Driving sustainability at early-stage innovation in production of zinc oxide nanoparticles

Driving sustainability at early-stage innovation in production of zinc oxide nanoparticles Israel Carreira-Barral a,1 , Julieta Díez-Hern´ andez b,1 , Elorri Igos c , Michael Saidani c , Tianran Ding c , Tiago Ramos da Silva d , Helena Monteiro d , Andreas Stingl e , Patricia M.A. Farias e,f , Olavo Cardozo e,f , Jesús Ib´ a˜ nez a , Ana García-Moral a , Juan Antonio Tamayo-Ramos a , Carlos Rumbo a , Rocío Barros a,* , Sonia Martel-Martín a,* a International Research Center in Critical Raw Materials for Advanced Industrial Technologies (ICCRAM), Universidad de Burgos, Centro de I+D+I, Plaza Misael Ba˜ nuelos, s/n, 09001 Burgos, Spain b Economy and Business Management Department, Faculty of Economics, Universidad de Burgos, Plaza Infanta Do˜ na Elena, s/n, 09001 Burgos, Spain c Environmental Research &Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, 4362 Belvaux, Luxembourg d Low Carbon &Resource Efficiency Unit, R&Di, Instituto de Soldadura e Qualidade (ISQ), R. do Mirante, 258, 4415-491 Grij´ o, Portugal e Phornano Holding GmbH, Kleineingersdorfer straße 24, 2100 Korneuburg, Austria f Advanced Materials Laboratory, Federal University of Pernambuco, Av. Prof. Moraes Rego, 1235, 50670-901 Recife, PE, Brazil ARTICLE INFO Editor: Prof. Raymond Tan Keywords: Zinc oxide nanoparticles Holistic sustainability Environmental Life Cycle Assessment Material Flow Cost Accounting Social Life Cycle Assessment Multi-Criteria Decision Analysis ABSTRACT Despite its industrial relevance and the methods that have been described for its synthesis, little is known about the performance of the production processes of ZnO nanoparticles (ZnO NPs), either pure or doped, from the sustainability perspective. The Safe-and-Sustainable-by-Design (SSbD) framework brings to this context an excellent opportunity to 1) evaluate the impacts of chemical processes from the safety and sustainability perspectives, and 2) design and test safety and sustainability strategies to study and optimise these key aspects in early innovation stages. This work aims at assessing the production of ZnO NPs using this approach, testing the sustainability of the materials, designed and produced by Phornano, an Austrian SME, under this scheme. Three scenarios were analysed: the original process (BS) and two alternatives resulting from the application of SSbD strategies to the former (S1 and S2). BS is a linear process in which Zn(NO 3 ) 2 ⋅6H 2 O, whey, water and a dopant (a Mn salt) are used as starting materials. However, obtention of the desired product entails the release of toxic fumes (SO x and NO x ) to the atmosphere. S1 and its scale-up version, S2, are circular processes in which SO x emissions are avoided, due to the replacement of whey by a non-aminated starch, and NO x are transformed into HNO 3 , which reacts with Zn powder to produce Zn(NO 3 ) 2 ⋅6H 2 O; in this way, no harmful substances are freed and the zinc salt employed as a raw material in BS is generated during the manufacture of ZnO NPs. Four well-known evaluation tools were employed to achieve a holistic sustainability perspective: Environmental Life Cycle Assessment (LCA), Material Flow Cost Accounting (MFCA), Social Life Cycle Assessment (S-LCA) and MultiCriteria Decision Analysis (MCDA), according to the standardised methodologies or the most broadly spread ones; the study was complemented with an uncertainty analysis. The results for the production of 1 kg of ZnO NPs show that the after-SSbD scenarios are remarkably more sustainable than BS: the environmental evaluation reveals that S2 outperforms BS for 10 environmental indicators, allowing a reduction of 67 % in terms of total aggregated impact (from 13.7 to 4.4 mPt); from the economic viewpoint, synthesis of ZnO NPs through S2 is around four times cheaper than that achieved via BS (512 vs 2206 € ); finally, the social footprint is reduced from 159 mPt in the original process to 21 mPt in S2. MCDA of BS, S1 and S2 considering the three assessments performed confirms that S2 is, with almost 100 % probability, the best-performing alternative from the sustainability perspective, followed by S1. Overall, this work, the most complete in this field to date, contributes to * Corresponding authors. E-mail addresses: [email protected] (I. Carreira-Barral), [email protected] (J. Díez-Hern´ andez), [email protected] (M. Saidani), [email protected] (T. Ding), [email protected] (T. Ramos da Silva), [email protected] (H. Monteiro), [email protected] (A. Stingl), [email protected] (P.M.A. Farias), [email protected] (O. Cardozo), [email protected] (J. Ib´ a˜ nez), [email protected] (A. García-Moral), [email protected] (J.A. Tamayo-Ramos), [email protected] (C. Rumbo), [email protected] (R. Barros), [email protected] (S. Martel-Martín). 1 These authors contributed equally to this work and share first authorship. Contents lists available at ScienceDirect Sustainable Production and Consumption journal homepage: www.elsevier.com/locate/spc https://doi.org/10.1016/j.spc.2025.03.003 Received 12 November 2024; Received in revised form 26 February 2025; Accepted 2 March 2025 Sustainable Production and Consumption 55 (2025) 353–372 Available online 6 March 2025 2352-5509/© 2025 The Authors. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). the sustainable synthesis of ZnO NPs and to the methodological advance of the SSbD framework through the revision of its limitations and opportunities. 1. Introduction Nanomaterials (NMs) are appealing because of their high stability, straightforward synthetic methods and easy control over aspects like size and shape (Negrescu et al., 2022). Among these materials, ZnO is the most widely used, since its chemical and optical properties can be easily tuned (Mandal et al., 2022). This work focuses on improving the sustainable synthesis of this compound, combining functionality with reduced environmental, economic and social impacts. ZnO exhibits excellent thermal and chemical stabilities (Skorenko et al., 2016;Heinonen et al., 2017), it is biocompatible (Stefanidou et al., 2006) and biodegradable (Kielbik et al., 2017), and the raw materials required for its production are accessible (El Faroudi et al., 2023). These advantages have turned ZnO nanoparticles (NPs) into a valuable multifunctional material, key to the development of Green Technologies (Klingshirn et al., 2010), that has been employed, for instance, in sunscreens (Schneider and Lim, 2019) and biomedical imaging (Hahm, 2014). These are the two main applications of the ZnO NPs manufactured by PHORNANO Holding GmbH (hereafter Phornano), the company that has produced ZnO NPs according to the processes described in this work. Several physical, chemical and biological methods have been reported for the synthesis of nanoscale materials altogether, and of ZnO NPs in particular (Mekuye and Abera, 2023); some of them are shown in Fig. 1. In this work, ZnO NPs were prepared through a hybrid strategy: a sol-gel method assisted by whey, a by-product of the production of cheese or casein (Soares et al., 2020), or by a non-aminated starch, a polysaccharide (Nain et al., 2020). Although ZnO NPs have been extensively studied, the potential of sustainable hybrid synthetic methodologies, particularly those incorporating renewable by-products, such as whey or starch, remains significantly underexplored. These approaches pose a promising avenue by utilising abundant, cost-effective and biodegradable resources, thereby aligning with the principles of sustainability and circular economy (United Nations, 1987). This research gap highlights the urgent necessity of developing environmentally sustainable synthetic strategies for the tailored functionalisation of ZnO NPs. In this context, this work aims at assessing and comparing the sustainability of the production of ZnO NPs before and after applying the Safe-andSustainable-by-Design (SSbD) framework (European Commission, 2022), implementing the identified safety and sustainability recommendations. Use of this framework, which is a general approach to steer innovation towards safe and sustainable materials (in this case, NMs) (Pizzol et al., 2023) throughout their life cycles (Furxhi et al., 2023;Caldeira et al., 2024), has allowed to pinpoint key sustainability hotspots and to evaluate the effectiveness of the proposed redesign measures to mitigate those impacts. The SSbD methodology consists of five steps, Steps 1, 2 and 3, associated to safety, and Steps 4 and 5, related to environmental and socio-economic analyses, respectively. Safe-by-Material (SbMD) and Safe-by-Process (SbPD) approaches, two particular sets of SSbD strategies, were applied to improve Phornano’s original production process (BS), leading to definition of two new scenarios, S1 and S2, that are described below; both SbMD and SbPD are part of the background research of this work, which is focused on Steps 4 and 5. Although the European Commission recommends the early application of SSbD in the innovation process (Abbate et al., 2024), challenges have been identified in various case studies (Caldeira et al., 2023), as well as difficulties experienced by companies when trying to implement SSbD (European Environment Agency, 2020;CEFIC, 2021, 2022;ChemSec, 2021). For instance, the homogenised terminology and SSbD criteria, evaluation tools, data availability and quality, methods for scaling up laboratory data to an industrial level, and others (Abbate et al., 2025). Within Steps 4 and 5, data availability, quality and uncertainty, and tools, are the main concerns (Caldeira et al., 2022;Abbate et al., 2025); besides, a holistic view of the application of the SSbD framework has been highlighted as a desirable approach to connect all value chain stakeholders and facilitate co-creation of SSbD solutions (Soeteman-Hern´ andez et al., 2024;Abbate et al., 2025). As Step 5 is an optional step of the framework and, differently from Step 4, there are not universally accepted methodologies to conduct the socio-economic evaluation, this work intends to assess the usefulness of the selected social and economic tools, and delve into data and uncertainty analysis for BS, S1 and S2 from the three sustainability pillars viewpoint, considering also the integration of the results through a Multi-Criteria Decision Analysis (MCDA) tool (Prado and Heijungs, 2018;Tschulkow, 2024), which allows for balancing complex trade-offs among environmental, social and economic factors. Altogether, this work contributes to filling some of the gaps found in the application of Steps 4 and 5 of the framework, and to the global analysis of the results of LCA, MFCA and S-LCA studies. As indicated in Section 2, sustainability studies about NMs Fig. 1. Some of the methods described in the literature for the synthesis of ZnO NPs: wet chemistry, eco-friendly routes and hybrid methods. I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 354 manufacturing processes in general, and of ZnO NPs in particular, are limited, which means a research gap in the identification of critical environmental, economic and social hotspots within those processes, and application of correcting measures to reduce the corresponding impacts, particularly according to the SSbD approach. Analysing the influence of uncertainties on the final results is also key, especially for low-TRL processes like those described here. This work aims at covering this gap by studying and comparing the hotspots of BS, S1 and S2, and the uncertainties of the final results, considering the three sustainability pillars and integration of results through the methods described in Section 3. The three scenarios are represented in Fig. 2. Briefly (for a more detailed discussion, see Section 3.1), BS is the initial alternative, in which Zn(NO 3 ) 2 ⋅6H 2 O, a Mn salt, whey and water are used as raw materials to ultimately yield the ZnO NPs, but with the concomitant formation of toxic fumes (SO x and NO x ). S1 and S2, the latter being an adapted and upscaled scenario with respect to S1, avoid such emissions by replacing whey by starch (SO x ), and by returning NO x to the system to produce useful reagents (HNO 3 ) that eventually react with Zn powder to generate Zn(NO 3 ) 2 ⋅6H 2 O, and from here the desired good. This study not only advances sustainable nanotechnology by optimising the synthesis of ZnO NPs for various applications but also provides practical guidance on the operationalisation of the socio-economic integration aspects of the SSbD approach. In particular, it contributes to the holistic sustainability evaluation, considering environmental, economic and social results. The findings of this research highlight its potential to support safety and sustainability in industrial processes from the earliest stages of innovation. By demonstrating the feasibility of hybrid green methods and the application of the SSbD approach to their study and improvement, this work lays a foundation for safer, more sustainable nanotechnology innovations, with results that are useful not only for academia but also for stakeholders and contribute to the development of these studies for NMs-based low-TRL technologies. 2. Literature review The blooming of nanotechnology and the ubiquity of NMs, such as the discussed ZnO, have encouraged the eco-friendly design and the sustainable use of these goods (Hutchison, 2016;Pokrajac et al., 2021; Chausali et al., 2023). Simultaneously, concerns about the sustainability of the production processes of NMs, including NPs emissions to the environment, have been raised (Buist et al., 2017;Salieri et al., 2019; Martínez et al., 2021;Arora et al., 2024). Among different evaluation tools, Life Cycle Assessment (LCA) has been commonly employed to quantify the impacts associated to the life cycle of a product. For an emerging technology such as NMs that often only functions at a lab or pilot scale, ex-ante LCA has become an essential instrument for identifying potential hotspots and guiding sustainable design (Cucurachi et al., 2018;Cucurachi and Blanco, 2022). For instance, Tan et al. (2018) performed an ex-ante LCA for cellulose nanocrystal foam along the R&D trajectory, describing the design improvements. Pallas et al. (2020) conducted an ex-ante LCA study of the emerging gallium-arsenide nanowire and provided a benchmark for the commercialisation of the technology. Recently, a framework for ex-ante LCA of nano-reinforced biopolymers at low TRL (Technology Readiness Level) was proposed and applied, and the environmental hotspots identified (Müller-Carneiro et al., 2023). However, the use of Life Cycle Assessment (LCA) (and, particularly, ex-ante LCA) as a tool to quantify the environmental footprint of the manufacturing of these materials (Meyer and Upadhyayula, 2014) is still at an early stage (Hachhach et al., 2022). According to a recent review, from 2001 to 2020 only 71 studies revolving around LCA of NMs were published (Nizam et al., 2021). Technoeconomic analyses of NMs production processes, in some cases complemented with LCA studies, have also been reported (de Assis et al., 2018;Ragadhita et al., 2019;Karadaghi et al., 2023;Rajendran et al., 2023), but S-LCA investigations in this context are scarce (Handy and Shaw, 2007;Stoycheva et al., 2022). Multi-objective problem-solving strategies are highly recommended to achieve a meaningful interpretation of results (Jia et al., 2016), since these approaches allow to simultaneously analyse and balance conflicting goals (e.g., reducing environmental impacts vs minimising operational costs); the holistic nature of the assessment and the consideration of uncertainties also permits to reduce bias, thus improving confidence in the outcome. Multi-Criteria Decision Analysis (MCDA) (Cinelli et al., 2017) is one of the tools used for this purpose; it is a valuable methodology to analyse several scenarios (as BS, S1 and S2; see Section 1) and various indicators (e.g., environmental, economic and social) at the same time. However, concerning NMs the use of MCDA is not as common as would be expected. Indeed, despite the comprehensive outcomes they provide, the former methodologies still raise challenges for their application to NPs, e.g., the complex modelling of the consequences of NPs release (Hischier et al., 2017) and the uncertain modelling of low-TRL technologies. In the case of ZnO NPs, to the best of our knowledge there is only one published LCA study, which addresses the microwave-assisted synthesis of this material, although not from a circular perspective (Papadaki et al., 2017). Few techno-economic assessments dealing with the production of this NM have been reported (Zahra et al., 2020;Yashni et al., 2021). Neither S-LCA nor MCDA studies have been found in the literature. The inclusion of S-LCA and MCDA in this work is therefore a progress beyond state of the art. Fig. 2. Simplified diagram of the manufacturing process of ZnO NPs through the baseline scenario (BS, highlighted in blue) and scenarios 1 and 2 (S1 and S2, respectively, highlighted in green; S2 is an adapted and upscaled version of S1). The last step (production of ZnO NPs) is shown as a green-blue gradient since it is a common step for the three processes. I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 355 3. Methods In order to perform the present investigation, four assessment tools were employed: LCA, MFCA, S-LCA and MCDA. LCA is a standardised methodology aiming at evaluating the environmental impacts of products, processes or services throughout their life cycle, according to ISO 14040 (2006) and 14044 (2006); this is the recommended approach within the SSbD framework. MFCA is standardised through ISO 14051 (2011); it focuses on material and energy flows to pinpoint and quantify waste and losses of production processes in monetary terms (Bierer et al., 2015). Differently from traditional costing methods, MFCA highlights not just the direct costs of undesirable outputs (in terms of lost sale revenue), but also all the upstream value loss in cost drivers like labour, raw materials and invested capital, that are also wasted (Schmidt, 2015). Thus, it is a useful tool not only in terms of cost accounting, but also in promoting improvements in resource efficiency and consequent reduction in environmental impact. The third tool, S-LCA, the least developed of this group, follows a similar approach to that of LCA, its purpose being to identify, prioritise and evaluate all the social impacts derived from the life of the object of study. Although there is not a universally accepted methodology to conduct S-LCA, the guidelines developed by UNEP/SETAC are the most widely spread attempt (Benoît Norris et al., 2020;Traverso et al., 2021). According to these guidelines, a Reference Scale Approach (RSA) was followed for the assessment through a database-assisted S-LCA. This method proposes to analyse the social performance opposite international and sectorial standards and statistics, which allows navigating uncertainties of low-TRL processes through a screening of potential hotspots. Finally, MCDA is a decisionsupporting method that addresses intricate problems with high uncertainties and considers mutual differences. This tool allows to integrate two or more scenarios (e.g., the production of ZnO nanopowder by three alternatives; vide infra) and two or more indicators (for instance, environmental, economic and social indicators) in a comparative study to decide which one is the best-performing process from a particular perspective (for example, sustainability). To perform this analysis, the Excel worksheet developed by Tschulkow (2024), based on the work by Prado and Heijungs (2018), was employed. 3.1. Case study This study was performed to evaluate the environmental, economic and social impacts derived from the production of doped ZnO NPs by Phornano, an Austrian SME (small and medium-sized enterprises) focused on the research, development and manufacturing of functional NMs. The manufactured Mn-doped ZnO NPs are used as a raw material for: (a) formulations leading to the development of sunscreens, as ZnO strongly absorbs UVA and UVB radiations (Antoniou et al., 2008), and (b) fluorescent nanoprobes for biomedical imaging, as replacement for the less safe Cd-based quantum dots (see Section 1) (Hahm, 2014). Initially, a process leading to the annual production of 2.5 kg of doped ZnO nanopowder (the baseline scenario, BS) was performed. By employing the Safe-and-Sustainable-by-Design (SSbD) methodology, key process inefficiencies were identified, and evidence-based recommendations provided to optimise performance, safety and global sustainability. Implementation of such recommendations resulted in two redesigned scenarios, S1 and S2, which were systematically re-evaluated to quantify their impact and validate the robustness of the proposed approach. Comprehensive physicochemical characterisation, integrated with toxicokinetic modelling, demonstrated that the reengineered materials and processes exhibited equivalent or superior functional properties compared to those of the original material (BS). The results of the sustainability studies conducted for the three processes are analysed in this work. The three alternatives were developed by Phornano within the framework of the ‘Diagonal’European research project (GA 953152). The synthesis of doped ZnO NPs according to BS starts with the dissolution of zinc nitrate hexahydrate (Zn(NO 3 ) 2 ⋅6H 2 O) and manganese(II) nitrate tetrahydrate (Mn(NO 3 ) 2 ⋅4H 2 O) in water under stirring and heating at 90 ◦C. This is followed by the insertion of a gelling agent; initially, whey was used as a chelating species (BS), but afterwards it was replaced by a non-aminated starch, a vegan alternative (i.e., free of animal sources or processing aids derived from animals, or animal by-products) to whey (S1 and S2). Both whey and starch avoid the use of citric acid and ethylene glycol, other commonly employed reagents in this process (Soares et al., 2020). Polymerisation takes place after the insertion of the chelating agent, driving to the formation of a gel that is heated in a drying oven for one hour at 200 ◦C to remove the water excess. A nanofoam is obtained and subsequently calcinated (400 ◦C), leading to the desired ZnO nanopowder. In the case of S1 and S2, the gel is treated in a closed reactor, thus avoiding the release of nitrogen oxides (NO x ), which flow into a water bath to turn them into nitric acid (HNO 3 ), that ultimately reacts with Zn powder to produce Zn (NO 3 ) 2 ⋅6H 2 O, restarting the cycle. In both scenarios the calcination step is circumvented, with the consequent energy saving (vide infra, and see also Figs. 3 and 4). After carrying out the physicochemical, toxicological and sustainability evaluations of BS, a set of Safe-by-Material and Safe-by-Process design alternatives (SbMD and SbPD, respectively) were identified and applied. Regarding SbMD, doping of ZnO with Mn was performed. As the VERDEQUANT process (Stingl et al., 2021;Phornano, 2024) supports doping of ZnO quite well (Picasso et al., 2022;Assis et al., 2024), samples were produced via this method. Concerning SbPD, three improvements were applied: •Creation of a NOFLOW box to enhance the safety of the handling process. The NOFLOW box is a workplace shielded against airflow and with a screen to protect the operator. Its design is similar to that of a flowbox, but without any ventilation to avoid nanopowders to be blown away during handling and filling. •Conversion of NO x into HNO 3 , thus allowing the circularity of the above-mentioned VERDEQUANT manufacturing process. •Substitution of whey by a non-aminated starch, a vegan formulation, thus avoiding the formation and release of sulfur oxides (SO x ), an unwanted by-product. These modifications in the production process led to the definition of two new scenarios: •Scenario 1 (S1): application of the above-mentioned SbMD and SbPD strategies to a small amount of product (2.5 kg). •Scenario 2 (S2): application of those strategies to a larger-scale scenario, leading to 100 kg of produced doped ZnO NPs. The linearity of BS and the circularity of S1 and S2 are explained in Fig. 3. S2 differs from S1 mainly in two aspects: on the one hand, in S1 both Zn(NO 3 ) 2 ⋅6H 2 O and Zn powder are employed as raw materials, whereas in S2 only Zn powder is used. On the other, S2 is an upscaled scenario: 100 kg of doped ZnO NPs are manufactured via this process, and 2.5 kg are obtained through S1. In S2, a larger reactor, with a higher energy efficiency, is employed. The selected functional unit (FU), as explained in the next section, is identical for the three studied processes, to allow for a proper comparison. Once the LCA, MFCA and S-LCA results for S1 and S2 were analysed, they were subjected, together with those of BS, to a Multi-Criteria Decision Analysis (MCDA) to determine which of the three approaches was the best-performing process from the sustainability point of view. 3.2. Goal and scope As indicated in Section 3.1, the main objective of this work is to study the environmental, economic and social performance and, altogether, the sustainability of the production process of doped ZnO NPs according to the experimental procedure conceived by Phornano, and to compare I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 356 this process with two scenarios resulting from the application of SSbD strategies to the former in an integrating study. Environmental, economic and societal arguments, analysed both separately and combined, are provided to select the most advisable alternative from the sustainability perspective. This, together with the life cycle inventory datasets included in this work, will contribute to the state-of-the-art knowledge of the sustainable synthesis of nanosized ZnO for the eventual production of solar lotions (Chauhan et al., 2022) and of medical-oriented materials (Weng et al., 2023), among other applications (Raha and Ahmaruzzaman, 2022;Sharma et al., 2022). The three scenarios were modelled consistently. Thus, the FU for BS, S1 and S2 is identical, so that the results can be compared: the production of 1 kg of ZnO nanopowder that is to be used in sunscreen formulations to achieve 50 SPF (Sun Protection Factor), at minimum 20 % concentration of the photoprotective agent (ZnO). This FU was also selected to carry out the three assessments described in this article (LCA, MFCA and S-LCA) and, consequently, MCDA. The system boundaries are also identical for the three scenarios and assessments. The present work is a ‘cradle-to-gate’study that starts with the raw materials extraction and finishes with the obtention of the product of interest (Fig. 4). It should be noted that only the inputs (e.g., raw materials, electricity consumption) and outputs (e.g., emissions, generated waste) were collected and included within the system boundaries. Upstream activities, such as extraction or transport, were obtained from the selected database (ecoinvent v3.10, 2024), which gathers and integrates average data for each input of the production process, or from the literature, whereas downstream activities (e.g., distribution, final use) were not considered in this work. 3.3. Environmental Life Cycle Assessment (LCA) 3.3.1. Life Cycle Inventory (LCI) Table 1 displays the data of raw materials, energy and emissions, among other items, involved in the synthesis of 2.5 or 100 kg of doped ZnO NPs according to the baseline and after-SSbD scenarios (data refer to the whole production process, without differentiating individual steps). The operational inventories were normalised to the production of 1 kg of doped ZnO NPs (reference flow); all of them can be found in the Fig. 3. Schematic representation of the production process of ZnO NPs through BS (a), and S1 and S2 (b). In the case of S1 and S2, the generated NO x are transformed into HNO 3 , which reacts with Zn powder to produce Zn(NO 3 ) 2 ⋅6H 2 O; in BS, the last reagent enters the process as such. After adding Mn(NO 3 ) 2 ⋅4H 2 O, the insertion of whey (BS) or a non-aminated starch (S1 and S2) takes place. Following a suitable treatment (Section 3.1), Mn-doped ZnO NPs are obtained, with the concomitant formation of NO x that enter the cycle again (S1 and S2). This design allows recirculating NO x and avoids the emission of SO x (BS). *In S1 a mixture of Zn(NO 3 ) 2 ⋅6H 2 O and Zn is employed. I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 357 ‘LCI_Modelling’worksheet of the SI. 3.3.2. Approximations, assumptions and limitations It was assumed that the losses of cleaning and process water are negligible (0 % losses applied) and, in order to model the transport (expressed in tons-kilometer, t⋅km) and production (mass unit used as reference for the ecoinvent v3.10 dataset) of whey, a density of 1 kg/L was supposed. Datasets from ecoinvent v3.10, a common LCI database, were chosen as much as representative in terms of technology (same materials and processes), geography (Austrian data if available, European or global data if not) and time (most recent data); geographical representativeness is indicated in brackets in the dataset name (AT for Austria, RER for Europe and GLO for the world). When the production processes and transport distances were known, materials were modelled with the so-called ‘production’datasets (mentioned at the end of the dataset name); otherwise, ‘market’datasets, which reflect the average production mix and transport means for the considered geographical area, were applied. In the case of Zn(NO 3 ) 2 ⋅6H 2 O and Mn(NO 3 ) 2 ⋅4H 2 O no background data could be found in ecoinvent v3.10 datasets. Therefore, a stoichiometric balance, coming from the reaction of ZnO and MnO, respectively, with HNO 3 , was employed to model these chemicals (MO +2HNO 3 ➔M(NO 3 ) 2 +H 2 O; M =Zn or Mn). All of this is reflected in Table 2. 3.3.3. Life Cycle Impact Assessment (LCIA) This phase allows transforming the data collected in the previous step into environmental impacts. The Environmental Footprint (EF, v3.1) method, based on the Environmental Footprint (EF) initiative, launched by the European Commission (2013) to create a harmonised EU methodology to communicate environmental performance of products and organisations, was selected as the LCIA method since this work was developed in the European Union under the shelter of a EuropeanCommission funded research project. This method consists of 16 midpoint impact categories (Fazio et al., 2018). As this article addresses the study of a low-TRL technology, there are no objective criteria to exclude any of the EF 3.1 indicators from the analysis and, therefore, the information provided by the 16 impact categories was deemed relevant for the work. Finally, creation of the models for the impact assessment calculation was conducted with SimaPro®9.6 (2024) by PR´ e Consultants. SimaPro®, being an internationally recognised software to perform LCA studies, is used both by industry and academia, integrates Fig. 4. Schematic view of the production process of Mn-doped ZnO nanopowder designed by Phornano through the three studied scenarios (BS, S1, S2). ‘NF’stands for ‘nanofoam’. Table 1 Operational inventory data of the baseline and after-SSbD scenarios (BS: baseline scenario; S1: scenario 1; S2: scenario 2) provided by Phornano for the annual production of 2.5 (BS and S1) and 100 (S2) kg of doped ZnO nanopowder, using 1 kg of doped ZnO as the reference flow. Flow Unit Normalised value Transport distance (km) BS S1 S2 BS S1 S2 Inputs Zn(NO₃)₂⋅6H₂O kg 4 1.8 –700 700 – Zn powder (99.9 %) kg –0.31 0.62 –700 700 Non-aminated starch kg –0.3 0.2 – – – Whey L 16 – – 20 – – Mn(NO 3 ) 2 ⋅4H₂O kg 0.4 0.4 0.1 700 700 700 HNO 3 (68 %) L –3 3 – – – Electricity from the grid, low voltage (for ZnO) kWh 4 12 9 – – – Cleaning water (tap) L 40 40 10 – – – Process water (deionised) L 10 10 10 – – – Outputs ZnO kg 1 1 1 – – – Wastewater from cleaning L 50 40 10 – – – NO 2 to air kg 1.12 0.55 0.37 – – – Table 2 LCI background data for the baseline and after-SSbD scenarios, obtained from ecoinvent datasets. Flow Background process Zn(NO 3 ) 2 ⋅6H 2 O 0.27 kg/kg ‘Zinc oxide {GLO}| market for’+0.42 kg/kg ‘Nitric acid, without water, in 50 % solution state {RER w/ o RU} | market for’+0.36 kg/kg ‘Tap water {Europe without Switzerland} | market for’ Zn powder (99.9 %) ‘Zinc oxide {GLO}| market for’ Non-aminated starch ‘Starch, from maize {GLO}| market for’ Whey ‘Whey {GLO}| cheese production, soft, from cow milk’ Mn(NO 3 ) 2 ⋅4H 2 O 0.40 kg/kg ‘Manganese(III) oxide {GLO}| market for’+ 0.70 kg/kg ‘Nitric acid, without water, in 50 % solution state {RER w/o RU} | market for’ HNO 3 (68 %) ‘Nitric acid, without water, in 50 % solution state {RER w/ o RU}| market for nitric acid, without water, in 50 % solution state | Cut-off, U’ Electricity ‘Electricity, low voltage {AT}| market for’ Water (process or for cleaning) ‘Tap water {Europe without Switzerland}| market for’ Wastewater from cleaning ‘Wastewater, average {Europe without Switzerland}| market for’ Lorry transport ‘Transport, freight, lorry, unspecified {RER}| market for’ I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 358 the above-mentioned ecoinvent database and EF method and allows to model and analyse complex scenarios, rendering key information for process optimisation, hotspots identification and sustainability improvement, thus facilitating decision-making. The EF single score (in mPt) (using the latest normalisation and weighting factors from EF 3.1) is computed as well to be used for the MCDA. The impacts of the reported new materials/processes were assumed to not significantly disturb the economy, so they were modelled following an attributional approach, where the impacts or flows from other sectors are allocated without considering a potential change in their operations. In addition, impacts due to waste treatment were assigned to the waste producer. If such waste had an economic value, those impacts were attributed to the future user. The cut-off approach was selected to be consistent with the stated modelling choices. Impacts from infrastructures were excluded from the assessment, since they generally have a low contribution to industrial processes and are subjected to large uncertainties. Environmental flows were classified and characterised depending on their effects, and for each flow the impact was the result of multiplying the mass of a given input/output by the associated characterisation factor (CF) (the impacts attributable to ZnO NPs emissions were not considered at this stage since CFs are not available, a matter that is currently under study in our laboratories). 3.4. Material Flow Cost Accounting (MFCA) analysis 3.4.1. Operational cost inventory and modelling In this paper, an MFCA analysis for both the baseline alternative and the after-SSbD scenarios (S1 and S2) was conducted, using the same system boundaries (Fig. 4), functional unit (1 kg of ZnO nanopowder) and mass and energy flows considered in the LCA. This methodology offers detailed cost insights into material and energy inefficiencies during manufacturing, and provides a robust framework aligned with the goals of sustainable production (Hunkeler et al., 2008). In addition, its certification under ISO 14051 (2011) allows to enhance the transparency, reproducibility and comparability of the results. According to MFCA, the production process is divided into Quantity Centres (QC), where input materials are compared with the products to judge material losses (material loss =input –products). Since the available data were aggregated for the whole production process, a single QC was defined, reporting the complete production process of the doped ZnO NPs from start to finish. Next, all costs associated with the entry and exit of material flows for the QC are assessed and attributed to those flows, in line with the Asian Productivity Organization (Tachikawa, 2014) and ISO 14051 (2011). In MFCA, these costs are broken down into four categories (materials, energy, system and waste management) (Tachikawa, 2014). In addition to the LCI of Table 1, inputs of equipment costs, Table 3 Aggregated cost inventory data of the baseline and after-SSbD scenarios (BS: baseline scenario; S1: scenario 1; S2: scenario 2) provided by Phornano for the annual production of 2.5 (BS and S1) and 100 (S2) kg of doped ZnO nanopowder, using 1 kg of doped ZnO as the reference flow. Inputs Unit Unit cost ( € /unit) Annual cost ( € /year) BS S1 S2 BS S1 S2 Raw materials Zn(NO 3 ) 2 ⋅6H 2 O kg 37.00 13.21 –370.00 59.45 – Zn powder (99.9 %) kg –16.80 14.43 –13.04 899.00 Non-aminated starch kg –5.00 5.00 –10.00 100.00 Whey L 3.50 – – 140.00 – – Mn(NO 3 ) 2 ⋅4H 2 O kg 100.00 100.00 100.00 100.00 100.00 1000.00 HNO 3 (68 %) L –21.20 20 –106.00 4000.00 Cleaning water (tap) L 0.0010 0.0010 0.0010 0.10 0.10 1.00 Process water (deionised) L 1.00 1.00 1.00 25.00 25.00 1000.00 Wide mouth bottles piece 5.00 5.00 5.00 500.00 250.00 1000.00 Shipping costs – – – – 68.40 107.19 255.00 Energy Electricity from the grid, low voltage kWh 0.50 0.32 0.32 10.00 9.60 288.00 Equipment Inputs Purchase cost ( € ) Annual depreciation (%) Annual cost ( € /year) BS S1 S2 BS S1 S2 BS S1 S2 Magnetic stirrer 400.00 400.00 1200.00 20 20 20 80.00 80.00 240.00 Reactor 300.00 300.00 10,000.00 20 20 20 60.00 60.00 2000.00 Muffle furnace 800.00 – – 20 – – 160.00 – – Maintenance Inputs Frequency Unit cost ( € /unit) Annual cost ( € /year) BS S1 S2 BS S1 S2 Equipment calibration Annual 400.00 400.00 400.00 400.00 400.00 400.00 Labour Type Unit Unit cost ( € /unit) Annual cost ( € /year) BS S1 S2 BS S1 S2 In-house labour cost € /hour 36.00 40.00 40.00 3600.00 4000.00 40,000.00 I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 359 maintenance tasks, labour expenses and shipping fees for the raw materials were considered. Output flows that have no potential economic value and are free of handling and disposal charges were disregarded, as they bear no effect on the cost results. Table 3 presents a compilation of the cost inventory. All costs are before taxes. The temporal system boundaries for this study refer to the period between 2022 and 2024 and are based on primary data from Phornano for raw materials, energy and labour. No future projections were included in the base case, as the focus was on analysing present-day production costs within the defined system boundaries. Future studies could expand the temporal boundaries to include projected costs for longer-term scenarios, incorporating factors such as technological advancements and market dynamics. 3.4.2. Approximations, assumptions and limitations In terms of equipment allocation, the listed machinery was assigned in full to the annual production amounts of the doped ZnO nanopowder. Also, the output wastewater was only assigned with its respective raw materials expenses, while the doped ZnO NPs wasted in the cleaning and packing processes were allocated with all the upstream cost inputs necessary for the doped ZnO NPs production. 3.5. Social Life Cycle Assessment (S-LCA) The social evaluation of the three scenarios aimed at the production of doped ZnO nanopowder involves two types of data: price information for the quantitative assessment, conducted with SimaPro®9.6 (2024), and qualitative data, for other indicators proposed by UNEP/SETAC in its methodological sheets (Traverso et al., 2021), which were used to evaluate the risks of potential impacts and to design pathways for improvement. The selected functional unit for the three scenarios (1 kg of doped ZnO NPs) was translated into monetary terms, and the considered activity variable was ‘worker hours’, which represents the intensity of work required by each country-specific sector directly related to production (Benoît Norris et al., 2018). The Social Hotspots Database (SHDB) was used for background information and to identify and assess potential upstream impacts (Benoît Norris et al., 2018). SHDB, being one of the most widely used databases, provides information on social risks and opportunities by country and sector, and on the composition and location of the supply chain, through a Global Input-Output Model (GTAP) (Benoît Norris et al., 2018). Only 5 of the 6 categories (labour rights and decent work, health and safety, society, governance and community) about which the database provides information were considered in this study, since the sixth one (socioeconomic contributions), being measured in economic terms and not in worker hours, could overlap with the MFCA analysis. 3.5.1. Social Life Cycle Inventory (S-LCI) and modelling To build the S-LCA inventory, both background and foreground data were employed. The former are the country-sector data coming from the database. In the case of the latter, information was gathered through a questionnaire including LCA, MFCA and S-LCA information. Quantitative information on the costs and sources of each system input was collected and, to perform the S-LCA, the reference flow considered for the three processes was the production of 1 kg of doped ZnO nanopowder, expressed in monetary terms; data from the MFCA assessment was employed for normalisation. Table 4 displays the cost of the inputs of the three scenarios, classified according to their industry and country of origin. To include the company performance, ad hoc worker hours for the ‘Labour’input were designed, following the formulae by Smith (2019) (Eqs. 1 and 2): Unit labourcost=Meanhourlysalaryfor thecountry−sector(peremployee) Grossannualoutput of thesectorinthecountry (1) Worker hours =Unit labour cost Mean hourly labour cost per employee (2) By employing these newly created company-specific worker hours and deleting the database original ones, generic for the Austrian chemical industry and the macroeconomic trade network connecting this industry to the rest of the world, the uncertainty pertaining to the use of generic data was eliminated. As, while the possibility of risks occurring in the sector remained, the use of audited first-handed data about labour conditions reduced the chances of these risks occurring in the company, which is reflected on the worker hours associated to this input. Concerning the specific worker hours calculus, an average hourly salary of 36 € per employee was considered, as stated in the inventory data provided by Phornano. According to the Austrian input-output tables for 2016 (Statistics Austria, 2020), the annual output of the Forschungs und Entwicklungs (Research and Development) sector was of 14,677,911 € , whereas the mean Technology and Development base salary in Austria amounted to 73,529 € per year. Phornano worker hours were calculated according to Eq. (2), throwing a result of 1.39 ×10 −7 worker hours (‘Phornano tailor-made worker hours’industry in Table 5). Table 5 shows the worker hours used from the database for each input’s country-specific sector. The ‘Reference Scale’method (also known as S-LCA Type I method) was selected to carry out the impact assessment of the three scenarios considered in this work. Such evaluation was conducted with the aid of SHDB and the Social Hotspots Index (SHI; Benoît Norris et al., 2018), which calculates the social risks associated to the product and the supply chain according to Eq. (3), measured in medium risk hours equivalent (mrheq): Social risks =Worker hours⋅cost of the input⋅indicatorʹs risk level (3) Risk levels work as characterisation factors. These correspond to the risks of the country-sector in compliance with the variable measured in the indicator and the severity of a situation, the distribution of values across the population of countries and sectors and experts’judgement. Table 4 Inputs for the S-LCA modelling ( € ) of the baseline and after-SSbD scenarios (BS: baseline scenario; S1: scenario 1; S2: scenario 2) for the annual production of doped ZnO (2.5 or 100 kg, depending on the scenario), using 1 kg of doped ZnO as the reference flow. Industry BS S1 a S2 Austria Germany Austria Germany Austria Germany Chemicals 66.00 188.00 10.00 87.62 10.00 58.99 Labour 1440.00 –1600.00 –400.00 – Electricity 4.00 –3.84 –2.88 – Water 0.04 –0.04 –0.01 – Bottles –200.00 –100.00 –10.00 Equipment 920.00 –216.00 –26.40 – Transport 27.36 –42.88 –2.55 – Corn – – 4.00 –1.00 – a In S1 some chemicals come from Brazil; such inputs amount to 23.78 € . I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 360 Then, these were aggregated for the different sub-categories and categories. Results were aggregated for each scenario (BS, S1 and S2) using the SHDB impact assessment method (SHI; Benoît Norris et al., 2018), which provides a single score in millipoints (mPt) based on the aggregation of the different sub-categories; this allows to compare the social performance of the product-system in the studied processes from a quantitative point of view. No weighting was applied to any indicator because of the uncertainties related to the innovative nature of the technology and its scale. 3.5.2. Approximations, assumptions and limitations The background modelling for this case study was designed through an iterative process trying to understand which of the existing countrysector pair in the database would fit best the product-system; the chosen ones for each group are presented in Table 5. Regarding the country of origin of the machinery and equipment used, all the suppliers were assumed to be sited in Austria. Since there is no section accounting for labour impacts in the SHDB, a new one was included (‘Phornano tailormade worker hours’). The risks assessed in this case study come from the valuations included in the SHDB, which were adapted whenever possible to reflect Phornano’s reality. Nevertheless, as the risk of a potentially occurring impact is measured according to macroeconomic measures, few risks were adapted; specifically, ‘Unemployment level’, as there were data not only for Austria altogether, but also for Korneuburg, the place in which the company is located. 3.6. Uncertainty analysis Uncertainty analysis was performed with two purposes: (a) to evaluate the quality of data used in the foreground and background inventory, and price volatility. For LCA, MFCA and S-LCA, each scenario was evaluated considering the inputs variability. This has allowed to understand how input data uncertainties influence the final results, and thus support SSbD endeavours; (b) to use the calculated uncertainties to carry out a Multi-Criteria Decision Analysis (MCDA) (Section 3.7). For the environmental, economic and social assessments the SimaPro®9.6 (2024) software was employed to conduct a Monte Carlo uncertainty analysis with 1000 runs. Two classes of uncertainties were considered: the basic uncertainty, which reflects the intrinsic variability, and the additional one, a consequence of the use of imperfect data (SantiagoHerrera et al., 2024). The basic uncertainty of input data was modelled through an ad hoc log-normal distribution representing the possible range of input values and the associated costs (i.e., on raw materials, chemicals, energy, emissions of pollutants, equipment and labour) provided by Phornano. The additional uncertainty was determined through the Pedigree matrix. This matrix considers five quality indicators (reliability, completeness, temporal correlation, geographic correlation and further technological correlation) and, depending on the quality of the data sources, a score from 1 to 5 is assigned to each of them (Pizzol et al., 2024). An uncertainty factor was determined for each indicator and each score. All these values were added up, yielding SD g95 , according to Eq. (4) (Muller et al., 2016): where U 1 =uncertainty factor of reliability, U 2 =uncertainty factor of completeness, U 3 =uncertainty factor of temporal correlation, U 4 = uncertainty factor of geographic correlation, U 5 =uncertainty factor of further technological correlation, and U b =basic uncertainty factor. For S-LCA, the Pedigree matrix was adapted to the particularities of this evaluation tool according to Mancini et al. (2018). The results of this matrix, translated into a single number employing Weidema et al.’s (2013) method in the absence of a specifically social translation model, built up the basic uncertainty of the social inventory, as it is inherited to the database functioning; the additional uncertainty for the social assessment was calculated based on the cost information inputted into the system. 3.7. Multi-Criteria Decision Analysis (MCDA) In the literature, LCA, S-LCA and economic assessments are usually conducted separately for a given product, process, or service, and then the results for the different scenarios are compared. This works when the object of comparison is single indicators, but it is not trivial when multiple indicators are involved. In these cases, the user would need to compare each indicator of each domain for all the options considered which, besides being time-consuming, may introduce bias when interpreting the results and, therefore, in the selection of the most preferable alternative. To avoid this, performance of a Multi-Criteria Decision Analysis (MCDA) is advisable. According to Dean (2020), MCDA comprises various classes of methods, techniques and tools which explicitly Table 5 Country-sector modelling and worker hours (wh) of the baseline and after-SSbD scenarios for the annual production of doped ZnO (2.5 or 100 kg, depending on the scenario), using 1 kg of doped ZnO as the reference flow. Industry Country-sector wh (all scenarios) a Chemicals Chemical, rubber, plastic products (crp)/AUT U 1.09 ×10 −5 Chemical, rubber, plastic products (crp)/DEU U 8.59 ×10 −6 Chemical, rubber, plastic products (crp)/BRA U – Labour Chemical, rubber, plastic products (crp)/AUT U 5.16 ×10 −5 Phornano tailor-made worker hours (AUT) 1.39 ×10 −7 Electricity Electricity (ely)/AUT U 4.08 ×10 −6 Water Water (wtr)/AUT U 1.10 ×10 −5 Bottles Manufactures nec (omf)/DEU U 5.27 ×10 −5 Equipment Machinery and equipment nec (ome)/AUT U 2.58 ×10 −5 Transport Transport nec (otp)/AUT U 2.81 ×10 −5 Corn Cereal grains nec (gro)/AUT U – a All the shown data are common to BS, S1 and S2, with the exception of the chemicals coming from Brazil, which apply only to S1 (in this case, wh =2.93 ×10 −4 ), and corn, applying only to S1 and S2 (in both cases, wh =1.17 ×10 −3 ). SDg95 ≅ σ 2 g=exp  [ln(U1)]2+[ln(U2)]2+[ln(U3)]2+[ln(U4)]2+[ln(U5)]2+[ln(Ub)]2 √(4) I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 361 uncertainties (Table 9) in the Excel worksheet triggered a 1000-iteration Monte-Carlo simulation. This means that, for each scenario and each indicator, 1000 values are randomly generated, and their average and standard deviation calculated. These numbers can be grouped by ranges; if the frequency of appearance of those ranges in the stochastic analysis is plotted against such ranges, Gaussian-like distributions are obtained, with their profile (shape or broad) depending on the uncertainty of the initial values. Hence, the distributions obtained for the three scenarios for a given indicator can be represented in the same graph, to compare the performance of the studied processes from the environmental, economic and social viewpoints. The results of this analysis for the three scenarios and the three reported indicators are shown in Fig. 9. Environmentally, S2 is the scenario with the lowest score (roughly 0.007 Pt), followed by S1 (around 0.011 Pt) and, finally, BS, with approximately 0.016 Pt (the average values resulting from the stochastic processes were considered; see Table 9). This means that the impacts generated when producing 1 kg of doped ZnO through S2 are less than half of those originated via BS, and 1.5 times lower than those generated by S1. Concerning the costs of such manufacturing, S2 is, by far, the most affordable option. Although S1 is less expensive than BS (2088 € vs 2205 € on average), S2 (512 € ) is roughly four times cheaper than the other two. Finally, S2 is also the least impactful process from the social point of view (0.021 Pt vs 0.082 Pt –S1–and 0.158 Pt –BS–). The results for the three indicators considered individually are in agreement with the main outcome of the MCDA: the 100-kg scaled-up scenario, S2, is the most sustainable of the three proposed options. In addition, S1 performs better than BS in the three indicators, which makes it the second most sustainable process of the study. After conducting this research, two additional MCDAs were proposed, each of them consisting of two scenarios, to confirm the abovepresented results: on the one hand, a study between S1 and S2 and, on the other, between the best-performing redesign resulting from this analysis and BS. The ranking obtained from the first of the two proposed MCDAs is shown in Fig. 8b. S2 is the most sustainable process. Indeed, this is the alternative that is more likely to occupy the first position (around 98 % likelihood), the chances of S1 to be ranked first being negligible. Therefore, the latter would be ranked second. This outcome is in line with that obtained from the former MCDA. In a similar fashion, the behaviour of S1 and S2 in the environmental (x), economic (y) and social (z) indicators was explored (Fig. S7). With respect to the former, S2 is 1.5 times less impactful than S1, whereas the cost of S2 is remarkably lower than that of S1 (around 512 € vs 2088 € ). Finally, the social impacts derived from the production of 1 kg of doped ZnO NPs through S2 are ca. 4 times lower than those generated by S1. Altogether, these results match those of the current MCDA and of that considering the three scenarios, where S2 was preferred over S1. Given this result, the next MCDA that was performed is that between BS and S2. Introduction of the values obtained for each indicator for both alternatives throwed the results presented in Figs. 8c and S8. Again, S2 is the best-performing process from the sustainability point of view (nearly 100 % likelihood), so the original design, BS, would be the least advisable process to produce 1 kg of doped ZnO NPs. This is also reflected in the indicators. Indeed, when studying the environmental impacts, S2 is roughly twice less impactful than BS, whereas in terms of social impacts the ratio is around 8:1 (BS:S2). Economically, S2 is also preferable, given that the cost of manufacturing 1 kg of doped ZnO NPs is 512 € in the case of S2 and 2205 € in the case of BS, on average. All of this makes S2 a superior option, in agreement with the current and former MCDAs. Thus, the 100-kg scaled-up scenario, in which circular economy principles were followed to reduce some raw materials inputs, is the most sustainable one of the three that have been studied in this work. Although at this stage of the research it was not possible to assign weights to the environmental, economic and social indicators, a sensitivity analysis was carried out to understand the impact of fixing weights in the analysis, instead of using randomly generated ones, considering either that the three indicators are equally relevant (33.33 % weight assigned to each one), or giving preference to one of them over the others, according to the procedure described in Section 3.7. The results, shown in SI (Fig. S9), allow to conclude that the global outcome depends neither on the nature of the weights, nor on the priority of one of them over the other two, as in all cases the trend is similar to that shown in Fig. 8, with S2 being the most likely process to be ranked first from the sustainability viewpoint, followed by S1. As indicated in Section 2, the literature about application of MCDA tools to analyse the holistic sustainability of NMs production processes is scarce, and no works focused on ZnO NPs have been found. Anyway, potential comparisons are difficult due to the different nature of collected data (qualitative or quantitative) and settings/tools (Hansen, 2010). It should be noted that, while MCDA proves to be a highly effective tool to visualise results and support decision-making in a process evaluation, as shown in this work, it is crucial to keep the interpretation of results separately for the three sustainability pillars. This ensures that potential trade-offs among these dimensions are explicitly recognised and mitigated, avoiding unintended compensations that could compromise truly sustainable outcomes. That said, the SSbD framework recommends the use of MCDA to evaluate and compare several alternatives, considering safety and sustainability factors Fig. 9. Analysis of the performance of options A (baseline scenario, BS), B (2.5kg redesign, S1) and C (100-kg scaled-up process, S2) in each indicator: (a) environmental, x, (b) economic, yand (c) social, z, for the Phornano case study. The Y axis represents the frequency of the corresponding range (X axis) resulting from the Monte Carlo analysis, and the X axis the Pt (environmental and social indicators) or € (economic indicator) of the analysed scenarios. I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 368 (Abbate et al., 2025). Particularly, MCDA allows for balancing complex trade-offs among environmental, social and economic criteria. This integration is in line with SSbD principles, as this methodology provides a solid foundation for decision-making from the earliest stages of development. 4.6. ZnO NPs: policy implications and commercial prospects As this work is the result of a EU-funded research project, European policies concerning NMs have been considered to carry out this investigation. In this context, the EU’s environmental regulations (e.g., REACH; European Commission, 2006) require NMs producers to evaluate and limit the impacts derived from the manufacture of these materials. As explained in Section 2, emissions of NPs during the production of these goods is a topic of special concern, so efforts to study the mechanisms accounting for their release and to determine their characterisation factors are being prioritised. By the same token, the EU is pushing for the adoption of cleaner, safer and more efficient methods to synthesise NPs. The SSbD framework (European Commission, 2022) is a voluntary approach to guide the innovation process for chemicals and materials. This work is a case study that explores the applicability of this framework and provides insights for further definition, with a particular focus on NMs, given their singular characteristics. As a result of this approach, significant conclusions to improve the production process of ZnO NPs have been ideated. The so-called ‘French process’, consisting in the vaporisation of metallic Zn followed by its oxidation with air at high temperatures and rapid cooling of the formed ZnO particles, is a typical way to obtain this product (Charnhattakorn et al., 2011). However, it is an energy-intensive method, and getting particles of uniform size is not trivial. In contrast, the processes presented in this work are environmentally friendly, in line with the EU’s Green Deal (European Commission, 2019), and controlling particles size is easier. Monitoring of the exposure of workers to NMs is also critical, as it may pose health risks, such as respiratory problems; thus, compliance of the manufacturing processes with the EU’s legislation on this subject (European Commission, 2006) and the European Chemicals Agency (ECHA) is mandatory. From the commercial perspective, the market for ZnO NPs is expected to grow significantly in the near future, from USD 254.4 million in 2020 to USD 425.2 million in 2027, representing a Compound Annual Growth Rate (CAGR) of 7.6 % (Chandrasekaran et al., 2024). ZnO NPs are currently used in several industries and products. Besides personal care goods, particularly sunscreens, wastewater treatment and biomedical devices are common applications of this material. Nonetheless, further research is necessary to improve its production and enhance its commercial viability and thus explore new opportunities to access untapped markets (Goswami et al., 2024). The scenarios studied in this paper allow to synthesise ZnO particles of nanometric size, which, in relation to the mentioned sunscreens, are more efficient in blocking UV light than microparticles (Kumari et al., 2010), that can result from the ‘French process’: if both sizes are compared, less NPs are needed to achieve a similar effect, which presents a clear commercial interest. Altogether, collaboration between academia, industry and regulatory bodies is key to boost commercialisation and technological innovations of ZnO NPs (Xie and Wang, 2021). 5. Conclusions In this work the Environmental, Economic and Social Life Cycle Assessments (LCA, MFCA and S-LCA, respectively) of three processes aimed at the production of Mn-doped ZnO NPs, complemented with a Multi-Criteria Decision Analysis (MCDA) to objectively decide which of them was the most sustainable one, were presented. According to the thorough literature review that was carried out, this is the most complete work in this field to date. The original production scenario conceived by Phornano (BS) was improved by applying suitable SbMD, SbPD and circularity strategies (S1 and S2, the latter being an adapted and upscaled version of the former). Environmentally, the three main impact drivers of the afterSSbD scenarios are the production of HNO 3 (main mass flow input), of Zn(NO 3 ) 2 (S1) or zinc powder (S2), and of electricity. Checking the single score, the reduction achieved in S1 and S2 with respect to BS was of 52 and 67 %, respectively, which confirmed the efficacy of the applied SSbD measures and, ultimately, of the scaled-up circular production of S2. This is also reflected in the economic assessment, since the production of 1 kg of Mn-doped ZnO nanopowder through S2 is roughly four times cheaper than that conducted via BS. Anyway, the main cost driver and hotspot in the two redesigned scenarios was the labour expense, accounting for more than 75 % of the total cost inputs. S-LCA also supports the suitability of the SSbD approaches, which led to a dramatic reduction of the social footprint of the complete productsystem: around 48 % from BS to S1, and about 87 % from BS to S2. This responds to a more efficient use of the resources, which translates into more outputs to distribute the risks across. The uncertainty analysis carried out for the environmental, economic and social evaluation of each scenario do not impact the interpretation of results, and allowed to perform MCDA. The MCDA outcome confirmed that the upscaled process, S2, was the best-performing scenario from the sustainability viewpoint, with almost 100 % likelihood of being placed in the first position of the ranking among the three options, and S1 ‘winning’the silver of the classification; the performed sensitivity analysis demonstrated that this trend is maintained regardless of the weights assigned to the environmental, economic and social indicators. However, more research is needed to effectively communicate integrated results in a clear and actionable way, ensuring that they are ultimately useful for informed decision-making, without the risk of overlooking relevant impacts, and to extend the application of the presented methodologies to other industrial sectors. On the other hand, impacts attributable to ZnO NPs emissions, together with their characterisation factors, should be considered in the analyses. Calculation of both is however not trivial and time-consuming, but they are being investigated in our laboratories and the results will be presented in a future publication, also related to the ‘Diagonal’project. In addition, it would be interesting to consider an expansion of the system boundaries, to include the use and end-of-life phases, when more data are available. Altogether, the findings of this work highlight the potential of combining hybrid green synthetic methods with structured evaluation frameworks to support safety and holistic sustainability in industrial scenarios from the earliest stages of innovation. Scaling up processes like S2 to real-world settings will help validate their technical, socioeconomic and environmental feasibility. Based on these findings, several recommendations can be made: for stakeholders in industry, the SSbD framework can support the adoption of sustainable processes like S1 and S2, aiming to reduce environmental impacts and production costs, while promoting circular economy strategies and social wellbeing. Finally, policy-makers should consider developing standardised guidelines for evaluating economic and social dimensions within the SSbD framework and incentivise the adoption of circular production methods. CRediT authorship contribution statement Israel Carreira-Barral: Writing –review &editing, Writing –original draft, Visualization, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation. Julieta Díez-Hern´ andez: Writing –review &editing, Writing –original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Elorri Igos: Writing –review &editing, Validation, Supervision, Methodology, Investigation, Conceptualization. Michael Saidani: Writing –review &editing, Writing –original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Tianran Ding: Writing –review &editing, Writing –original draft, I. Carreira-Barral et al. Sustainable Production and Consumption 55 (2025) 353–372 369 Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Tiago Ramos da Silva: Writing –review &editing, Writing –original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Helena Monteiro: Writing – review &editing, Writing –original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Andreas Stingl: Writing –review &editing, Validation, Methodology, Investigation. Patricia M.A. Farias: Writing –review &editing, Validation, Methodology, Investigation, Data curation. Olavo Cardozo: Writing – review &editing, Validation, Methodology, Investigation, Data curation. Jesús Ib´ a˜ nez: Writing –review &editing, Validation, Methodology, Investigation, Formal analysis. Ana García-Moral: Writing – review &editing, Validation, Methodology, Investigation, Formal analysis. Juan Antonio Tamayo-Ramos: Project administration, Funding acquisition. Carlos Rumbo: Writing –review &editing, Validation, Supervision, Project administration, Funding acquisition. Rocío Barros: Writing –review &editing, Validation, Supervision, Project administration, Conceptualization. Sonia Martel-Martín: Writing – review &editing, Writing –original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Funding This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 953152) in the context of the ‘Diagonal’project. Declaration of competing interest 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. Acknowledgements Julieta Díez-Hern´ andez thanks the Spanish Ministry of Universities for her pre-doctoral contract (University Teachers’Training Programme, ref. FPU/00605-2021). The authors thank Daniel Cort´ esBatista for designing the graphical abstract and some of the figures shown in the article. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.spc.2025.03.003. 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