span products in this study demonstrated high data quality and representativeness. The price factors of surfactants and the characterization factors of oleic acid and ethylene oxide were identified as key parameters affecting the LCA outcomes. When carbon uptake was considered, the carbon footprints of Tween and Span products decreased significantly (44.8%). Additionally, this study examined Tween 80-assisted synthesis of silicon–carbon anodes for LIBs as a representative case. Although surfactant use increases the manufacturing-stage carbon footprint and energy consumption, the resulting improvements in battery performance reduce life cycle environmental impacts. Thus, these surfactants contribute to the development of sustainable batteries.

The results were compared with Ecoinvent databases (Fig. S12).⁵³ Considering product characteristics, Tween and Span products were benchmarked against ethylene oxide-derived and fatty acid-derived nonionic surfactants in the Ecoinvent database, respectively. To align system boundaries, plant construction data from the database were removed, and identical electricity structures were applied. Significant discrepancies were observed between the results of Ecoinvent and this study. For Tween products, the GWP and CED in Ecoinvent were 14% and 71% higher, respectively. For Span products, the GWP and CED were 92% and 71% higher, respectively. These differences arose because the Ecoinvent proxies are based on different chemical feedstocks and reactions (e.g., ethoxylation of dodecanol) and older, generalized European industry data, which do not accurately represent the specific sorbitol–fatty acid chemistry and modern, dedicated production of Tween and Span. Consequently, Ecoinvent data should serve only as provisional substitutes in early-stage studies lacking primary data and cannot accurately represent Tween/Span LCA results.

Some academic studies have used nonionic surfactant data from the Ecoinvent database for LCAs, potentially overestimating results.³⁴ In the case of this study, substituting our Tween dataset with an ethylene oxide-derived non-ionic surfactant from the Ecoinvent database increased the energy demand for producing 1 kg of Si/C-TW80 by an additional 18%.¹⁷ Such overestimations could hinder the development and adoption of Tween or Span surfactants in the field of batteries.

Although this study rigorously verified the reliability of inventory data and traced upstream data for certain raw materials, some background data originated from the Ecoinvent database and may deviate from actual conditions. Uncertainty and sensitivity analyses were conducted to evaluate the impact of fluctuations in key materials on LCA outcomes. More accurate background data would further reduce uncertainties. In summary, our results demonstrate the sustainability of Tween and Span surfactants, providing valuable insights for advancing green reagents in batteries. Future research should integrate LCAs with diverse surfactant types and battery systems to comprehensively assess surfactant suitability for sustainable battery technologies.

5. Conclusion

This study establishes cradle-to-gate life cycle profiles for Tween and Span surfactants using primary industrial data, providing critical insights for their sustainable application in batteries and beyond. We found that the production is energy- and carbon-intensive, predominantly driven by the upstream supply of oleic acid, sorbitol, and ethylene oxide. Our analysis validates that employing primary data is crucial, as standard proxies (e.g., Ecoinvent) significantly overestimate the environmental footprint. Furthermore, the case study on a silicon–carbon anode demonstrates that while surfactant addition increases the immediate burden of electrode production, the substantial enhancement in the battery cycle life ultimately reduces the environmental impact per functional unit, proving their role in enabling more sustainable batteries. Future efforts to reduce the footprint of these surfactants should prioritize greening the production of key raw materials, with our high-quality LCI serving as an essential benchmark for guiding green chemistry in energy storage.

Author contributions

Shiyu Wang: conceptualization, methodology, data curation, formal analysis, and writing – original draft. Likun Zhao: methodology. He Ye: data curation. Zhan Shi: writing – review & editing. Huakui Zhang: visualization. Fengyin Zhou: writing – review & editing. Simin Xu: writing – review & editing. Lei Xing: resources. Dihua Wang: writing – review & editing and funding acquisition. Huayi Yin: writing – review & editing, supervision, and funding acquisition.

Conflicts of interest

The authors declare no competing financial interest.

Data availability

The data supporting this article have been included as part of the supplementary information (SI). Supplementary information is available. See DOI: https://doi.org/10.1039/d5gc03411f.

Acknowledgements

We greatly acknowledge the financial support from the National Natural Science Foundation of China (No. 52374308, 52031008, and U22B2071) and the Fundamental Research Funds for the Central Universities (No. 2042023kf0214).

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