What if adding more metal-binding groups made a silica sorbent perform worse? This study shows that pore structure and group accessibility can matter more than simply counting nitrogen sites. Heavy metals are well-known environmental pollutants because of their toxicity and ability to bioaccumulate in the bodies of living organisms at various stages of the food chain.

The reported negative changes affect plants, fish, mammals, and humans, so the problem spans multiple parts of the food chain. The study places both water purification and recovery of valuable metals for reuse among the important goals of environmental management.

Limited mining possibilities and increasing costs for metals such as copper, nickel, and europium make recovery from industrial waters, wastewater, and deposited wastes a promising strategy. Sorption is presented as a desirable recovery approach because it is effective, easy to use, low cost, and environmentally sustainable.

The study connects adsorption-process development with adsorbents that have developed porous structures, large surface areas, and high contents of functional groups. Amino-containing organosilicas are good sorbents for removing metal ions from water and wastewater because amino groups form complexes and the porous structure can be adjusted.

Earlier work showed the central design problem: increasing the proportion of APTES increased amino groups but produced non-porous samples and inaccessible amino groups. The study therefore used N-[3-(trimethoxysilyl)propyl]ethylenediamine as a functional agent to increase surface adsorption centers.

The stated aim was to synthesize mesoporous spherical silica particles with high ethylenediamine contents and determine what affects adsorption of copper, nickel, and europium ions. The samples were synthesized with a one-pot modified Stöber method in an ammonia medium, using three different structuring agents.

The synthesis scheme shown here sets up the central comparison between silica-building components and the surface diamine functionality. Figure one schematically shows the one-pot modified Stöber synthesis of organosilica adsorbents bearing surface (propyl)ethylenediamine groups, supplied by TMPEDA, in an ammonia medium.

TEOS leads to the polysiloxane samples TNN2, TNN3, and TNN6, while the bridged silanes BTESE and BTESB produce ethylene-bridged samples ENN1, ENN2, and ENN4, and phenylene-bridged samples BNN1, BNN2, and BNN4. The figure matters because it connects the choice of structuring silane with the resulting organosilica framework and surface functionality.

The TEOS-based samples were TNN2, TNN3, and TNN6; ethylene-bridged BTESE produced ENN1, ENN2, and ENN4; and phenylene-bridged BTESB produced BNN1, BNN2, and BNN4. TMPEDA supplied the propyl ethylenediamine functional groups on the organosilica surfaces.

Bissilanes were used to create mesoporosity without template synthesis, and the method used an alkaline catalyst rather than an acidic one. The structuring-to-functional-silane molar ratio was varied to adjust the porosity of the final materials.

With a five-micrometer scale bar in each panel, the images reveal predominantly particulate or textured surfaces, while TNN2 appears more monolithic than the loosely packed spherical particles described for TNN3 and TNN6. This comparison matters because it shows how the structuring component influences morphology, a feature relevant to the materials’ porous structures.

The synthesized materials were predominantly spherical particles ranging from fifty to one hundred nanometers, with morphology depending on the structuring component's nature and content. TNN3 and TNN6 contained loosely packed spheres of about one hundred nanometers, while TNN2 was monolithic and formed by spheres smaller than fifty nanometers.

Ethylene bridges produced relatively loose packing and particles from fifty to eighty nanometers in ENN1 and ENN2, while more alkyl bridges grew ENN4 particles to one hundred nanometers. Phenylene bridges increased polydispersity and heterogeneity, and BNN2 and BNN4 formed agglomerates from three hundred to seven hundred nanometers and even larger.

Nitrogen adsorption and desorption were measured to characterize the porous structures and assess how the structuring silanes affected sample porosity. The measurements were used to calculate specific surface area, pore volume, and pore-size distribution, and to estimate contributions from different pore types.

The porosity depended on both reaction-component ratios and functional-group size, producing non-porous particles at the lowest structuring-to-functional-silane ratios for TEOS and BTESE. Larger proportions of TEOS and BTESE produced high specific surface areas.

Across the three series, increasing the structuring agent proportion produced a more developed porous structure. Figure three pairs low-temperature nitrogen adsorption–desorption isotherms with pore-size distributions calculated using the SCV/SCR method.

This matters because the authors use these distributions to connect adsorption behavior with the different pore types and particle packing that contribute to total porosity. For ethylene-bridged samples, increasing the bridging-silane contribution improved specific surface area and pore volume.

ENN2 mainly had slit-like mesopores, while ENN4 had gaps and more nanopores, and its isotherm could be attributed to the Langmuir type. Phenylene-bridged samples were usually porous regardless of component ratios, except that BNN2 was non-porous under these conditions. BNN1 had a type-four isotherm with the characteristic hysteresis loop of mesoporous materials and mainly cylindrical mesopores.

For BNN4, the specific surface area increased because of slit-shaped and cylindrical nanopores and mesopores. Nitrogen content, and therefore amino-group content, increased as the proportion of TMPEDA increased and the proportions of structuring agents decreased.

The functional-group amounts were 1.95 to 3.40 millimoles per gram for polysiloxanes, 1.50 to 3.40 for BTESE-based polysilsesquioxanes, and 1.14 to 2.91 for BTESB-based polysilsesquioxanes. Figure 5 plots zeta potential against pH for polysiloxane TNN samples in panel a, and polysilsesquioxane ENN and BNN samples in panels b and c.

The curves show how the particles’ electrokinetic surface charge changes across acidic to alkaline conditions, reflecting protonation of ethylenediamine groups and contributions from silanol groups. The authors use these pH-dependent profiles, particularly the shifts in the point of zero charge, to interpret differences in metal-ion adsorption, including the reported connection between lower pI values and reduced nickel two-plus uptake.

The zeta potentials were between minus twenty and plus twenty millivolts across the studied pH range, indicating unstable suspensions and a tendency for particles to agglomerate. All sample groups shifted their isoelectric point toward a more acidic environment as nitrogen-containing groups decreased.

The shift may also be connected with more silanol groups on the surface from bissilane polycondensation. Polysiloxane TNN samples had positive zeta potentials from pH two to nine and alkaline isoelectric points from pH nine point three two to ten point seven seven, while ethylene-bridged samples had acidic points from pH three point zero three to four point five nine.

The synthesized ethylenediamine groups were expected to interact with copper, nickel, and europium ions. The adsorption isotherms and their linearized graphs are shown in Figure 6, with parameters provided in Table 3. Figure six shows adsorption isotherms for copper(II), nickel(II), and europium(III) ions on ethylenediamine-bearing polysiloxane TNN, and polysilsesquioxanes ENN and BNN.

Each material is paired with linearized plots using the Langmuir coordinates, Ceq divided by Aeq versus Ceq, and the Freundlich coordinates, ln Aeq versus ln Ceq. The authors use these plots, alongside the parameters in Table three, to evaluate how the organosilicas interact with the three metal ions.

Table 3 reports Langmuir and Freundlich isotherm parameters for copper(II), nickel(II), and europium(III) sorption on organosilica adsorbents, including maximum capacity, adsorption constants, empirical n values, and correlation coefficients. The authors use these fitted parameters to characterize monolayer capacity and adsorption intensity, while the bold correlation coefficients identify the selected model for each case.

The table also records functional-group content, sorption capacity, and formal group-to-metal ratios, connecting sorption behavior with adsorbent structure. For copper(II), the highest-affinity samples ranked as ENN1 at one point eight millimoles per gram, TNN6 at one point seven nine, ENN2 at one point six two, and BNN2 at one point three eight.

For nickel(II), the highest-affinity samples ranked as BNN1 at zero point eight three millimoles per gram, TNN2 at zero point seven two, TNN3 at zero point six nine, and ENN1 at zero point four six. For europium(III), the highest-affinity samples ranked as TNN3 at zero point five five millimoles per gram, BNN1 at zero point five two, ENN1 at zero point four zero, and ENN2 at zero point three seven.

There was no direct dependence between functional-group number and static sorption-capacity values. The cation-adsorption differences therefore need to be analyzed through structure features and group availability for each organosilica type.

The study compared adsorption across samples made from the same synthesis components while considering ionic radii of seventy-three picometers for copper two plus, sixty-nine for nickel two plus, and one hundred six point six for europium three plus. Figure seven illustrates proposed copper two plus binding at aminopropyl and ethylenediamine groups on TEOS-based polysiloxane surfaces.

The diagrams show copper coordinated by nearby nitrogen donors, while the reported surface site capacities are SSC equals one point two millimoles per gram and SSC equals one point seven nine millimoles per gram. The visual matters because it connects the longer amino-organic fragment with the coordination environment used to interpret copper uptake, while also showing how surface structure and group accessibility may shape adsorption.

An important contrast appeared in the phenylene-bridged series: BNN2 had the highest copper(II) sorption but the lowest nickel(II) and europium(III) sorption. This pattern supports a role for silanol and phenylene residues in interactions with the target cations.

Across the obtained samples, both polysiloxanes and polysilsesquioxanes were best for taking up copper(II) ions from water, while nickel(II) and europium(III) sorption was worse. A longer amino-organic fragment increased adsorption only for copper on TEOS-based polysiloxane particles.

Copper complexes with ethylenediamine and polysiloxanes can involve two nitrogen atoms from the same group, which may explain higher static sorption capacities for those samples. Co-condensation of TMPEDA with TEOS, BTESE, or BTESB in an ammonium-hydroxide basic environment formed spherical organosilica particles from fifty to one hundred nanometers with surface propyl ethylenediamine groups.

Functional-group content increased with a growing TMPEDA proportion, reaching ranges of 1.95 to 3.40 millimoles per gram for TEOS, 1.5 to 3.4 for ethylene-bridged polysilsesquioxanes, and 1.14 to 2.91 for phenylene-bridged polysilsesquioxanes. The main condition for mesoporous samples was increasing the proportion of TEOS or bissilanes in the reaction medium.

Both material families removed copper(II) better than nickel(II) or europium(III) from aqueous solutions. The highest copper(II) static sorption capacity was one point eight millimoles per gram for an ethylenediamine-bearing polysilsesquioxane made at a BTESE-to-TMPEDA ratio of one to one, with a functional-group content of three point four millimoles per gram.

Ethylene bridges prevented nitrogen-containing groups from back-sticking, making them available for interactions with target cations. The key lesson is that organosilica performance depends on matching pore structure and accessible ethylenediamine groups to the target ion.

More functional groups alone did not guarantee higher uptake, while copper removal was generally strongest.