Synthetic urine oversimplification results in misleading membrane fouling mechanisms in bipolar membrane electrodialysis

Domain: chemistry
Published: 2026-04-10
Editorial outcome: minor_revision

Abstract
Lab-scale wastewater treatment studies, including urine recovery, often rely on oversimplified synthetic wastewater, thereby compromising the reliability of results and data. Here, we systematically evaluate how using full-component versus simplified synthetic urine formulations affect the performance and engineering-economic assessment of bipolar membrane electrodialysis. Our findings reveal that simplification fundamentally alters fouling mechanisms. While urea alone causes significant damage to anion exchange membranes through hydrogen bonding aggregation, natural organic co-components in real urine mitigate fouling via inhibitory interactions—a mechanism confirmed by molecular dynamics simulations and experimental characterization. After seven batches, the full-component formulations showed 29.3% less performance decay and 10–14% higher urea recovery than the simplified formulation. Moreover, the complete removal of organic components disrupts ion-organic co-aggregation pathways on cation exchange membranes, shifting fouling toward mineral crystallization on bipolar membranes. Simplification also distorts the engineering-economic assessment, overestimating cleaning costs by 15.9% and underestimating membrane lifespan by 12.5%. These findings necessitate identifying key wastewater constituents and ensuring experimental integrity to bridge lab-industry gaps, advocating designs comprehensively addressing multi-component interactions.

Introduction
Advances in sustainable water treatment technologies are pivotal in addressing global resource and environmental challenges1–3. However, a significant gap remains between fundamental research and engineering practice, particularly in wastewater treatment, where lab-derived findings often fail to translate into reliable real-world applications. Current experimental frameworks commonly rely on simplified synthetic formulations to characterize complex wastewater systems—a pragmatic approach driven by cost efficiency and reproducibility4,5. While this allows controlled investigations, it inherently disrupts the interconnected mechanisms of multidimensional interfacial processes (physical, chemical, and biological) that characterize real-world systems. For instance, neglecting trace organics, colloidal particles, and dynamic ionic compositions in synthetic formulations distorts degradation kinetics, misestimates treatment performance, and disrupts microbial community succession6–8. Such issues undermine the reliability of performance predictions for industrial-scale systems. Consequently, applying simplified experimental formulations to the inherent complexity of actual wastewater systems poses significant challenges.

This issue is starkly exemplified in the field of urine resource recovery5,9,10. Systematic reviews indicate that fewer than 20% of studies employ real human urine, with most relying on synthetic formulations deficient in critical organic metabolites9,11. Key omissions (e.g., creatinine and uric acid) disrupt urea hydrolysis, membrane stability, and microbial synergy12, yet overly idealized synthetic formulations persist, creating an artificial paradigm that misguides decentralized sanitation technology development. In recent years, bipolar membrane electrodialysis (BMED) has demonstrated significant potential in resource recovery by utilizing the hydrolysis properties of bipolar membrane (BPM) under an electric field, enabling the recovery of nutrients from urine in the form of acids, bases, and fertilizers13–17. However, these studies predominantly rely on oversimplified synthetic formulations, limiting its guidance for industrial-scale applications.

An illustrative example of this overestimation lies in BMED systems tested with synthetic urine (e.g., 1.75 g L−1 total nitrogen), which reported high nitrogen removal efficiencies (80–89%) under a current density of 150 Am-2. These results were overestimated owing to neglecting key organic constituents typically found in actual wastewater systems18. The transition to actual urine (2.0 g L−1 Na+, 4.9 g L−1 NH4+, and 15 g L−1 CO3 2-) revealed the evident limitations of simplified compositions: competitive cation migration (Na+ vs. NH4+) reduced nitrogen mass transfer efficiency by over 50% due to preferential occupation of cation-exchange membrane channels, while the dynamic carbonate-phosphate buffering system shifted acid chamber pH from synthetic formulation values (pH 1.07) to near-neutral conditions (pH 7.50) through CO2 back-diffusion and phosphate protonation11,17. These phenomena are absent in synthetic formulations. Further compounding this issue is the neglect of organic-inorganic interactions. Current studies often isolate inorganic ions (e.g., 3772 mg L−1 NH4+ and 3080 mg L−1 Cl-) or single organic species (e.g., 15 g L−1 urea), while actual urine comprises a complex matrix of metabolites that synergistically regulate interfacial processes in BMED. Previous research reported that in a bioelectrically enhanced BMED system (BBGS), creatinine acted as an electron shuttle, enhancing microbial urease activity and facilitating the complete hydrolysis of urea (1717 mg L−1) to ammonium. Simultaneously, histidine stabilized the pH gradient across BPM via its imidazole group, thereby increasing ammonia recovery to 90.39%16. These mechanisms, which rely on the coexistence of organic and inorganic constituents, are absent in the simplified formulations commonly used in studies that neglect cross-component synergy. Such persistent oversimplification distorts the identification of fouling drivers, biasing predictive models toward idealized interactions. Consequently, it creates a fundamental knowledge gap: How do complex component synergies—routinely ignored in oversimplified synthetic formulations—impact treatment performance and obscure experimental interpretability of performance-limiting mechanisms in BMED systems? Critically, the prevailing reliance on oversimplified formulations (e.g., containing only urea) creates a systematic blind spot5,10,16,17. This paradigm is fundamentally limited, as it inherently fails to capture the full spectrum of intermolecular interactions—particularly the overlooked antagonistic interactions that, counterintuitively, mitigate rather than exacerbate membrane fouling. This oversight not only obscures key fouling mechanisms but also risks a complete misdiagnosis of fouling risks in real-world systems.

To address this knowledge gap, this study systematically investigates how compositional fidelity in synthetic urine formulations—ranging from basic salt systems to complex mixtures with organic metabolites—impacts the performance of BMED system. By constructing synthetic urine formulations with graded complexity and integrating system stability testing and engineering-economic assessment, we quantitatively dissect the effects of formulation variations on the efficiency of nutrient recovery. Through experimental characterization and molecular dynamics simulations, this study clarifies the intrinsic correlations between compositional fidelity, nutrient recovery efficiency, and operational stability. This study advocates for a paradigm in water research, where compositional fidelity is a cornerstone of technological validation, addressing critical oversimplifications in environmental engineering water treatment and ensuring robust scalability assessments for next-generation wastewater systems.

Results
Urine complexity impacts BMED performance
As demonstrated in Fig. 1a, applied electric fields drive selective ion migration: HnPO4 (3-n)-, NH4+ and K+ traverse ion-exchange membranes (IEMs) into the acid and base chambers, respectively, where they combine with H+/OH- generated by the BPM to produce acid and base. Under alkaline conditions, NH4+ is converted into gaseous NH3. The NH3 then diffuses through the hollow fiber membrane (HFM) and is absorbed by circulating sulfuric acid in the absorption chamber, forming (NH4)2SO4. Meanwhile, the untransferred HnPO4 (3-n)- and electrically neutral urea remain confined to the feed chamber, where they can be further utilized as raw materials for liquid fertilizer production.

To bridge the gap between synthetic and real wastewater, the formulations were designed based on comprehensive analysis of urban urine composition (Supplementary Table 1-2). Group A represents the conventional simplified formulation9,17, containing only dominant inorganic salts (e.g., NaCl, KCl) and urea. Group B expands this by incorporating core small organic metabolites—creatinine and uric acid—which are not only universally present in actual urine but are also consistently employed in rigorous studies to enhance the representativeness of synthetic urine formulations5,16,17. Group C further introduces macromolecular organics, using Bovine Serum Albumin (BSA) as a well-established model protein. This choice is justified by its high structural and functional similarity to human serum albumin, a key proteinaceous component in urine, allowing for a controlled investigation of macromolecular fouling mechanisms relevant to real systems19–21. This hierarchical design covers key categories of actual urine components—from ions to small organics and macromolecules—enabling a comprehensive analysis of their synergistic effects. Under a constant voltage of 15V and a flow rate of 0.4 L min−1, desalination efficiencies of 85.9%, 95.4%, and 95.4% were obtained from the first batch of 2-hour experiments for Groups A, B, and C, respectively (Fig. 1b). However, performance progressively degraded in subsequent batches, with efficiencies dropping to 55.7%, 67.4%, and 69.0% by the seventh batch. Notably, the ratio of performance decay was inversely related to compositional complexity (A < B < C), with decay ratios of 54.2%, 41.4%, and 38.3%, respectively (Fig. 1c). Despite the differential performance decay, the core functionality of the BMED process remained relatively stable, as reflected in the consistent pH evolution of the key chambers (Supplementary Fig. 1). Both the acid and base chambers established their respective strongly acidic (pH < 2.5) and alkaline (pH > 10.5) environments within the first 30 minutes of operation and maintained them consistently throughout the experiments, providing a stable driving force for nutrient recovery and ammonia stripping. The feed chamber pH remained stable at approximately 3, a consequence of inherent proton leakage in the BMED process22. This phenomenon, however, conferred a critical advantage by serendipitously suppressing urea hydrolysis, thereby obviating the need for exogenous acid addition and significantly facilitating the storage and transportation of urine resources23. While classical membrane fouling theory suggests that increased compositional complexity exacerbates synergistic fouling (e.g., organic-inorganic co-deposition24,25), our results revealed a counterintuitive trend: higher formulation complexity correlated with slower performance decay. Specifically, Group A (simplified formulation) exhibited a >50% performance decay by the seventh batch, exceeding typical maintenance thresholds for acceptable decay, whereas Groups B and C showed more gradual performance degradation (Fig. 1c).

Subsequently, this study further examined the cascade effects of compositional complexity on resource recovery efficiency in the BMED system (Fig. 1d-f). As operational batches increased, the system exhibited performance changes, with desalination efficiency declining by 26.4–30.2%, resulting in a corresponding reduction in overall nutrient recovery efficiency. As shown in Fig. 1d, NH4+ recovery remained largely unaffected by performance decay due to its stable transmembrane migration, a finding consistent with results from previous studies17,26. In contrast, urea recovery efficiency exhibited significant variability across experimental groups: Group A (urea-only) showed a 3.2% decline, whereas Groups B and C, supplemented with creatinine, uric acid and BSA, demonstrated 10–14% improvements in recovery efficiency. These findings suggested that the addition of small and large molecules in Groups B and C contributed to the stabilization of system performance, potentially by reducing urea migration through specific interactions that limit its transmembrane passage. Further analysis revealed that the transmembrane migration ratios of phosphorus (P) and potassium (K) in the seventh batch increased from 16% and 64% in Group A to 23% and 84% in Group B, and further to 24% and 83% in Group C, respectively (Fig. 1e, f). These results suggested that the complex interactions within Groups B and C stabilized BMED performance, thereby facilitating electromigration-driven transmembrane transport of ions such as HnPO4 (3-n)- and K+. This enhancement in ion transport directly facilitated the generation of high-value acids and bases in the BMED system, not only improving urinary nutrient recovery efficiency but also enhancing system economic viability. Pressure-driven technologies such as nanofiltration and reverse osmosis exhibit heightened sensitivity to compositional complexity, with studies27–29 demonstrating severe flux declines (e.g., 65% reduction with 1 mM calcium ion30, synergistic organic-inorganic fouling27). Such discrepancies highlighted a critical knowledge gap: oversimplified urine compositions disproportionately amplify single-component fouling risks (e.g., exaggerated treatment performance decline in Group A), while failing to capture emergent stabilizing interactions in multi-component systems. These findings suggested that compositional complexity likely stabilizes BMED performance through interactions among urinary components—a mechanism often overlooked in simplified formulations—that may extend to other complex aqueous systems rich in organics and inorganics.

While traditional membrane systems exhibited performance losses due to actual wastewater complexities, our research revealed an inverse relationship, where increased complexity enhances operational stability. Both phenomena converged on a central idea: oversimplified compositions can lead to misdiagnosis of fouling mechanisms. These insights necessitated integrative approaches to wastewater treatment optimization, where component interaction mapping supersedes single-factor fouling assessments.

Organic interactions reduce anion-exchange membrane fouling
Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) investigates the spatial distribution and semi-quantitative elemental composition of the fouling layer of the anion-exchange membrane (AEM) exposed to various synthetic urine formulations. Obviously, the surface deposits of the BMED-treated groups were predominantly composed of C, N, and O (Fig. 2a, b), indicating preferential adsorption of nitrogen-rich organic compounds onto the AEM surface. A comparative analysis further demonstrated that Group A (urea-only) exhibited severe membrane fouling, characterized by a significantly higher density of particulate deposits on the AEM surface compared to other groups. This phenomenon was directly associated with the observed over 50% performance decay in Group A after seven batches of operation (Fig. 1c). In contrast, Group B, which introduced creatinine and uric acid in addition to urea, exhibited a marked reduction in surface deposit density on the AEM compared to Group A. This suggested that the coexistence of small-molecule organics suppressed pollutant accumulation, thereby maintaining the treatment stability of the BMED system. When BSA was added to form Group C, the distribution of sulfur elements (Fig. 2b, Supplementary Fig. 2) corresponded to cysteine residues in the BSA molecule, indicating that proteins participated in the fouling process through interfacial adsorption. Notably, in Group A, spherical aggregates with a diameter of approximately 1.4 μm were observed (Fig. 2a), three orders of magnitude larger than the theoretical diameter of urea molecules (~0.4 nm)31,32. Since Group A contained only urea as the sole organic component, these aggregates were likely urea self-aggregates. Previous studies confirmed through molecular dynamics simulations that urea forms self-aggregates in aqueous solution via hydrogen bond weakening and hydrophobic interactions, but such aggregation phenomena only occur at higher concentrations (>1 M)32,33. However, this study demonstrated that membrane interfaces could induce urea molecules to overcome the random aggregation patterns of bulk solutions, forming micrometer-sized structures through interface-induced nucleation. The phenomenon suggested that the AEM surface provided nucleation sites, enhancing intermolecular interactions between urea molecules and the AEM surface. X-ray diffraction detected characteristic crystal plane signals, and EDS showed N enrichment in the aggregated regions, collectively validating the interface-induced urea self-aggregation process (Fig. 2b, Supplementary Fig. 3).

To elucidate the molecular mechanism of urea self-aggregation at membrane interfaces, this study employed EDS, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and Molecular dynamics (MD) simulations (Fig. 2b-e and Supplementary Fig. 4-5). FTIR analysis revealed significant spectral shifts, indicating critical hydrogen-bonding interactions: the N-H stretching vibration exhibited a marked redshift (Δν≈100 cm−1) from its characteristic 3500 cm−1 position, demonstrating hydrogen-bond donation from -NH2 groups of urea to acceptor sites on the AEM surface. Concurrently, reduced intensities of C=O (1640 cm−1) and N-H (1600 cm−1) bands in urea-fouled AEM (Group A) compared to pristine AEM confirmed that carbonyl and amine groups governed the hydrogen-bonding interactions, facilitating the assembly of cross-linked molecular networks (Fig. 2c). These findings were consistent with previous studies34,35, providing molecular-level evidence for interface-induced urea self-aggregates. XPS analysis complemented the findings with intensity increases at specific binding energies: ~288.1 eV (C=O) and ~286.2 eV (C-N) collectively confirming cross-linked structure formation via hydrogen bonding in urea molecules (Supplementary Fig. 4).

MD simulations served as a visual complement to the mechanisms revealed by experimental characterizations, with snapshot images clearly depicting the aggregation of urea molecules on the AEM surface (Supplementary Fig. 5). Radial distribution function (RDF) analysis revealed a prominent peak at the interface of the AEM, indicating a preferential accumulation of urea molecules at this distance scale on the AEM surface, thereby highlighting a significant interaction between urea and the AEM interface (Fig. 2d). This conclusion was consistent with experimental characterization results. Furthermore, the peak observed at 5.9 Å in the RDF significantly exceeded the typical hydrogen bond length range (2.5–3.5 Å), suggesting that, in addition to hydrogen bonding, other interactions, such as van der Waals forces, may have also played a role in facilitating the aggregation of urea on the membrane surface. Thus, the urea -NH2 groups preferentially interacted strongly with the AEM interface via hydrogen bonding, driving a specific molecular orientation at the interface: the polar -NH2 groups anchored onto the AEM surface, with the C=O groups oriented outward. These outward-facing C=O groups then served as binding sites for the -NH2 groups of adjacent urea molecules, forming spherical aggregates through hydrogen bonding. The reason for the lower N content in the surface of AEM in Group A compared to Groups B and C can also be attributed to this mechanism, as observed in the EDS (Fig. 2b).

Building on these results, this study further examined the multi-scale interplay governing the stability of urea aggregation on the membrane under BMED treatment conditions, including acid-enhanced hydrogen bonding, van der Waals forces, hydrophobic effects, shear dissipation, and thermal perturbations (details in Supplementary Method 1). Theoretical simulations demonstrated that although the total hydrogen-bonding energy (1.57×10−9 J) was insufficient to counteract thermal and shear disturbances (1.77×10-9 J), its strong directional preference and high binding energy drove the formation of a cross-linked hydrogen-bonded network within urea aggregates. This was supported by MD simulations and experimental characterizations (Fig. 2c-f, Supplementary Fig. 5). The network achieved dynamic stability by reducing Helmholtz free energy, while protonation in acidic microenvironments further enhanced binding energy36,37. Notably, van der Waals interactions and hydrophobic effects (3.28×10−10 J) synergistically reinforced network integrity, enabling urea aggregates to maintain a characteristic size (>1.4 μm). Consequently, the stabilization mechanism consisted of two complementary components: hydrogen-bond networks provided molecular recognition and orientation constraints, while long-range weak interactions formed an energy buffer system. Their multiscale coupling established a dynamic equilibrium resistant to shear and thermal fluctuations.

The introduction of creatinine and uric acid (Group B) reduced membrane fouling compared to Group A (Fig. 2a). SEM-EDS analyses showed suppressed urea self-aggregation in Group B, consistent with the inverse relationship between system component complexity and performance decay observed in the BMED system (Fig. 1b-c). Furthermore, in Group B, the RDF peak between urea and the AEM surface vanished, being replaced by a peak for creatinine and AEM (Fig. 2e), indicating that the addition of creatinine weakens the interfacial interaction between urea and AEM. This observation suggested that hydrogen bond competition involving creatinine disrupted the hydrogen bonding between the urea -NH2 groups and the membrane surface, thereby inhibiting urea self-aggregation. Additional evidence for this mechanism came from FTIR spectral shifts—the narrowing of the NH2 peak and a blueshift—indicating competitive hydrogen bonding via preferential adsorption of other nitrogen-containing groups (such as =NH, -NH-) from creatinine and uric acid (Fig. 2c). XPS further supported this mechanism: attenuated C-N (~286.2 eV) signals and the varying intensity of the -NH2 (~401.6 eV) peak, reflecting the competitive occupation of adsorption sites, confirmed the disruption of urea hydrogen-bond networks (Supplementary Fig. 4). In the treatment of Group C AEM, SEM-EDS confirmed the uniform coverage of the membrane surface by a BSA layer, with sulfur distribution matching BSA composition (Fig. 2a-b, Supplementary Fig. 2). FTIR spectroscopy revealed a redshift of the -NH2 stretching vibration to 3210 cm−1 upon BSA adsorption, indicative of hydrogen bonding (N-H···O) restricting vibrational modes of -NH2. This observation agreed with previous studies38,39. Concurrently, intensified absorption in the 1000–1300 cm−1 region was observed, suggesting stabilization of surface groups through hydrogen-bonded BSA overlayers. XPS analysis demonstrated a downward shift in the C=O binding energy from 531.78 eV to 531.48 eV (Supplementary Fig. 4), implying enhanced electron density around oxygen atoms. These results were consistent with the anti-fouling mechanism of membrane protein coatings40–42: (1) Charge shielding through hydrogen bonding between BSA amino groups and membrane oxygen-containing groups (C=O), which neutralized charge activity; and (2) Steric exclusion from the dense BSA layer, physically blocking pollutant access. Although previous studies43,44 have suggested that BSA fouling may negatively affect ion transport, our results indicate that, compared to urea self-aggregation fouling, BSA fouling exerts a lesser impact on membrane performance. Besides, BSA-fouled membranes (Group C) exhibited higher hydrophilicity than urea-fouled (Group A) counterparts (Δ surface free energy: +13.18 mJ m-2, Supplementary Fig. 6), facilitating enhanced ion transport (Fig. 1b-c). Thus, the BSA-fouling not only enhanced ion transport but also suppressed urea fouling, further stabilizing BMED performance.

In summary, the various interactions occurring at the urea-membrane interface and the resulting self-aggregation phenomenon were the primary factors responsible for the significant performance decay of the BMED system. Simplified formulation (Group A) neglected antagonistic interactions among urinary inherent organic molecules—such as competitive adsorption between urea, creatinine, and uric acid, and steric exclusion by proteins—thereby overestimating the individual fouling risk of urea while masking the anti-fouling mechanisms inherent in complex components (Fig. 2f). This oversight biased lab-scale interpretations by amplifying fouling risks of single compounds and obscuring the synergistic anti-fouling effects in actual urine. These differences underscored the limitations of oversimplified wastewater component models for BMED systems, as their lab-level data failed to guide practical industrial applications.

Organic-inorganic synergy shifts membrane fouling patterns
During urine treatment via the BMED system, both cation-exchange membrane (CEM) and AEM exhibited substantial urea aggregation, while the underlying mechanisms differed. Similar to AEM, Groups B and C (containing creatinine/uric acid or BSA) exhibited a marked reduction in urea-only (Group A) fouling on the surface of CEM (Fig. 3a), reaffirming the inherent antifouling synergy of natural urine components. However, the CEM surface displayed distinct fouling characteristics: SEM imaging showed that the fouling particles were more compact and larger compared to those on AEM. EDS analysis further indicated a significant enrichment of hardness ions (Ca2+, Mg2+) in CEM deposits (Fig. 3b), with Ca2+ concentrations ranging from 0.5-3 wt% and Mg2+ from 1-6 wt% across Groups A, B, and C, providing semi-quantitative evidence that these ions were critical mediators of foulant formation. Prior studies45,46 proposed that hardness ions act as mineralization cores, attracting organic molecules like urea to establish aggregation layers—a mechanism corroborated by our findings. Additionally, these ions also induce electron cloud redistribution in organic molecules, thereby stabilizing extended aggregation structures47. Such ion-bridging effects, which synergize urea self-aggregation and hardness ion co-aggregation, led to the formation of larger, more densely distributed foulant particles on CEM surfaces, ultimately compromising membrane ion-transport efficiency and long-term operational stability.

FTIR and XPS analyses revealed the urea aggregation mechanisms on CEM surfaces and their synergistic interplay with Ca2+ and Mg2+. FTIR spectroscopy demonstrated that the absorption intensities of C=O (~1650 cm−1) and -NH2 stretching (~3400 cm−1) vibrations on the CEM surface in Groups A-C were readily detectable and more intense than those of the pristine membrane (Fig. 3c), indicating hydrogen bond formation between the carbonyl/amino groups of urea and the oxygen atoms of sulfonate groups (-SO3-), which are inherent to the CEM. The broadening and intensification of the N-H bond peak (~1600 cm−1) further evidenced hydrogen-bond-driven urea adsorption and local aggregation on the CEM. Combined with EDS and XPS, urea-aggregated regions on the CEM surface exhibited spatial co-aggregation of Ca2+ and Mg2+ (Fig. 3b, Fig. 3e and Supplementary Fig. 7). The chemical shift and intensification of the O1s C=O peak corroborated carbonyloxy oxygen coordination with hardness ions, while the Ca2p features at ~347.4 eV and ~350.9 eV, indicative of Ca-O coordination with organic ligands, provided direct evidence for metal-ligand bond formation. This coordination likely facilitated the densification of hydrogen-bond networks, as indicated by the shifted C-N/C=O components in the C1s spectrum and reconfigured -NH2/C-N signals in the N 1s spectrum. MD simulations visually demonstrated the co-aggregation of hardness ions and urea molecules on the CEM surface, with RDF showing pronounced interaction maxima for CEM-Ca2+ and CEM-Mg2+ (Fig. 3d and Supplementary Fig. 8), indicative of exceptional compactness and strong specific coordination48–51. The consistent findings from EDS, FTIR, and XPS collectively suggest that ionic electrostatic interactions and metal-ligand coordination jointly mediated the adsorption of Ca2+ and Mg2+ on CEM, which in turn promoted urea aggregation via two pathways: (1) direct coordination between metal ions and carbonyl/amino groups of urea, and (2) intermolecular bridging by charged ions to stabilize urea aggregates.

To investigate hardness ion roles, this study designed Group D (inorganic-only urine, Supplementary Table 2) and conducted seven batch continuous experiments. Unexpectedly, BPM exhibited severe scaling in Group D (Supplementary Fig. 9a). This finding stood in stark contrast to the results observed with organic-containing formulations (Groups A-C), where BPM remained largely unscaled (Supplementary Fig. 9b). SEM mapping revealed ion-specific deposition patterns: Ca2+ forming calcium hydroxide scale on the BPM anion side, while Mg2+ forming magnesium hydroxide on the CEM base chamber side (Supplementary Figs. 9 and 10; as detailed in Supplementary Note 1). This divergence arose from distinct coordination chemistries: Ca2+ (ionic radius: 0.99 Å, with lower charge density and higher polarizability52,53) preferentially bound urea’s groups as nucleation cores in organic-containing formulations (Groups A-C), forming steric aggregates that anchored ions to CEM. The distinct hydration properties of Ca2+ and Mg2+ further governed their migration paths. Mg2+ (ionic radius: 0.71 Å), with its higher charge density, possesses a larger hydration shell and higher hydration energy (-1922 kJ mol−1 for Mg2+ vs -1577 kJ mol−1 for Ca2+), which restricted its mobility and enhanced its retention on the CEM17,54. This preference, in turn, conferred greater mobility upon the more weakly hydrated Ca2+, facilitating its transit across the CEM and subsequent precipitation at the BPM interface. Conventional theories suggest that low fouling on BPM is due to IEMs barrier effects and its self-cleaning via H+/OH- generation15,55,56. As a result, in the inorganic-only Group D, mobile Ca2+ migrated across the CEM, reacting with OH- generated by the BPM to form scales, which then deposited on its surface. Mg2+, however, adsorbed to CEM via electrostatic interactions, regardless of urea presence. These findings indicated that Group D (inorganic-only), compared with Groups A-C (organic-containing), exhibited discrepancies in assessing fouling risk and locations, further revealing the risks of oversimplifying urine components.

Key findings from Group D (inorganic-only) experiments highlighted the essential role of organic-inorganic interactions in governing fouling pathways. Unlike organic-containing Groups A-C, where Ca2+ and Mg2+ stabilized urea aggregates on CEM via electrostatic bridging and coordination, Group D exhibited severe scaling on BPM, driven by freely migrating Ca2+ forming inorganic deposits (Fig. 3f). This underscored that organic (such as urea) acted as both an adsorption matrix and ion anchor: in its absence, hardness ions lost stabilization, leading to unpredictable scale migration (e.g., BPM anion-side Ca2+ accumulation vs. CEM-based Mg2+ adsorption). Critically, many studies5,9,10,18 suffered from limitations in experimental design by using inorganic-only urine components similar to those in Group D. This oversimplified experimental design, neglecting organic-inorganic interactions, distorts fouling mechanism interpretation, compromising experimental integrity and yielding unreliable predictions of membrane performance in treating actual wastewater.

Economic consequences of formulation simplification
The engineering-economic assessment of electrochemical membrane systems is closely tied to their long-term stability57. To demonstrate how formulation simplification can systematically bias such assessments, we performed a proof-of-concept cost-revenue analysis building on prior research and based on our lab-scale data (Fig. 4a and Supplementary Table 3)17,58,59. This analysis is not intended as a precise industrial forecast, but to quantify the economic risks of relying on oversimplified compositions. Building on previous studies17,57,60,61, our cost-revenue analysis considered urine compositional complexities. This study provided a comprehensive assessment of energy costs, cleaning costs, fixed costs, and revenue dynamics in the treatment of urine using the BMED system. As shown in Fig. 4b, fixed costs dominate the total expenditure, accounting for approximately 67%, with membrane costs representing the largest share—a finding consistent with prior studies60,62. Using mathematical modeling, we predicted cleaning frequencies at the lab-scale (Supplementary Method 2), revealing that cleaning costs constitute approximately 32% of total costs, with modeling approaches diverging between experimental groups. Group A followed an asymptotic decay pattern, while Groups B and C exhibited multi-stage nonlinear dynamics, requiring fifth-order polynomial models that reflect composition-driven interaction complexities in their systems (Supplementary Fig. 11 and Supplementary Table 4). This increase in cleaning costs was mainly attributed to the amplified fouling effects inherent in small-scale systems57. While energy costs and revenue remained relatively modest at this stage, the scalability of the process offers significant economic potential17. As production scales up, fixed costs are expected to be amortized over larger outputs, reducing unit costs and enhancing economic viability. Furthermore, the effective recovery of resources and generation of high-value products ensures long-term economic sustainability, a conclusion supported by multiple studies63,64.

The engineering-economic assessment also revealed a counterintuitive relationship between urine component complexity and operational costs in BMED. As synthetic urine composition transitioned from simplified (Group A) to complex formulations (Group C), membrane cleaning intervals were extended from 12 to 14 hours, reducing annual cleaning batches from 634 to 547 and resulting in a stepwise decrease in total process costs (from $2638.27 per year to $2299.16 per year). Although the complex components (Groups B and C) exhibited higher product yields (Group B: 9.74 kg m-3, Group C: 10.05 kg m-3), representing an 8.8%–12.3% increase compared to Group A (8.95 kg m-3), their annual energy consumption (Group B: 7.97 kWh, Group C: 7.94 kWh) was 10.89%–11.31% higher than that of Group A (7.16 kWh). This was due to the prolonged operation time resulting from reduced cleaning frequency. In contrast, the higher product revenues of Group B ($1.67 year−1) and Group C ($1.72 year−1) compared to Group A ($1.52 year−1) underscored the economic advantages of more complex systems. Notably, the higher cleaning frequency of Group A resulted in higher cleaning costs of 868.63 $ year−1, significantly surpassing those of Groups B and C (749.44 $ year−1). Furthermore, the higher frequency of cleaning triggered a cascade effect, leading to a marked reduction in the lifespan of the membranes, membrane stacks, and peripheral equipment, thus raising fixed costs (from $1543.5 to $1764 per year). This explains why, in the BMED system, the proportions of components in engineering-economic assessment remain consistent despite varying levels of membrane fouling (Fig. 4b).

The engineering-economic assessment further underscored that extrapolating laboratory data derived from simplified formulations to real-world engineering applications could lead to severe misjudgments of long-term operational costs and profits. For instance, cost analyses based on Group A data underestimated the energy requirements in actual urine (where Group A’s consumption was 9.82% lower than Group C’s), which could potentially distort the predicted return on investment during the pilot phase. Therefore, molecular interactions among urine components not only directly impacted the operational performance of BMED by governing membrane fouling mechanisms but also indirectly influenced the economic projections of the entire system through system stability.

Methods
Materials and chemical reagents
All chemicals (analytical reagent grade) used in this study were obtained from Macklin and Aladdin. Three representative synthetic urine formulations were prepared based on the composition and concentrations found in actual urine, ranging from simple inorganic ion solutions to complex mixtures of urea, small organic molecules, and large proteins (Supplementary Table 2). Three sets of AEM and four sets of BPM in the BMED were provided by Lanran Co., China, while three sets of CEM were purchased from Astom Co., Japan. Additionally, a single ePTFE-based HFM filament was provided by Hanchen Co., China. Deionized (DI) water was used throughout the study.

Fabrication of the BMED system
The BMED is a custom-designed electrodialysis unit consisting of three repeated four-chamber sections: the acid, base, feed, and electrode chambers, each separated by 1 mm thick spacers. Each membrane has an effective area of 55 cm2, and the electrodes are titanium alloy coated with ruthenium and iridium. To enhance nutrient recovery from urine, an HFM was integrated into the base chamber to capture volatilized ammonia nitrogen. The shell side of the HFM was exposed to the base solution and ammonia gas, while its lumen, with an effective area of 500 cm2, was connected to the absorption chamber, circulating H2SO4.

Operation of the BMED system
Initially, the acid and base chambers were filled with DI water, and 4 wt.% NaOH was used as the electrolyte, with urine introduced into the feed chamber. Each chamber contained 0.15 L of the initial solution. A peristaltic pump (JIHPUMP, China) circulated the solution through the membrane at a flow rate of 0.4 L min−1. A regulated DC power supply (ITECH, China) applied a constant voltage of 15V, operating in continuous cycling mode for nutrient separation and recovery from urine. The batches were processed for 2 hours, taking both energy consumption and performance into account. During the process, real-time monitoring was conducted to measure the pH, conductivity, and solution volume in each chamber.

Analytical methods
Urea concentrations were determined by mixing 1 mL of sample with 2 mL of acid-ferric and 1 mL of diacetyl monoxime-thiosemicarbazide solutions, followed by heating at 100°C for 20 min and spectrophotometric measurement at 525 nm66. The anions (Cl-, SO4 2−, and PO4 3−) and cations (Na+, K+, NH4+, Mg2+, and Ca2+) were detected using an ion chromatograph (ICS-1100, Thermo, USA). Surface analysis of fouled membranes was conducted using a combination of non-destructive, surface-sensitive techniques to preserve the integrity of the foulant-membrane interface and to enable cross-validation of chemical and morphological data. Surface morphology and elemental composition of the fouled membrane were characterized by SEM (MIRA LMS, Tescan, Czech Republic) and EDS (X-MaxN, Oxford, UK). ATR-FTIR (Nicolet iS20, Thermo, USA) and XPS (K-Alpha, Thermo, USA) were utilized to analyze the surface chemical compositions of different membranes. For XPS data analysis, the spectra were calibrated to C1s (284.8 eV) and fitted using a Shirley background and the fixed Gaussian-Lorentzian peak shape. The surface free energy of the membrane were measured using a Contact Angle Goniometer (SDC200S, SINDIN, China).

Calculations of the BMED system performance evaluation
Desalination efficiency serves as a core metric for evaluating the performance of BMED system. The recovery efficiency of individual nutrients represents a key parameter for assessing the efficacy of BMED system in achieving resource recovery from urine treatment; importantly, the performance decay rate stands as a critical determinant for evaluating the long-term operational sustainability and reliability of such systems.

The desalination efficiency (DE) of urine was calculated using Eq. 1.

DE = (σ0 - σt) / σ0 × 100% (1)

where σ0 and σt are the conductivity of urine in the feed chamber at times 0 and t (min).

The recovery efficiencies of each nutrient were calculated according to Eq. 2.

R = (Ca,t / Ca,0) × 100% (2)

where R denotes the recovery efficiency of each nutrient; Ca,t (mg L−1) represents the concentration of each nutrient in the recovery chamber—specifically, NH4+ in the absorption chamber, urea in the feed chamber, PO4 3- in both acid and feed chambers, and K+ in both base and feed chambers—at time t (min). And Ca,0 (mg L−1) corresponds to the initial concentration of each nutrient in urine.

The performance decay ratio (P) of the BMED system was determined using Eq. 3.

P = (P0 - Pn) / P0 × 100% (3)

where P0 denotes the initial performance of BMED system in urine treatment (specifically, desalination efficiency), while Pn represents the performance observed during the nth batch of BMED processing.

Notably, the nutrient recovery decay rate (N) was calculated to evaluate the decrease in recovery efficiency over consecutive batches, as shown in Eq. (4).

N = (N0 - Nn) / N0 × 100% (4)

where N0 and Nn represent the nutrient recovery content (e.g., nitrogen or phosphorus) in the first batch and the nth, respectively.

Theoretical calculation of surface free energy
Surface free energy reflects the intermolecular forces at a material’s surface and is crucial in determining interactions between membrane materials and pollutants, although it cannot be directly measured. In this study, surface free energy was estimated through contact angle measurements using liquids with known surface free energies (water and diiodomethane), applying the Owens-Wendt model. This model decomposes the membrane’s surface free energy and the liquid’s surface tension into two components: dispersive (d) and polar (p)67, as described in Eqs. 5 and 6.

γS = γd S + γp S (5)

γL = γd L + γp L (6)

where γS represents the total free energy of the membrane, with γd S and γp S denoting the dispersive and polar components, respectively; γL is the surface tension of the liquid, while γd L and γp L correspond to the dispersive and polar components of the liquid’s surface free energy.

The model was derived from the Young’s equation and the Good-van Oss theory (Eq. 7):

γL (1 + cosθ) = 2 (γd S × γd L)1/2 + 2 (γp S × γp L)1/2 (7)

In this study, polar water and nonpolar diiodomethane were selected as titration liquids, with contact angles denoted as θ1 and θ2, respectively, as described in Eqs. 8 and 9.

γL1 (1 + cosθ1) = 2 (γd S × γd L1)1/2 + 2 (γp S × γp L1)1/2 (8)

γL2 (1 + cosθ2) = 2 (γd S × γd L2)1/2 + 2 (γp S × γp L2)1/2 (9)

For water, γL1 = 72.8 mN m−1, γd L1 = 21.8 mN m−1, γp L1 = 51.0 mN m−1; for diiodomethane, γL2 = 50.8 mN m−1, γd L2 = 50.8 mN m−1, γp L2 = 0 mN m−1. Solving this system of equations simultaneously gives γd S and γp S, with the γS equal to their sum.

Molecular dynamics simulation
The interactions between ions and the fragments (with functional groups) of AEM and CEM were simulated in the FORCITE module and COMPASS II force fields in Material Studio 201968,69. In the simulation process, solution-CEM-solution (urea/urea mixture) and solution-AEM-solution (urea/urea mixed) were constructed. All partial atomic charges were defined using the adopted force field. A simulation box with dimensions of 28.28 Å × 28.28 Å × 74.44 Å was created. These four systems were geometrically optimized, followed by model construction. With the optimized structure, all calculations were conducted in the NPT and NVT ensembles at 298 K, with a timestep of 1 fs and a total simulation time of 600 ps, during which simulation trajectories were recorded every 1000 steps. The Ewald scheme and the atomic truncation method were used to handle electrostatic and van der Waals interactions, respectively. Data were collected in the NVT ensemble. The radial distribution function (RDF) was used to describe the interactions between ion exchange membranes and solution contents (urea, ions, uric acid, and creatinine).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this work, Yang et al. pointed out that oversimplified wastewater composition, which has been used quite often in previous studies focusing on membrane fouling, can mislead the result. I totally support their view. They demonstrated that this was the case when oversimplified synthetic urine was tested with an electrodialysis system: a very simple composition caused more severe fouling than did more complex compositions. It is a very interesting finding, but it is still dangerous to generalize the obtained results for various membrane processes. I suggest the authors to change the title of this work to avoid such misleading. I feel that this is not the case with most of pressure-driven membrane processes.\nThey carried out a cost analysis in this work. I do not see the rational and the necessity for it, as they still tested simplified urines in this study. To what extent do they think did the “complex urine” such as Group C represent the real urine? They demonstrated that the very simplified urine (Group A) exhibited more severe fouling than Groups B/C. However, this does not mean Group A still exhibits more severe fouling than does the real urine.\nThe fundamental information, pH of the solutions measured during the tests, is not shown in the current manuscript. This certainly lowers the quality of this work. Discussion in this work should be made with pH.\nL134-140\nThey need to justify the use of creatinine, uric acid and BSA much more carefully. Are they really dominant metabolites and macromolecular organics in the urine? Particularly, the use of BSA is questionable. BSA does not necessarily represent proteins in the real urine.\nL161\nThere is no data by which we believe that rapid transmembrane migration occurred.\nFigs 2 and 3\nQuantitative discussion on the basis of EDS and/or FTIR is dangerous. They should have scraped the deposits and analyzed them directly.\nL369\nHow can it be claimed “significantly higher”?\nL395\nAre there any possibilities of formation of MAP? This could be confirmed by sampling the deposits.", "reviewer_2": "In the manuscript \" Why oversimplified wastewater composition undermines experimental integrity and biases membrane fouling interpretation\", a critical gap in wastewater treatment research by demonstrating how oversimplified synthetic urine formulations distort membrane fouling mechanisms and economic assessments in BMED systems was addressed.\nHowever, before its publication some amendments have to be done. Authors must work on the following points:\n1. The topic is not focused and the scope is too broad. Suggest similar modifications: \"Oversimplified Wastewater Composition Compromises Experimental Integrity and Biases Membrane Fouling Interpretation in Electrodialysis Systems.\"\n2. While focused on urine, the title and abstract broadly implicate \"wastewater.\" Clarify whether conclusions extend to other waste streams (e.g., municipal/industrial). If not, modify claims to avoid overgeneralization (e.g., replace \"wastewater\" with \"source-separated urine\" in key statements).\n3. Abstract lacks quantitative conclusions. Add quantitative key findings (e.g., \"29.3% lower decay rate in multi-component systems\") to highlight impact.\n4. Introduction lacks sufficient emphasis on how this work fundamentally advances beyond prior literature. Explicitly contrast findings with existing studies to highlight novelty (e.g., the discovery of antagonistic organic interactions that stabilize BMED performance).\n5. The hierarchical formulation design (Groups A–C) effectively bridges lab-industry gaps. Yet, the rationale for selecting specific metabolites (creatinine, uric acid, BSA) needs strengthening. Include quantitative data from actual urine (e.g., concentration ranges in Supplementary Table 1) to justify their inclusion and representativeness.\n6. Cost analysis is based on lab-scale operations. Projections for industrial-scale viability require sensitivity analysis of key variables (e.g., membrane lifespan under real-world cleaning protocols, fluctuations in energy/chemical costs). Discuss scalability limitations (e.g., electrode degradation, flow distribution challenges).\n7. SEM images (Fig. 2a/3a): Include scale bars for all micrographs. Label deposits (e.g., \"urea aggregate,\" \"Ca²⁺-BSA co-deposit\").\n8. Group D (inorganic-only): Clarify why Mg²⁺ scaling occurred on CEM while Ca²⁺ migrated to BPM. Elaborate on the role of ionic radius/polarizability beyond citing references.\n9. Relate cost deviations (15.9% cleaning cost overestimation) to industry implications in Discussion.\n10. Add error bars to multiple quantitative data graphs to address statistical significance."}, "author_response": "Point-to-point reply\nWe are sincerely grateful to the editors and reviewers for their thorough review and\ninsightful comments, which have been instrumental in enhancing the rigor and clarity of\nour manuscript. All suggestions have been incorporated into the revised version. Our point-\nby-point responses and the associated revisions are outlined below.\nResponse Letter to Reviewer 1\nDear Reviewer,\nThank you for reviewing our paper. The paper was modified according to your valuable\nsuggestions and comments.\n1. In this work, Yang et al. pointed out that oversimplified wastewater composition, which\nhas been used quite often in previous studies focusing on membrane fouling, can mislead\nthe result. I totally support their view. They demonstrated that this was the case when\noversimplified synthetic urine was tested with an electrodialysis system: a very simple\ncomposition caused more severe fouling than did more complex compositions. It is a very\ninteresting finding, but it is still dangerous to generalize the obtained results for various\nmembrane processes. I suggest the authors to change the title of this work to avoid such\nmisleading. I feel that this is not the case with most of pressure-driven membrane\nprocesses.\nResponse\nWe thank the reviewer for their positive feedback on our core premise and their valuable\nsuggestion to refine the scope of our work. We agree that our conclusions should be\ncarefully framed to avoid overgeneralization across membrane processes. To prevent any\npotential misunderstanding, we have revised the title of the manuscript to \"The\noversimplification pitfall: How synthetic urine misleads mechanistic understanding of\nmembrane fouling in bipolar membrane electrodialysis\" and have carefully refined the\nlanguage throughout the manuscript—particularly in the abstract and discussion—to\nconsistently frame our conclusions within the context of electrodialysis. We hope that these\nexplanations adequately address your concerns.\nWe appreciate your valuable suggestions, which have helped us improve the clarity and\nprofessionalism of our manuscript.\nRevised as: Title (line 1-3), line 98-101, line 357-359: and line 515-518.\nTitle (Line 1-3): The oversimplification pitfall: How synthetic urine misleads mechanistic\nunderstanding of membrane fouling in bipolar membrane electrodialysis\nLine 98-101: Consequently, it creates a fundamental knowledge gap: How do complex\ncomponent synergies—routinely ignored in oversimplified synthetic formulations—impact\ntreatment performance and obscure experimental interpretability of performance-limiting\nmechanisms in BMED systems?\nLine 357-359: These differences underscored the limitations of oversimplified wastewater\ncomponent models for BMED systems, as their lab-level data failed to guide practical\nindustrial applications.\nLine 515-518: This study highlights a critical yet overlooked limitation in membrane-based\nsource-separated urine treatment research, demonstrating using the case of BMED\nsystems that oversimplified synthetic compositions fundamentally distort membrane\nfouling mechanisms and compromise the translational validity of laboratory findings.\n2. They carried out a cost analysis in this work. I do not see the rational and the necessity\nfor it, as they still tested simplified urines in this study. To what extent do they think did the\n“complex urine” such as Group C represent the real urine? They demonstrated that the\nvery simplified urine (Group A) exhibited more severe fouling than Groups B/C. However,\nthis does not mean Group A still exhibits more severe fouling than does the real urine.\nResponse\nWe thank the reviewer for this insightful comment, which raises important questions\nregarding the representativeness of our synthetic formulations and the rationale for the\ncost analysis. We agree that our most complex formulation (Group C) remains a model\nsystem and does not fully capture the complete complexity of real urine. In revising our\nmanuscript, we have sought to better articulate the intent behind our experimental design\nand the economic assessment.\nThe primary objective of the cost analysis was not to provide a precise industrial cost\nforecast, but rather to serve as a proof-of-concept demonstration of how compositional\noversimplification can systematically bias economic evaluations. We have clarified this\npoint in the text (Lines 459-463). To address the reviewer's specific concern about\nrepresentativeness, we have now explicitly stated that Group C was designed to\nincorporate key organic constituent categories—urea, small-molecule metabolites\n(creatinine, uric acid), and a model protein (BSA)—that are universally present in real urine\nand are known to critically influence interfacial processes. Our molecular dynamics\nsimulations and experimental characterizations confirm that these components actively\ngovern the fouling mechanisms under study, making Group C a more physiologically\nrelevant model than the highly simplified formulations commonly used in the literature.\nFurthermore, we have reframed the discussion to focus squarely on the comparative\nanalysis between the synthetic formulations that were experimentally tested. The\nmonotonic improvement in system stability and the corresponding reduction in projected\ncosts from Group A to Group C demonstrates that the choice of synthetic formulation itself\nfundamentally alters the perceived performance and economic outlook. This internal\ncomparison provides a direct, quantitative measure of how oversimplification (as in Group\nA) can bias experimental outcomes and subsequent engineering assessments compared\nto a more representative model (as in Group C). The cost overestimation identified with\nGroup A data quantifies the risk of misjudgment inherent in selecting an oversimplified\nmodel over a more complex alternative. This reinforces our study's primary contribution: to\nhighlight that the choice of lab-scale model composition is a critical methodological factor\nthat can predetermine technical and economic conclusions.\nWe hope that these revisions, which include a more cautious interpretation of our cost\nanalysis and a clearer justification of our model formulations, adequately address the\nreviewer's concerns. The feedback has been invaluable in helping us refine the narrative\nof our study and better highlight its central message: that overlooking key wastewater\nconstituents can lead to both technical and economic misjudgments in process evaluation.\nRevised as: Line 459-463, line 547-554 and new references.\nLine 459-463: To demonstrate how formulation simplification can systematically bias such\nassessments, we performed a proof-of-concept cost-revenue analysis building on prior\nresearch and based on our lab-scale data (Fig. 4a and Supplementary Table 3)17,57,58. This\nanalysis is not intended as a precise industrial forecast, but to quantify the economic risks\nof relying on oversimplified compositions.\nLine 547-554: It is crucial to note that this cost analysis, while revealing a systematic bias,\nis derived from lab-scale operations. To assess the robustness of our finding—that\nsimplified formulations overestimate costs—we first dissected the cost structure. As\nsynthesized in Supplementary Fig. 12, the complex formulation (Group C) achieves a lower\ntotal cost primarily by reducing the costs associated with membrane replacement and\ncleaning (both fixed and operational costs). The economic advantage of Group C lies in its\nmore stable operation, which persists even though it incurs marginally higher energy costs\ndue to longer runtime.\nReference:\ncomplete resource recovery and zero-discharge of urine: Performance\noptimization and evaluation. Water Research 263, 122175 (2024).\nprepared from readily available materials: Characteristics and economics. PLoS\nWater 2, e0000178 (2023).\nA. A circular economy strategy for valorizing industrial saline wastewaters: Techno-\neconomics and environmental impacts. Chemical Engineering Journal 472,\n144819 (2023).\n3. The fundamental information, pH of the solutions measured during the tests, is not\nshown in the current manuscript. This certainly lowers the quality of this work. Discussion\nin this work should be made with pH.\nResponse\nWe thank the reviewer for this insightful comment, which helps strengthen our argument.\nWe agree that the pH is a fundamental parameter governing the BMED process and the\ndiscussed fouling mechanisms, and its omission was a significant oversight.\nIn response, we have now included the pH data for all key chambers (acid, base, and\nfeed) across the operational batches as Supplementary Fig. 1. We have chosen to present\nthis dataset in the Supplementary Information to maintain the focus of the main text figures\non the primary performance metrics (e.g., conductivity, nutrient recovery efficiency, and\nfouling characterization) that directly underpin the central thesis of formulation\nsimplification-induced bias. However, recognizing its critical role in interpreting the process\nstability and fouling mechanisms, we have fully integrated the discussion of the pH data\ninto the main text. Furthermore, we have revised the manuscript to integrate the discussion\nof pH where it is most relevant, using the data to explain the stable process driving force\nand the serendipitous suppression of urea hydrolysis.\nWe are grateful for this constructive feedback, which has significantly improved our\nmanuscript.\nRevised as: Supplementary Fig. 1, line 161-171 and new references.\nSupplementary Fig. 1 | pH in BMED chambers during urine treatment over 7 batches: (a)\nacid, (b) base, and (c) feed chambers.\nLine 161-171: Despite the differential performance decay, the core functionality of the\nBMED process remained relatively stable, as reflected in the consistent pH evolution of the\nkey chambers (Supplementary Fig. 1). Both the acid and base chambers established their\nrespective strongly acidic (pH < 2.5) and alkaline (pH > 10.5) environments within the first\n5 minutes of operation and maintained them consistently throughout the experiments,\nproviding a stable driving force for nutrient recovery and ammonia stripping. The feed\nchamber pH remained stable at approximately 3, a consequence of inherent proton\nleakage in the BMED process22. This phenomenon, however, conferred a critical\nadvantage by serendipitously suppressing urea hydrolysis, thereby obviating the need for\nexogenous acid addition and significantly facilitating the storage and transportation of urine\nresources23.\nReference:\ncircular lithium economy with electrodialysis: upcycling spent battery leachates with\nselective and bipolar ion-exchange membranes. Environmental Science &\nTechnology 58, 19486-19500 (2024).\nNa-chabazite as urease inhibitor. Resources, Conservation and Recycling 212, 107955\n(2025).\nL134-140\n4. They need to justify the use of creatinine, uric acid and BSA much more carefully. Are\nthey really dominant metabolites and macromolecular organics in the urine? Particularly,\nthe use of BSA is questionable. BSA does not necessarily represent proteins in the real\nurine.\nResponse\nWe thank the reviewer for this important question regarding our selection of organic\ncomponents. In response, we have strengthened the justification in the manuscript. The\nsmall organic metabolites, creatinine and uric acid, were selected because they are\nconsistently identified in literature as core organic constituents of human urine and are\nfrequently employed in studies seeking to enhance the realism of synthetic formulations.\nFor the macromolecular component, BSA was chosen as a representative model protein\nfor several key reasons. Firstly, from a practical standpoint, obtaining a stable and\nconsistent supply of purified human urinary proteins (which encompass a wide range,\nincluding albumin, uromodulin, and others) for controlled laboratory experiments is\nchallenging and cost-prohibitive. Secondly, and most importantly, BSA is a scientifically\njustified surrogate. Extensive literature demonstrates its high structural and functional\nsimilarity to Human Serum Albumin (HSA), a key proteinaceous component present in\nurine. As such, BSA serves as an excellent model for the albumin fraction of urinary protein.\nFinally, the use of BSA as a representative proteinaceous foulant is a well-established and\nwidely accepted paradigm in fundamental membrane fouling studies. This allows for a\ncontrolled, mechanistic investigation of macromolecular interactions (e.g., adsorption,\naggregation) that are relevant to the broader class of proteins in urine, providing a critical\nand reproducible foundation for our hierarchical experimental design. Our hierarchical\ndesign (Groups A–C) is intended to systematically bridge the complexity gap by introducing\nthese key, often-overlooked organic categories, thereby providing a more robust platform\nfor analyzing multi-component interactions than single-solute simplifications.\nWe believe these additions not only address your concerns but also set the stage for\nmore realistic and applicable future research. Thank you for the opportunity to enhance\nour work based on your constructive feedback.\nRevised as: Line 145-152 and new references.\nLine 145-152: Group B expands this by incorporating core small organic metabolites—\ncreatinine and uric acid—which are not only universally present in actual urine but are also\nconsistently employed in rigorous studies to enhance the representativeness of synthetic\nurine formulations5,16,17. Group C further introduces macromolecular organics, using\nBovine Serum Albumin (BSA) as a well-established model protein. This choice is justified\nby its high structural and functional similarity to human serum albumin, a key proteinaceous\ncomponent in urine, allowing for a controlled investigation of macromolecular fouling\nmechanisms relevant to real systems19-21.\nReference:\nurine: experiments, modeling, and performance analysis. Environmental Science\n& Technology 55, 14886-14896 (2021).\nfrom source-separated urine based on bioelectrically enhanced bipolar membrane-\ndriven in-situ alkali production coupling with gas-permeable membrane system.\nChemical Engineering Journal 431, 134161 (2022).\ncomplete resource recovery and zero-discharge of urine: Performance\noptimization and evaluation. Water Research 263, 122175 (2024).\nprobe for discriminating and quantifying similar proteins. Chemical Science 16,\n3228-3237 (2025).\nresponse to HSA and BSA: quantitative detection of HSA in urine. Journal of\nMaterials Chemistry B 12, 9037-9049 (2024).\nand sensitive detection of human serum albumin (HSA) in biological matrices and\nmonitoring of HSA purity and degradation. Talanta 286, 127471 (2025).\nL161\n5. There is no data by which we believe that rapid transmembrane migration occurred.\nResponse\nThank you for this insightful comment. We agree that the term \"rapid\" overstepped what\ncould be directly supported by our kinetic data. In response, we have revised the text to\nuse the term \"stable\" to describe NH + migration, which is accurately reflected by its\n4\nconsistent recovery performance throughout the batches (Fig. 1d) and does not imply a\nspecific rate.\nWe appreciate your feedback in strengthening the precision of our wording.\nRevised as: Line 182-184.\nLine 182-184: As shown in Fig. 1d, NH + recovery remained largely unaffected by\n4\nperformance decay due to its stable transmembrane migration, a finding consistent with\nresults from previous studies17,26.\nFigs 2 and 3\n6. Quantitative discussion on the basis of EDS and/or FTIR is dangerous. They should\nhave scraped the deposits and analyzed them directly.\nResponse\nWe are grateful to the reviewer for raising this important point regarding analytical rigor.\nWe fully acknowledge that mechanical scraping of deposits, followed by bulk analysis, is a\npowerful and direct method for foulant characterization. Under circumstances where a\nstable, scrapable foulant layer can be obtained, it undoubtedly provides invaluable\ncompositional insights.\nHowever, within the specific context of bipolar membrane electrodialysis (BMED)\noperated under constant voltage, this approach is often precluded by fundamental process\nconstraints. As noted in previous studies1-3, severe fouling in electromembrane processes\nprimarily manifests as a drastic increase in electrical resistance rather than the formation\nof a thick, physical cake. This resistance surge leads to excessive Joule heating and risks\nirreversible membrane damage long before a mechanically stable, scrapable layer is\nformed. Indeed, as shown in the Fig. R1, which compares the initial membranes (a: pristine\nCEM; c: pristine AEM) with those subjected to seven batches of BMED operation under\nGroup A conditions (b: fouled CEM; d: fouled AEM), no macroscopic or mechanically\nscrapable foulant layer is visually discernible on the fouled membranes, despite the severe\nperformance decay recorded (Fig. 1c). This operational limitation, which necessitates\nalternative characterization strategies, is a recognized challenge in the electromembrane\nfield4,5.\nFig. R1. Surface comparison of initial and fouled membranes. (a) Pristine CEM; (b) Fouled CEM from\nGroup A after seven batches; (c) Pristine AEM; (d) Fouled AEM from Group A after seven batches. All\nmembranes exhibit uniform surfaces without macroscopic foulant layers, consistent with the typical\nmanifestation of electrodialysis fouling as a thin, adherent film that elevates electrical resistance.\nTherefore, in alignment with established practices for such systems, our experimental\nstrategy was designed around a suite of non-destructive, surface-sensitive techniques\n(SEM-EDS, FTIR, XPS, XRD). This multi-methodological approach is widely adopted to\npreserve the integrity of the fragile foulant-membrane interface and to enable cross-\nvalidation of data4,5. In direct response to the reviewer's valid concern about over-\ninterpretation, we have revised the manuscript to explicitly emphasize the semi-quantitative\nnature of the EDS analysis and to clarify that all mechanistic conclusions are drawn from\nthe convergence of evidence provided by these complementary techniques, rather than\nrelying on any single method. We believe this in-situ, multi-technique framework not only\naddresses a technical constraint but also provides a robust, multi-faceted understanding\nof the fouling layer.\nWe thank the reviewer again for pushing us to articulate these methodological\nconsiderations more clearly.\nReference:\nmembranes response to their exposure to electric current during electrodialysis of\na Ca2+ containing solution. Journal of Membrane Science 644, 120149 (2022).\nelectrodialysis: impacts of the molecular size of dissolved organic matter. Journal\nof Membrane Science 685, 121974 (2023).\nstructural and physicochemical investigation. Journal of Membrane Science 436,\n68-78 (2013).\nduring selective electrodialysis for metal ions removal from ionic liquid aqueous\nsolutions. Journal of Environmental Chemical Engineering 11, 109246 (2023).\nthe ageing behavior of ion-exchange membranes during electrodialysis treatment\nof flue gas desulfurization wastewater. Water Research 271, 122897 (2025).\nRevised as: Line 217-220, line 368-372, line 413-417 and line 616-619.\nLine 217-220: Scanning electron microscopy coupled with energy-dispersive X-ray\nspectroscopy (SEM-EDS) investigates the spatial distribution and semi-quantitative\nelemental composition of the fouling layer of the anion-exchange membrane (AEM)\nexposed to various synthetic urine formulations.\nLine 368-372: EDS analysis further indicated a significant enrichment of hardness ions\n(Ca2+, Mg2+) in CEM deposits (Fig. 3b), with Ca2+ concentrations ranging from 0.5-3 wt%\nand Mg2+ from 1-6 wt% across Groups A, B, and C, providing semi-quantitative evidence\nthat these ions were critical mediators of foulant formation.\nLine 413-417: The consistent findings from EDS, FTIR, and XPS collectively suggest that\nionic electrostatic interactions and metal-ligand coordination jointly mediated the\nadsorption of Ca2+ and Mg2+ on CEM, which in turn promoted urea aggregation via two\npathways: (1) direct coordination between metal ions and carbonyl/amino groups of urea,\nand (2) intermolecular bridging by charged ions to stabilize urea aggregates.\nLine 616-619: Surface analysis of fouled membranes was conducted using a combination\nof non-destructive, surface-sensitive techniques to preserve the integrity of the foulant-\nmembrane interface and to enable cross-validation of chemical and morphological data.\nL369\n7. How can it be claimed “significantly higher”?\nResponse\nThank you for your valuable comments. We agree that the term \"significantly higher\" was\ninappropriate without supporting statistical testing. We have revised the manuscript to\nreplace this and similar subjective terms with more accurate, descriptive language that\nreflects the observed differences in intensity or concentration without implying statistical\nsignificance. We appreciate your feedback, which has enhanced the precision of our\nscientific language.\nRevised as: Line 395-400.\nLine 395-400: FTIR spectroscopy demonstrated that the absorption intensities of C=O\n(~1650 cm-1) and N-H stretching (~3400 cm-1) vibrations on the CEM surface in Groups A-\nC were readily detectable and more intense than those of the pristine membrane (Fig. 3c),\nindicating hydrogen bond formation between the carbonyl/amino groups of urea and the\noxygen atoms of sulfonate groups (-SO -), which are inherent to the CEM.\n8. Are there any possibilities of formation of magnesium ammonium phosphate (MAP)?\nThis could be confirmed by sampling the deposits.\nResponse\nWe are grateful to the reviewer for raising this pertinent point regarding the potential\nformation of magnesium ammonium phosphate (struvite, MAP), which is indeed an\nimportant consideration in urine treatment processes. In response, we have supplemented\nthe elemental mapping analysis of the fouled membranes to include nitrogen distribution.\nAs now detailed in the revised Supplementary Note 2 and Supplementary Fig. 9, the\nscalants identified on the membrane surfaces show a complete absence of nitrogen signal.\nThis finding is critical, as MAP (MgNH PO ·6H O) necessarily contains nitrogen in its\n4 4 2\nstructure; the lack of it in the deposits allows us to definitively exclude MAP as a constituent.\nThis experimental observation is entirely consistent with, and further reinforced by, the\nintrinsic ion-separation mechanism of the bipolar membrane electrodialysis system. In our\nconfiguration, phosphate anions are electromigrated through the anion-exchange\nmembranes into the acid chamber, rendering the base chamber—where Mg2+ and NH +\n4\nare present—effectively free of phosphate. The physical segregation of these essential\nprecursors makes the formation of MAP thermodynamically and kinetically implausible.\nConsequently, the scaling must be attributed to species that are locally available. The\nstrong correlation between Mg and O, in the absence of both N and P, on the CEM surface\nfacing the base chamber, together with the highly alkaline conditions, provides coherent\nand conclusive support for the formation of magnesium hydroxide. We have revised the\nmanuscript and Supplementary Note 2 accordingly to present this integrated argument.\nThank you for this critical question, which was essential for us to robustly justify our\nidentification of the scaling constituents.\nRevised as: Line 422-425, Supplementary Note 2 and Supplementary Fig. 9.\nLine 422-425: SEM mapping revealed ion-specific deposition patterns: Ca2+ forming\ncalcium hydroxide scale on the BPM anion side, while Mg2+ forming magnesium hydroxide\non the CEM base chamber side (Supplementary Figs. 9 and 10; as detailed in\nSupplementary Note 2).\nSupplementary Note 2: Analysis of hydroxide scaling formation on membrane\nsurfaces\nThe spatial distribution of inorganic scales on the membrane surfaces, as revealed by\nSEM-EDS elemental mapping (Supplementary Figs. 9 and 10), provides direct evidence\nfor the formation of distinct hydroxide deposits. The mapping shows a clear spatial\ncorrelation between Ca and O on the anion-exchange side of the bipolar membrane (BPM),\nand a concurrent correlation between Mg and O on the base chamber-side surface of the\ncation exchange membrane (CEM). Critically, the absence of a nitrogen signal in these\ndeposits definitively excludes nitrogen-containing scales such as struvite\n(MgNH PO ·6H O, MAP). Furthermore, the system design inherently excludes phosphate\n4 4 2\nfrom the base chamber, as phosphate anions electromigrate through the anion exchange\nmembrane (AEM) toward the acid chamber. This elemental co-localization, in the absence\nof other scaling precursor ions, identifies the primary deposits as Ca(OH) and Mg(OH) ,\n2 2\nrespectively.\nThe divergent scaling locations are a direct result of the differential mobility of Ca2+ and\nMg2+ under the applied electric field. Mg2+, with its high charge density and strong hydration\nenergy (-1922 kJ/mol), interacts more strongly with the CEM surface and possesses lower\nmobility. This results in its preferential accumulation and rapid precipitation as Mg(OH)\n2\nupon encountering the high-pH environment at the CEM surface, effectively hindering its\nfurther transport. In contrast, the more weakly hydrated Ca2+ (hydration energy: -1577\nkJ/mol) exhibits greater mobility, facilitating its transit across the CEM21,22. Once in the base\nchamber, Ca2+ is further driven towards the BPM by the electric field, where it encounters\nthe most concentrated OH- flux and precipitates as Ca(OH) on the BPM's anion-exchange\n2\nlayer.\nIn conclusion, the scaling pattern is not stochastic but is a deterministic outcome of the\nsystem's electrochemistry. The combined evidence from elemental mapping and\nthermodynamic analysis confirms that the observed deposits are Ca(OH) and Mg(OH) ,\n2 2\nwhose formation sites are kinetically controlled by the differential migration of the hardness\nions toward a pervasive high-pH sink.\n(a) D-BP-AEM Ca O N\n(b) A-BP-AEM B-BP-AEM C-BP-AEM\nSupplementary Fig. 9 | a Surface mapping analysis after treatment with Group D. BP-\nAEM: Cathode side of the bipolar membrane, i.e., the OH- production side and b\nContamination characteristic of BP-AEM surfaces after seven batches of BMED treatment\nwith different urine components (Groups A to C).\nReference:\ncomplete resource recovery and zero-discharge of urine: Performance\noptimization and evaluation. Water Research 263, 122175 (2024).\nferrite: role of swift heavy ion irradiation. Journal of Magnetism and Magnetic\nMaterials 471, 521-528 (2019).\nResponse Letter to Reviewer 2\nDear Reviewer,\nThank you for reviewing our paper. The paper was modified according to your valuable\nsuggestions and comments.\nIn the manuscript \"Why oversimplified wastewater composition undermines experimental\nintegrity and biases membrane fouling interpretation\", a critical gap in wastewater\ntreatment research by demonstrating how oversimplified synthetic urine formulations\ndistort membrane fouling mechanisms and economic assessments in BMED systems was\naddressed. However, before its publication some amendments have to be done. Authors\nmust work on the following points:\n1. The topic is not focused and the scope is too broad. Suggest similar modifications:\n\"Oversimplified Wastewater Composition Compromises Experimental Integrity and Biases\nMembrane Fouling Interpretation in Electrodialysis Systems.\"\nResponse\nWe sincerely thank the reviewer for their insightful suggestion to refine our title to better\nhighlight the core concern of oversimplification. Inspired by this excellent advice, we have\nrefined the title to: \"The oversimplification pitfall: How synthetic urine misleads mechanistic\nunderstanding of membrane fouling in bipolar membrane electrodialysis\". We believe this\nversion captures the essence of the reviewer's input while more precisely framing our study\nwithin the specific context of synthetic urine and bipolar membrane electrodialysis.\nAccordingly, we have updated the abstract, introduction, and discussion throughout the\nmanuscript to ensure full alignment with this focused narrative.\nWe are truly grateful for this suggestion, which has significantly strengthened the\npresentation of our work.\nRevised as: Title (line 1-3), line 98-101, line 357-359: and line 515-518.\nTitle (line 1-3): The oversimplification pitfall: How synthetic urine misleads mechanistic\nunderstanding of membrane fouling in bipolar membrane electrodialysis\nLine 98-101: Consequently, it creates a fundamental knowledge gap: How do complex\ncomponent synergies—routinely ignored in oversimplified synthetic formulations—impact\ntreatment performance and obscure experimental interpretability of performance-limiting\nmechanisms in BMED systems?\nLine 357-359: These differences underscored the limitations of oversimplified wastewater\ncomponent models for BMED systems, as their lab-level data failed to guide practical\nindustrial applications.\nLine 515-518: This study highlights a critical yet overlooked limitation in membrane-based\nsource-separated urine treatment research, demonstrating using the case of BMED\nsystems that oversimplified synthetic compositions fundamentally distort membrane\nfouling mechanisms and compromise the translational validity of laboratory findings.\n2. While focused on urine, the title and abstract broadly implicate \"wastewater.\" Clarify\nwhether conclusions extend to other waste streams (e.g., municipal/industrial). If not,\nmodify claims to avoid overgeneralization (e.g., replace \"wastewater\" with \"source-\nseparated urine\" in key statements).\nResponse\nWe thank the reviewer for this important comment, which helped us to clarify the scope\nof our claims. We recognize that the use of the broad term \"wastewater\" could cause\nconfusion regarding the direct applicability of our findings. In response, we have revised\nthe manuscript to more precisely articulate the basis and implications of our work.\nSpecifically, we have replaced overarching uses of \"wastewater\" with \"urine\" in the\nmanuscript to accurately reflect our experimental system. More importantly, we have added\na clarifying paragraph in the Discussion to explicitly state that while our experimental\nvalidation was performed on urine, the methodological insight—that oversimplification\nneglects critical intermolecular interactions—is a fundamental principle likely applicable to\nthe electrodialysis treatment of other complex aqueous streams. This revision ensures our\nclaims are firmly grounded in our data while justifiably highlighting the broader relevance\nof our conclusions for the field.\nWe hope that these explanations adequately address your concerns. Thank you again\nfor your review and valuable suggestions.\nRevised as: Line 46-47, line 205-208 and line 563-569. lines 153, 202, 357, 442, 464, 509,\n511.\nLine 46-47: Keywords: Experimental integrity, Oversimplified urine compositions, Bipolar\nmembrane electrodialysis, Membrane fouling, Engineering-economic assessment.\nLine 205-208: These findings suggested that compositional complexity likely stabilizes\nBMED performance through interactions among urinary components—a mechanism often\noverlooked in simplified formulations—that may extend to other complex aqueous systems\nrich in organics and inorganics.\nLine 563-569: Notably, although the mechanistic insights presented here are derived from\nurine, they illuminate a methodological principle with broader implications: reliable\nprediction of BMED performance requires synthetic formulations that preserve the core\nintermolecular interactions of the target stream. This understanding suggests that when\ntreating other complex waste streams (e.g., from food processing or agricultural operations)\nwhere membrane performance depends on multi-constituent interplay, adopting\ncomponent-representative designs becomes essential.\n3. Abstract lacks quantitative conclusions. Add quantitative key findings (e.g., \"29.3% lower\ndecay rate in multi-component systems\") to highlight impact.\nResponse\nWe thank the reviewer for this excellent suggestion to enhance the impact of our abstract.\nWe agree that including quantitative key findings will provide readers with a more\nimmediate and concrete understanding of our study's contributions. Following this advice,\nwe have revised the abstract to incorporate the most salient quantitative results, including\nthe 29.3% reduction in performance decay, the 15.9% overestimation of cleaning costs,\nand key improvements in nutrient recovery efficiency.\nThank you once again for your constructive feedback.\nRevised as: Line 35-37 and line 40-41.\nLine 35-37: After seven batches, the full-component formulations showed 29.3% less\nperformance decay and 10–14% higher urea recovery than the simplified formulation.\nLine 40-41: Simplification also distorts the engineering-economic assessment,\noverestimating cleaning costs by 15.9% and underestimating membrane lifespan by 12.5%.\n4. Introduction lacks sufficient emphasis on how this work fundamentally advances beyond\nprior literature. Explicitly contrast findings with existing studies to highlight novelty (e.g., the\ndiscovery of antagonistic organic interactions that stabilize BMED performance).\nResponse\nWe thank the reviewer for this critical suggestion to better articulate the novel\ncontributions of our work. We agree that a stronger emphasis on the conceptual advance\nbeyond prior literature would enhance the introduction. In response, we have substantially\nrevised the introduction to explicitly contrast the established paradigm with our\ncounterintuitive findings, specifically highlighting the discovery of antagonistic organic\ninteractions that stabilize BMED performance—a phenomenon that challenges the\nconventional wisdom in membrane fouling. Additionally, we have reinforced this point in\nthe Discussion section to further underscore the novelty of our findings.\nThank you once again for your constructive feedback.\nRevised as: Line 102-107, line 530-534 and new references.\nLine 102-107: Critically, the prevailing reliance on oversimplified formulations (e.g.,\ncontaining only urea) creates a systematic blind spot5,10,16,17. This paradigm is\nfundamentally limited, as it inherently fails to capture the full spectrum of intermolecular\ninteractions—particularly the overlooked antagonistic interactions that, counterintuitively,\nmitigate rather than exacerbate membrane fouling. This oversight not only obscures key\nfouling mechanisms but also risks a complete misdiagnosis of fouling risks in real-world\nsystems.\nLine 530-534: In contrast to the prevailing focus on synergistic fouling, this work\nestablishes that antagonistic organic interactions decisively stabilize BMED performance.\nThis finding challenges the paradigm that wastewater complexity inherently compromises\nmembrane operation and provides a new mechanistic foundation for predicting fouling in\nreal-world systems.\nReference:\nurine: experiments, modeling, and performance analysis. Environmental Science\n& Technology 55, 14886-14896 (2021).\nelectrode capacitive deionization (FCDI)-based technology. ACS ES&T Water 1,\n175-184 (2020).\nfrom source-separated urine based on bioelectrically enhanced bipolar membrane-\ndriven in-situ alkali production coupling with gas-permeable membrane system.\nChemical Engineering Journal 431, 134161 (2022).\ncomplete resource recovery and zero-discharge of urine: Performance\noptimization and evaluation. Water Research 263, 122175 (2024).\n5. The hierarchical formulation design (Groups A–C) effectively bridges lab-industry gaps.\nYet, the rationale for selecting specific metabolites (creatinine, uric acid, BSA) needs\nstrengthening. Include quantitative data from actual urine (e.g., concentration ranges in\nSupplementary Table 1) to justify their inclusion and representativeness.\nResponse\nWe sincerely thank the reviewer for this valuable suggestion. In response, we have\nstrengthened the rationale for the selection of specific metabolites (creatinine, uric acid,\nBSA) in our synthetic formulations. Specifically, we have added a new Supplementary\nTable 1 to provide quantitative data on their physiological concentrations and have revised\nthe manuscript text to elaborate on the scientific reasoning behind their inclusion. We are\ngrateful for this insightful comment, which has helped strengthen the methodological\nfoundation of our study.\nRevised as: Line 142-152, Supplementary Table 1 and new references.\nLine 142-152: To bridge the gap between synthetic and real wastewater, the formulations\nwere designed based on comprehensive analysis of urban urine composition\n(Supplementary Table 1-2). Group A represents the conventional simplified formulation9,17,\ncontaining only dominant inorganic salts (e.g., NaCl, KCl) and urea. Group B expands this\nby incorporating core small organic metabolites—creatinine and uric acid—which are not\nonly universally present in actual urine but are also consistently employed in rigorous\nstudies to enhance the representativeness of synthetic urine formulations5,16,17. Group C\nfurther introduces macromolecular organics, using Bovine Serum Albumin (BSA) as a well-\nestablished model protein. This choice is justified by its high structural and functional\nsimilarity to human serum albumin, a key proteinaceous component in urine, allowing for\na controlled investigation of macromolecular fouling mechanisms relevant to real\nsystems19-21.\nSupplementary Table 1 | Typical concentration ranges of major components in human\nurine.\nComposition Normal range in humans\nUrea (CH N O) 10–35 g/d\n4 2\nUric acid (C H N O ) <750 mg/d\n5 4 4 3\nMales: 955-2936 mg/d\nCreatinine (C H N O)\n4 7 3\nFemales: 601-1689mg/d\nCitrate (C H O 3-) 221-1191 mg/d\n6 5 7\nSodium (Na+) 41-227 mmol/d\nPotassium (K+) 17-77 mmol/d\nAmmonium (NH +) 15-56 mmol/d\nCalcium (Ca2+)\nFemales: < 200 mg/d\nMagnesium (Mg2+) 51-269 mg/d\nChloride (Cl-) 40-224 mmol/d\nOxalate (C O 2-) 0.11-0.46 mmol/d\n2 4\nSulphate (SO 2-) 7-47 mmol/d\nReference:\nurine: experiments, modeling, and performance analysis. Environmental Science\n& Technology 55, 14886-14896 (2021).\nfrom source-separated urine based on bioelectrically enhanced bipolar membrane-\ndriven in-situ alkali production coupling with gas-permeable membrane system.\nChemical Engineering Journal 431, 134161 (2022).\ncomplete resource recovery and zero-discharge of urine: Performance\noptimization and evaluation. Water Research 263, 122175 (2024).\nprobe for discriminating and quantifying similar proteins. Chemical Science 16,\n3228-3237 (2025).\nresponse to HSA and BSA: quantitative detection of HSA in urine. Journal of\nMaterials Chemistry B 12, 9037-9049 (2024).\nand sensitive detection of human serum albumin (HSA) in biological matrices and\nmonitoring of HSA purity and degradation. Talanta 286, 127471 (2025).\nhuman urine. Scientific reports 9, 20159 (2019).\nin decentralized sanitation research and advancing protocols for preparing\nsynthetic urine. Frontiers in Environmental Science 12, 1367982 (2024).\n6. Cost analysis is based on lab-scale operations. Projections for industrial-scale viability\nrequire sensitivity analysis of key variables (e.g., membrane lifespan under real-world\ncleaning protocols, fluctuations in energy/chemical costs). Discuss scalability limitations\n(e.g., electrode degradation, flow distribution challenges).\nResponse\nWe sincerely thank the reviewer for this critical comment, which has significantly\nstrengthened the robustness of our economic discussion. In direct response, we have\nperformed a comprehensive economic sensitivity analysis. The key result is synthesized\nin the new Supplementary Fig. 12, which visually demonstrates how the superior fouling\nresistance of the multi-component formulation (Group C) translates into tangible economic\nbenefits—primarily through extended membrane lifespan and reduced cleaning\nfrequency—despite a marginally higher energy cost. This analysis, based on industrially-\ninformed parameter variations (Supplementary Table 5), confirms that the cost advantage\nof the representative formulation is robust. Furthermore, we have revised the Discussion\nsection to explicitly acknowledge the lab-scale nature of our analysis and to incorporate\nthe reviewer's point regarding scalability limitations.\nWe are grateful for this suggestion, which has enabled us to provide a more balanced\nand credible perspective on the technological translation of our findings.\nRevised as: Line 547-562, Supplementary Table 5 and Supplementary Fig. 12.\nLine 547-562: It is crucial to note that this cost analysis, while revealing a systematic bias,\nis derived from lab-scale operations. To assess the robustness of our finding—that\nsimplified formulations overestimate costs—we first dissected the cost structure. As\nsynthesized in Supplementary Fig. 12, the complex formulation (Group C) achieves a lower\ntotal cost primarily by reducing the costs associated with membrane replacement and\ncleaning (both fixed and operational costs). The economic advantage of Group C lies in its\nmore stable operation, which persists even though it incurs marginally higher energy costs\ndue to longer runtime. We then performed a single-parameter sensitivity analysis on key\nparameters (e.g., membrane lifespan, energy cost) using industrially-informed variation\nranges (Supplementary Table 5)65. The result confirms that the economic superiority of\nGroup C remains robust across all tested scenarios. While a full industrial assessment\nmust consider additional scale-dependent factors such as electrode degradation and flow\ndistribution challenges, our lab-scale model unequivocally demonstrates that\ncompositional simplification is a fundamental source of economic misjudgment by\ndistorting the key fouling-driven parameters that govern operational expenses.\nSupplementary Table 5 | Key parameters and assumed ranges for economic sensitivity\nanalysis of urine treatment via BMED.\nVariation\nParameter Rationale for variation\nrange\nThe typical service life of industrial bipolar membranes is\nMembrane ±50% (2-6\n3-5 years, with 6 years being the upper limit under ideal\nlifetime years)\nmaintenance conditions.\nGroup A:\n±16.7% Cleaning frequency is affected by the influent pollutant\nCleaning\nGroups load, and in industrial operation, it typically fluctuates by\ninterval\nB/C: ±15-20%, depending on the membrane fouling rate.\n±14.3%\nEnergy price fluctuations are often set at ±20% in\nEnergy price ±20% international industrial contexts, reflecting regional\ndifferences and energy market movements.\nThe prices of acid and alkali reagents are influenced by\nChemical\n±20% raw material and transportation costs, and industry\ncost\nanalysis often adopts a ±20% fluctuation.\nLabour costs are affected by regional minimum wage\nLabour cost ±15% policies and labour market supply and demand, with the\nfluctuation range typically being ±10-15%.\nSupplementary Fig. 12 | Relative cost comparison of urine treatment via BMED: complex\n(Group C) versus simplified (Group A) formulation. The chart shows the ratio of each major\ncost component for Group C relative to Group A (baseline = 1.0). Values were calculated\nusing the cost data in Supplementary Table 3 and parameter ranges in Supplementary\nTable 5. Although Group C incurs a higher energy cost (ratio > 1.0) due to its longer, stable\noperation, this is outweighed by substantial savings in membrane replacement, chemical,\nand labor costs (all ratios < 1.0), which result from its superior fouling resistance and\nextended cleaning intervals.\nReference:\nVol. 1 (Oxford University Press, 2004).\n7. SEM images (Fig. 2a/3a): Include scale bars for all micrographs. Label deposits (e.g.,\n\"urea aggregate,\" \"Ca2+-BSA co-deposit\").\nResponse\nWe thank the reviewer for these critical suggestions to improve the clarity of our data.\nIn response, we have added scale bars to all SEM micrographs in Figures 2a and 3a.\nFurthermore, to precisely identify the foulants without cluttering the images, we have now\ndetailed their chemical composition directly in the figure captions, with each label explicitly\nlinked to the corresponding morphological features in the micrographs and cross-validated\nby our EDS analysis. We believe this approach enhances clarity while maintaining a clean\npresentation.\nWe appreciate your feedback, which has significantly improved the presentation of our\nresults.\nRevised as: Figs. 2a and 3a. Line 252-256 and line 384-387.\n(a)\nLine 252-256: Fig. 2 | AEM fouling in the BMED system during urine nutrient recovery from\ndifferent groups over seven batches: a SEM morphological analysis (In Group A (urea-\nonly), spherical primary urea aggregates constitute the fouling layer. In Group B (with\ncreatinine/uric acid), the density of urea aggregates is significantly reduced due to\ncompetitive inhibition, resulting in a cleaner membrane surface. In Group C (with BSA), a\nuniform proteinaceous layer predominates, demonstrating steric hindrance.);\n(a)\nLine 384-387: Fig. 3 | CEM fouling in the BMED system during urine nutrient recovery from\ndifferent groups over seven batches. a SEM morphological analysis (Dominant Ca2+-Mg2+-\nurea co-deposits in Group A; a heterogeneous composite incorporating creatinine and uric\nacid in Group B; and foulants superseded by a BSA-Ca2+-Mg2+ complex in Group C.);\n8. Group D (inorganic-only): Clarify why Mg2+ scaling occurred on CEM while Ca2+ migrated\nto BPM. Elaborate on the role of ionic radius/polarizability beyond citing references.\nResponse\nWe thank the reviewer for raising this point to clarify the ion-specific behaviors. We have\nrevised the manuscript to provide a more detailed explanation, elaborating on the distinct\nhydration properties (ionic radius and hydration energy) of Ca2+ and Mg2+ to account for\ntheir different scaling locations.\nThank you once again for your constructive feedback.\nRevised as: Line 425-435 and new references.\nLine 425-435: This divergence arose from distinct coordination chemistries: Ca2+ (ionic\nradius: 0.99 Å, with lower charge density and higher polarizability52,53) preferentially bound\nurea’s groups as nucleation cores in organic-containing formulations (Groups A-C),\nforming steric aggregates that anchored ions to CEM. The distinct hydration properties of\nCa2+ and Mg2+ further governed their migration paths. Mg2+ (ionic radius: 0.71 Å), with its\nhigher charge density, possesses a larger hydration shell and higher hydration energy (-\n1922 kJ/mol for Mg2+ vs -1577 kJ/mol for Ca2+), which restricted its mobility and enhanced\nits retention on the CEM17,54. This preference, in turn, conferred greater mobility upon the\nmore weakly hydrated Ca2+, facilitating its transit across the CEM and subsequent\nprecipitation at the BPM interface.\nReference:\ncomplete resource recovery and zero-discharge of urine: Performance\noptimization and evaluation. Water Research 263, 122175 (2024).\nCa2+ site in photosystem II: evidence for direct involvement of Ca2+ in O formation.\nMg2+ solvation using polarizable atomic multipole potential. The journal of physical\nchemistry B 110, 18553-18559 (2006).\nferrite: role of swift heavy ion irradiation. Journal of Magnetism and Magnetic\nMaterials 471, 521-528 (2019).\n9. Relate cost deviations (15.9% cleaning cost overestimation) to industry implications in\nDiscussion.\nResponse\nWe thank the reviewer for this valuable suggestion to strengthen the practical relevance\nof our findings. We have revised the Discussion section to explicitly connect the quantified\ncost deviations (e.g., the 15.9% overestimation of cleaning costs) to their potential\nimplications for industrial decision-making and techno-economic assessments.\nThank you once again for your constructive feedback. We believe these revisions\nenhance the robustness and relevance of our study.\nRevised as: Line 544-547.\nLine 544-547: When scaled to industrial levels, these systematic errors would cascade into\nsubstantial financial miscalculations for full-scale plants, distorting projections of operating\nexpenses, misguiding capital investment decisions, and ultimately misrepresenting the\neconomic competitiveness of the BMED technology.\n10. Add error bars to multiple quantitative data graphs to address statistical significance.\nResponse\nWe sincerely thank the reviewer for this insightful comment regarding statistical\nsignificance. We agree that replicating experiments is a cornerstone of robust scientific\npractice.\nIn designing this study, our primary objective was to mechanistically characterize the\nevolution of membrane fouling and its impact on system performance over an extended,\ncontinuous operation. This approach, using a single reactor run through multiple\nconsecutive batches, is particularly suited to capturing the inherent trends and progressive\ndecay mechanisms in electrochemical systems, which can be masked by averaging across\nindependent runs. We acknowledge that this design choice means we cannot provide\ntraditional error bars derived from parallel reactors for the performance data in Fig. 1b-f.\nHowever, we wish to emphasize that the observed performance trends are robustly and\nconsistently supported by multiple, deterministic lines of evidence within the same\nexperimental sequence:\n1. Strong correlation with fouling characterization: The performance decay directly\ncorrelates with the spatially and chemically resolved formation of foulants, as definitively\nshown by post-operation analysis (SEM-EDS, FTIR, XPS in Figs. 2 and 3). This provides\na mechanistic, causal link to the observed performance drop.\n2. Internal consistency of process parameters: Other key parameters monitored throughout\nthe run, such as the stable establishment of pH gradients (Supplementary Fig. 1) and the\nlogical progression of product concentration changes, show a coherent and consistent\nnarrative that aligns perfectly with the performance decay trend.\n3. Initial system stability: The high and stable performance in the initial batches confirms\nthe baseline reliability of our experimental setup before significant fouling occurred.\nIn response to the reviewer's valid point, we have revised the figure captions to explicitly\nstate: \"Data points represent measurements from a single, continuous operational\nsequence for each group.\"\nWe fully acknowledge that future work incorporating multiple independent runs would\nprovide valuable statistical validation of absolute performance values. We are grateful for\nthis comment, which has improved the clarity of our manuscript and will directly inform the\ndesign of our subsequent validation studies.\nRevised as: Line 141.\nLine 141: Data points represent measurements from a single, continuous operational\nsequence for each group.\nPoint-to-point reply\nWe are sincerely grateful to the Editor for the meticulous final check and insightful\ncomments regarding the data presentation, which have been instrumental in enhancing\nthe rigor and accuracy of our manuscript. All suggestions have been incorporated into the\nrevised version. Our point-by-point responses and the associated revisions are outlined\nbelow.\nResponse Letter to Editor\nDear Editor,\nThank you for the final technical assessment of our paper. The manuscript and figures\nwere modified strictly according to your requirements regarding the XPS data analysis.\n1. However, while performing the final checks on the manuscript we noticed issues with\npeak fitting the XPS data. Consider the figure 3e (Ca and Mg), the fitted curve goes beyond\nthe acquired XPS data. Similar issues are present in most, if not all, XPS plots. Kindly\nrecheck the peak fitting in the XPS data and revise the manuscript accordingly. Please also\ndescribe the model used to fit the data in the methods section.\nResponse\nWe thank the Editor for the detailed inspection of our data visualization. We fully agree\nthat the cumulative fitted curve must strictly align with the acquired experimental data to\nensure physical accuracy. We have rigorously re-examined the raw data and re-processed\nthe peak fitting for Figure 3e as well as all other relevant XPS spectra included in the\nmanuscript and Supplementary Information. We have corrected the fitting parameters—\nspecifically optimizing the background subtraction and constraining the peak widths—to\nensure that the sum of the fitted components does not exceed the intensity of the acquired\nraw signal. The revised figures now present a physically accurate representation of the\nsurface chemistry. Furthermore, we have expanded the \"Characterization\" section in the\nmethods to explicitly describe the peak fitting model, including the background subtraction\nmethod and the line-shape function. We appreciate your rigorous check, which has helped\nus improve the accuracy and quality of our data reporting.\nRevised as: Line 623-625, Fig. 3e, Supplementary Fig. 4 and Supplementary Fig. 7.\nLine 623-625: For XPS data analysis, the spectra were calibrated to C 1s (284.8 eV) and\nfitted using a Shirley background and mixed Gaussian–Lorentzian function. Doublets were\nconstrained by fixed spin-orbit splitting and area ratios.\nFig. 3 | CEM fouling in the BMED system during urine nutrient recovery from different groups over\nseven batches. a SEM morphological analysis (Dominant Ca2+-Mg2+-urea co-deposits in Group A; a\nheterogeneous composite incorporating creatinine and uric acid in Group B; and foulants superseded by\na BSA-Ca2+-Mg2+ complex in Group C.); b EDS quantitative detection of fouling components; c FTIR\ncharacterization; d RDF analyses of interactions between urea, Ca, Mg and CEM; e XPS analysis of urine\nfouling in Group A; f Scaling site variation and mechanisms: In urea-containing groups (Groups A-C), Ca2+\nco-aggregates with urea, forming deposits predominantly on the feed side of CEM. In contrast, in the\ninorganic-only group (Group D), Ca2+ rapidly migrates through the CEM and deposits on BPM, triggered\nby precipitation reactions with OH- generated by BPM. Mg2+ consistently forms scaling on the alkaline\nside of CEM through interaction with OH- during its transport across the membrane.\nSupplementary Fig. 4 | XPS characterization of C-N-O functional groups on AEM surfaces:\nFine spectra of pristine, Group A, Group B, and Group C after BMED of different urine\ncomponents.\nSupplementary Fig. 7 | XPS characterization of C-N-O functional groups on CEM\nsurfaces: Fine spectra of pristine, Group A, Group B, and Group C after BMED of different\nurine components."}, {"round": 2, "reviewer_comments": {"reviewer_1": "All of the concerned raised by this reviewer has been adequately addressed.", "reviewer_2": "The author has addressed all the concerns and suggestions of the reviewers, and this version can be accepted for publication."}, "author_response": ""}]

Decision letter