Environmental Impact and Cost of Bio-based Hydrophobic Multifunctional Coatings

Environmental Impact and Cost of Bio-based Hydrophobic Multifunctional Coatings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental Impact and Cost of Bio-based Hydrophobic Multifunctional Coatings Pooja Yadav, Paula Nousiainen, Muhammad Farooq This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6853367/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2026 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract The circular bioeconomy supports climate action and biodiversity preservation by promoting the use of renewable materials in sustainable production. In this study bio-based betulin, lignin and suberin were used as raw materials for producing the multifunctional hydrophobic coatings. Life cycle assessment (LCA) was used to study the environmental impact of these protective coatings from cradle to gate. The foreground data were collected from laboratory experiments and literature, while background data were sourced from the ecoinvent 3.10 database. The functional unit (FU) used was coating production and application on 1 m 2 of fabric. The environmental impacts and cost were evaluated using Recipe (H) 2016 midpoint method in SimaPro 9.6. The results indicated that per FU, the global warming potential (GWP) was 2.92 kg CO 2 eq. for suberin coating, 2.39 kg CO 2 eq. for betulin coating, and 2.01 kg CO 2 eq. for lignin coating. The sensitivity analysis indicated that replacing ethanol with bioethanol reduced the burden on GWP and fossil resource scarcity (FRS) but increased the burden on the land use (LU), terrestrial ecotoxicity (TE) and human non-carcinogenic toxicity (HNCT). Additionally, the source of energy in the process particularly participation of nuclear and bio-based electricity, was found to influence the results on GWP, IR and LU impact categories. The recycling rate of solvents and the production process of feed stocks (suberin, betulin and lignin) also significantly impacted the results. Betulin Environmental Impact and cost Hydrophobic coating Life cycle assessment Lignin Suberin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Highlights • Environmental impact and cost assessed for three hydrophobic bio-based coatings. • Suberin, betulin and lignin production process influence the results. • Energy source selection and use of bioethanol found promise in reducing GWP. • Suberin, betulin and lignin-based coating provides benefits over fossil coatings. 1. Introduction Advancing bio-based chemical production methods to replace fossil-derived alternatives is vital for addressing the contemporary global challenges (Harman-Ware et al. 2021 ). Forest biomass is a sustainable and renewable resource, but it has limitations to use it, and for that reason more efficient utilization of biomass is required (Oettel and Lapin 2021 ; Yadav et al. 2024 ). Factors such as available land, materials, and other sustainability constraints limit the amount of biomass that can be responsibly extracted (Carlqvist et al. 2022 ). Bark is a lignocellulosic material, and it has attracted interest in recent years for its potential in various value-added utilization (Kwan et al. 2022 ). The extractives found in barks offer potential uses in various applications, including surface coatings, textiles, and food ingredients (Almeida et al. 2019 ; Kwan et al. 2022 ). Bark of birch is an important byproduct of biorefineries, has mainly been used for energy production (Yadav et al. 2024 ). It is also holding a great potential as a source of bioactive compounds, including betulin, lignin, suberin, oleanolic acid, and lupeol (Zhao et al. 2020 ). Suberin, and betulin are potential materials for multifunctional hydrophobic coatings for textiles sectors and packaging (Kumar et al. 2022 ). Lignin is inherently amphiphilic, rather than strongly hydrophobic, and requires modification for effective use in hydrophobic applications (Ruwoldt et al. 2023 ). In birch outer bark, suberin constitutes the largest proportion of bark components, reaching up to 45% by weight of its solid matter (Kumar et al. 2022 ; Yadav et al. 2024 ). While suberin cannot be extracted in its intact farm, it can be break down via alkaline hydrolysis, methanolysis and ionic liquid extraction (Li et al. 2016 ). Suberin is a hydrophobic biopolymer, its potential industrial application has been explored in previously published studies (Quilter et al. 2024 ). Suberin has potential to be used in hydrophobic coatings production (Kumar et al. 2022 ). In addition to suberin, the outer bark of silver birch contains 24.5 betulin by weight (Demets 2022; Yadav et al. 2024 ), which is a co-extracted with suberin during processing. Betulin, the most abundant triterpenoid of the lupan series, possesses valuable properties such as pharmacological, antiviral, antitumor, antibacterial, hypolipidemic and hydrophobic character (Demets et al. 2022 ; Yadav et al. 2024 ). Betulin-derived compounds offer eco-friendly alternatives for producing water-repellent textiles (Huang et al. 2019). Lignin is a natural resource, and the second most abundant plant-based biopolymer material on Earth, as well as the most abundant aromatic biopolymer (Priyadarshi et al. 2024 ). Lignin is generated as by-product of the of pulp and paper industry, as well as biorefineries, with an estimation annual production of 50 million tonnes (Mobredi et al. 2024 ; Priyadarshi et al. 2024 ;). Currently, around 1–2% of the annual lignin produced is used for the development value-added products, while the remainder serves as a fuel source for energy generation in power plants (Mobredi et al. 2024 ). Due to its amphiphilic nature, lignin holds potential for use in multifunctional coatings applications (Souza et al. 2019 ). Several research studies have explored the wettability of coatings based on lignin. Numerous studies have investigated the wettability of lignin-based coatings, revealing that different types of lignin display varying degrees of hydrophobicity (Mobredi et al. 2024 ). Additionally, lignin’s inherent UV-protection, antimicrobial resistance and antioxidant activity provide beneficial multifunctional properties to the coating applications (Ruwoldt et al. 2023 ). The textile industry increasingly seeks sustainable alternatives to conventional hydrophobic coatings, which are often derived from petroleum-based chemicals. Bio-based hydrophobic coatings for fabrics offer an eco-friendly solution by utilizing renewable resources. These coatings provide water repellence while maintaining breathability and comfort, which are crucial for applications in clothing, upholstery, and technical textiles (Babaeipour et al. 2024 ; Khan et al. 2022 ). Combining permeability and waterproofness in a garment creates a material with two main functions that somewhat contradicts each other (Babaeipour et al. 2024 ). The development and implementation of renewable bio-based fluorine-free formulations for hydrophobic coating treatments can reduce the adverse environmental and biological impacts typically associated with the synthesis of conventional liquid-repellent coatings (Mates et al. 2016 ). Over the past few decades, various methods have been developed to fabricate hydrophobic surfaces, but synthesis processes of these coatings frequently involve toxic organic solvents (Tang et al. 2013 ; Mates et al. 2016 ), complicated processing methods, and use of fluorinated chemistries (Gao & He 2013 ). However, the previously published methods are not practically feasible for large-scale commercial applications (Mates et al. 2016 ), as well as the environmental impact of these process are not available. Duan et al. ( 2016 ) studied chitosan-based coatings, a biopolymer from chitin, known for its potential in fabric coatings. Chitosan can be chemically modified to improve its hydrophobic properties. Sehaqui et al. ( 2014 ) investigated the use of cellulose nanocrystals from plant cellulose to create hydrophobic coatings for fabric substrates, enhancing water repellence. Rahmadhani et al. ( 2024 ) developed a nanocomposite coating using chitosan and silicon dioxide (SiO₂) to impart hydrophobic and antibacterial properties to textiles. The SiO₂ was derived from rice husk ash, while the chitosan was extracted from crustacean shells. It is important to assess the environmental impact of hydrophobic coating treatments and their production process before advancing to large-scale research or commercial applications. Climate change is a global challenge of increasing concern, has prompted action across multiple sectors, including the European Union (EU). However, addressing the sustainability of new technologies, such as production of biobased hydrophobic coatings is essential to ensure they contribute positively to sustainable development goals. It requires a thorough quantitative environmental impact analysis, and Life Cycle Analysis (LCA) is a tool that is increasingly being used to assess and compare environmental impacts of products and processes at their early stage (Fidan et al. 2024 ). Yadav et al. ( 2024 ) evaluated the environmental load of producing betulin and suberin from birch bark using extraction and alkaline hydrolysis methods. Lecart et al. ( 2023 ) evaluated the impact of suberin coating. Bernier et al. ( 2013 ), Hermansson et al. ( 2020 ) and Kumaniaev et al. ( 2020 ) have conducted LCA and examined the environmental impacts of kraft lignin production as side product or main product from biorefineries that used for fuels. As per our knowledge none of the previously published study used suberin, betulin and lignin bio-based materials to produce the multifunctional hydrophobic coatings and assessed their environmental impacts and cost. This study is the first to assess the environmental sustainability of three different bio-based coatings derived from suberin, betulin, and lignin. These coatings were compared based on environmental impact and cost to identify the most environmentally friendly coatings overall from material selection to self-assembly or production of nanoparticles (NPs) and then their application process. These coatings have the potential to replace fossil-based coatings, such as Teflon, used for textiles. The contribution analysis was conducted to identify the environmental hotspots within the process. This study will also help to find out the way to reduce the environmental burden of production and application of coating by performing the sensitivity analysis using different energy sources and bio-based solvents. The reliability of the data and results was confirmed by conducting an uncertainty analysis using the Monte Carlo simulation features in the SimaPro 9.6 software, utilizing primary and secondary data. 2. Materials and Methods 2.1. Goal and scope definition This study investigates the environmental implications across the life cycle of betulin, lignin, and suberin-based hydrophobic coatings for textile and packaging applications. 2.1.1: Functional unit The functional unit (FU) is a comparative unit for LCA studies. Here, 1 m 2 of biobased coating cotton fabric materials were used as FU that cotton fabric required 2.86-liter coating solution as per our laboratory experiments. The use of 1 m 2 as the FU is most common in the textile industry, thus allowing for the possibility of comparison with literature. 2.1.2: System boundary System boundaries start from raw materials that were obtained from forest, with production of suberin and betulin from outer bark of birch hardwood, and kraft lignin as a residue from a softwood kraft pulp mill. The process involves the production of betulin, suberin, and lignin, production of NPs, and followed by application of coating dispersion on fabric. The “cradle-to-gate” system boundary was followed for hydrophobic functional coating material production, with a focus on its application phase. 2.2 Life Cycle Inventory Analysis Primary data collection for the life cycle inventory (LCI) primarily relied on laboratory experiments conducted at Aalto University (Table S1 ). Additional data were obtained from scientific publications and the ecoinvent 3.10 database, which provided secondary data for Finnish electricity mix, ethanol production, acetone, water, and other chemical inputs (Table S2). 2.2.1: Kraft Lignin Production Lignin was received from UPM (Lappeenranta, Finland) as part of the support and collaboration for this project. The lignin was produced using the kraft pulping process from softwood and purified according to the company’s general protocol. The obtained kraft lignin was dried in powder form and was of the grade UPM BioPiva™ 395. The water content of the product was 5%. The material was used as such without further purification or drying for preparation of lignin nanoparticles ( LNPs ). The kraft process is the most dominant process in pulp and paper industries and considered a traditional method to separate lignin from the lignocellulosic biomass (Bernier et al. 2013 ; Hermansson et al. 2019; Gordobil et al. 2021 ; Bilal et al. 2021 ). Fig. S1 shows lignin production process from kraft pulp the procedure is described in detail in Bernier et al. ( 2013 ). 2.2.2: Betulin and Suberin Production Suberin hydrolysate and betulin fraction were obtained from Natural Resources Institute Finland (LUKE). Both fractions were isolated from hardwood birch ( Betula pendula ) stems. The procedure is described in detail in Yadav at al. (2024) and Fig. S2. In short, silver birch (Punkaharju, Finland) outer bark was milled using a Fritsch Pulverisette cutting mill (Fritsch GmbH, Germany). The powder was subjected to ethanol extraction and subsequent alkaline ethanolic hydrolysis. The extraction in ethanol: water was done in a 2.0-liter Büchi Glas Uster stirred autoclave (Büchi AG, Uster, Switzerland) at 90°C, and isolated by evaporation under reduced pressure. The residual extracted bark was hydrolysed according to Korpinen et al. ( 2019 ). Ethanol and 20 w% NaOH in water was used in reactor at 90°C for 60 min and the hydrolysate was collected and evaporated. The residue was washed with boiling water to isolate additional betulin fraction to yield a combined betulinol-rich residue (BF) approximately 36% of the charged fractionated outer bark. The filtrate was finally acidified to pH 4, to precipitate out water insoluble suberin fatty acids. The yield of the suberin fatty acid rich fraction (SH) was 26% calculated from original charged outer bark. The details of conduction LCA for production of the suberin and betulin was described in Yadav et al. ( 2024 ). 2.2.3. Nanoparticle formation (self-assembly) from Betulin, Lignin and Suberin Self-assembly of lignin to produce LNPs was performed according to Table 1 . following the solvent exchange method by Zou et al (2019). Shortly, 1 w% of kraft lignin was dissolved in acetone for 1 hour. The solution was then filtered using paper filter (Whatman, pore size 0.7 µm) to remove any undissolved residues. Spherical particles were formed through self-assembly by pouring solution into vigorously stirred deionized water at a 1:3 (v/v) ratio. Acetone was removed from the LNP using rotary evaporation at 40°C under reduced pressure. Supramolecular self-assembly of suberin rich hydrolysate (SH) and betulin rich fraction (BF ) solutions containing 1 wt% of SH and BF in acetone were formulated according to Table 1 . The mixtures were initially agitated at 600 rpm for a duration of 15 minutes at 65°C, ensued by constant stirring at ambient temperature for a period of 1 hour to facilitate dissolution. The solutions were centrifuged at 10,000 rpm for 30 min to eliminate any undissolved residues. Self-assembly was achieved by rapidly transferring the dilutions or mixtures into deionized water that was vortex-stirred, with a solution-to-water ratio of 1:5 (v/v). Subsequently, the dispersed particles underwent dialysis against water for approximately 48 hours using Spectra/Por 1 tubing with a molecular weight cut-off (MWCO) of 6–8 kDa to eliminate the organic solvent. Dilutions and mixtures in this study were prepared using freshly made solutions, following the established protocol to prevent crystallization. 2.2.4. Application of Coating (based on Suberin, Betulin, and Lignin) on fabric Cotton fabric was cut into small strips (5 × 3.5 cm), then cleaned with ethanol and deionized water to remove any possible contaminants. The strips were soaked in water for a few minutes prior to the layer-by-layer deposition process. Each strip was immersed in a 0.1 wt% cationic starch solution for 5 minutes, followed by three rinses with deionized water to ensure uniform deposition of the cationic polyelectrolyte and to eliminate loosely bound molecules. The strips were then immersed in NPs dispersions for 20 minutes, followed by rinsing to remove unadsorbed particles. This dip-coating process was repeated layer by layer until two bilayers were formed on the cotton fabric. Finally, the coated samples were dried at room temperature. The TENCEL™ fabric samples measuring 5 cm × 3.5 cm were excised and washed with ethanol, followed by a rinse with deionized water to eliminate any potential contaminants present on the fabric. Subsequently, fabric samples were immersed in a solution of 0.1 wt% cationic starch for 20 minutes. Following this, the fabric was rinsed for a further 5 minutes with deionized water. The fabric was immersed in NPs dispersions for 20 minutes, followed by a subsequent rinsing for 5 minutes. To ensure adequate coverage, the fabric specimens were left to dry overnight. Subsequently, a second layer of the dispersion was applied, followed by a 5-minute rinse with deionized water. Table 1 Inputs and outputs of nanoparticle formation, coating material formation and application of coating solution on fabric, dm = dry matter. Materials Lignin solution Betulin solution Suberin solution Unit Source/reference Lignin (dry matter) 0.1 - - g (dm) UPM BioPiva 395 Betulin (dry matter) - 0.1 - g Laboratory (Luke) Suberin (dry matter) - - 0.1 g Laboratory (Luke) Acetone (concentration. 100%) 10 10 10 ml VWR Stirring (10 min at 65 ℃) - 0.65 0.65 kcal 1000 W stirrer Stirring for 1 hr 1 1 1 kWh 1000 W stirrer for 1 hr Total 10 10 10 ml Nanoparticle formation (self-assembly) Deionized water 7 7 7 ml Lignin Solution 2 - - ml Betulin solution - 2 - ml Suberin solution - - 2 ml Acetone Recycling rate 99 99 99 % (Ashok et al. 2018 ; Rivière et al. 2021 ) Electricity for recycling of acetone 0.11 0.11 0.11 kWh/kg (Capello et al. 2005 ) Filtration (cellulose membrane) 12.5 12.5 12.5 cm Marchery-Nagel Cellulose membrane 1.05 1.05 1.05 gm weight of membrane Acetone removal Bio-membrane 2 2 2 cm need membrane Total nanoparticle dispersion 7 7 7 ml Coating application process for 5 cm (L) and 3.5 cm (W) fabric Ethanol (concentration 96%) 1 1 1 ml Anora Group, Finland Deionized water 3 3 3 ml Cationic starch solution (concentration 0.1 wt. %) 5 5 5 ml Chemigate Oy, Finland Coating dispersion solution 5 5 5 ml Total 14 14 14 ml Different values kg CO 2 per kg used in study for Suberin, Betulin and Lignin Baseline (S1) S2 S3 unit (Bernier et al. 2013 ) (Hermansson et al. 2020 ) Lignin 3.22 0.91 0.47 kg CO 2 / kg Betulin 7.23 1.66 1.40 kg CO 2 / kg (Yadav et al. 2024 ) Suberin 13.72 8.57 2.48 kg CO 2 / kg 2.3. Life Cycle Impact Assessment The life cycle environmental impact was calculated using ReCiPe 2016 Midpoint (H) V1.09 / World (2010) H method (Huijbregts et al. 2017 ) is well suited for global prospective (Huijbregts et al. 2017 ). The environmental impacts were calculated on Global warming potential (GWP), Ionizing radiation (IR), Ozone formation (OF), Fine particulate matter formation (FPMF), Freshwater eutrophication (FWE), Terrestrial acidification (TA), Freshwater ecotoxicity (FE), Terrestrial ecotoxicity (TE), Human carcinogenic toxicity (HCT), Marine ecotoxicity (ME), Human non-carcinogenic toxicity (HNC), Mineral resource scarcity (MRS), Fossil resource scarcity (FRS), Land use (LU), and Water consumption (WC). The background data were adapted from the database Ecoinvent 3.10 using cut off model (Table 1 ). It is important to discuss the biogenic carbon when dealing with wood-based biomass products, and for that reason Environmental Footprint (E.F) 3.1 (Andreasi et al. 2023) method was used for calculating the biogenic carbon emissions. The energy demand in the process was calculated using Cumulative Energy Demand (LHV) V1.01 method (Frischknecht et al. 2007 ). 2.4 Environmental and production cost The environmental and production cost analysis was performed to identify the most economical scenario. The capital investment costs associated with high uncertainty, for that reason the analysis primarily focused on environmental cost and production cost. The production costs for suberin, betulin, and lignin-based coatings were estimated based on the inputs (chemicals, mass and energy balance calculations) (Thunman et al. 2019 ; Yadav et al. 2020 ; Yadav et al. 2021a ). The raw material prices were as follows: suberin at 25 €/kg (CORDIS 2024), betulin at 570 €/kg (NST Chemicals 2024 ), and kraft lignin at 380 €/t (Bajwa et al. 2019 ). The prices of acetone (1253 €/t) and ethanol (805 €/t) were obtained from Kuittinen et al. ( 2022 ), while water was priced at 2.52 €/m³ and bioethanol price was 776 €/t (Yadav et la. 2021; Chembid 2020 ). The electricity price of 0.089 €/kWh was sourced from Eurostat ( 2023 ). Production costs were estimated for1 m² of coating material produced from suberin, betulin, and lignin consistent with the FU. The production cost excluded VAT and taxes, labour cost, and subsidies, and added in details in supplementary. The environmental price for each environmental indicators were assessed at the midpoint level, as reported by CE Delft ( 2024 ). The environmental prices were determined, by multiplying the environmental impact of each impact category by its external costs (Shanmugam et al. 2019 ; Yadav et al. 2021b ). In this study, external costs for all impact categories were considered at midpoint level from Environmental Prices Handbook 2024: EU27 (CE Delft 2024 ) in detailed mentioned in Table S3. External cost studies the possible damage to society when one kilogram of additional pollutant finds its way into the environment (CE Delft 2024 ). 2.5 Sensitivity Analysis The sensitivity assessment was performed by replacing the (a) source of electricity, (b) and replacing the bioethanol instead of fossil ethanol and (c) by changing the GWP values (environmental load) of suberin, betulin and lignin production. In LCA, environmental impact of a product can differ significantly depending on how it is produced, and the allocation method used, as well as whether the product is the primary output or a side product within the same system boundary. These variations can substantially influence the CO₂ emissions per kilogram of suberin, betulin, and lignin, which are the primary raw materials in the coating material analysed in this study. To account for this variability, sensitivity analysis was performed using two different values of CO 2 emissions of per kg suberin, betulin and lignin. The values for lignin were sourced from literature (Hermansson et al. 2020 ), while the values of suberin and betulin were obtained from Yadav et al. ( 2024 ). 2.6 Uncertainty analysis The manufacturing of coating affects different unit processes, that causing uncertainty in data. To evaluate uncertainties of all three coatings production and application, uncertainty analysis was done by using Monte Carlo simulations in SimaPro 9.6 using 95% confidence (SimaPro 2025; Yadav et al. 2021b ) using the life cycle inventory data (water, electricity, acetone, and ethanol) for production of hydrophobic coating solution 2.86 liter that applied on FU. A semi-quantitative approach was employed using the Ecoinvent 3.10 database, where ratings were assigned based on five data quality indicators: completeness, reliability, temporal relevance, geographical correlation, and further technological applicability (Yadav et al. 2021b ; Patel and Singh 2024 ). For assessing the uncertainty of all inputs (electricity, water, acetone, and ethanol) in process a pedigree numbers between 1 and 5 based indicating the quality of data in SimaPro 9.6 was assigned using Ecoinvent database 3.10. In the database, a parameter scores of 1 symbolizes the excellent or reliable data quality, although a score of 5 indicates the poor data quality (Ciroth et al. 2012 ). For example, in the case of electricity, the assigned scores were (1, 1, 1, 1, 1, not applicable). This indicates that the data quality used in this study was reliable. A score of 1 means the data is verified and based on direct measurements. C ompleteness : The score was also 1, meaning the data is representative of all relevant sites for the market considered. Temporal correlation : The score of 1 indicates that the data is less than three years different from the time of the dataset. Geographical correlation : The score of 1 signifies that the data originates from the area under study. Technological correlation : The score of 1 reflects that the data comes from companies, materials and processes, under study. The sample size was unspecified , which is why it was marked as "not applicable." To derive measures of uncertainty, 1,000 iterations were performed using ReCiPe (H) midpoint environmental impact categories. 3. Results and Discussion The selection of sustainable solutions, both environmental and economic, are crucial in developing a new bio-based product for the market. As part of this, the environmental impact of suberin, betulin, and lignin coatings were valuated across seventeen environmental indicators. The environmental impact on GWP of suberin coating was 2.92 kg CO 2 eq., for betulin coating 2.39 kg CO 2 eq., and for lignin coting 2.01 kg CO 2 eq. per FU. The environmental impacts on other impact categories per FU are presented in Table 2 . The lab-scale coating application process requires chemicals such as cationic starch solution, ethanol, and water. For this reason, the environmental impact of producing 1 m² of coating has been also calculated and it is presented in Table 2 . The GWP of suberin coating without application per 1 m 2 was 1.99 kg CO 2 eq., which was 31.85% lower than per FU. Betulin coating without application per 1 m 2 was 1.46 kg CO 2 eq. that was 38.91% lower than per FU. The GWP of lignin coating without application per m 2 was 1.08 kg CO 2 eq., that was 46.26% lower than per FU. Table 2 Environmental impact of different coating materials production, with and without application of coating on fabric. Impact categories Unit Suberin coating Production Betulin coating Production Lignin coating Production Suberin coating per FU Betulin coating per FU Lignin coating per FU GWP kg CO 2 eq. 1.985 1.455 1.078 2.916 2.386 2.009 IR kBq Co-60 eq. 2.248 1.619 0.907 2.274 1.646 0.934 OF kg NO x eq. 0.003 0.003 0.002 0.005 0.005 0.004 FPMF kg PM 2.5 eq. 0.003 0.002 0.001 0.004 0.003 0.002 TA kg SO 2 eq. 0.008 0.004 0.003 0.011 0.006 0.005 FWE kg P eq. 0.001 0.000 0.000 0.001 0.001 0.001 TE kg 1.4-DCB 9.420 8.693 8.685 11.555 10.828 10.820 FE kg 1.4-DCB 0.028 0.022 0.017 0.035 0.029 0.024 ME kg 1.4-DCB 0.046 0.037 0.030 0.055 0.046 0.039 HCT kg 1.4-DCB 0.058 0.046 0.035 0.074 0.062 0.051 HNCT kg 1.4-DCB 1.303 0.989 0.645 1.548 1.235 0.891 LU m 2 a crop eq. 0.330 0.171 0.117 0.804 0.645 0.591 MRS kg Cu eq. 0.003 0.002 0.002 0.003 0.003 0.002 FRS kg oil eq. 0.600 0.439 0.331 1.112 0.950 0.842 WC m 3 0.245 0.214 0.239 0.277 0.247 0.239 GW-biogenic kg CO 2 -eq 0.003 0.004 0.003 0.003 0.004 0.003 CED (LHV) MJ 84.07 59.53 39.39 111.93 86.81 66.68 Note : Global warming potential (GWP), Ionizing radiation (IR), Ozone formation (OF), Fine particulate matter formation (FPMF),Terrestrial acidification (TA), Freshwater eutrophication (FWE), Terrestrial ecotoxicity (TE), Freshwater ecotoxicity (FE), Marine ecotoxicity (ME), Human carcinogenic toxicity (HCT), Human non-carcinogenic toxicity (HNC), Land use (LU), Mineral resource scarcity (MRS), Fossil resource scarcity (FRS), Water consumption (WC) and Global warming (GW) biogenic and CED cumulative Energy Demand (LHV). The production of suberin and betulin -based coatings were found to have higher environmental impacts than lignin-based coating. The betulin coating had 27% lower GWP than suberin coating. The betulin coating production had lower environmental impact on IR (28%), OF (11%), FPMF (40%), TA (54%), FWE (29%), TE (8%), FE (21%), ME (19%), HCT (21%), HNCT (24%), LU (48%), MRS (21%), FRS (27%) and WC (12%) impact categories compared to suberin-based coating. Specifically, the lignin-based coating had the lowest environmental impact among the three coatings on all impact categories. The environmental impacts of lignin-based coating on GWP (46%), IR (60%), OF (39%), FPMF (60%), TA (67%), FWE (57%), TE (8%), FE (40%), ME (35%), HCT (40%), HNCT (51%), LU (65%), MRS (42%), FRS (45%) and WC (2%) were lower than suberin coating. While betulin coating showed lower water consumption than lignin coating (Table 2 ). However, the biogenic carbon dioxide emissions of all three coatings are approximately similar (Table 2 ). The environmental impacts of these coatings are highly dependent on their production methods and production of raw materials. Specifically, the environmental impact observed in the present study is influenced by production process and maturity of technology associated with suberin, betulin and kraft lignin. However, there are still several issues that need to be overcome before the industrialized production of suberin and betulin can be realized, as detailed in our previous LCA study by Yadav et al. ( 2024 ). Since environmental impact of betulin and suberin production is extended from Yadav et al. ( 2024 ). However, the production of kraft lignin is an established technology with relatively low environmental impact (Bernier et al. 2013 ). The results of this study are based on the environmental impact, while the quality of hydrophobicity and functionality of coating plays an important role in decision making factor, which have not been covered in this study. When assessing the environmental affect of any process, it is essential to evaluate its energy efficiency. The energy required for the process is calculated using the cumulative energy demand (CED) method based on the lower heating value (LHV). The CED consists of a combination of non-renewable (77.31%) and renewable (22.69%) energy sources to produce all three coatings. The non-renewable component includes fossil fuels, nuclear energy, and biomass, while the renewable component comprises biomass, wind, solar, water, and geothermal energy. Suberin coating production required 84.07 MJ energy, betulin (59.53 MJ) and lignin required 39.9 MJ. The energy demand always reduces by upscaling the process. The relative contribution of environmental impact categories of across different stages of coatings production, including dissolution of suberin, betulin and lignin, NPs, and application of coating onto fabric, is presented in Fig. 2 , Fig. 3 , and Fig. 4 . The findings indicated that the environmental impacts of the NPs, and coating application stages are approximately the same for all three coatings, due to identical inputs and outputs in these processes. However, the suberin dissolution stage GWP is 1.37 kg CO 2 eq. that is higher than the GWP of LNPs formation and application of coating on fabric, and the same trend is observed for IR, TE, FPMF, TE, HCT and HNCT impact categories. The contribution analysis shows that the GWP of betulin solution (0.84 kg CO 2 eq.) and lignin solution (0.47 kg CO 2 eq.), are higher than that of NPs formation but lower than the coating application stage (Fig. 3 and Fig. 4 and Table S4). When comparing the dissolution stages of the three coatings the betulin dissolution stage had higher environmental impact than lignin but lower than suberin. This was due to the production processes of suberin, betulin and lignin. Especially, the suberin and betulin production processes were intensive in terms of ethanol, energy, and water usage (Fig. S2). The environmental indicators to produce betulin and suberin depend on the process's mass and energy balance, the energy source, the ethanol recycling rate, and the type of allocation method employed. An efficient industrial process could reduce the GWP of suberin (Fig. S2) and betulin (Yadav et al. 2024 ). In the lignin coating production process, lignin dissolution had a GWP 0.47 kg CO 2 eq., which was lower than the environmental impacts of NPs formation (0.61 kg CO 2 eq.) and application stages (0.93 kg CO 2 eq.). This was attributed to lignin solution formation phase that required 0.65 kcal less energy compared to suberin and betulin that required 0.65 kcal per 10 ml solution for stirring at 10 min at 85℃ (Table 1 ). This lower energy requirement is one reason for reduced environmental impact of lignin coatings compared to betulin and suberin coatings. The lignin used in this study is a byproduct of pulp industry, as shown in Fig. S1 , with inventory data sourced from Bernier et al. ( 2013 ) presented in Table S1 . The use of lignin as a renewable feedstock in coating production successfully lowers the environmental impact compared to suberin and betulin on considered environmental impact categories. The choice of renewable feedstocks (lignin, betulin, and suberin) significantly influences the environmental outcomes of the process and emerges as a key determinant affecting the results. The environmental impacts of suberin, betulin, and lignin are contingent upon their production methods, as influenced by the system boundaries, allocation strategies employed, efficiency of the process, size of scale and quality of data used. The contribution analysis outcomes for lignin-based coatings are illustrated in Fig. 4 . The results of other materials in the market that are used for hydrophobic coating in the textile industry are compared with our study results. The previously published studies were found to focused mainly on the GWP. The commonly used hydrophobic material in the market, Teflon, has a GWP of 13.11 kg CO 2 eq. per m 2 (Gore and Associates 2013) application that is higher than the GWP values of this study’s coatings [suberin (2.92 kg CO 2 / FU), betulin (1.38 kg CO 2 /FU) and lignin (2.01 kg CO 2 /FU)]. Silicones have a GWP of 1.88 kg CO 2 /m 2 (Althaus et al. 2007 ), which is lower than suberin (1.99 kg CO 2 /m 2 ) but higher than betulin (1.46 kg CO 2 /m 2 ) and lignin (1.07 kg CO 2 /m 2 ). While silicon-based coatings production is well-established, our results based on lab-scale experiments for suberin, betulin and lignin-based coatings may evolve as the technology matures. Improved materials and energy efficiency, along with recycling chemicals in a closed-loop system and utilizing resources like water more effectively, will likely reduce the environmental impacts. 3.1 Results of contribution analysis Environmental hotspots were identified by analysing the contributions of components such as suberin, betulin, lignin, electricity, acetone, ethanol, cationic starch, water, and filters in the process. The results of contribution assessment of different components used per FU for suberin coating are presented in Fig. 5 . The production of suberin raw material (1.12 kg CO 2 eq.) was the main contributor to GWP, followed using ethanol (0.63 kg CO 2 eq.) during the application stage. Electricity use was the third highest contributor (0.42 kg CO 2 eq.), and water had the lowest impact (0.03 kg CO 2 eq.) among all components used in coating production and application process. In addition, cationic starch (0.30 kg CO 2 eq.), filter paper (0.29 kg CO 2 eq.), and acetone (0.12 kg CO 2 eq.) had moderate contributions to GWP. Suberin was the main contributor to IR (1.35 kBq Co-60 eq.), FPMF (0.002 kg PM 2.5 eq.), TA (0.006kg SO 2 eq.), FEW (0.004 kg P eq.), MRS (0.0012 kg Cu eq.), HCT (0.025 kg 1,4-DCB), HNCT (0.68 kg 1,4-DCB) and LU (0.21 m 2 a crop eq.) impact categories (Table S5). The use of chemicals like ethanol and sulfuric acid in suberin production significantly contributes to environmental impacts in various categories. Electricity was main contributor to IR, and MRS impact categories, primarily due to use of Finnish mix electricity. Acetone on the other hand contributed to the TE impact category due to its production process. The environmental impacts varied significantly across different input materials and processes. Ethanol emerged as the primary contributor to GWP, OF, and FRS, while betulin was the second highest contributor to GWP, IR, OF, FPMF, FE and FRS (Fig. 6 ). Electricity consumption predominantly influenced IR and MRS, while showing minimal impact on other categories. LU were primarily driven by cationic starch production, followed by betulin, cellulosic filter, and electricity consumption (Table S6). These LU impacts can be attributed to the cultivation of raw materials required for starch, cellulose, and betulin production, as well as the Finnish electricity mix. WC was directly impacted by process water requirements. The environmental impact contributions of different components in the lignin-based coating and application process are shown in Fig. 7 and Table S7. Ethanol was the primary contributor to GWP, while acetone dominated TE impacts. Water consumption directly influenced WC, and cationic starch was the main driver of LU. Lignin production significantly affected the IR, HCT, and HNCT categories. 3.2 Results of Sensitivity Analysis 3.2.1 Change in source of electricity The LCA outcomes are primarily influenced by two key factors the source of electricity and the type of ethanol used in the process. Figures 8 , 9 , and 10 show that the highest GWP occurred when the global (GLO) electricity mix (Table S8) was used, whereas the lowest GWP was observed with the Finnish (FI) electricity mix. The reason is high percentage of electricity generation from coal at a global level. In contrast, the Finnish electricity mix has a lower percentage of fossil and coal-based electricity and a higher percentage of renewable electricity (Energy statistics Finland 2023). Finland's electricity mix comprises biomass (25%), nuclear power (27.4%), hydropower (19.2%), wind power (9.7%), solar power (0.3%), and imported electricity-18.4% from Russia and the remainder from Sweden (Energy statistics Finland 2023; Yadav et al. 2024 ). Sweden generates 48% of its electricity from nuclear power, while 64% of Russia's electricity is produced from fossil fuels (EMBER 2023 ). Among all the coatings, the highest impact on the IRP category was observed with the use of nuclear power in Finland's electricity grid mix. In contrast, the application of GLO electricity resulted in the lowest impact within the IRP category (Fig. 8 ). The percentage changes in environmental impact when Finnish mix electricity was replaced by Europe (EU) and global (GLO) electricity mixes are shown in Table S8. In suberin coating, by replacing FI by EU and GLO mix electricity, GWP increased by 63% and 19%, respectively. Conversely, IR and LU impact categories were reduced by 26% and 11%, respectively, with the GLO and EU mixes. The same trends were observed for betulin and lignin coatings in GWP, IR and LU impact categories (Table S8). Europe power grid composition is 40% renewable energy, 38.6% fossil fuels, and 20% nuclear power and others. Natural gas is the main fossil energy source used to generate electricity (19.6%), followed by coal (15.8%) (Europa 2023 ). Globally, fossil fuels remain the highest source of electricity generation, with the GLO mix grid composition being different from the EU power grid. The coal-based energy is 35.5%, while natural gas follows with a 23% share, and China, India, and the United States accounted for the largest contributors (STATISTA 2023). For lignin coating, GWP increased by 28% and 91% when FI was replaced by EU and GLO electricity, respectively. HCTP increased by 91%, whereas IRP decreased 64% by replacing FI by GLO electricity, respectively (Table S9). 3.2.2 Bioethanol as an alternative to ethanol The substitution of fossil ethanol with bioethanol demonstrated varying environmental impacts across the three coating systems. A modest reduction in GWP was observed for suberin coating, decreasing from 2.92 kg CO 2 to 2.53 kg CO 2 , with similar trends noted for betulin and lignin coatings (Fig. 11 , Fig. 12 and Fig. 13 ). While minimal changes were observed in IR, FE, and ME impact categories, significant variations emerged in LU, FRS, TA, and WC. Notably, LUP increased substantially by 102% (from 0.80 m 2 a to 1.62 m 2 a crop) due to wood resource requirements in bioethanol production. This LU increase pattern was consistent across betulin and lignin coating systems. Detailed comparative results for all impact categories are presented in Table S10. 3.2.3 Effect of Feedstock Production Changes on GWP The analysis demonstrated that changes in the production methods of raw materials (suberin, betulin and lignin) significantly changes the environmental impact outcomes (Fig. 14 ). The biobased chemicals produced at different scales (laboratory, pilot and industrial) exhibited different environmental impacts for the same product. At laboratory scale, the environmental impacts are generally higher because of less efficient processes, lower energy and material efficiency, higher waste generation, and limited possibility for recycling and reusing chemicals in process. However, at industrial scale the possibility of recycling and reuse of chemicals and materials maximizes as the technology matures. The results showed that GWP decreased by 18–47% due to changes in suberin production process (Yadav et al. 2024 ). GWP decreased by 18–25% for betulin coating and by 16–17% for lignin coating. 3.3 Production cost and environmental cost Since the betulin, suberin, and lignin coating production processes are precommercial for that reason industrial data is unavailable, and capital and investment costs are highly uncertain at the trial stage. Both production and environmental costs vary according to the raw materials utilized in each coating. Environmental costs were calculated by multiplying the impact of each environmental indicator by its associated externality cost (results listed in Table S11). The external cost for suberin coating is 1.50 €/kg per FU, for betulin coating is 1.20 €/kg per FU and for lignin coating is 0.96 €/kg per FU, while without coating application is 1.10 €/kg for suberin, 0.80 €/kg for betulin, and 0.57 €/kg for lignin. Environmental cost is highly dependent of external prices of FPMF, that is 99.2 €/kg PM 2.5 eq. whether environmental impact on FPMF was low for all coatings and that did not significantly influence the external costs of coatings. The production cost of suberin is 2.21 €/kg, for betulin is 1.23 €/kg, and for lignin is 0.20 €/kg (Table S12). Suberin and betulin have higher market prices in comparison to lignin, because the production of suberin and betulin raw material is more costly than lignin. This is the main reason for higher prices of suberin and betulin coatings. The production of lignin is a well-established industrial process, which lowers its price significantly. Table S12 also shows the changes in price per FU, with and without the application stage. Ethanol is only needed at application stage and for that reason the cost is provided in two parts: one is production cost without application, and another is total cost per FU. The price is also influenced by other components such as electricity, bark, and other chemicals used in process. 3.4 Uncertainty analysis The results of the uncertainty analysis provide insight into the reliability and quality of the data across various parameters, as outlined in the methodology section (Section 2.6 ). Detailed results of uncertainty analysis were reported in Table S13 on different impact categories, including the mean, median, standard deviation (SD), coefficient of variation (CV), and standard error of the mean (SEM). In general, CV less than 10% is considered a good stability indicator, whereas more than 10% indicates that the results are more variable. The results showed that CV values for GWP were within ± 10%, while the values for LU were higher than 10%. A high SD indicates that the data is widely spread, making it less reliable, whereas a low SD suggests that the data is closely clustered around the mean, indicating greater reliability, as shown in Table S13. The SD values range from 0.15 to 10.18 across various impact categories. The GWP category had an SD of 0.15, indicating high precision and consistency in the results. In contrast, the WC and HNCT impact categories showed SD values greater than 10, suggesting higher variability and less reliability. 4. Conclusions This life cycle assessment provides comprehensive insights into the environmental performance of bio-based hydrophobic multifunctional coatings derived from wood components: suberin, betulin, and lignin - demonstrating their potential as sustainable alternatives to conventional fossil-based coatings like Teflon. Energy source selection emerged as a critical factor, with the Finnish electricity mix offering environmental advantages due to its low fossil fuel dependency. While bioethanol substitution showed promise in reducing global warming potential, it highlighted important sustainability trade-offs, particularly in land use impacts. These findings contribute to the growing body of knowledge on sustainable coating technologies and provide valuable insights for future development of environmentally conscious manufacturing processes in the textile industry. Further optimization through renewable energy integration presents opportunities for enhancing the environmental performance of these bio-based coating systems. The production cost and environmental cost of the three coatings were calculated. The production cost depends on the inputs used in the process and their market price. The environmental cost depends on the emissions produced during the coating’s production and application. It was found that the environmental cost is mainly affected by the fine particulate matter formation (FPMF), because it has a high external cost. The environmental impact assessment, encompassing both production and economic dimensions, revealed complex interrelationships between process variables and environmental outcomes. Declarations 5.1 Acknowledgement and Funding The authors gratefully acknowledge the financial support provided by the ENZYFUNC Project (349052 and 348870 RRF Green and Digital Transition), funded by the Research Council of Finland. Support from the Academy of Finland’s Flagship Program through projects no. 318890 and 318891 (Competence Centre for Materials Bioeconomy, Finn CERES) is also sincerely acknowledged 5.2 Authors’ Contributions Pooja Yadav: Writing-original draft, Writing-review & editing, Visualization, Validation, Supervision, Software modelling, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Paula Nousiainen: Writing- review & editing, Writing -experimental part, Data curation. Farooq Muhammad: Writing- review & editing, Writing. 5.3 Ethical Approval: This is not applicable for this manuscript. 5.4 Consent to Participate : This is not applicable for this manuscript. 5.5 Consent to Publish: This is not applicable for this manuscript. 5.6 Clinical trial number: not applicable. 5.7 Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 5.8 Data Availability Statement: Data will be made available on reasonable request. References Almeida RSR, Taccini MM, de Moura LF, Ceribelli UL, Brito JO, Gloria EM (2019) Potential of Pyroligneous Extract of Eucalyptus Wood as a Preservative of Cosmetic and Sanitizing Products. Waste Biomass Valoriz 10:1111–1118 Althaus HJ, Chudacoff M, Hischier R, Jungbluth N, Osses M, Primas A (2007) Life cycle inventories of chemicals. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6853367","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486884714,"identity":"ad20ee29-55ce-449f-89ff-2fa05d2a5cb1","order_by":0,"name":"Pooja Yadav","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYJACAzB5gIHxwQOGBAYG5oMNRGthNkgAaWFLJKyFAaqFTQKiJQG/QnP25gMFPxgOy/MdP2NWkdiWJsfAxozfFsueYwmGPQyHDWeeyTG7kdiWY8zAxohfi8GNHAMDHobDjBsOpKUBtVQkNsg3EtZi+IfhsP2G88/SCoBa6huIscUYaEvihhvJxxiADksg6DCQX4xlDNKTZ954fFgi4VyaYRshLcAQO2b4psLatu98YuOHD2XJ8vxs7A/wOwwYDwYMBs0IETa86iFamIGG1hFSNwpGwSgYBSMZAABGEEpDlkw7DAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-6204-2911","institution":"Natural Resources Institute Finland: Luonnonvarakeskus","correspondingAuthor":true,"prefix":"","firstName":"Pooja","middleName":"","lastName":"Yadav","suffix":""},{"id":486884715,"identity":"e5482d59-a05d-479a-bee5-022c944a6d05","order_by":1,"name":"Paula Nousiainen","email":"","orcid":"","institution":"Aalto University School of Chemical Engineering: Aalto-yliopisto Kemian tekniikan korkeakoulu","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Nousiainen","suffix":""},{"id":486884716,"identity":"b96bc63c-58c8-4286-b903-7f13da893420","order_by":2,"name":"Muhammad Farooq","email":"","orcid":"","institution":"Aalto University School of Chemical Engineering: Aalto-yliopisto Kemian tekniikan korkeakoulu","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Farooq","suffix":""}],"badges":[],"createdAt":"2025-06-09 10:20:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6853367/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6853367/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-026-37701-3","type":"published","date":"2026-04-07T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87328598,"identity":"59eea579-cdc1-42dc-9917-ae1309170fd4","added_by":"auto","created_at":"2025-07-22 18:15:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172502,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the production process (system boundary) for suberin, betulin, and lignin nanoparticle dispersion coatings and their application onto cotton fabric.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/0bf362110a8ee36caf5eeddc.jpg"},{"id":87327969,"identity":"ba55ebd5-ca1a-45f7-b38a-284ad5faa7d2","added_by":"auto","created_at":"2025-07-22 18:07:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":315712,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental impact of suberin coating per functional unit based on the contribution of different stages.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/df7412d931b6b40cdb06b0a1.jpg"},{"id":87328846,"identity":"fed72360-9aa8-4717-8ea4-1e1f84ef6d71","added_by":"auto","created_at":"2025-07-22 18:23:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":312994,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental impact of betulin coating per functional unit based on the contribution of different stages.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/66f0984aafe1fff2b6596764.jpg"},{"id":87327970,"identity":"3c3cd3c2-62fb-410c-8120-5398dcbd78ed","added_by":"auto","created_at":"2025-07-22 18:07:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":325604,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental impact of lignin coating per functional unit based on the contribution of different stages.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/4747abc731a9ebac90a7370a.jpg"},{"id":87328600,"identity":"8d8e1151-9424-47e1-83f4-a6f29ff6981a","added_by":"auto","created_at":"2025-07-22 18:15:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":359281,"visible":true,"origin":"","legend":"\u003cp\u003eResults of contribution analysis of different inputs used in the process to production of suberin coating per FU.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/d7dc4ba2a9469248fa8d18db.jpg"},{"id":87328848,"identity":"bd1b7569-5d9f-4e97-add5-ff081b816956","added_by":"auto","created_at":"2025-07-22 18:23:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":369372,"visible":true,"origin":"","legend":"\u003cp\u003eResults of contribution analysis of different inputs used in the process to production of betulin coating per FU.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/506c011e15c4a93d44771db8.jpg"},{"id":87327973,"identity":"0fe94d43-95c7-42dd-96f2-a7f8bc550a5b","added_by":"auto","created_at":"2025-07-22 18:07:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":353052,"visible":true,"origin":"","legend":"\u003cp\u003eResults of contribution analysis of different inputs used in the process to production of lignin coating per FU.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/258efe46beaab90249a4fe36.jpg"},{"id":87329376,"identity":"aa452e62-175f-4408-a15a-40c2aaea7eea","added_by":"auto","created_at":"2025-07-22 18:31:08","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":294719,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the results suberin coating per FU by changing Finnish average mix (FI) electricity to European average mix (EU) or global mix (GLO).\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/b8bfdaea3aa86e4dc18841ea.jpg"},{"id":87328601,"identity":"763a07e9-1e80-49bd-8eb7-cf50b360f5dc","added_by":"auto","created_at":"2025-07-22 18:15:08","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":273433,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the results of betulin coating per FU by changing Finnish average mix electricity (FI) to European average mix (EU) and global mix (GLO).\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/587f2781fe6c02bcf650d203.jpg"},{"id":87327977,"identity":"98cab880-9356-4959-b972-69a00a489283","added_by":"auto","created_at":"2025-07-22 18:07:08","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":299829,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the results production of lignin coating by changing Finnish average mix (FI) electricity to European average mix (EU) or global mix (GLO).\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/2950eb7834b470b9f452b408.jpg"},{"id":87328850,"identity":"9e8bb8a8-d1c9-48cd-8f95-93e479e2023a","added_by":"auto","created_at":"2025-07-22 18:23:08","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":347818,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the results production of suberin coating by changing ethanol by bioethanol per FU.\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/fff79be57265b67c5ac12d92.jpg"},{"id":87327983,"identity":"2e27c893-2952-4383-8de2-5563f918cd61","added_by":"auto","created_at":"2025-07-22 18:07:08","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":302259,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the results production of betulin coating by changing ethanol by bioethanol per FU.\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/9de81aaf49bf1e8915447609.jpg"},{"id":87327975,"identity":"98fbaeab-79a1-4ac7-93c4-23db6c975c2c","added_by":"auto","created_at":"2025-07-22 18:07:08","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":325689,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the results production of lignin coating by changing ethanol by bioethanol per FU.\u003c/p\u003e","description":"","filename":"Picture13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/906a523e0de902851a0cafe4.jpg"},{"id":87327985,"identity":"01d694e3-ccbb-4a14-bbc8-6c477955e4b9","added_by":"auto","created_at":"2025-07-22 18:07:08","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":197403,"visible":true,"origin":"","legend":"\u003cp\u003eResults of sensitivity analysis of production of suberin coating, betulin and lignin coating.\u003c/p\u003e","description":"","filename":"Picture14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/4c77470016f3b1e9112a7814.jpg"},{"id":106809007,"identity":"479ea4f1-b2e5-4d08-824c-e778f6b2734d","added_by":"auto","created_at":"2026-04-13 16:05:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5750625,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/038fb381-f14b-40a9-9f91-b91457c6d011.pdf"},{"id":87328605,"identity":"b77d21be-08c4-4670-ba43-5c5ac73cc8bb","added_by":"auto","created_at":"2025-07-22 18:15:08","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":305369,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6853367/v1/4c36b88408da1ac86d10843b.docx"}],"financialInterests":"","formattedTitle":"Environmental Impact and Cost of Bio-based Hydrophobic Multifunctional Coatings","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Environmental impact and cost assessed for three hydrophobic bio-based coatings.\u003c/p\u003e\u003cp\u003e\u0026bull; Suberin, betulin and lignin production process influence the results.\u003c/p\u003e\u003cp\u003e\u0026bull; Energy source selection and use of bioethanol found promise in reducing GWP.\u003c/p\u003e\u003cp\u003e\u0026bull; Suberin, betulin and lignin-based coating provides benefits over fossil coatings.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eAdvancing bio-based chemical production methods to replace fossil-derived alternatives is vital for addressing the contemporary global challenges (Harman-Ware et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Forest biomass is a sustainable and renewable resource, but it has limitations to use it, and for that reason more efficient utilization of biomass is required (Oettel and Lapin \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Factors such as available land, materials, and other sustainability constraints limit the amount of biomass that can be responsibly extracted (Carlqvist et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bark is a lignocellulosic material, and it has attracted interest in recent years for its potential in various value-added utilization (Kwan et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The extractives found in barks offer potential uses in various applications, including surface coatings, textiles, and food ingredients (Almeida et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kwan et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bark of birch is an important byproduct of biorefineries, has mainly been used for energy production (Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is also holding a great potential as a source of bioactive compounds, including betulin, lignin, suberin, oleanolic acid, and lupeol (Zhao et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Suberin, and betulin are potential materials for multifunctional hydrophobic coatings for textiles sectors and packaging (Kumar et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lignin is inherently amphiphilic, rather than strongly hydrophobic, and requires modification for effective use in hydrophobic applications (Ruwoldt et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn birch outer bark, suberin constitutes the largest proportion of bark components, reaching up to 45% by weight of its solid matter (Kumar et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While suberin cannot be extracted in its intact farm, it can be break down via alkaline hydrolysis, methanolysis and ionic liquid extraction (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Suberin is a hydrophobic biopolymer, its potential industrial application has been explored in previously published studies (Quilter et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Suberin has potential to be used in hydrophobic coatings production (Kumar et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition to suberin, the outer bark of silver birch contains 24.5 betulin by weight (Demets 2022; Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which is a co-extracted with suberin during processing. Betulin, the most abundant triterpenoid of the lupan series, possesses valuable properties such as pharmacological, antiviral, antitumor, antibacterial, hypolipidemic and hydrophobic character (Demets et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Betulin-derived compounds offer eco-friendly alternatives for producing water-repellent textiles (Huang et al. 2019).\u003c/p\u003e\u003cp\u003eLignin is a natural resource, and the second most abundant plant-based biopolymer material on Earth, as well as the most abundant aromatic biopolymer (Priyadarshi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Lignin is generated as by-product of the of pulp and paper industry, as well as biorefineries, with an estimation annual production of 50\u0026nbsp;million tonnes (Mobredi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Priyadarshi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e;). Currently, around 1\u0026ndash;2% of the annual lignin produced is used for the development value-added products, while the remainder serves as a fuel source for energy generation in power plants (Mobredi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to its amphiphilic nature, lignin holds potential for use in multifunctional coatings applications (Souza et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several research studies have explored the wettability of coatings based on lignin. Numerous studies have investigated the wettability of lignin-based coatings, revealing that different types of lignin display varying degrees of hydrophobicity (Mobredi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, lignin\u0026rsquo;s inherent UV-protection, antimicrobial resistance and antioxidant activity provide beneficial multifunctional properties to the coating applications (Ruwoldt et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe textile industry increasingly seeks sustainable alternatives to conventional hydrophobic coatings, which are often derived from petroleum-based chemicals. Bio-based hydrophobic coatings for fabrics offer an eco-friendly solution by utilizing renewable resources. These coatings provide water repellence while maintaining breathability and comfort, which are crucial for applications in clothing, upholstery, and technical textiles (Babaeipour et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Combining permeability and waterproofness in a garment creates a material with two main functions that somewhat contradicts each other (Babaeipour et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The development and implementation of renewable bio-based fluorine-free formulations for hydrophobic coating treatments can reduce the adverse environmental and biological impacts typically associated with the synthesis of conventional liquid-repellent coatings (Mates et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOver the past few decades, various methods have been developed to fabricate hydrophobic surfaces, but synthesis processes of these coatings frequently involve toxic organic solvents (Tang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mates et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), complicated processing methods, and use of fluorinated chemistries (Gao \u0026amp; He \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, the previously published methods are not practically feasible for large-scale commercial applications (Mates et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), as well as the environmental impact of these process are not available. Duan et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) studied chitosan-based coatings, a biopolymer from chitin, known for its potential in fabric coatings. Chitosan can be chemically modified to improve its hydrophobic properties. Sehaqui et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) investigated the use of cellulose nanocrystals from plant cellulose to create hydrophobic coatings for fabric substrates, enhancing water repellence. Rahmadhani et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) developed a nanocomposite coating using chitosan and silicon dioxide (SiO₂) to impart hydrophobic and antibacterial properties to textiles. The SiO₂ was derived from rice husk ash, while the chitosan was extracted from crustacean shells.\u003c/p\u003e\u003cp\u003eIt is important to assess the environmental impact of hydrophobic coating treatments and their production process before advancing to large-scale research or commercial applications. Climate change is a global challenge of increasing concern, has prompted action across multiple sectors, including the European Union (EU). However, addressing the sustainability of new technologies, such as production of biobased hydrophobic coatings is essential to ensure they contribute positively to sustainable development goals. It requires a thorough quantitative environmental impact analysis, and Life Cycle Analysis (LCA) is a tool that is increasingly being used to assess and compare environmental impacts of products and processes at their early stage (Fidan et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Yadav et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) evaluated the environmental load of producing betulin and suberin from birch bark using extraction and alkaline hydrolysis methods. Lecart et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) evaluated the impact of suberin coating. Bernier et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Hermansson et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Kumaniaev et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have conducted LCA and examined the environmental impacts of kraft lignin production as side product or main product from biorefineries that used for fuels.\u003c/p\u003e\u003cp\u003eAs per our knowledge none of the previously published study used suberin, betulin and lignin bio-based materials to produce the multifunctional hydrophobic coatings and assessed their environmental impacts and cost. This study is the first to assess the environmental sustainability of three different bio-based coatings derived from suberin, betulin, and lignin. These coatings were compared based on environmental impact and cost to identify the most environmentally friendly coatings overall from material selection to self-assembly or production of nanoparticles (NPs) and then their application process. These coatings have the potential to replace fossil-based coatings, such as Teflon, used for textiles. The contribution analysis was conducted to identify the environmental hotspots within the process. This study will also help to find out the way to reduce the environmental burden of production and application of coating by performing the sensitivity analysis using different energy sources and bio-based solvents. The reliability of the data and results was confirmed by conducting an uncertainty analysis using the Monte Carlo simulation features in the SimaPro 9.6 software, utilizing primary and secondary data.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Goal and scope definition\u003c/h2\u003e\u003cp\u003eThis study investigates the environmental implications across the life cycle of betulin, lignin, and suberin-based hydrophobic coatings for textile and packaging applications.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1: Functional unit\u003c/h2\u003e\u003cp\u003eThe functional unit (FU) is a comparative unit for LCA studies. Here, 1 m\u003csup\u003e2\u003c/sup\u003e of biobased coating cotton fabric materials were used as FU that cotton fabric required 2.86-liter coating solution as per our laboratory experiments. The use of 1 m\u003csup\u003e2\u003c/sup\u003e as the FU is most common in the textile industry, thus allowing for the possibility of comparison with literature.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2: System boundary\u003c/h2\u003e\u003cp\u003eSystem boundaries start from raw materials that were obtained from forest, with production of suberin and betulin from outer bark of birch hardwood, and kraft lignin as a residue from a softwood kraft pulp mill. The process involves the production of betulin, suberin, and lignin, production of NPs, and followed by application of coating dispersion on fabric. The \u0026ldquo;cradle-to-gate\u0026rdquo; system boundary was followed for hydrophobic functional coating material production, with a focus on its application phase.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Life Cycle Inventory Analysis\u003c/h2\u003e\u003cp\u003ePrimary data collection for the life cycle inventory (LCI) primarily relied on laboratory experiments conducted at Aalto University (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additional data were obtained from scientific publications and the ecoinvent 3.10 database, which provided secondary data for Finnish electricity mix, ethanol production, acetone, water, and other chemical inputs (Table S2).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1: Kraft Lignin Production\u003c/h2\u003e\u003cp\u003eLignin was received from UPM (Lappeenranta, Finland) as part of the support and collaboration for this project. The lignin was produced using the kraft pulping process from softwood and purified according to the company\u0026rsquo;s general protocol. The obtained kraft lignin was dried in powder form and was of the grade UPM BioPiva\u0026trade; 395. The water content of the product was 5%. The material was used as such without further purification or drying for preparation of lignin nanoparticles (\u003cem\u003eLNPs\u003c/em\u003e). The kraft process is the most dominant process in pulp and paper industries and considered a traditional method to separate lignin from the lignocellulosic biomass (Bernier et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hermansson et al. 2019; Gordobil et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bilal et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e shows lignin production process from kraft pulp the procedure is described in detail in Bernier et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2: Betulin and Suberin Production\u003c/h2\u003e\u003cp\u003eSuberin hydrolysate and betulin fraction were obtained from Natural Resources Institute Finland (LUKE). Both fractions were isolated from hardwood birch (\u003cem\u003eBetula pendula\u003c/em\u003e) stems. The procedure is described in detail in Yadav at al. (2024) and Fig. S2. In short, silver birch (Punkaharju, Finland) outer bark was milled using a Fritsch Pulverisette cutting mill (Fritsch GmbH, Germany). The powder was subjected to ethanol extraction and subsequent alkaline ethanolic hydrolysis. The extraction in ethanol: water was done in a 2.0-liter B\u0026uuml;chi Glas Uster stirred autoclave (B\u0026uuml;chi AG, Uster, Switzerland) at 90\u0026deg;C, and isolated by evaporation under reduced pressure. The residual extracted bark was hydrolysed according to Korpinen et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Ethanol and 20 w% NaOH in water was used in reactor at 90\u0026deg;C for 60 min and the hydrolysate was collected and evaporated. The residue was washed with boiling water to isolate additional betulin fraction to yield a combined betulinol-rich residue (BF) approximately 36% of the charged fractionated outer bark. The filtrate was finally acidified to pH 4, to precipitate out water insoluble suberin fatty acids. The yield of the suberin fatty acid rich fraction (SH) was 26% calculated from original charged outer bark. The details of conduction LCA for production of the suberin and betulin was described in Yadav et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3. Nanoparticle formation (self-assembly) from Betulin, Lignin and Suberin\u003c/h2\u003e\u003cp\u003eSelf-assembly of lignin to produce \u003cem\u003eLNPs\u003c/em\u003e was performed according to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. following the solvent exchange method by Zou et al (2019). Shortly, 1 w% of kraft lignin was dissolved in acetone for 1 hour. The solution was then filtered using paper filter (Whatman, pore size 0.7 \u0026micro;m) to remove any undissolved residues. Spherical particles were formed through self-assembly by pouring solution into vigorously stirred deionized water at a 1:3 (v/v) ratio. Acetone was removed from the LNP using rotary evaporation at 40\u0026deg;C under reduced pressure. Supramolecular self-assembly of suberin rich hydrolysate (SH) and betulin rich fraction (BF\u003cem\u003e)\u003c/em\u003e solutions containing 1 wt% of SH and BF in acetone were formulated according to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mixtures were initially agitated at 600 rpm for a duration of 15 minutes at 65\u0026deg;C, ensued by constant stirring at ambient temperature for a period of 1 hour to facilitate dissolution. The solutions were centrifuged at 10,000 rpm for 30 min to eliminate any undissolved residues. Self-assembly was achieved by rapidly transferring the dilutions or mixtures into deionized water that was vortex-stirred, with a solution-to-water ratio of 1:5 (v/v). Subsequently, the dispersed particles underwent dialysis against water for approximately 48 hours using Spectra/Por 1 tubing with a molecular weight cut-off (MWCO) of 6\u0026ndash;8 kDa to eliminate the organic solvent. Dilutions and mixtures in this study were prepared using freshly made solutions, following the established protocol to prevent crystallization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4. Application of Coating (based on Suberin, Betulin, and Lignin) on fabric\u003c/h2\u003e\u003cp\u003eCotton fabric was cut into small strips (5 \u0026times; 3.5 cm), then cleaned with ethanol and deionized water to remove any possible contaminants. The strips were soaked in water for a few minutes prior to the layer-by-layer deposition process. Each strip was immersed in a 0.1 wt% cationic starch solution for 5 minutes, followed by three rinses with deionized water to ensure uniform deposition of the cationic polyelectrolyte and to eliminate loosely bound molecules. The strips were then immersed in NPs dispersions for 20 minutes, followed by rinsing to remove unadsorbed particles. This dip-coating process was repeated layer by layer until two bilayers were formed on the cotton fabric. Finally, the coated samples were dried at room temperature. The TENCEL\u0026trade; fabric samples measuring 5 cm \u0026times; 3.5 cm were excised and washed with ethanol, followed by a rinse with deionized water to eliminate any potential contaminants present on the fabric. Subsequently, fabric samples were immersed in a solution of 0.1 wt% cationic starch for 20 minutes. Following this, the fabric was rinsed for a further 5 minutes with deionized water. The fabric was immersed in NPs dispersions for 20 minutes, followed by a subsequent rinsing for 5 minutes. To ensure adequate coverage, the fabric specimens were left to dry overnight. Subsequently, a second layer of the dispersion was applied, followed by a 5-minute rinse with deionized water.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInputs and outputs of nanoparticle formation, coating material formation and application of coating solution on fabric, dm\u0026thinsp;=\u0026thinsp;dry matter.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaterials\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLignin solution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBetulin solution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSuberin solution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSource/reference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLignin (dry matter)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eg (dm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUPM BioPiva 395\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetulin (dry matter)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLaboratory (Luke)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuberin (dry matter)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLaboratory (Luke)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcetone (concentration. 100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVWR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStirring (10 min at 65 ℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ekcal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1000 W stirrer\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStirring for 1 hr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ekWh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1000 W stirrer for 1 hr\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eml\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNanoparticle formation (self-assembly)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeionized water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLignin Solution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetulin solution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuberin solution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcetone Recycling rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(Ashok et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rivi\u0026egrave;re et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElectricity for recycling of acetone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ekWh/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(Capello et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFiltration (cellulose membrane)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMarchery-Nagel\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCellulose membrane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003egm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eweight of membrane\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcetone removal Bio-membrane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eneed membrane\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal nanoparticle dispersion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eml\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCoating application process for 5 cm (L) and 3.5 cm (W) fabric\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthanol (concentration 96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnora Group, Finland\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeionized water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCationic starch solution\u003c/p\u003e\u003cp\u003e(concentration 0.1 wt. %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChemigate Oy, Finland\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoating dispersion solution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eml\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDifferent values kg CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eper kg used in study for Suberin, Betulin and Lignin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBaseline (S1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eS2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eS3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eunit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e(Bernier et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) (Hermansson et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLignin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e/ kg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e/ kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e(Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuberin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e/ kg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Life Cycle Impact Assessment\u003c/h2\u003e\u003cp\u003eThe life cycle environmental impact was calculated using ReCiPe 2016 Midpoint (H) V1.09 / World (2010) H method (Huijbregts et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) is well suited for global prospective (Huijbregts et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The environmental impacts were calculated on Global warming potential (GWP), Ionizing radiation (IR), Ozone formation (OF), Fine particulate matter formation (FPMF), Freshwater eutrophication (FWE), Terrestrial acidification (TA), Freshwater ecotoxicity (FE), Terrestrial ecotoxicity (TE), Human carcinogenic toxicity (HCT), Marine ecotoxicity (ME), Human non-carcinogenic toxicity (HNC), Mineral resource scarcity (MRS), Fossil resource scarcity (FRS), Land use (LU), and Water consumption (WC). The background data were adapted from the database Ecoinvent 3.10 using cut off model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is important to discuss the biogenic carbon when dealing with wood-based biomass products, and for that reason Environmental Footprint (E.F) 3.1 (Andreasi et al. 2023) method was used for calculating the biogenic carbon emissions. The energy demand in the process was calculated using Cumulative Energy Demand (LHV) V1.01 method (Frischknecht et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Environmental and production cost\u003c/h2\u003e\u003cp\u003eThe environmental and production cost analysis was performed to identify the most economical scenario. The capital investment costs associated with high uncertainty, for that reason the analysis primarily focused on environmental cost and production cost. The production costs for suberin, betulin, and lignin-based coatings were estimated based on the inputs (chemicals, mass and energy balance calculations) (Thunman et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). The raw material prices were as follows: suberin at 25 \u0026euro;/kg (CORDIS 2024), betulin at 570 \u0026euro;/kg (NST Chemicals \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and kraft lignin at 380 \u0026euro;/t (Bajwa et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The prices of acetone (1253 \u0026euro;/t) and ethanol (805 \u0026euro;/t) were obtained from Kuittinen et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while water was priced at 2.52 \u0026euro;/m\u0026sup3; and bioethanol price was 776 \u0026euro;/t (Yadav et la. 2021; Chembid \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The electricity price of 0.089 \u0026euro;/kWh was sourced from Eurostat (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Production costs were estimated for1 m\u0026sup2; of coating material produced from suberin, betulin, and lignin consistent with the FU. The production cost excluded VAT and taxes, labour cost, and subsidies, and added in details in supplementary.\u003c/p\u003e\u003cp\u003eThe environmental price for each environmental indicators were assessed at the midpoint level, as reported by CE Delft (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The environmental prices were determined, by multiplying the environmental impact of each impact category by its external costs (Shanmugam et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). In this study, external costs for all impact categories were considered at midpoint level from Environmental Prices Handbook 2024: EU27 (CE Delft \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in detailed mentioned in Table S3. External cost studies the possible damage to society when one kilogram of additional pollutant finds its way into the environment (CE Delft \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Sensitivity Analysis\u003c/h2\u003e\u003cp\u003eThe sensitivity assessment was performed by replacing the (a) source of electricity, (b) and replacing the bioethanol instead of fossil ethanol and (c) by changing the GWP values (environmental load) of suberin, betulin and lignin production. In LCA, environmental impact of a product can differ significantly depending on how it is produced, and the allocation method used, as well as whether the product is the primary output or a side product within the same system boundary. These variations can substantially influence the CO₂ emissions per kilogram of suberin, betulin, and lignin, which are the primary raw materials in the coating material analysed in this study. To account for this variability, sensitivity analysis was performed using two different values of CO\u003csub\u003e2\u003c/sub\u003e emissions of per kg suberin, betulin and lignin. The values for lignin were sourced from literature (Hermansson et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while the values of suberin and betulin were obtained from Yadav et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Uncertainty analysis\u003c/h2\u003e\u003cp\u003eThe manufacturing of coating affects different unit processes, that causing uncertainty in data. To evaluate uncertainties of all three coatings production and application, uncertainty analysis was done by using Monte Carlo simulations in SimaPro 9.6 using 95% confidence (SimaPro 2025; Yadav et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) using the life cycle inventory data (water, electricity, acetone, and ethanol) for production of hydrophobic coating solution 2.86 liter that applied on FU. A semi-quantitative approach was employed using the Ecoinvent 3.10 database, where ratings were assigned based on five data quality indicators: completeness, reliability, temporal relevance, geographical correlation, and further technological applicability (Yadav et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Patel and Singh \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For assessing the uncertainty of all inputs (electricity, water, acetone, and ethanol) in process a pedigree numbers between 1 and 5 based indicating the quality of data in SimaPro 9.6 was assigned using Ecoinvent database 3.10. In the database, a parameter scores of 1 symbolizes the excellent or reliable data quality, although a score of 5 indicates the poor data quality (Ciroth et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For example, in the case of electricity, the assigned scores were (1, 1, 1, 1, 1, not applicable). This indicates that the data quality used in this study was reliable. A score of 1 means the data is verified and based on direct measurements. C\u003cb\u003eompleteness\u003c/b\u003e: The score was also 1, meaning the data is representative of all relevant sites for the market considered. \u003cb\u003eTemporal correlation\u003c/b\u003e: The score of 1 indicates that the data is less than three years different from the time of the dataset. \u003cb\u003eGeographical correlation\u003c/b\u003e: The score of 1 signifies that the data originates from the area under study. \u003cb\u003eTechnological correlation\u003c/b\u003e: The score of 1 reflects that the data comes from companies, materials and processes, under study. \u003cb\u003eThe sample size was unspecified\u003c/b\u003e, which is why it was marked as \"not applicable.\" To derive measures of uncertainty, 1,000 iterations were performed using ReCiPe (H) midpoint environmental impact categories.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eThe selection of sustainable solutions, both environmental and economic, are crucial in developing a new bio-based product for the market. As part of this, the environmental impact of suberin, betulin, and lignin coatings were valuated across seventeen environmental indicators. The environmental impact on GWP of suberin coating was 2.92 kg CO\u003csub\u003e2\u003c/sub\u003e eq., for betulin coating 2.39 kg CO\u003csub\u003e2\u003c/sub\u003e eq., and for lignin coting 2.01 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;per FU. The environmental impacts on other impact categories per FU are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The lab-scale coating application process requires chemicals such as cationic starch solution, ethanol, and water. For this reason, the environmental impact of producing 1 m\u0026sup2; of coating has been also calculated and it is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The GWP of suberin coating without application per 1 m\u003csup\u003e2\u003c/sup\u003e was 1.99 kg CO\u003csub\u003e2\u003c/sub\u003e eq., which was 31.85% lower than per FU. Betulin coating without application per 1 m\u003csup\u003e2\u003c/sup\u003e was 1.46 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;that was 38.91% lower than per FU. The GWP of lignin coating without application per m\u003csup\u003e2\u003c/sup\u003e was 1.08 kg CO\u003csub\u003e2\u003c/sub\u003e eq., that was 46.26% lower than per FU.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEnvironmental impact of different coating materials production, with and without application of coating on fabric.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImpact categories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSuberin coating Production\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBetulin coating Production\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLignin coating Production\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSuberin coating per FU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBetulin coating per FU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLignin coating per FU\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGWP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e eq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekBq Co-60 eq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg NO\u003csub\u003ex\u003c/sub\u003e eq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg PM\u003csup\u003e2.5\u003c/sup\u003e eq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg SO\u003csub\u003e2\u003c/sub\u003e eq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFWE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg P eq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg 1.4-DCB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.820\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg 1.4-DCB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg 1.4-DCB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg 1.4-DCB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHNCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg 1.4-DCB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003em\u003csup\u003e2\u003c/sup\u003ea crop eq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg Cu eq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg oil eq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003em\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGW-biogenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e-eq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCED (LHV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMJ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e111.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e86.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e66.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: Global warming potential (GWP), Ionizing radiation (IR), Ozone formation (OF), Fine particulate matter formation (FPMF),Terrestrial acidification (TA), Freshwater eutrophication (FWE), Terrestrial ecotoxicity (TE), Freshwater ecotoxicity (FE), Marine ecotoxicity (ME), Human carcinogenic toxicity (HCT), Human non-carcinogenic toxicity (HNC), Land use (LU), Mineral resource scarcity (MRS), Fossil resource scarcity (FRS), Water consumption (WC) and Global warming (GW) biogenic and CED cumulative Energy Demand (LHV).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe production of suberin and betulin -based coatings were found to have higher environmental impacts than lignin-based coating. The betulin coating had 27% lower GWP than suberin coating. The betulin coating production had lower environmental impact on IR (28%), OF (11%), FPMF (40%), TA (54%), FWE (29%), TE (8%), FE (21%), ME (19%), HCT (21%), HNCT (24%), LU (48%), MRS (21%), FRS (27%) and WC (12%) impact categories compared to suberin-based coating. Specifically, the lignin-based coating had the lowest environmental impact among the three coatings on all impact categories. The environmental impacts of lignin-based coating on GWP (46%), IR (60%), OF (39%), FPMF (60%), TA (67%), FWE (57%), TE (8%), FE (40%), ME (35%), HCT (40%), HNCT (51%), LU (65%), MRS (42%), FRS (45%) and WC (2%) were lower than suberin coating. While betulin coating showed lower water consumption than lignin coating (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the biogenic carbon dioxide emissions of all three coatings are approximately similar (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The environmental impacts of these coatings are highly dependent on their production methods and production of raw materials. Specifically, the environmental impact observed in the present study is influenced by production process and maturity of technology associated with suberin, betulin and kraft lignin. However, there are still several issues that need to be overcome before the industrialized production of suberin and betulin can be realized, as detailed in our previous LCA study by Yadav et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Since environmental impact of betulin and suberin production is extended from Yadav et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the production of kraft lignin is an established technology with relatively low environmental impact (Bernier et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The results of this study are based on the environmental impact, while the quality of hydrophobicity and functionality of coating plays an important role in decision making factor, which have not been covered in this study.\u003c/p\u003e\u003cp\u003eWhen assessing the environmental affect of any process, it is essential to evaluate its energy efficiency. The energy required for the process is calculated using the cumulative energy demand (CED) method based on the lower heating value (LHV). The CED consists of a combination of non-renewable (77.31%) and renewable (22.69%) energy sources to produce all three coatings. The non-renewable component includes fossil fuels, nuclear energy, and biomass, while the renewable component comprises biomass, wind, solar, water, and geothermal energy. Suberin coating production required 84.07 MJ energy, betulin (59.53 MJ) and lignin required 39.9 MJ. The energy demand always reduces by upscaling the process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe relative contribution of environmental impact categories of across different stages of coatings production, including dissolution of suberin, betulin and lignin, NPs, and application of coating onto fabric, is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The findings indicated that the environmental impacts of the NPs, and coating application stages are approximately the same for all three coatings, due to identical inputs and outputs in these processes. However, the suberin dissolution stage GWP is 1.37 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;that is higher than the GWP of \u003cem\u003eLNPs\u003c/em\u003e formation and application of coating on fabric, and the same trend is observed for IR, TE, FPMF, TE, HCT and HNCT impact categories. The contribution analysis shows that the GWP of betulin solution (0.84 kg CO\u003csub\u003e2\u003c/sub\u003e eq.) and lignin solution (0.47 kg CO\u003csub\u003e2\u003c/sub\u003e eq.), are higher than that of NPs formation but lower than the coating application stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table S4). When comparing the dissolution stages of the three coatings the betulin dissolution stage had higher environmental impact than lignin but lower than suberin. This was due to the production processes of suberin, betulin and lignin. Especially, the suberin and betulin production processes were intensive in terms of ethanol, energy, and water usage (Fig. S2). The environmental indicators to produce betulin and suberin depend on the process's mass and energy balance, the energy source, the ethanol recycling rate, and the type of allocation method employed. An efficient industrial process could reduce the GWP of suberin (Fig. S2) and betulin (Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the lignin coating production process, lignin dissolution had a GWP 0.47 kg CO\u003csub\u003e2\u003c/sub\u003e eq., which was lower than the environmental impacts of NPs formation (0.61 kg CO\u003csub\u003e2\u003c/sub\u003e eq.) and application stages (0.93 kg CO\u003csub\u003e2\u003c/sub\u003e eq.). This was attributed to lignin solution formation phase that required 0.65 kcal less energy compared to suberin and betulin that required 0.65 kcal per 10 ml solution for stirring at 10 min at 85℃ (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This lower energy requirement is one reason for reduced environmental impact of lignin coatings compared to betulin and suberin coatings. The lignin used in this study is a byproduct of pulp industry, as shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, with inventory data sourced from Bernier et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The use of lignin as a renewable feedstock in coating production successfully lowers the environmental impact compared to suberin and betulin on considered environmental impact categories. The choice of renewable feedstocks (lignin, betulin, and suberin) significantly influences the environmental outcomes of the process and emerges as a key determinant affecting the results. The environmental impacts of suberin, betulin, and lignin are contingent upon their production methods, as influenced by the system boundaries, allocation strategies employed, efficiency of the process, size of scale and quality of data used. The contribution analysis outcomes for lignin-based coatings are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results of other materials in the market that are used for hydrophobic coating in the textile industry are compared with our study results. The previously published studies were found to focused mainly on the GWP. The commonly used hydrophobic material in the market, Teflon, has a GWP of 13.11 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;per m\u003csup\u003e2\u003c/sup\u003e (Gore and Associates 2013) application that is higher than the GWP values of this study\u0026rsquo;s coatings [suberin (2.92 kg CO\u003csub\u003e2\u003c/sub\u003e/ FU), betulin (1.38 kg CO\u003csub\u003e2\u003c/sub\u003e/FU) and lignin (2.01 kg CO\u003csub\u003e2\u003c/sub\u003e/FU)]. Silicones have a GWP of 1.88 kg CO\u003csub\u003e2\u003c/sub\u003e/m\u003csup\u003e2\u003c/sup\u003e (Althaus et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which is lower than suberin (1.99 kg CO\u003csub\u003e2\u003c/sub\u003e/m\u003csup\u003e2\u003c/sup\u003e) but higher than betulin (1.46 kg CO\u003csub\u003e2\u003c/sub\u003e/m\u003csup\u003e2\u003c/sup\u003e) and lignin (1.07 kg CO\u003csub\u003e2\u003c/sub\u003e/m\u003csup\u003e2\u003c/sup\u003e). While silicon-based coatings production is well-established, our results based on lab-scale experiments for suberin, betulin and lignin-based coatings may evolve as the technology matures. Improved materials and energy efficiency, along with recycling chemicals in a closed-loop system and utilizing resources like water more effectively, will likely reduce the environmental impacts.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Results of contribution analysis\u003c/h2\u003e\u003cp\u003eEnvironmental hotspots were identified by analysing the contributions of components such as suberin, betulin, lignin, electricity, acetone, ethanol, cationic starch, water, and filters in the process. The results of contribution assessment of different components used per FU for suberin coating are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The production of suberin raw material (1.12 kg CO\u003csub\u003e2\u003c/sub\u003e eq.) was the main contributor to GWP, followed using ethanol (0.63 kg CO\u003csub\u003e2\u003c/sub\u003e eq.) during the application stage. Electricity use was the third highest contributor (0.42 kg CO\u003csub\u003e2\u003c/sub\u003e eq.), and water had the lowest impact (0.03 kg CO\u003csub\u003e2\u003c/sub\u003e eq.) among all components used in coating production and application process. In addition, cationic starch (0.30 kg CO\u003csub\u003e2\u003c/sub\u003e eq.), filter paper (0.29 kg CO\u003csub\u003e2\u003c/sub\u003e eq.), and acetone (0.12 kg CO\u003csub\u003e2\u003c/sub\u003e eq.) had moderate contributions to GWP.\u003c/p\u003e\u003cp\u003eSuberin was the main contributor to IR (1.35 kBq Co-60 eq.), FPMF (0.002 kg PM\u003csup\u003e2.5\u003c/sup\u003e eq.), TA (0.006kg SO\u003csub\u003e2\u003c/sub\u003e eq.), FEW (0.004 kg P eq.), MRS (0.0012 kg Cu eq.), HCT (0.025 kg 1,4-DCB), HNCT (0.68 kg 1,4-DCB) and LU (0.21 m\u003csup\u003e2\u003c/sup\u003ea crop eq.) impact categories (Table S5). The use of chemicals like ethanol and sulfuric acid in suberin production significantly contributes to environmental impacts in various categories. Electricity was main contributor to IR, and MRS impact categories, primarily due to use of Finnish mix electricity. Acetone on the other hand contributed to the TE impact category due to its production process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe environmental impacts varied significantly across different input materials and processes. Ethanol emerged as the primary contributor to GWP, OF, and FRS, while betulin was the second highest contributor to GWP, IR, OF, FPMF, FE and FRS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Electricity consumption predominantly influenced IR and MRS, while showing minimal impact on other categories. LU were primarily driven by cationic starch production, followed by betulin, cellulosic filter, and electricity consumption (Table S6). These LU impacts can be attributed to the cultivation of raw materials required for starch, cellulose, and betulin production, as well as the Finnish electricity mix. WC was directly impacted by process water requirements.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe environmental impact contributions of different components in the lignin-based coating and application process are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table S7. Ethanol was the primary contributor to GWP, while acetone dominated TE impacts. Water consumption directly influenced WC, and cationic starch was the main driver of LU. Lignin production significantly affected the IR, HCT, and HNCT categories.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Results of Sensitivity Analysis\u003c/h2\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Change in source of electricity\u003c/h2\u003e\u003cp\u003eThe LCA outcomes are primarily influenced by two key factors the source of electricity and the type of ethanol used in the process. Figures\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e show that the highest GWP occurred when the global (GLO) electricity mix (Table S8) was used, whereas the lowest GWP was observed with the Finnish (FI) electricity mix. The reason is high percentage of electricity generation from coal at a global level. In contrast, the Finnish electricity mix has a lower percentage of fossil and coal-based electricity and a higher percentage of renewable electricity (Energy statistics Finland 2023). Finland's electricity mix comprises biomass (25%), nuclear power (27.4%), hydropower (19.2%), wind power (9.7%), solar power (0.3%), and imported electricity-18.4% from Russia and the remainder from Sweden (Energy statistics Finland 2023; Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sweden generates 48% of its electricity from nuclear power, while 64% of Russia's electricity is produced from fossil fuels (EMBER \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among all the coatings, the highest impact on the IRP category was observed with the use of nuclear power in Finland's electricity grid mix. In contrast, the application of GLO electricity resulted in the lowest impact within the IRP category (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe percentage changes in environmental impact when Finnish mix electricity was replaced by Europe (EU) and global (GLO) electricity mixes are shown in Table S8. In suberin coating, by replacing FI by EU and GLO mix electricity, GWP increased by 63% and 19%, respectively. Conversely, IR and LU impact categories were reduced by 26% and 11%, respectively, with the GLO and EU mixes. The same trends were observed for betulin and lignin coatings in GWP, IR and LU impact categories (Table S8). Europe power grid composition is 40% renewable energy, 38.6% fossil fuels, and 20% nuclear power and others. Natural gas is the main fossil energy source used \u003cb\u003eto generate electricity (19.6%), followed by coal (15.8%)\u003c/b\u003e (Europa \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Globally, fossil fuels remain the highest source of electricity generation, with the GLO mix grid composition being different from the EU power grid. The coal-based energy is 35.5%, while natural gas follows with a 23% share, and China, India, and the United States accounted for the largest contributors (STATISTA 2023). For lignin coating, GWP increased by 28% and 91% when FI was replaced by EU and GLO electricity, respectively. HCTP increased by 91%, whereas IRP decreased 64% by replacing FI by GLO electricity, respectively (Table S9).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Bioethanol as an alternative to ethanol\u003c/h2\u003e\u003cp\u003eThe substitution of fossil ethanol with bioethanol demonstrated varying environmental impacts across the three coating systems. A modest reduction in GWP was observed for suberin coating, decreasing from 2.92 kg CO\u003csub\u003e2\u003c/sub\u003e to 2.53 kg CO\u003csub\u003e2\u003c/sub\u003e, with similar trends noted for betulin and lignin coatings (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). While minimal changes were observed in IR, FE, and ME impact categories, significant variations emerged in LU, FRS, TA, and WC. Notably, LUP increased substantially by 102% (from 0.80 m\u003csup\u003e2\u003c/sup\u003ea to 1.62 m\u003csup\u003e2\u003c/sup\u003ea crop) due to wood resource requirements in bioethanol production. This LU increase pattern was consistent across betulin and lignin coating systems. Detailed comparative results for all impact categories are presented in Table S10.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Effect of Feedstock Production Changes on GWP\u003c/h2\u003e\u003cp\u003eThe analysis demonstrated that changes in the production methods of raw materials (suberin, betulin and lignin) significantly changes the environmental impact outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e). The biobased chemicals produced at different scales (laboratory, pilot and industrial) exhibited different environmental impacts for the same product. At laboratory scale, the environmental impacts are generally higher because of less efficient processes, lower energy and material efficiency, higher waste generation, and limited possibility for recycling and reusing chemicals in process. However, at industrial scale the possibility of recycling and reuse of chemicals and materials maximizes as the technology matures. The results showed that GWP decreased by 18\u0026ndash;47% due to changes in suberin production process (Yadav et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). GWP decreased by 18\u0026ndash;25% for betulin coating and by 16\u0026ndash;17% for lignin coating.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Production cost and environmental cost\u003c/h2\u003e\u003cp\u003eSince the betulin, suberin, and lignin coating production processes are precommercial for that reason industrial data is unavailable, and capital and investment costs are highly uncertain at the trial stage. Both production and environmental costs vary according to the raw materials utilized in each coating. Environmental costs were calculated by multiplying the impact of each environmental indicator by its associated externality cost (results listed in Table S11).\u003c/p\u003e\u003cp\u003eThe external cost for suberin coating is 1.50 \u0026euro;/kg per FU, for betulin coating is 1.20 \u0026euro;/kg per FU and for lignin coating is 0.96 \u0026euro;/kg per FU, while without coating application is 1.10 \u0026euro;/kg for suberin, 0.80 \u0026euro;/kg for betulin, and 0.57 \u0026euro;/kg for lignin. Environmental cost is highly dependent of external prices of FPMF, that is 99.2 \u0026euro;/kg PM\u003csup\u003e2.5\u003c/sup\u003e eq.\u0026nbsp;whether environmental impact on FPMF was low for all coatings and that did not significantly influence the external costs of coatings.\u003c/p\u003e\u003cp\u003eThe production cost of suberin is 2.21 \u0026euro;/kg, for betulin is 1.23 \u0026euro;/kg, and for lignin is 0.20 \u0026euro;/kg (Table S12). Suberin and betulin have higher market prices in comparison to lignin, because the production of suberin and betulin raw material is more costly than lignin. This is the main reason for higher prices of suberin and betulin coatings. The production of lignin is a well-established industrial process, which lowers its price significantly. Table S12 also shows the changes in price per FU, with and without the application stage. Ethanol is only needed at application stage and for that reason the cost is provided in two parts: one is production cost without application, and another is total cost per FU. The price is also influenced by other components such as electricity, bark, and other chemicals used in process.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Uncertainty analysis\u003c/h2\u003e\u003cp\u003eThe results of the uncertainty analysis provide insight into the reliability and quality of the data across various parameters, as outlined in the methodology section (Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e2.6\u003c/span\u003e). Detailed results of uncertainty analysis were reported in Table S13 on different impact categories, including the mean, median, standard deviation (SD), coefficient of variation (CV), and standard error of the mean (SEM). In general, CV less than 10% is considered a good stability indicator, whereas more than 10% indicates that the results are more variable. The results showed that CV values for GWP were within \u0026plusmn;\u0026thinsp;10%, while the values for LU were higher than 10%. A high SD indicates that the data is widely spread, making it less reliable, whereas a low SD suggests that the data is closely clustered around the mean, indicating greater reliability, as shown in Table S13. The SD values range from 0.15 to 10.18 across various impact categories. The GWP category had an SD of 0.15, indicating high precision and consistency in the results. In contrast, the WC and HNCT impact categories showed SD values greater than 10, suggesting higher variability and less reliability.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis life cycle assessment provides comprehensive insights into the environmental performance of bio-based hydrophobic multifunctional coatings derived from wood components: suberin, betulin, and lignin - demonstrating their potential as sustainable alternatives to conventional fossil-based coatings like Teflon. Energy source selection emerged as a critical factor, with the Finnish electricity mix offering environmental advantages due to its low fossil fuel dependency. While bioethanol substitution showed promise in reducing global warming potential, it highlighted important sustainability trade-offs, particularly in land use impacts. These findings contribute to the growing body of knowledge on sustainable coating technologies and provide valuable insights for future development of environmentally conscious manufacturing processes in the textile industry. Further optimization through renewable energy integration presents opportunities for enhancing the environmental performance of these bio-based coating systems. The production cost and environmental cost of the three coatings were calculated. The production cost depends on the inputs used in the process and their market price. The environmental cost depends on the emissions produced during the coating\u0026rsquo;s production and application. It was found that the environmental cost is mainly affected by the fine particulate matter formation (FPMF), because it has a high external cost. The environmental impact assessment, encompassing both production and economic dimensions, revealed complex interrelationships between process variables and environmental outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5.1 Acknowledgement and Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the financial support provided by the ENZYFUNC Project (349052 and 348870 RRF Green and Digital Transition), funded by the Research Council of Finland. Support from the Academy of Finland’s Flagship Program through projects no. 318890 and 318891 (Competence Centre for Materials Bioeconomy, Finn CERES) is also sincerely acknowledged\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Authors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePooja Yadav: Writing-original draft, Writing-review \u0026amp; editing, Visualization, Validation, Supervision, Software modelling, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Paula Nousiainen: \u0026nbsp;Writing- review \u0026amp; editing, Writing -experimental part, Data curation. Farooq Muhammad: Writing- review \u0026amp; editing, Writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Ethical Approval:\u003c/strong\u003e This is not applicable for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Consent to Participate\u003c/strong\u003e:\u0026nbsp;This is not applicable for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.5 Consent to Publish:\u003c/strong\u003e This is not applicable for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.6 Clinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.7 Competing Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.8 Data Availability Statement:\u003c/strong\u003e Data will be made available on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmeida RSR, Taccini MM, de Moura LF, Ceribelli UL, Brito JO, Gloria EM (2019) Potential of Pyroligneous Extract of Eucalyptus Wood as a Preservative of Cosmetic and Sanitizing Products. 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Holzforschung 78(5):303\u0026ndash;316\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahmadhani D, Yuliani KD, Frida E, Taufiq A (2024) Hydrophobic and antibacterial properties of textiles using nanocomposite chitosan and SiO2 from rice husk ash as-coating. S Afr J Chem Eng 48:366\u0026ndash;374\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRivi\u0026egrave;re GN, Pion F, Farooq M, Sipponen MH, Koivula H, Jayabalan T, Pandard P, Marlair G, Liao X, Baumberger S, \u0026Ouml;sterberg M (2021) Toward waste valorization by converting bioethanol production residues into nanoparticles and nanocomposite films. Sustainable Mater Technol 28:e00269\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuwoldt J, Blindheim FH, Chinga-Carrasco G (2023) Functional surfaces, films, and coatings with lignin\u0026ndash;a critical review. RSC Adv 13(18):12529\u0026ndash;12553\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSehaqui H, Liu A, Zhou Q, Berglund LA (2014) Fast preparation procedure for large, flat cellulose and cellulose/inorganic nanopaper structures. Biomacromolecules 11(9):2195\u0026ndash;2198\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShanmugam K, Gadhamshetty V, Yadav P, Athanassiadis D, Tysklind M, Upadhyayula VK (2019) Advanced high-strength steel and carbon fiber reinforced polymer composite body in white for passenger cars: Environmental performance and sustainable return on investment under different propulsion modes. 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Journal of Analytical and Applied Pyrolysis, 150, p.104843\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Betulin, Environmental Impact and cost, Hydrophobic coating, Life cycle assessment, Lignin, Suberin","lastPublishedDoi":"10.21203/rs.3.rs-6853367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6853367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe circular bioeconomy supports climate action and biodiversity preservation by promoting the use of renewable materials in sustainable production. In this study bio-based betulin, lignin and suberin were used as raw materials for producing the multifunctional hydrophobic coatings. Life cycle assessment (LCA) was used to study the environmental impact of these protective coatings from cradle to gate. The foreground data were collected from laboratory experiments and literature, while background data were sourced from the ecoinvent 3.10 database. The functional unit (FU) used was coating production and application on 1 m\u003csup\u003e2\u003c/sup\u003e of fabric. The environmental impacts and cost were evaluated using Recipe (H) 2016 midpoint method in SimaPro 9.6. The results indicated that per FU, the global warming potential (GWP) was 2.92 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;for suberin coating, 2.39 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;for betulin coating, and 2.01 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;for lignin coating. The sensitivity analysis indicated that replacing ethanol with bioethanol reduced the burden on GWP and fossil resource scarcity (FRS) but increased the burden on the land use (LU), terrestrial ecotoxicity (TE) and human non-carcinogenic toxicity (HNCT). Additionally, the source of energy in the process particularly participation of nuclear and bio-based electricity, was found to influence the results on GWP, IR and LU impact categories. The recycling rate of solvents and the production process of feed stocks (suberin, betulin and lignin) also significantly impacted the results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Environmental Impact and Cost of Bio-based Hydrophobic Multifunctional Coatings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 18:07:03","doi":"10.21203/rs.3.rs-6853367/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2026-01-26T09:38:52+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-12-04T13:20:09+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T12:22:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2025-07-04T09:53:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T04:21:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2025-06-11T11:45:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4a584669-a43f-47cb-b5db-e5b194950b21","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:01:32+00:00","versionOfRecord":{"articleIdentity":"rs-6853367","link":"https://doi.org/10.1007/s11356-026-37701-3","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2026-04-07 15:58:24","publishedOnDateReadable":"April 7th, 2026"},"versionCreatedAt":"2025-07-22 18:07:03","video":"","vorDoi":"10.1007/s11356-026-37701-3","vorDoiUrl":"https://doi.org/10.1007/s11356-026-37701-3","workflowStages":[]},"version":"v1","identity":"rs-6853367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6853367","identity":"rs-6853367","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}