Precursor-encoded supramolecular topology governs metal-humic stability

Precursor-encoded supramolecular topology governs metal-humic stability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Precursor-encoded supramolecular topology governs metal-humic stability Kui Cheng, Yu Li, Mengxin Wu, Shuang Ai, Xianghui Meng, Jianghao Cheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9252807/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The thermodynamic stability of metal–humic complexes governs metal mobility and persistence in natural environments, yet remains difficult to predict because of the molecular heterogeneity of humic matter. Existing frameworks mainly emphasize functional group abundance, while overlooking how precursor chemistry is encoded into supramolecular network topology during humification. Here, using artificial humic acids as a controllable model system, we show that precursor-inherited supramolecular topology governs the thermodynamic stability of metal–humic complexes. By integrating ultrahigh-resolution mass spectrometry, synchrotron X-ray absorption spectroscopy, and molecular dynamics simulations, we identify two contrasting thermodynamic trajectories determined by network architecture. Rigid, aromatic-dominated networks undergo thermodynamic hardening, generating stable, multidentate, and interference-resistant coordination domains, whereas flexible, nitrogen-rich networks exhibit thermodynamic softening, favoring kinetically accessible but metastable metal binding. Using cadmium as a probe metal, we further show that supramolecular rigidity enhances coordination saturation and entropic stabilization, increasing resistance to competitive ion displacement, while flexible topologies favor adsorption capacity at the cost of long-term stability. These results establish supramolecular topology as a governing principle linking precursor chemistry to the thermodynamic fate of metals in humic matter. Physical sciences/Chemistry/Environmental chemistry/Geochemistry Physical sciences/Chemistry/Green chemistry/Sustainability Supramolecular topology Network rigidity Metal–humic interactions Thermodynamic stability Environmental geochemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction In natural environments, interactions between metal ions and organic matter (OM) constitute a fundamental and ubiquitous physicochemical mechanism that underpins biogeochemical cycling, biological metabolism, and materials synthesis 1 , 2 . The complexation of metal ions with organic ligands dictates the partition of metals between solution and solid phases to govern their mobility and bioavailability 3 . Conversely, metal binding modulates the aggregation state and accessibility of OM functional groups through mechanisms that include charge shielding, cation bridging, and local cross-linking 4 . This process subsequently redirects the reaction pathways and persistence of OM 5 . Consequently, organic-metal complexation is characterized by a dynamic and bidirectional coupling where ligand selectivity determines the environmental fate of metals while metal participation fundamentally reshapes the supramolecular architecture and evolutionary trajectory of OM. Although the fundamental principles of organic-metal complexation are established 6 , the formation and evolution of ligand pools in nature remain highly dynamic and context-dependent 7 . This complexity impedes our ability to predict metal fate and its coupling with carbon stability under global change scenarios. A critical impediment is the high heterogeneity of natural soil organic matter (SOM) and its ligand structures 8 . The interplay of source components, microbial processing, and selective mineral adsorption shapes the spatial distribution and energy landscape of coordination sites 9 . As a result, macroscopic indices frequently fail to map directly onto verifiable coordination microenvironments. This disconnect elucidates a prevalent paradox in remediation where similar OM inputs trigger diametrically opposite metal responses that range from effective immobilization to unexpected remobilization 10 . This phenomenon indicates that the quantity of OM is an insufficient predictor and underscores that the composition and supramolecular organization of the ligands are the decisive variables. Traditional models that treat SOM as a homogeneous ligand pool are thus inadequate for rationalizing divergent metal coordination behaviors among chemically similar organic substrates 11 . Humic acid (HA) represents a highly reactive fraction of SOM that plays a pivotal role in metal complexation and organic carbon stabilization 12 , 13 . Given that HA originates from the decomposition of plant and microbial residues, differences in precursor chemical composition are hypothesized to drive its structural diversity and functional differentiation 14 . To advance this understanding from correlation to mechanism, a controlled comparative system is required to preserve molecular complexity while isolating the variable of precursor origin from environmental boundary conditions. The artificial humic acid (A-HA) system satisfies this requirement 15 . By simulating humification under controlled conditions, we can generate products with natural-like complexity to facilitate a systematic comparison of precursor-driven differences 16 . Furthermore, the integration of ultra-high-resolution molecular omics with spectroscopic characterization enables the tracing of complexation-induced recombination at the molecular scale 17 . This approach permits a thermodynamic analysis of ligand hardening or softening to establish molecular design rules for both environmental prediction and materials engineering 18 . In this study, we utilized rice straw (RS) and soybean straw (SS) as contrasting model precursors. RS is rich in cellulose and silica with complex lignin structures and recalcitrant nitrogen forms. In contrast, SS is protein-rich and possesses abundant labile nitrogen alongside a relatively simple lignin composition 19 20 . We employed cadmium (Cd) as a model metal to integrate Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with synchrotron-based extended X-ray absorption fine structure (EXAFS) spectroscopy and molecular dynamics (MD) simulations. Our objective was to map precursor composition differences to the supramolecular flexibility, coordination geometry, and binding energetics of the resulting A-HA. Specifically, we aim to elucidate how chemical heterogeneity in plant residues is encoded into the molecular structure of HA during humification to govern the mechanism and stability of metal coordination. This work provides a mechanistic basis that links the chemical inputs of plant residues to the environmental function of their humified products. 2. Results and discussion 2.1 Binding characteristics of HA-Cd complexes In this study, we hypothesize that the chemical signatures inherited from plant residues are translated during hydrothermal humification into distinct humic-acid network topologies, which determine the spatial arrangement and accessibility of chelating motifs and thereby modulate the enthalpy–entropy balance controlling Cd²⁺ complexation stability. To test this, we prepared artificial humic acids (A-HAs) from rice straw (RS) and soybean straw (SS) via the same hydrothermal process (Fig. 1 a) and compared their structural features and Cd-binding behaviors; the resulting materials are denoted RS-HA and SS-HA and exhibit markedly different compositions and reactivities. Elemental analysis revealed that SS-HA, inheriting the nitrogen-rich signature of its leguminous precursor, possessed significantly higher nitrogen (3.82%) and oxygen (32.27%) contents, as well as a higher (O + N)/C atomic ratio (0.62) compared to RS-HA (Supplementary Table 1), indicating a higher density of functional groups. While minor differences in H/C ratios suggested subtle variations in aromaticity, both A-HAs maintained the high condensation features typical of humic structures 21 . This divergence in precursor origin manifested as a distinct trade-off between capacity and stability. Adsorption kinetics demonstrated that SS-HA achieved an equilibrium capacity 36% higher than that of RS-HA (Supplementary Fig. 1a and Supplementary Table 2), likely attributable to the high accessibility of its active domains. However, this capacity advantage proved fragile under ionic interference. In the presence of competing cations such as Ca²⁺ and Pb²⁺, the Cd²⁺ retention of SS-HA declined by 10.2–25.4%, whereas RS-HA maintained robust performance (Supplementary Fig. 1b). We speculate that while the sheer quantity of functional groups in SS-HA facilitates rapid uptake, it fails to confer the requisite stability within complex environmental matrices. Spectroscopic profiling revealed the structural basis of this divergence. SS-HA exhibited higher fluorescence intensity for humic-like substances (Fmax1 = 14394.48), likely attributed to polycondensation induced by the abundant nitrogen components in SS. In contrast, RS-HA displayed prominent tryptophan-like (Trp) signals (Fmax2 = 11373.64). Notably, the humification index (HIX) and biological index (BIX = 0.98) of RS-HA were significantly higher than those of SS-HA, suggesting a greater degree of humification (Fig. 1 b and Supplementary Figs. 2–3) 22–24 . Two-dimensional fluorescence correlation spectroscopy (2D-SFS-COS) uncovered diametrically opposed Cd²⁺ binding sequences 25 . In RS-HA, Cd²⁺ interacted first with Trp species followed by the HA backbone (Trp > HA), whereas SS-HA showed the reverse order (HA > Trp) (Fig. 1 c and Supplementary Table 3). The aromatic domains in SS-HA, enriched via nitrogen-driven polycondensation, favor carboxyl-dominated ion exchange and support broad-spectrum binding through linear electron transfer 26 . However, this open-channel architecture renders SS-HA highly susceptible to competition from charge-similar cations (e.g., Ca²⁺). Conversely, RS-HA initiates Cd²⁺ complexation via discrete Trp microdomains, engaging in N-coordination and π-cation interactions 27 . This spatially restricted topology provides steric protection, thereby enhancing coordination stability under ionic stress 28 , 29 . EXAFS spectroscopy provided atomic-scale insights into the Cd²⁺ coordination environments 30 . The X-ray absorption near-edge structure (XANES) spectra revealed a consistent edge energy (E₀ ≈ 26,714 eV) for both complexes, confirming that Cd²⁺ retains its oxidation state and primarily engages in coordination bonding (Fig. 2 a). Fourier-transformed EXAFS spectra (Supplementary Fig. 4a) exhibited three main coordination shells for SS-HA-Cd at approximately 1.31 Å (Cd-N), 1.78 Å (Cd-O), and 2.26 Å (Cd-S). Notably, RS-HA-Cd displayed analogous features but with systematically shifted peak positions and greater oscillation amplitudes, suggesting a more saturated and ordered coordination environment. This difference likely arises from unique electronic structures and coordination geometries formed during complexation. Quantitative fitting revealed that RS-HA-Cd exhibited higher coordination numbers of 2.73 (Cd–N), 3.46 (Cd–O), and 1.39 (Cd–S) compared to the values of 1.89, 3.17, and 1.03 respectively for SS-HA-Cd, accompanied by elongated bond lengths (Fig. 2 b, Supplementary Fig. 4b-f, and Supplementary Table 4) 31 . These differences likely stem from distinct Cd–ligand interactions modulated by the local chemical environment. Specifically, RS favors the formation of condensed aromatic domains, which facilitate π-electron delocalization and d-orbital hybridization, enhancing Cd binding capacity. Furthermore, a higher defect density in RS-HA may increase ligand accessibility, contributing to bond elongation and spectral broadening. Wavelet transform analysis (Fig. 2 c and Supplementary Fig. 5) confirmed all three coordination shells and revealed clear edge splitting in RS-HA-Cd, indicative of stratified coordination domains formed via crosslinking. Although the RS-HA-Cd complex exhibits slightly elongated Cd-N/O bond lengths compared to SS-HA which likely pointing to steric crowding within the rigid aromatic scaffold, it displays a significantly higher coordination number (CN = 2.73). This indicates a shift from monodentate surface adsorption to multidentate chelation. According to the chelate effect, this high-order coordination structure creates a substantial entropic advantage, overriding the enthalpic penalty of slightly longer individual bonds and resulting in superior overall thermodynamic stability. MD simulations elucidated the differences in coordination dynamics and structural stability. Given the structural complexity, gallic acid (GA) was selected as a representative model compound 32 . Within a 4.45 Å radius of Cd centers, RS-HA exhibited a more compact and ordered coordination geometry, consistent with its rigid supramolecular framework (Fig. 2 d). This structural compactness translated into a lower Cd²⁺ diffusion coefficient (0.03558, compared to 0.04061 in SS-HA). Similarly, GA and Trp components diffused more rapidly in SS-HA (0.01308 and 0.01836, respectively) compared to RS-HA (0.00866 and 0.01311) (Supplementary Fig. 6), indicating stronger ligand–metal interactions in RS-HA. Post-relaxation radial distribution functions confirmed enhanced affinity of RS-HA toward carboxyl oxygen atoms and hydrogen-bond donors. Cd-Trp coordination was also strengthened via interactions with aromatic nitrogen, amine groups, hydroxyl oxygens, and carboxyl oxygens (Fig. 2 e-f), aligning with EXAFS observations of higher coordination numbers and longer average bond lengths. Collectively, this points to a more saturated and stable Cd coordination environment in RS-HA. In contrast, the higher molecular mobility of SS-HA reflects its flexible topology and weaker coordination, explaining its low resistance to ion competition. Mean squared displacement (MSD) analysis confirmed these differences, with all key species (Cd, Cl⁻, GA, Trp) displaying greater mobility in SS-HA, especially tryptophan-like components (Supplementary Fig. 7a). The elevated dynamic flexibility in SS-HA aligns with its carboxyl-dominated but competition-prone ion exchange mechanism. Conversely, the restricted diffusion and multidentate coordination geometry of RS-HA impart steric shielding, stabilizing Cd²⁺ retention under perturbation. Hydrogen bond network analysis (Supplementary Fig. 7b) further supported this, showing RS-HA maintains a marginally higher average number of hydrogen bonds (3510) than SS-HA (3470), reinforcing its tighter molecular packing and greater intermolecular cohesion. These observations highlight how the topological rigidity in RS-HA enhances the dynamic stability and selectivity of Cd²⁺ binding through the synergistic effects of π-cation interactions, steric protection, and hydrophobic microenvironments 33 . In summary, the chemical disparity of feedstocks influences metal interaction behaviors by driving structural variations in HA where this effect extends beyond mere functional group abundance to include the resultant coordination strategies (Fig. 2 g). SS-HA leverages its oxygen-rich, flexible architecture for rapid ion exchange, but its relatively loose coordination framework renders it highly susceptible to competitive ion interference. Conversely, the structurally rigid aromatic matrix of RS-HA exhibits enhanced Cd²⁺ retention, enabling π-cation interactions, multidentate N/O coordination, and hydrogen-bond stabilization. 2.2 HA and HA-Cd molecular composition, distribution, and properties FT-ICR MS analysis revealed molecular-level reorganization pathways underlying Cd 2+ complexation by A-HAs, offering mechanistic insights beyond macroscopic observations (Fig. 3 a, b and Supplementary Table 5) 34 . Upon Cd 2+ binding, RS-HA underwent a net molecular loss of 341 species (from 4568 decrease to 4227), involving 3414 transformed molecules, 1157 removed and 813 generated. In parallel, SS-HA exhibited a more extensive reorganization, with a net loss of 660 species (from 4604 decrease to 3944), stemming from 3350 transformations, 1250 removals and only 596 newly formed compounds (Fig. 3 c). Molecular class analysis revealed divergent structural trajectories. RS-HA exhibited an increase in condensed aromatics (CA, from 41 to 48) and a concurrent decrease in lignin-like (LG) molecules (from 3983 to 3669), suggesting dealkylation-driven condensation via -CH₂/-C₂H₄ loss and enhanced aromaticity (Supplementary Fig. 8). This structural evolution supports the formation of π-delocalized domains conducive to cation–π interactions 35 . Conversely, SS-HA underwent aromatic ring cleavage, as evidenced by a decrease in CA compounds (31 to 23) and a substantial increase in lipid-like (LP) species (306 to 425). This trend indicates fragmentation and aliphatic chain proliferation, disrupting the spatial continuity of carboxylate networks and diminishing Cd²⁺ binding efficiency. Additionally, sharp reductions in phenolic acids (PA, 153 to 50) and CHON-class molecules (1983 to 1527) point to functional group loss, particularly nitrogenous moieties critical for metal chelation. Transformation pathway analysis (Fig. 3 d) highlighted these fundamental differences. RS-HA predominantly underwent oxidation (32.8%) and dealkylation (29.1%) modifications, yielding 813 oxygen-enriched molecules. By contrast, SS-HA transformations skewed toward non-oxidative dealkylation (34.7%), resulting in fewer products (596) with diminished O/C ratios (from 0.3511 to 0.3281), indicating degradation of the carboxylate matrix. The frequent detection of -CH₂O losses (60 occurrences) in SS-HA reflects glycosidic bond cleavage, likely creating less-specific hydroxyl-rich surfaces 36 . Molecular weight profiles further confirmed this divergence (Supplementary Fig. 9). RS-HA displayed a modest increase in average m/z (451.70 to 453.37) despite a reduction in molecular count (from 4,568 to 4,227), signifying selection of high-affinity binding motifs, where fewer but more functionally efficient structures emerge. Conversely, SS-HA exhibited a decline in m/z (454.85 to 453.64) and a shift toward heterogeneous, aliphatic-rich species, suggesting quantity-driven but functionally diffuse complexation. Integrated with fluorescence spectroscopy data, these findings support distinct binding models. RS-HA initiates Cd²⁺ complexation via tryptophan-like domains, followed by stabilization through condensed aromatic structures, forming a multilayered retention architecture. SS-HA, while initially advantaged by its carboxyl-rich network, experiences coordination site degradation via fragmentation and aliphatic expansion, compromising long-term stability. Finally, the functional transformation efficiency, defined as newly generated binding-relevant molecules per 1000 total transformations, was markedly higher for RS-HA (238 vs. 178 in SS-HA), underscoring its more targeted and effective molecular reconfiguration in response to Cd²⁺ exposure. A persistent challenge in DOM research is deciphering the complex molecular reorganization processes that underpin its lifecycle and functional behavior 17 . Here, we quantified transformation fluxes and reaction networks across different A-HAs types, demonstrating how precursor composition modulates structural evolution to optimize Cd²⁺ complexation. Using precise tracking of functional group alterations in molecular formulas (e.g., demethylation or decarboxylation), we uncovered systematic differences in transformation pathways between SS-HA and RS-HA. Across all samples, LG compounds dominated the transformation landscape regardless of reaction type (Fig. 4 a and Supplementary Table 6), highlighting their structural resilience. While LG species underwent extensive modifications (including functional group changes, conformational adjustments, or direct Cd binding), their aromatic scaffolds remain largely preserved. Transformation topologies diverged substantially between the two A-HAs. RS-HA displayed a conservative reaction network, characterized by intra-class conversions within LG, partial oxidation or demethylation of PA into LG and minimal involvement of LP species. In contrast, SS-HA exhibited a highly interconnected and reactive network, wherein LG species not only underwent internal rearrangements but also frequently converted into tannins (TN) molecules. PA compounds in SS-HA underwent intense decarboxylation, often yielding LP-like products. Notably, LP compounds acted as central hubs, participating in inter-class conversions, particularly demethylation-driven exchanges involving LG, PA, and TN classes. This high molecular flux and compositional plasticity in SS-HA likely enhances binding adaptability, albeit at the cost of coordination stability. Conversely, the structurally rigid LG-PA network in RS-HA may foster defined and selective binding domains (Fig. 4 b). To explore thermodynamic underpinnings of these differences, we examined the redox characteristics of each compound class, using combustion enthalpy (ΔH, kJ mol⁻¹ C) as a proxy for Gibbs free energy of oxidation 37 . A strong linear correlations (R² = 0.972) between ΔH and nominal oxidation state of carbon (NOSC) was observed across all compound classes (Fig. 4 c and Supplementary Table 7) 38 , 39 . Upon transformation, most compound classes exhibited higher ΔH and NOSC values in RS-HA, indicating greater recalcitrance and thermodynamic stability compared to SS-HA (Supplementary Table 8). These trends were further supported by intensity-weighted ΔH-NOSC relationships, reinforcing that RS-HA harbors more oxidized, energetically stable species. Distribution analysis revealed that SS-HA molecules predominantly occupy NOSC negative regions (Supplementary Fig. 10), reflecting reduced, chemically labile structures prone to oxidation or ion exchange, while RS-HA species shift toward NOSC positive domains, representing more oxidized, structurally inert compounds 40 . These redox profiles suggest that SS-HA forms reactive but transient coordination environments, whereas RS-HA establishes more robust and redox-stable chelation frameworks. To further dissect structural divergence, we performed principal component analysis (PCA) on FT-ICR-MS datasets (Fig. 4 d). RS-HA exhibited tight clustering among LG, LP, and PA components, indicative of greater molecular homogeneity. In contrast, SS-HA showed wide separation between PA and LP clusters, reflecting its greater chemical diversity. Cd²⁺ addition induced distinct shifts in molecular category positions for both HA types, validating that complexation is a selective process targeting specific structural motifs. In SS-HA, PCA revealed a post-complexation convergence between LG and LP clusters, consistent with the frequent LG-LP transformations (e.g., demethylation) observed in reaction network, suggesting that Cd 2+ induces lipid-lignin hybridization and expands binding motifs. Conversely, RS-HA displayed increased category separation post-complexation, corroborating its rigid, functionally defined coordination framework. Together, these findings establish that precursor-driven transformation topologies and redox characteristics fundamentally shape the coordination landscapes of A-HAs. SS-HA evolves dynamic but less selective networks, whereas RS-HA promotes structurally conserved, thermodynamically stable chelation domains, explaining their distinct Cd²⁺ binding performance. 2.3 Unique molecular constituents drive differential binding To dissect the molecular underpinnings of differential Cd²⁺ complexation, we interrogated the distinctive molecular subsets identified from FT-ICR MS data. Venn diagram analysis revealed 4,079 shared molecules alongside 489 and 525 unique species for RS-HA and SS-HA, respectively (Fig. 3 e). These unique molecular pools were subjected to thermodynamic and network-based analysis to elucidate structure function relationships underpinning their divergent binding behaviors (Fig. 5 a-b) 18 . Among 489 unique molecular formulas of RS-HA, 57 underwent thermodynamically favorable processes (TFP), including 29 intragroup transformations and 28 intergroup conversions, indicating a high degree of structural plasticity and synergistic transformation potential (Supplementary Table 9). In contrast, 525 unique formulas of SS-HA yielded only 44 TFPs (33 intragroup, 11 intergroup), suggesting more constrained and compartmentalized transformation dynamics. This disparity reflects superior propensity of RS-HA for cross-category reorganization and multidimensional complexation networks, while SS-HA remains largely confined to intra-class exchanges, a structural trait associated with lower resistance to competitive adsorption. Cd²⁺ complexation significantly altered these thermodynamic patterns. RS-HA exhibited a notable reduction in both TFPs (from 57 to 18, including 15 intragroup and 3 intergroup) and thermodynamically limited processes (TLPs) (from 48 to 13), consistent with a “thermodynamic hardening” phenomenon, increased energy barriers restrict further molecular rearrangement, yielding enhanced complex stability. Conversely, SS-HA underwent “thermodynamic softening”, with TFPs increasing from 44 to 73 (33 to 46 intragroup) and TLPs doubling from 32 to 60, signifying a more flexible, yet less stable coordination environment post-complexation. These divergent thermodynamic trajectories are governed by the chemical composition of the precursors, which shapes molecular topology and transformation accessibility. Network visualization of transformation pathways further reveals contrasting topological architectures. RS-HA forms highly interconnected three-dimensional networks (average node connectivity = 6.03), where condensed aromatics, lignin-like, and proteinaceous components serve as key hubs molecules (Fig. 5 c and Supplementary Table 10). This topological redundancy allows for functional compensation and cascade activation, wherein the formation of one complex initiate sequential node rearrangement, ultimately enhancing chelation robustness under environmental perturbations. In contrast, SS-HA exhibits a hierarchical and compartmentalized network, organized into 12 sub-pathways, three of which are nitrogen-specific routes contribute 28% of TFP conversions (Fig. 5 d). Although these pathways offer parallel coordination routes, notably a dual-mode system where amino carboxylates form stable bidentate complexes and the dominant carboxyl network facilitates rapid ion exchange, they remain energetically decoupled, reducing overall structural resilience. FT-ICR MS data revealed a substantial increase in aliphatic components following Cd²⁺ complexation (from 6.6% to 10.8%), accompanied by a 25.8% decrease in condensed aromatics (31 to 23 molecules) (Supplementary Fig. 11). This aromatic ring cleavage coupled with aliphatic chain proliferation temporarily enhances site density but ultimately disrupts the spatial arrangement of carboxyl network through steric hindrance, thereby reducing effective binding site accessibility. Competitive adsorption experiments confirm this structural vulnerability. SS-HA exhibited a 10.2% decreased in Cd²⁺ binding capacity reduction under Ca²⁺ challenge, which further dropped by 25.4% in a quaternary-ion systems. This behavior underscores its limited interference resistance, attributable to insufficient energetic coupling between core carboxyl and auxiliary nitrogen coordination pathways. These findings establish molecular network robustness as a new functional criterion for evaluating humic acids. Traditional metrics based on functional group density fail to reconcile the paradoxical combination of high capacity and low stability of SS-HA. Instead, our data demonstrate that the chemical composition of the precursors fundamentally dictates network topology and, consequently, functional output. SS gives rise to hierarchical architectures optimized for site density, whereas RS fosters topologically rigid, three-dimensional networks with enhanced pathway redundancy. This mechanistic coupling between microstructural topology and macroscopic functionality highlights the critical role of topological rigidity in selective metal complexation. 3. Discussion Organo metal interactions are fundamental to environmental chemistry because they couple metal speciation to the structure, reactivity, and evolution of natural organic matter. However, these interactions are still often described using composition level averages, such as functional group inventories or bulk indices, with an implicit assumption that binding outcomes scale with the abundance of binding sites. This description is increasingly insufficient. Complexation commonly exhibits a kinetic thermodynamic divergence in which rapid binding does not necessarily develop into persistent coordination and stable coordination does not necessarily arise from the most accessible motifs. In addition, complexation is not simply a passive occupation of preexisting sites. Exposure to metals can actively reorganize organic ensembles and alter molecular connectivity and accessibility, which then redirects subsequent transformation pathways. As a result, the effective ligand pool becomes contingent on environmental history and molecular assembly. A mechanistic framework has been lacking that links precursor inputs and organic assembly to coordination microenvironments, and then connects those microenvironments to the feedback of metal exposure on organic matter evolution. In this work we show that supramolecular topology provides this missing link. The decisive control is not only which functional groups are present, but how they are organized within an architecture that constrains or permits molecular motion and reorganization. When topology is treated as a governing descriptor, the long standing paradox that more sites do not necessarily yield more stable binding becomes a predictable outcome rather than an anomaly. Architectures that emphasize openness and accessibility naturally promote fast binding through exchange compatible coordination motifs. Architectures that are more constrained and more connected favor cooperative coordination and shielding, which increases coordination saturation and biases binding toward more persistent states. This topological perspective also clarifies why similar metal organic encounters can follow divergent thermodynamic trajectories. A hardening trajectory emerges when binding progressively becomes less exchange prone as reorganization pathways are restricted and coordination saturation increases. A softening trajectory emerges when binding remains dynamically permissive because reorganization pathways remain accessible and exchange pathways remain active. We validated this topology-based mechanism by integrating evidence across complementary scales while avoiding reliance on composition level averages alone. Coordination spectroscopy establishes that the systems occupy distinct coordination regimes that differ in coordination saturation and exchange susceptibility, which directly links macroscopic persistence to coordination microstructure. Molecular simulations provide the dynamic correlate of topology by showing that constrained architectures suppress mobility and reduce access to exchange pathways, whereas permissive architectures remain dynamically open and facilitate rapid association while increasing vulnerability to displacement and continual rearrangement. Ultrahigh resolution mass spectrometry further demonstrates that complexation acts as a selective perturbation to the organic ensemble rather than a bookkeeping exercise of site occupancy. Metal exposure reshapes molecular pools along precursor dependent transformation topologies, either restricting accessible routes and concentrating stability in the hardening direction or expanding accessible routes and sustaining plasticity in the softening direction. These independent lines of evidence converge on a single causal chain in which topology gates reorganization, reorganization controls coordination saturation and exchangeability, and these properties determine whether binding matures into persistent coordination or remains transient. This framework is useful in several ways. Conceptually, it reframes metal organic chemistry from static complexation to metal driven organic matter evolution under topological constraints. It elevates topology from a descriptive attribute to a mechanistic state variable that reconciles kinetic accessibility with thermodynamic persistence. Practically, it provides a transferable explanation for why different precursor sources and humification trajectories can yield qualitatively different metal organic outcomes even when bulk composition appears similar. The critical difference lies in architectural organization and network robustness rather than in chemical inventory alone. Predictively, the framework suggests measurable handles that can be incorporated into models of metal organic coupling, including descriptors related to coordination saturation, transformation accessibility, and network robustness. These descriptors can bridge molecular resolved organic chemistry with forecasts of how metals interact with organic reactivity, aggregation and dispersion behavior, and longer term carbon processing under changing environmental conditions. The framework also produces testable expectations. Increasing architectural constraint should bias complexation toward hardening and more saturated coordination. Increased fragmentation and pathway proliferation should bias complexation toward softening and greater exchange susceptibility. Early engagement of protected microdomains should be associated with deeper and more persistent coordination states than binding initiated within open and highly accessible domains. 4. Methods 4.1 A-HA sample preparation A-HA was synthesized via HTH following established protocols 41 . Specifically, 1.20 g portions of RS or SS were individually combined with varying KOH quantities in a 50 mL autoclave. The sealed reactor was heated at 200°C for 24 h. Following the reaction, the system cooled naturally to ambient temperature, yielding liquid products. These liquids were acidified to pH 3 using 6.0 mol·L⁻¹ HCl. Subsequent filtration isolated the solid humic acid fraction from the liquid phase. The recovered solid was then washed to eliminate residual acids and impurities, yielding purified A-HA (marked as RS-HA and SS-HA). 4.2 A-HA − Cd Complexation Experiments. A-HA working solution was added to 40 mL brown glass bottles, and the final dissolved organic carbon (DOC) concentrations was 5 mg L − 1 . Subsequently, metal ions were added to the solution, and the final metal ion concentrations ranged from 0 to 4 mg L − 1 . This concentration range was utilized to obtain the complete complexation curves of A-HA-Cd complexes. Finally, the mixed solutions were shaken on a shaker at 140 rpm and 25°C.After 24h complexation reaction, the solutions were filtered through a 0.45 µm membrane to remove precipitates formed during complexation, and A-HA − Cd complexes were obtained 42 . The filtrate was analyzed using a Three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy (Hitachi, Tokyo, Japan) to characterize the binding properties of the complexes, as described in Supplementary Method 1. All experiments were repeated at least three times. 4.3 FT-ICR MS Detection. The Agilent Bond Elut PPL cartridge (200 mg per 3 mL) was used to remove inorganic salts from DOM samples before FT-ICR MS detection, with an extraction efficiency > 57%. Filtration with a 0.45 µm membrane was adopted to obtain a more comprehensive molecular composition of the samples. DOM enrichment (200 µL) of the PPL resin was analyzed on a SolariX 15T FT-ICR mass spectrometer (Bruker, Karlsruhe, Germany) in negative ionization mode (Supplementary Method 2). Molecules detected by FT-ICR MS were classified by element composition, including CHO, CHON, CHOS, and CHONS (Supplementary Method 3) 43 . The H/C and O/C ratios were characterized using van Krevelen diagrams, and the relevant stoichiometric ranges of each classification are listed in Supplementary Table 10. The modified aromatic index (AI mod ) 44 , unsaturation index ((DBE-O)/C) and Kendrick mass defect (KMD) were utilized to describe the character is tics of the detected molecular formulas, which were calculated as described in Text S4. 4.4 Other Analytical Methods. Parallel factor analysis (PARAFAC) was performed on the fluorescence spectra of the A-HA–Cd complexes using MATLAB R2018a and the DOM-Fluor toolbox. The maximum fluorescence intensity of each PARAFAC-resolved component was determined (Fmax1 and Fmax2). In addition, fluorescence-derived indices were calculated from the excitation–emission matrix (EEM) spectra, including the fluorescence index (FluI), freshness index (FreI), biological index (BIX), and humification index (HIX), following established definitions 45 . The changing degree and order of different functional groups were revealed via two-dimensional correlation spectra (2D-COS) (Supplementary Method 4) 46 . Identification of potential biochemical transformation processes of A-HA molecules was performed based on precise mass differences between FT-ICR mass spectral peaks 47 . A-HA molecules were divided into four regions taking into account the two molecular trait dimensions of reactivity and activity: labile-active (LA; number of transformations > 10, H/C ≥ 1.5), labile-inactive (LI; number of transformations ≤ 1, H/C ≥ 1.5), recalcitrant-active (RA; number of transformations > 10, H/C < 1.5), and recalcitrant-inactive (RI; number of transformations ≤ 1, H/C < 1.5) 48 . The molecular transformation process also involves energy changes. Thus, the Gibbs free energy before and after the molecular transformations was calculated. These transformations were categorized into thermodynamically favorable processes (TFP) and thermodynamically limited processes (TLP), according to thermodynamic spontaneity 49 . Details of the calculation are provided in Text S6. The between-molecule mass difference within 1 ppm was matched to the expected mass of the transformation. Using these pairwise mass differences and transformation associations, the transformation networks in which the nodes represent individual molecular formulas and the edges represent definitive molecular transformations were constructed and visualized using Gephi version 0.9.2 software (Mathieu Bastian and Sebastien Heymann, Paris, France) 50 . Declarations Funding This study was funded by the National Key Research and Development Program of China (2024YFD1500503), the Outstanding Youth Project of Heilongjiang Province (JQ2024D001) the financial support from Longjiang Scholars for young scientist and Heilongjiang Provincial Undergraduate Institutions Support Plan for Outstanding Young Teachers in Fundamental Research (YQGH2023191). Competing interests All authors declare no financial or non-financial competing interests. Author's contributions Yu Li: Writing – original draft, Investigation. Mengxin Wu: Software. Shuang Ai: Investigation. Xianghui Meng: Validation, Investigation. Jianghao Cheng: Validation, Investigation. Liu Cui: Software, Supervision. Fan Yang: Supervision, Funding acquisition. 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Y.; Bertoni, M. I., Quantitative analysis of Cu XANES spectra using linear combination fitting of binary mixtures simulated by FEFF9. Radiation Physics and Chemistry 2023, 202 , 110548. Šolc, R.; Gerzabek, M. H.; Lischka, H.; Tunega, D., Radical sites in humic acids: A theoretical study on protocatechuic and gallic acids. Computational and Theoretical Chemistry 2014, 1032 , 42–49. Infield, D. T.; Rasouli, A.; Galles, G. D.; Chipot, C.; Tajkhorshid, E.; Ahern, C. A., Cation-π Interactions and their Functional Roles in Membrane Proteins. Journal of Molecular Biology 2021, 433 , (17), 167035. Fu, Q.-L.; Chen, C.; Liu, Y.; Fujii, M.; Fu, P., FT-ICR MS Spectral Improvement of Dissolved Organic Matter by the Absorption Mode: A Comparison of the Electrospray Ionization in Positive-Ion and Negative-Ion Modes. Analytical Chemistry 2024, 96 , (1), 522–530. Jirásek, M.; Rickhaus, M.; Tejerina, L.; Anderson, H. L., Experimental and Theoretical Evidence for Aromatic Stabilization Energy in Large Macrocycles. Journal of the American Chemical Society 2021, 143 , (5), 2403–2412. Lenz, S. A.; Kohout, J. D.; Wetmore, S. D., Hydrolytic Glycosidic Bond Cleavage in RNA Nucleosides: Effects of the 2'-Hydroxy Group and Acid-Base Catalysis. The journal of physical chemistry. B 2016, 120 , (50), 12795–12806. Xu, Z., Mechanics of metal-catecholate complexes: The roles of coordination state and metal types. Scientific Reports 2013, 3 , (1), 2914. LaRowe, D. E.; Van Cappellen, P., Degradation of natural organic matter: A thermodynamic analysis. Geochimica et Cosmochimica Acta 2011, 75 , (8), 2030–2042. Gunina, A.; Kuzyakov, Y., From energy to (soil organic) matter. Global Change Biology 2022, 28 , (7), 2169–2182. Freeman, L. A.; Walley, J. E.; Gilliard, R. J., Synthesis and reactivity of low-oxidation-state alkaline earth metal complexes. Nature Synthesis 2022, 1 , (6), 439–448. Yang, F.; Zhang, S.; Cheng, K.; Antonietti, M., A hydrothermal process to turn waste biomass into artificial fulvic and humic acids for soil remediation. Science of the Total Environment 2019, 686 , 1140–1151. Hu, Q.; Lou, M.; Wang, R.; Bai, S.; Guo, H.; Zhou, J.; Ma, Q.; Wang, T.; Zhu, L.; Zhang, X., Complexation with Metal Ions Affects Chlorination Reactivity of Dissolved Organic Matter: Structural Reactomics of Emerging Disinfection Byproducts. Environmental Science & Technology 2024, 58 , (31), 13890–13903. Bramer, L. M.; White, A. M.; Stratton, K. G.; Thompson, A. M.; Claborne, D.; Hofmockel, K.; McCue, L. A., ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data. PLOS Computational Biology 2020, 16 , (3), e1007654. Koch, B. P.; Dittmar, T., From mass to structure: an aromaticity index for high-resolution mass data of natural organic matter. Rapid Communications in Mass Spectrometry 2006, 20 , (5), 926–932. Stedmon, C. A.; Bro, R., Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnology and Oceanography: Methods 2008, 6 , (11), 572–579. Noda, I., Two-dimensional codistribution spectroscopy to determine the sequential order of distributed presence of species. Journal of Molecular Structure 2014, 1069 , 50–59. Cheng, Z.; Li, A.; Wang, R.; Hu, Q.; Zhou, J.; Li, M.; Wang, T.; He, D.; Zhu, L., Long-term straw return promotes accumulation of stable soil dissolved organic matter by driving molecular-level activity and diversity. Agriculture, Ecosystems & Environment 2024, 374 , 109155. Hu, A.; Jang, K.-S.; Meng, F.; Stegen, J.; Tanentzap, A. J.; Choi, M.; Lennon, J. T.; Soininen, J.; Wang, J., Microbial and Environmental Processes Shape the Link between Organic Matter Functional Traits and Composition. Environmental Science & Technology 2022, 56 , (14), 10504–10516. Wu, M.; Li, P.; Li, G.; Liu, K.; Gao, G.; Ma, S.; Qiu, C.; Li, Z., Using Potential Molecular Transformation To Understand the Molecular Trade-Offs in Soil Dissolved Organic Matter. Environmental Science & Technology 2022, 56 , (16), 11827–11834. Xiang, Y.; Gonsior, M.; Schmitt-Kopplin, P.; Shang, C., Influence of the UV/H2O2 Advanced Oxidation Process on Dissolved Organic Matter and the Connection between Elemental Composition and Disinfection Byproduct Formation. Environmental Science & Technology 2020, 54 , (23), 14964–14973. Additional Declarations There is NO Competing Interest. Supplementary Files liyusupport2.5.doc Supplementary information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9252807","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619798482,"identity":"7090fdcd-f37b-4308-a4cf-62cca296b627","order_by":0,"name":"Kui Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACCRBxgIGHH8pnbCBai2QDqVoYDA4Qq4V/dvOxh1/OHJYxvt188DMPg43shgPMzx7gteTOsXRjmRuHeczuHEuW5mFIM95wgM3cAJ8WA4kcM2mJD0AtN3IMgFoOJ244wMMmgV9L/jewFuMZ+Z9/8zD8J0ZLDpvkB6DDQAygLQcIa5G4kWYmzXAmnQfEsJxjkGw88zCbGV4t/DOSn0n+OGZtD2Q8vvGmwk6273jzM7xaQICZB+FOEJeQeiBg/EGEolEwCkbBKBjBAAB84EfSE4Mv8gAAAABJRU5ErkJggg==","orcid":"","institution":"College of Engineering, Northeast Agricultural University, Harbin, China.","correspondingAuthor":true,"prefix":"","firstName":"Kui","middleName":"","lastName":"Cheng","suffix":""},{"id":619798483,"identity":"114d5e1c-49d9-4ab4-a08c-ba6b3f171796","order_by":1,"name":"Yu Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Li","suffix":""},{"id":619798484,"identity":"fe548f2e-0451-4432-9a34-b196b0b78d49","order_by":2,"name":"Mengxin Wu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mengxin","middleName":"","lastName":"Wu","suffix":""},{"id":619798485,"identity":"777e5eb5-dfe3-4d98-92c6-f370a1d194b8","order_by":3,"name":"Shuang Ai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Ai","suffix":""},{"id":619798486,"identity":"5c90424a-8afa-428e-a2dd-6d606b2e02cf","order_by":4,"name":"Xianghui Meng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xianghui","middleName":"","lastName":"Meng","suffix":""},{"id":619798487,"identity":"3b6a2031-1801-41e0-987a-144f0a697af3","order_by":5,"name":"Jianghao Cheng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jianghao","middleName":"","lastName":"Cheng","suffix":""},{"id":619798488,"identity":"8dbc8f99-0ce5-4e3d-96f2-39aa6dd3f38a","order_by":6,"name":"Liu Cui","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Cui","suffix":""},{"id":619798489,"identity":"207beec9-e6ed-47e2-b0da-7c9ccb969e53","order_by":7,"name":"Fan Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-03-28 12:45:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9252807/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9252807/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107076099,"identity":"875d076f-d15d-46dc-8929-6fdd7b426c6f","added_by":"auto","created_at":"2026-04-16 13:19:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":822605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrecursor-driven humification generates distinct fluorescence signatures and binding responses to Cd\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e. \u003c/strong\u003e(a) Conceptual schematic of hydrothermal humification of rice straw and soybean straw to form A-HA (RS-HA and SS-HA) with contrasting network architectures, followed by Cd\u003csup\u003e2+\u003c/sup\u003e complexation to metal–humic assemblies. (b) PARAFAC-derived fluorescence component intensities (Fmax1, Fmax2) and optical indices (FluI, FreI, HIX, BIX) as a function of Cd concentration for RS-HA and SS-HA (points with fitted trends). (c) The synchronous and asynchronous 2D-SFS-COS maps with increasing Cd concentration. Red indicates positive values and green indicates negative values in 2D-SFS-COS maps.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9252807/v1/f703e05529cc6ccc86c360a4.png"},{"id":107076101,"identity":"b875ed7c-fd9c-4609-836e-96059ad3bef4","added_by":"auto","created_at":"2026-04-16 13:19:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":623632,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eXAFS and MD reveal coordination microenvironments and dynamics. \u003c/strong\u003eStructural analysis of HA-Cd: (a) comparison of Cd K-edge normalized XANES spectra. (b) Fitting curves of fourier-transformed EXAFS spectra in R space. (c) Wavelet transform of the Cd K-edge. (d) Coordination structures of organic molecules with Cd within a distance of less than 4.45 Å. (e-f) The RDFs and coordination numbers for Cd²⁺ ions coordinating with oxygen atoms from GA and nitrogen atoms from Trp in the simulated system. Full lines and dashed lines are g(r) and CN as the function of distance, respectively. (g) Mechanistic summary linking precursor-programmed topology to coordination microstructure (multidentate anchoring vs monodentate hanging) and to dynamical landscapes (rigid cage vs flexible net).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9252807/v1/787f85dbc623e26d5e233c92.png"},{"id":107481581,"identity":"6720c2fb-1645-483b-ad0a-048ced687afe","added_by":"auto","created_at":"2026-04-22 02:19:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":301775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrecursor-dependent compositional reorganization upon Cd²⁺ binding. \u003c/strong\u003eThe relative abundance according to formula classes (a) and compound classes (d). (c) Van Krevelen diagrams showing the molecule transformation of RS-HA and SS-HA during the Cd²⁺ chelation process. (d) Venn diagram of molecular distribution in HA and HA-Cd. (e) The bubble diagrams shows the number of possible precursor-product pairs during the Cd²⁺ chelation process.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9252807/v1/7b498f8b591c715b8ccd11b6.png"},{"id":107076102,"identity":"26d1cc45-dbff-40b0-af2a-4f55cf79b747","added_by":"auto","created_at":"2026-04-16 13:19:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":380051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClass-resolved molecular shifts during Cd²⁺ complexation. \u003c/strong\u003e(a) Possible chemical reactions occurring between different compound classes: oxygenation, dealkylation, decarboxylation and deamination between HA and HA-Cd molecules, and quantitative data for chemical reactions occurred among various compound classes is available in Table S7. (b) Parallel plots for comparing compound classes, element combinations and NOSC between HA and HA-Cd samples. (c) The relationship between average ΔH (kJ mol\u003csup\u003e−1\u003c/sup\u003e C) and NOSC values of compound classes of HA and HA-Cd. Linear relationship validated using known compounds in HA and HA-Cd, with the shaded area representing the 95% prediction interval of the fitted linear regression model. (d) PCA of the relationship between molecular composition and structural factors: H/C ratio, DBE, AI\u003csub\u003emod\u003c/sub\u003e and other relevant parameters.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9252807/v1/89a9160a3700d2da4341f2be.png"},{"id":107480901,"identity":"04c86247-9b89-46dd-9df9-464510e8d035","added_by":"auto","created_at":"2026-04-22 02:14:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":718823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReaction pathways and thermodynamic signatures of Cd²⁺ complexation. \u003c/strong\u003e(a) Molecular characteristics of unique molecular formulas in RS-HA and SS-HA systems, including distribution patterns of C and O atoms, phylogenetic tree analysis, and reaction types occurring during Cd complexation. (b) Bar chart illustrating the classification of molecular transformations, Sankey diagram quantifying the fluxes of TFP and TLP both within and between molecular transformations, and chord diagram revealing specific intergroup and intragroup conversion pathways. Reaction network between HA and HA-Cd. Comparative reaction network analysis between: (c) RS-HA and RS-HA-Cd, and (d) SS-HA and SS-HA-Cd systems. (e) Subnetwork of SS-HA\u003csub\u003e \u003c/sub\u003eand SS-HA-Cd systems.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9252807/v1/e73f563731e411e4a5052d2c.png"},{"id":107076104,"identity":"8a4f1ff0-3b3c-4796-a2ab-83974dd5852e","added_by":"auto","created_at":"2026-04-16 13:19:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":433816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual schematic illustrating precursor-encoded network topology in organo–metal interactions. \u003c/strong\u003eThe diagram summarizes how distinct biomass precursors are transformed during humification into supramolecular networks (rigid vs flexible), and highlights the associated coordination microenvironment and dynamic accessibility in organo–metal complexation, together with the three descriptors used for evaluation (binding kinetics, thermodynamics, and coordination saturation).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9252807/v1/7f90450e0165e8edb98b55da.png"},{"id":108490921,"identity":"8a70cf55-755e-4c54-86ae-bd80b5024b9f","added_by":"auto","created_at":"2026-05-05 09:50:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3412412,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9252807/v1/6d606605-b1f4-44b4-acf0-e1abcd40dce1.pdf"},{"id":107480796,"identity":"248d0ff0-ce6f-4fe9-bfa7-a799d7c2f7d3","added_by":"auto","created_at":"2026-04-22 02:13:39","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6999040,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"liyusupport2.5.doc","url":"https://assets-eu.researchsquare.com/files/rs-9252807/v1/b45df58ad8d65ba580b12b6c.doc"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Precursor-encoded supramolecular topology governs metal-humic stability","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn natural environments, interactions between metal ions and organic matter (OM) constitute a fundamental and ubiquitous physicochemical mechanism that underpins biogeochemical cycling, biological metabolism, and materials synthesis \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The complexation of metal ions with organic ligands dictates the partition of metals between solution and solid phases to govern their mobility and bioavailability \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Conversely, metal binding modulates the aggregation state and accessibility of OM functional groups through mechanisms that include charge shielding, cation bridging, and local cross-linking \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This process subsequently redirects the reaction pathways and persistence of OM \u003csup\u003e5\u003c/sup\u003e. Consequently, organic-metal complexation is characterized by a dynamic and bidirectional coupling where ligand selectivity determines the environmental fate of metals while metal participation fundamentally reshapes the supramolecular architecture and evolutionary trajectory of OM.\u003c/p\u003e \u003cp\u003eAlthough the fundamental principles of organic-metal complexation are established \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, the formation and evolution of ligand pools in nature remain highly dynamic and context-dependent \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This complexity impedes our ability to predict metal fate and its coupling with carbon stability under global change scenarios. A critical impediment is the high heterogeneity of natural soil organic matter (SOM) and its ligand structures \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The interplay of source components, microbial processing, and selective mineral adsorption shapes the spatial distribution and energy landscape of coordination sites\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. As a result, macroscopic indices frequently fail to map directly onto verifiable coordination microenvironments. This disconnect elucidates a prevalent paradox in remediation where similar OM inputs trigger diametrically opposite metal responses that range from effective immobilization to unexpected remobilization \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This phenomenon indicates that the quantity of OM is an insufficient predictor and underscores that the composition and supramolecular organization of the ligands are the decisive variables. Traditional models that treat SOM as a homogeneous ligand pool are thus inadequate for rationalizing divergent metal coordination behaviors among chemically similar organic substrates \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHumic acid (HA) represents a highly reactive fraction of SOM that plays a pivotal role in metal complexation and organic carbon stabilization \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Given that HA originates from the decomposition of plant and microbial residues, differences in precursor chemical composition are hypothesized to drive its structural diversity and functional differentiation \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. To advance this understanding from correlation to mechanism, a controlled comparative system is required to preserve molecular complexity while isolating the variable of precursor origin from environmental boundary conditions. The artificial humic acid (A-HA) system satisfies this requirement \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. By simulating humification under controlled conditions, we can generate products with natural-like complexity to facilitate a systematic comparison of precursor-driven differences\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Furthermore, the integration of ultra-high-resolution molecular omics with spectroscopic characterization enables the tracing of complexation-induced recombination at the molecular scale \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This approach permits a thermodynamic analysis of ligand hardening or softening to establish molecular design rules for both environmental prediction and materials engineering \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we utilized rice straw (RS) and soybean straw (SS) as contrasting model precursors. RS is rich in cellulose and silica with complex lignin structures and recalcitrant nitrogen forms. In contrast, SS is protein-rich and possesses abundant labile nitrogen alongside a relatively simple lignin composition \u003csup\u003e19 20\u003c/sup\u003e. We employed cadmium (Cd) as a model metal to integrate Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with synchrotron-based extended X-ray absorption fine structure (EXAFS) spectroscopy and molecular dynamics (MD) simulations. Our objective was to map precursor composition differences to the supramolecular flexibility, coordination geometry, and binding energetics of the resulting A-HA. Specifically, we aim to elucidate how chemical heterogeneity in plant residues is encoded into the molecular structure of HA during humification to govern the mechanism and stability of metal coordination. This work provides a mechanistic basis that links the chemical inputs of plant residues to the environmental function of their humified products.\u003c/p\u003e"},{"header":"2. Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Binding characteristics of HA-Cd complexes\u003c/h2\u003e \u003cp\u003eIn this study, we hypothesize that the chemical signatures inherited from plant residues are translated during hydrothermal humification into distinct humic-acid network topologies, which determine the spatial arrangement and accessibility of chelating motifs and thereby modulate the enthalpy\u0026ndash;entropy balance controlling Cd\u0026sup2;⁺ complexation stability. To test this, we prepared artificial humic acids (A-HAs) from rice straw (RS) and soybean straw (SS) via the same hydrothermal process (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and compared their structural features and Cd-binding behaviors; the resulting materials are denoted RS-HA and SS-HA and exhibit markedly different compositions and reactivities. Elemental analysis revealed that SS-HA, inheriting the nitrogen-rich signature of its leguminous precursor, possessed significantly higher nitrogen (3.82%) and oxygen (32.27%) contents, as well as a higher (O\u0026thinsp;+\u0026thinsp;N)/C atomic ratio (0.62) compared to RS-HA (Supplementary Table\u0026nbsp;1), indicating a higher density of functional groups. While minor differences in H/C ratios suggested subtle variations in aromaticity, both A-HAs maintained the high condensation features typical of humic structures \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This divergence in precursor origin manifested as a distinct trade-off between capacity and stability. Adsorption kinetics demonstrated that SS-HA achieved an equilibrium capacity 36% higher than that of RS-HA (Supplementary Fig.\u0026nbsp;1a and Supplementary Table\u0026nbsp;2), likely attributable to the high accessibility of its active domains. However, this capacity advantage proved fragile under ionic interference. In the presence of competing cations such as Ca\u0026sup2;⁺ and Pb\u0026sup2;⁺, the Cd\u0026sup2;⁺ retention of SS-HA declined by 10.2\u0026ndash;25.4%, whereas RS-HA maintained robust performance (Supplementary Fig.\u0026nbsp;1b). We speculate that while the sheer quantity of functional groups in SS-HA facilitates rapid uptake, it fails to confer the requisite stability within complex environmental matrices. Spectroscopic profiling revealed the structural basis of this divergence. SS-HA exhibited higher fluorescence intensity for humic-like substances (Fmax1\u0026thinsp;=\u0026thinsp;14394.48), likely attributed to polycondensation induced by the abundant nitrogen components in SS. In contrast, RS-HA displayed prominent tryptophan-like (Trp) signals (Fmax2\u0026thinsp;=\u0026thinsp;11373.64). Notably, the humification index (HIX) and biological index (BIX\u0026thinsp;=\u0026thinsp;0.98) of RS-HA were significantly higher than those of SS-HA, suggesting a greater degree of humification (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and Supplementary Figs.\u0026nbsp;2\u0026ndash;3) \u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. Two-dimensional fluorescence correlation spectroscopy (2D-SFS-COS) uncovered diametrically opposed Cd\u0026sup2;⁺ binding sequences \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In RS-HA, Cd\u0026sup2;⁺ interacted first with Trp species followed by the HA backbone (Trp\u0026thinsp;\u0026gt;\u0026thinsp;HA), whereas SS-HA showed the reverse order (HA\u0026thinsp;\u0026gt;\u0026thinsp;Trp) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Supplementary Table\u0026nbsp;3). The aromatic domains in SS-HA, enriched via nitrogen-driven polycondensation, favor carboxyl-dominated ion exchange and support broad-spectrum binding through linear electron transfer \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, this open-channel architecture renders SS-HA highly susceptible to competition from charge-similar cations (e.g., Ca\u0026sup2;⁺). Conversely, RS-HA initiates Cd\u0026sup2;⁺ complexation via discrete Trp microdomains, engaging in N-coordination and π-cation interactions \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This spatially restricted topology provides steric protection, thereby enhancing coordination stability under ionic stress \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEXAFS spectroscopy provided atomic-scale insights into the Cd\u0026sup2;⁺ coordination environments \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The X-ray absorption near-edge structure (XANES) spectra revealed a consistent edge energy (E₀ \u0026asymp; 26,714 eV) for both complexes, confirming that Cd\u0026sup2;⁺ retains its oxidation state and primarily engages in coordination bonding (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Fourier-transformed EXAFS spectra (Supplementary Fig.\u0026nbsp;4a) exhibited three main coordination shells for SS-HA-Cd at approximately 1.31 \u0026Aring; (Cd-N), 1.78 \u0026Aring; (Cd-O), and 2.26 \u0026Aring; (Cd-S). Notably, RS-HA-Cd displayed analogous features but with systematically shifted peak positions and greater oscillation amplitudes, suggesting a more saturated and ordered coordination environment. This difference likely arises from unique electronic structures and coordination geometries formed during complexation. Quantitative fitting revealed that RS-HA-Cd exhibited higher coordination numbers of 2.73 (Cd\u0026ndash;N), 3.46 (Cd\u0026ndash;O), and 1.39 (Cd\u0026ndash;S) compared to the values of 1.89, 3.17, and 1.03 respectively for SS-HA-Cd, accompanied by elongated bond lengths (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Fig.\u0026nbsp;4b-f, and Supplementary Table\u0026nbsp;4) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These differences likely stem from distinct Cd\u0026ndash;ligand interactions modulated by the local chemical environment. Specifically, RS favors the formation of condensed aromatic domains, which facilitate π-electron delocalization and d-orbital hybridization, enhancing Cd binding capacity. Furthermore, a higher defect density in RS-HA may increase ligand accessibility, contributing to bond elongation and spectral broadening. Wavelet transform analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;5) confirmed all three coordination shells and revealed clear edge splitting in RS-HA-Cd, indicative of stratified coordination domains formed via crosslinking. Although the RS-HA-Cd complex exhibits slightly elongated Cd-N/O bond lengths compared to SS-HA which likely pointing to steric crowding within the rigid aromatic scaffold, it displays a significantly higher coordination number (CN\u0026thinsp;=\u0026thinsp;2.73). This indicates a shift from monodentate surface adsorption to multidentate chelation. According to the chelate effect, this high-order coordination structure creates a substantial entropic advantage, overriding the enthalpic penalty of slightly longer individual bonds and resulting in superior overall thermodynamic stability.\u003c/p\u003e \u003cp\u003eMD simulations elucidated the differences in coordination dynamics and structural stability. Given the structural complexity, gallic acid (GA) was selected as a representative model compound \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Within a 4.45 \u0026Aring; radius of Cd centers, RS-HA exhibited a more compact and ordered coordination geometry, consistent with its rigid supramolecular framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). This structural compactness translated into a lower Cd\u0026sup2;⁺ diffusion coefficient (0.03558, compared to 0.04061 in SS-HA). Similarly, GA and Trp components diffused more rapidly in SS-HA (0.01308 and 0.01836, respectively) compared to RS-HA (0.00866 and 0.01311) (Supplementary Fig.\u0026nbsp;6), indicating stronger ligand\u0026ndash;metal interactions in RS-HA. Post-relaxation radial distribution functions confirmed enhanced affinity of RS-HA toward carboxyl oxygen atoms and hydrogen-bond donors. Cd-Trp coordination was also strengthened via interactions with aromatic nitrogen, amine groups, hydroxyl oxygens, and carboxyl oxygens (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-f), aligning with EXAFS observations of higher coordination numbers and longer average bond lengths. Collectively, this points to a more saturated and stable Cd coordination environment in RS-HA. In contrast, the higher molecular mobility of SS-HA reflects its flexible topology and weaker coordination, explaining its low resistance to ion competition. Mean squared displacement (MSD) analysis confirmed these differences, with all key species (Cd, Cl⁻, GA, Trp) displaying greater mobility in SS-HA, especially tryptophan-like components (Supplementary Fig.\u0026nbsp;7a). The elevated dynamic flexibility in SS-HA aligns with its carboxyl-dominated but competition-prone ion exchange mechanism. Conversely, the restricted diffusion and multidentate coordination geometry of RS-HA impart steric shielding, stabilizing Cd\u0026sup2;⁺ retention under perturbation. Hydrogen bond network analysis (Supplementary Fig.\u0026nbsp;7b) further supported this, showing RS-HA maintains a marginally higher average number of hydrogen bonds (3510) than SS-HA (3470), reinforcing its tighter molecular packing and greater intermolecular cohesion. These observations highlight how the topological rigidity in RS-HA enhances the dynamic stability and selectivity of Cd\u0026sup2;⁺ binding through the synergistic effects of π-cation interactions, steric protection, and hydrophobic microenvironments \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, the chemical disparity of feedstocks influences metal interaction behaviors by driving structural variations in HA where this effect extends beyond mere functional group abundance to include the resultant coordination strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). SS-HA leverages its oxygen-rich, flexible architecture for rapid ion exchange, but its relatively loose coordination framework renders it highly susceptible to competitive ion interference. Conversely, the structurally rigid aromatic matrix of RS-HA exhibits enhanced Cd\u0026sup2;⁺ retention, enabling π-cation interactions, multidentate N/O coordination, and hydrogen-bond stabilization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2 HA and HA-Cd molecular composition, distribution, and properties\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eFT-ICR MS analysis revealed molecular-level reorganization pathways underlying Cd\u003csup\u003e2+\u003c/sup\u003e complexation by A-HAs, offering mechanistic insights beyond macroscopic observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b and Supplementary Table\u0026nbsp;5)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Upon Cd\u003csup\u003e2+\u003c/sup\u003e binding, RS-HA underwent a net molecular loss of 341 species (from 4568 decrease to 4227), involving 3414 transformed molecules, 1157 removed and 813 generated. In parallel, SS-HA exhibited a more extensive reorganization, with a net loss of 660 species (from 4604 decrease to 3944), stemming from 3350 transformations, 1250 removals and only 596 newly formed compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Molecular class analysis revealed divergent structural trajectories. RS-HA exhibited an increase in condensed aromatics (CA, from 41 to 48) and a concurrent decrease in lignin-like (LG) molecules (from 3983 to 3669), suggesting dealkylation-driven condensation via -CH₂/-C₂H₄ loss and enhanced aromaticity (Supplementary Fig.\u0026nbsp;8). This structural evolution supports the formation of π-delocalized domains conducive to cation\u0026ndash;π interactions \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Conversely, SS-HA underwent aromatic ring cleavage, as evidenced by a decrease in CA compounds (31 to 23) and a substantial increase in lipid-like (LP) species (306 to 425). This trend indicates fragmentation and aliphatic chain proliferation, disrupting the spatial continuity of carboxylate networks and diminishing Cd\u0026sup2;⁺ binding efficiency. Additionally, sharp reductions in phenolic acids (PA, 153 to 50) and CHON-class molecules (1983 to 1527) point to functional group loss, particularly nitrogenous moieties critical for metal chelation.\u003c/p\u003e \u003cp\u003eTransformation pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) highlighted these fundamental differences. RS-HA predominantly underwent oxidation (32.8%) and dealkylation (29.1%) modifications, yielding 813 oxygen-enriched molecules. By contrast, SS-HA transformations skewed toward non-oxidative dealkylation (34.7%), resulting in fewer products (596) with diminished O/C ratios (from 0.3511 to 0.3281), indicating degradation of the carboxylate matrix. The frequent detection of -CH₂O losses (60 occurrences) in SS-HA reflects glycosidic bond cleavage, likely creating less-specific hydroxyl-rich surfaces\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Molecular weight profiles further confirmed this divergence (Supplementary Fig.\u0026nbsp;9). RS-HA displayed a modest increase in average m/z (451.70 to 453.37) despite a reduction in molecular count (from 4,568 to 4,227), signifying selection of high-affinity binding motifs, where fewer but more functionally efficient structures emerge. Conversely, SS-HA exhibited a decline in m/z (454.85 to 453.64) and a shift toward heterogeneous, aliphatic-rich species, suggesting quantity-driven but functionally diffuse complexation. Integrated with fluorescence spectroscopy data, these findings support distinct binding models. RS-HA initiates Cd\u0026sup2;⁺ complexation via tryptophan-like domains, followed by stabilization through condensed aromatic structures, forming a multilayered retention architecture. SS-HA, while initially advantaged by its carboxyl-rich network, experiences coordination site degradation via fragmentation and aliphatic expansion, compromising long-term stability. Finally, the functional transformation efficiency, defined as newly generated binding-relevant molecules per 1000 total transformations, was markedly higher for RS-HA (238 vs. 178 in SS-HA), underscoring its more targeted and effective molecular reconfiguration in response to Cd\u0026sup2;⁺ exposure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA persistent challenge in DOM research is deciphering the complex molecular reorganization processes that underpin its lifecycle and functional behavior\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Here, we quantified transformation fluxes and reaction networks across different A-HAs types, demonstrating how precursor composition modulates structural evolution to optimize Cd\u0026sup2;⁺ complexation. Using precise tracking of functional group alterations in molecular formulas (e.g., demethylation or decarboxylation), we uncovered systematic differences in transformation pathways between SS-HA and RS-HA. Across all samples, LG compounds dominated the transformation landscape regardless of reaction type (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and Supplementary Table\u0026nbsp;6), highlighting their structural resilience. While LG species underwent extensive modifications (including functional group changes, conformational adjustments, or direct Cd binding), their aromatic scaffolds remain largely preserved. Transformation topologies diverged substantially between the two A-HAs. RS-HA displayed a conservative reaction network, characterized by intra-class conversions within LG, partial oxidation or demethylation of PA into LG and minimal involvement of LP species. In contrast, SS-HA exhibited a highly interconnected and reactive network, wherein LG species not only underwent internal rearrangements but also frequently converted into tannins (TN) molecules. PA compounds in SS-HA underwent intense decarboxylation, often yielding LP-like products. Notably, LP compounds acted as central hubs, participating in inter-class conversions, particularly demethylation-driven exchanges involving LG, PA, and TN classes. This high molecular flux and compositional plasticity in SS-HA likely enhances binding adaptability, albeit at the cost of coordination stability. Conversely, the structurally rigid LG-PA network in RS-HA may foster defined and selective binding domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). To explore thermodynamic underpinnings of these differences, we examined the redox characteristics of each compound class, using combustion enthalpy (ΔH, kJ mol⁻\u0026sup1; C) as a proxy for Gibbs free energy of oxidation\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. A strong linear correlations (R\u0026sup2; = 0.972) between ΔH and nominal oxidation state of carbon (NOSC) was observed across all compound classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and Supplementary Table\u0026nbsp;7)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Upon transformation, most compound classes exhibited higher ΔH and NOSC values in RS-HA, indicating greater recalcitrance and thermodynamic stability compared to SS-HA (Supplementary Table\u0026nbsp;8). These trends were further supported by intensity-weighted ΔH-NOSC relationships, reinforcing that RS-HA harbors more oxidized, energetically stable species. Distribution analysis revealed that SS-HA molecules predominantly occupy NOSC negative regions (Supplementary Fig.\u0026nbsp;10), reflecting reduced, chemically labile structures prone to oxidation or ion exchange, while RS-HA species shift toward NOSC positive domains, representing more oxidized, structurally inert compounds \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. These redox profiles suggest that SS-HA forms reactive but transient coordination environments, whereas RS-HA establishes more robust and redox-stable chelation frameworks. To further dissect structural divergence, we performed principal component analysis (PCA) on FT-ICR-MS datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). RS-HA exhibited tight clustering among LG, LP, and PA components, indicative of greater molecular homogeneity. In contrast, SS-HA showed wide separation between PA and LP clusters, reflecting its greater chemical diversity. Cd\u0026sup2;⁺ addition induced distinct shifts in molecular category positions for both HA types, validating that complexation is a selective process targeting specific structural motifs. In SS-HA, PCA revealed a post-complexation convergence between LG and LP clusters, consistent with the frequent LG-LP transformations (e.g., demethylation) observed in reaction network, suggesting that Cd\u003csup\u003e2+\u003c/sup\u003e induces lipid-lignin hybridization and expands binding motifs. Conversely, RS-HA displayed increased category separation post-complexation, corroborating its rigid, functionally defined coordination framework. Together, these findings establish that precursor-driven transformation topologies and redox characteristics fundamentally shape the coordination landscapes of A-HAs. SS-HA evolves dynamic but less selective networks, whereas RS-HA promotes structurally conserved, thermodynamically stable chelation domains, explaining their distinct Cd\u0026sup2;⁺ binding performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Unique molecular constituents drive differential binding\u003c/h2\u003e \u003cp\u003eTo dissect the molecular underpinnings of differential Cd\u0026sup2;⁺ complexation, we interrogated the distinctive molecular subsets identified from FT-ICR MS data. Venn diagram analysis revealed 4,079 shared molecules alongside 489 and 525 unique species for RS-HA and SS-HA, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). These unique molecular pools were subjected to thermodynamic and network-based analysis to elucidate structure function relationships underpinning their divergent binding behaviors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b)\u003csup\u003e18\u003c/sup\u003e. Among 489 unique molecular formulas of RS-HA, 57 underwent thermodynamically favorable processes (TFP), including 29 intragroup transformations and 28 intergroup conversions, indicating a high degree of structural plasticity and synergistic transformation potential (Supplementary Table\u0026nbsp;9). In contrast, 525 unique formulas of SS-HA yielded only 44 TFPs (33 intragroup, 11 intergroup), suggesting more constrained and compartmentalized transformation dynamics. This disparity reflects superior propensity of RS-HA for cross-category reorganization and multidimensional complexation networks, while SS-HA remains largely confined to intra-class exchanges, a structural trait associated with lower resistance to competitive adsorption. Cd\u0026sup2;⁺ complexation significantly altered these thermodynamic patterns. RS-HA exhibited a notable reduction in both TFPs (from 57 to 18, including 15 intragroup and 3 intergroup) and thermodynamically limited processes (TLPs) (from 48 to 13), consistent with a \u0026ldquo;thermodynamic hardening\u0026rdquo; phenomenon, increased energy barriers restrict further molecular rearrangement, yielding enhanced complex stability. Conversely, SS-HA underwent \u0026ldquo;thermodynamic softening\u0026rdquo;, with TFPs increasing from 44 to 73 (33 to 46 intragroup) and TLPs doubling from 32 to 60, signifying a more flexible, yet less stable coordination environment post-complexation. These divergent thermodynamic trajectories are governed by the chemical composition of the precursors, which shapes molecular topology and transformation accessibility. Network visualization of transformation pathways further reveals contrasting topological architectures. RS-HA forms highly interconnected three-dimensional networks (average node connectivity\u0026thinsp;=\u0026thinsp;6.03), where condensed aromatics, lignin-like, and proteinaceous components serve as key hubs molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec and Supplementary Table\u0026nbsp;10). This topological redundancy allows for functional compensation and cascade activation, wherein the formation of one complex initiate sequential node rearrangement, ultimately enhancing chelation robustness under environmental perturbations. In contrast, SS-HA exhibits a hierarchical and compartmentalized network, organized into 12 sub-pathways, three of which are nitrogen-specific routes contribute 28% of TFP conversions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Although these pathways offer parallel coordination routes, notably a dual-mode system where amino carboxylates form stable bidentate complexes and the dominant carboxyl network facilitates rapid ion exchange, they remain energetically decoupled, reducing overall structural resilience. FT-ICR MS data revealed a substantial increase in aliphatic components following Cd\u0026sup2;⁺ complexation (from 6.6% to 10.8%), accompanied by a 25.8% decrease in condensed aromatics (31 to 23 molecules) (Supplementary Fig.\u0026nbsp;11). This aromatic ring cleavage coupled with aliphatic chain proliferation temporarily enhances site density but ultimately disrupts the spatial arrangement of carboxyl network through steric hindrance, thereby reducing effective binding site accessibility. Competitive adsorption experiments confirm this structural vulnerability. SS-HA exhibited a 10.2% decreased in Cd\u0026sup2;⁺ binding capacity reduction under Ca\u0026sup2;⁺ challenge, which further dropped by 25.4% in a quaternary-ion systems. This behavior underscores its limited interference resistance, attributable to insufficient energetic coupling between core carboxyl and auxiliary nitrogen coordination pathways.\u003c/p\u003e \u003cp\u003eThese findings establish molecular network robustness as a new functional criterion for evaluating humic acids. Traditional metrics based on functional group density fail to reconcile the paradoxical combination of high capacity and low stability of SS-HA. Instead, our data demonstrate that the chemical composition of the precursors fundamentally dictates network topology and, consequently, functional output. SS gives rise to hierarchical architectures optimized for site density, whereas RS fosters topologically rigid, three-dimensional networks with enhanced pathway redundancy. This mechanistic coupling between microstructural topology and macroscopic functionality highlights the critical role of topological rigidity in selective metal complexation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eOrgano metal interactions are fundamental to environmental chemistry because they couple metal speciation to the structure, reactivity, and evolution of natural organic matter. However, these interactions are still often described using composition level averages, such as functional group inventories or bulk indices, with an implicit assumption that binding outcomes scale with the abundance of binding sites. This description is increasingly insufficient. Complexation commonly exhibits a kinetic thermodynamic divergence in which rapid binding does not necessarily develop into persistent coordination and stable coordination does not necessarily arise from the most accessible motifs. In addition, complexation is not simply a passive occupation of preexisting sites. Exposure to metals can actively reorganize organic ensembles and alter molecular connectivity and accessibility, which then redirects subsequent transformation pathways. As a result, the effective ligand pool becomes contingent on environmental history and molecular assembly. A mechanistic framework has been lacking that links precursor inputs and organic assembly to coordination microenvironments, and then connects those microenvironments to the feedback of metal exposure on organic matter evolution.\u003c/p\u003e \u003cp\u003eIn this work we show that supramolecular topology provides this missing link. The decisive control is not only which functional groups are present, but how they are organized within an architecture that constrains or permits molecular motion and reorganization. When topology is treated as a governing descriptor, the long standing paradox that more sites do not necessarily yield more stable binding becomes a predictable outcome rather than an anomaly. Architectures that emphasize openness and accessibility naturally promote fast binding through exchange compatible coordination motifs. Architectures that are more constrained and more connected favor cooperative coordination and shielding, which increases coordination saturation and biases binding toward more persistent states. This topological perspective also clarifies why similar metal organic encounters can follow divergent thermodynamic trajectories. A hardening trajectory emerges when binding progressively becomes less exchange prone as reorganization pathways are restricted and coordination saturation increases. A softening trajectory emerges when binding remains dynamically permissive because reorganization pathways remain accessible and exchange pathways remain active.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe validated this topology-based mechanism by integrating evidence across complementary scales while avoiding reliance on composition level averages alone. Coordination spectroscopy establishes that the systems occupy distinct coordination regimes that differ in coordination saturation and exchange susceptibility, which directly links macroscopic persistence to coordination microstructure. Molecular simulations provide the dynamic correlate of topology by showing that constrained architectures suppress mobility and reduce access to exchange pathways, whereas permissive architectures remain dynamically open and facilitate rapid association while increasing vulnerability to displacement and continual rearrangement. Ultrahigh resolution mass spectrometry further demonstrates that complexation acts as a selective perturbation to the organic ensemble rather than a bookkeeping exercise of site occupancy. Metal exposure reshapes molecular pools along precursor dependent transformation topologies, either restricting accessible routes and concentrating stability in the hardening direction or expanding accessible routes and sustaining plasticity in the softening direction. These independent lines of evidence converge on a single causal chain in which topology gates reorganization, reorganization controls coordination saturation and exchangeability, and these properties determine whether binding matures into persistent coordination or remains transient.\u003c/p\u003e \u003cp\u003eThis framework is useful in several ways. Conceptually, it reframes metal organic chemistry from static complexation to metal driven organic matter evolution under topological constraints. It elevates topology from a descriptive attribute to a mechanistic state variable that reconciles kinetic accessibility with thermodynamic persistence. Practically, it provides a transferable explanation for why different precursor sources and humification trajectories can yield qualitatively different metal organic outcomes even when bulk composition appears similar. The critical difference lies in architectural organization and network robustness rather than in chemical inventory alone. Predictively, the framework suggests measurable handles that can be incorporated into models of metal organic coupling, including descriptors related to coordination saturation, transformation accessibility, and network robustness. These descriptors can bridge molecular resolved organic chemistry with forecasts of how metals interact with organic reactivity, aggregation and dispersion behavior, and longer term carbon processing under changing environmental conditions. The framework also produces testable expectations. Increasing architectural constraint should bias complexation toward hardening and more saturated coordination. Increased fragmentation and pathway proliferation should bias complexation toward softening and greater exchange susceptibility. Early engagement of protected microdomains should be associated with deeper and more persistent coordination states than binding initiated within open and highly accessible domains.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 A-HA sample preparation\u003c/h2\u003e \u003cp\u003eA-HA was synthesized via HTH following established protocols\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Specifically, 1.20 g portions of RS or SS were individually combined with varying KOH quantities in a 50 mL autoclave. The sealed reactor was heated at 200\u0026deg;C for 24 h. Following the reaction, the system cooled naturally to ambient temperature, yielding liquid products. These liquids were acidified to pH 3 using 6.0 mol\u0026middot;L⁻\u0026sup1; HCl. Subsequent filtration isolated the solid humic acid fraction from the liquid phase. The recovered solid was then washed to eliminate residual acids and impurities, yielding purified A-HA (marked as RS-HA and SS-HA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 A-HA\u0026thinsp;\u0026minus;\u0026thinsp;Cd Complexation Experiments.\u003c/h2\u003e \u003cp\u003eA-HA working solution was added to 40 mL brown glass bottles, and the final dissolved organic carbon (DOC) concentrations was 5 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Subsequently, metal ions were added to the solution, and the final metal ion concentrations ranged from 0 to 4 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. This concentration range was utilized to obtain the complete complexation curves of A-HA-Cd complexes. Finally, the mixed solutions were shaken on a shaker at 140 rpm and 25\u0026deg;C.After 24h complexation reaction, the solutions were filtered through a 0.45 \u0026micro;m membrane to remove precipitates formed during complexation, and A-HA\u0026thinsp;\u0026minus;\u0026thinsp;Cd complexes were obtained\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The filtrate was analyzed using a Three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy (Hitachi, Tokyo, Japan) to characterize the binding properties of the complexes, as described in Supplementary Method 1. All experiments were repeated at least three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 FT-ICR MS Detection.\u003c/h2\u003e \u003cp\u003eThe Agilent Bond Elut PPL cartridge (200 mg per 3 mL) was used to remove inorganic salts from DOM samples before FT-ICR MS detection, with an extraction efficiency\u0026thinsp;\u0026gt;\u0026thinsp;57%. Filtration with a 0.45 \u0026micro;m membrane was adopted to obtain a more comprehensive molecular composition of the samples. DOM enrichment (200 \u0026micro;L) of the PPL resin was analyzed on a SolariX 15T FT-ICR mass spectrometer (Bruker, Karlsruhe, Germany) in negative ionization mode (Supplementary Method 2). Molecules detected by FT-ICR MS were classified by element composition, including CHO, CHON, CHOS, and CHONS (Supplementary Method 3)\u003csup\u003e43\u003c/sup\u003e. The H/C and O/C ratios were characterized using van Krevelen diagrams, and the relevant stoichiometric ranges of each classification are listed in Supplementary Table\u0026nbsp;10. The modified aromatic index (AI\u003csub\u003emod\u003c/sub\u003e)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, unsaturation index ((DBE-O)/C) and Kendrick mass defect (KMD) were utilized to describe the character is tics of the detected molecular formulas, which were calculated as described in Text S4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Other Analytical Methods.\u003c/h2\u003e \u003cp\u003eParallel factor analysis (PARAFAC) was performed on the fluorescence spectra of the A-HA\u0026ndash;Cd complexes using MATLAB R2018a and the DOM-Fluor toolbox. The maximum fluorescence intensity of each PARAFAC-resolved component was determined (Fmax1 and Fmax2). In addition, fluorescence-derived indices were calculated from the excitation\u0026ndash;emission matrix (EEM) spectra, including the fluorescence index (FluI), freshness index (FreI), biological index (BIX), and humification index (HIX), following established definitions\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The changing degree and order of different functional groups were revealed via two-dimensional correlation spectra (2D-COS) (Supplementary Method 4)\u003csup\u003e46\u003c/sup\u003e. Identification of potential biochemical transformation processes of A-HA molecules was performed based on precise mass differences between FT-ICR mass spectral peaks \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. A-HA molecules were divided into four regions taking into account the two molecular trait dimensions of reactivity and activity: labile-active (LA; number of transformations\u0026thinsp;\u0026gt;\u0026thinsp;10, H/C\u0026thinsp;\u0026ge;\u0026thinsp;1.5), labile-inactive (LI; number of transformations\u0026thinsp;\u0026le;\u0026thinsp;1, H/C\u0026thinsp;\u0026ge;\u0026thinsp;1.5), recalcitrant-active (RA; number of transformations\u0026thinsp;\u0026gt;\u0026thinsp;10, H/C\u0026thinsp;\u0026lt;\u0026thinsp;1.5), and recalcitrant-inactive (RI; number of transformations\u0026thinsp;\u0026le;\u0026thinsp;1, H/C\u0026thinsp;\u0026lt;\u0026thinsp;1.5) \u003csup\u003e48\u003c/sup\u003e. The molecular transformation process also involves energy changes. Thus, the Gibbs free energy before and after the molecular transformations was calculated. These transformations were categorized into thermodynamically favorable processes (TFP) and thermodynamically limited processes (TLP), according to thermodynamic spontaneity\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Details of the calculation are provided in Text S6. The between-molecule mass difference within 1 ppm was matched to the expected mass of the transformation. Using these pairwise mass differences and transformation associations, the transformation networks in which the nodes represent individual molecular formulas and the edges represent definitive molecular transformations were constructed and visualized using Gephi version 0.9.2 software (Mathieu Bastian and Sebastien Heymann, Paris, France) \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Key Research and Development Program of China (2024YFD1500503), the Outstanding Youth Project of Heilongjiang Province (JQ2024D001) the financial support from Longjiang Scholars for young scientist and Heilongjiang Provincial Undergraduate Institutions Support Plan for Outstanding Young Teachers in Fundamental Research (YQGH2023191).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor's contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYu Li: Writing – original draft, Investigation. Mengxin Wu: Software. Shuang Ai: Investigation. Xianghui Meng: Validation, Investigation. Jianghao Cheng: Validation, Investigation. Liu Cui: Software, Supervision. Fan Yang: Supervision, Funding acquisition. Kui Cheng:\u0026nbsp;preservation of the original data on which the paper is based, verification that the figures and conclusions accurately reflect the data collected and that manipulations to images are in accordance with \u003cem\u003eNature\u003c/em\u003e journal guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKleber, M.; Bourg, I. C.; Coward, E. K.; Hansel, C. M.; Myneni, S. C. 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Existing frameworks mainly emphasize functional group abundance, while overlooking how precursor chemistry is encoded into supramolecular network topology during humification. Here, using artificial humic acids as a controllable model system, we show that precursor-inherited supramolecular topology governs the thermodynamic stability of metal\u0026ndash;humic complexes. By integrating ultrahigh-resolution mass spectrometry, synchrotron X-ray absorption spectroscopy, and molecular dynamics simulations, we identify two contrasting thermodynamic trajectories determined by network architecture. Rigid, aromatic-dominated networks undergo thermodynamic hardening, generating stable, multidentate, and interference-resistant coordination domains, whereas flexible, nitrogen-rich networks exhibit thermodynamic softening, favoring kinetically accessible but metastable metal binding. Using cadmium as a probe metal, we further show that supramolecular rigidity enhances coordination saturation and entropic stabilization, increasing resistance to competitive ion displacement, while flexible topologies favor adsorption capacity at the cost of long-term stability. These results establish supramolecular topology as a governing principle linking precursor chemistry to the thermodynamic fate of metals in humic matter.\u003c/p\u003e","manuscriptTitle":"Precursor-encoded supramolecular topology governs metal-humic stability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 13:19:28","doi":"10.21203/rs.3.rs-9252807/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-chemistry","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commschem","sideBox":"Learn more about [Communications Chemistry](http://www.nature.com/commschem/)","snPcode":"","submissionUrl":"","title":"Communications Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d79ce9ce-a721-454f-b066-a276ad8c85c2","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"revise","date":"2026-05-05T13:02:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-03T11:59:17+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-02T05:32:14+00:00","index":1,"fulltext":"This content is not available."}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65959087,"name":"Physical sciences/Chemistry/Environmental chemistry/Geochemistry"},{"id":65959088,"name":"Physical sciences/Chemistry/Green chemistry/Sustainability"}],"tags":[],"updatedAt":"2026-05-05T13:07:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 13:19:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9252807","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9252807","identity":"rs-9252807","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}