Simulation supports graphene-oxide SPR sensor for metal-ion water conditions

Computer simulations indicate that a multilayer surface plasmon resonance sensor combining borosilicate glass, aluminum, aluminum oxide and graphene oxide could register small refractive-index changes associated with mercury, lead and zinc in water. Researchers from institutions in Ecuador and Italy optimized the proposed architecture for individual Hg(II), Pb(II) and Zn(II) conditions and for two binary mixtures containing mercury. The modeled design produced distinct shifts in its predicted surface plasmon resonance angle, but the sensor has not been fabricated or tested with real environmental water samples.

The peer-reviewed computational study, published in Electron on July 13, 2026, examined how a thin-film optical structure might translate changes near a sensing surface into changes in the angle at which light resonates. Metal ions can enter aquatic environments through industrial and other human activities, while detecting these pollutants at low concentrations often requires specialized analytical equipment and trained personnel. The simulations therefore investigated a possible optical sensing approach that could eventually support faster, simpler and more accessible monitoring. The present work is a numerical design and optimization stage, not an experimental demonstration of a working chemical sensor.

At the center of the proposed device is surface plasmon resonance, or SPR. In the modeled system, variations in the aqueous environment near the sensor surface correspond to changes in refractive index, an optical property describing how light propagates through a material. The researchers represented the metal-ion conditions through such refractive-index differences and calculated the resulting shifts in the predicted SPR angle. Their simulations showed that subtle optical changes in the modeled environment generated measurable changes in resonance position, providing a numerical basis for a possible water-sensing architecture.

The final sensor design contains four principal layers. A borosilicate glass prism serves as the optical coupling component, followed by a 60-nanometer aluminum film, a 32-nanometer aluminum oxide layer and an outer graphene oxide sensing layer. The prism was selected after computational comparison with other candidate materials and provided a favorable balance between optical response and resonance sharpness under the investigated conditions. Aluminum supplies the plasmonic metallic layer and was examined as a potentially abundant and comparatively inexpensive alternative to gold, which is commonly associated with SPR sensing but was not selected for the final architecture.

Aluminum also oxidizes readily, so the optimized design incorporates aluminum oxide, Al₂O₃. In the model, that 32-nanometer layer serves both as a protective or passivating component and as an integral part of the optical architecture. Graphene oxide, a carbon-based nanomaterial containing oxygen-bearing chemical groups, completes the multilayer structure as the sensing surface. Together, the four components were modeled to provide a strong and sufficiently narrow resonance whose displacement could in principle reveal small changes in the surrounding water.

The researchers compared four carbon nanomaterials as candidate sensing layers: graphene oxide, reduced graphene oxide, graphene and semiconducting single-walled carbon nanotubes. Some of the alternatives generated larger shifts in the resonance angle than graphene oxide, but a larger displacement did not automatically make a material more useful. A useful optical signal must also remain narrow and well defined so that it can be measured precisely. Single-walled carbon nanotubes produced a particularly large angular response but also extreme resonance broadening, while reduced graphene oxide and graphene presented other compromises between signal displacement and resonance width.

Among the four nanomaterials evaluated, graphene oxide provided the most balanced modeled performance, combining a measurable resonance shift with a comparatively narrow resonance. This comparison highlighted the optimization trade-off in the study: the material with the strongest response was not necessarily the material with the clearest signal. The choice of graphene oxide therefore emerged from the joint assessment of angular sensitivity and resonance linewidth rather than from a single performance measure.

After optimizing the multilayer structure, the team modeled aqueous environments corresponding to Hg(II), Pb(II) and Zn(II), as well as two binary mixtures containing mercury. Among the individual metal-ion conditions, Zn(II) produced the highest modeled angular sensitivity, while Hg(II) produced the lowest. Both binary mixtures retained sensitivities above 333° per refractive-index unit, showing that the simulated optical response remained strong under the mixed conditions included in the analysis. Modeling mixtures extends the computational investigation beyond an idealized single-solute environment, although the results do not demonstrate that the sensor can identify particular metal ions in real samples.

The modeled distinction among the investigated conditions came entirely from differences in refractive index. The study did not establish that unmodified graphene oxide is intrinsically selective for mercury, lead or zinc. Real water may also contain sodium, calcium and magnesium ions, while pH, temperature, ionic strength and dissolved organic substances can alter the optical environment. Competitive interactions among different ions and the kinetics of adsorption at the sensing surface were outside the scope of the numerical model. Future versions may require selective surface functionalization, involving chemical recognition elements designed to interact preferentially with target ions, before chemical selectivity could be demonstrated.

The simulations produced optical limits of detection on the order of 10⁻⁵ refractive-index units. This figure is not a mercury, lead or zinc concentration and cannot be interpreted directly in parts per million or parts per billion. It represents the smallest refractive-index variation that the optical system could theoretically resolve under the assumptions used in the calculations. Relating that optical threshold to actual contaminant concentrations will require calibration with experimentally prepared samples and measurement of how metal-specific refractive-index changes vary with known concentrations. The sensor’s current status is therefore strictly computational.

Turning the numerical blueprint into a physical instrument would introduce additional variables. Deposited films can contain surface roughness and thickness variations, coatings may be incomplete or nonuniform, and aluminum may continue to oxidize over time. Such imperfections could alter the resonance observed in laboratory measurements relative to the idealized model. Environmental matrices pose a separate challenge because they can contain many substances absent from the simulations. The next stages identified by the researchers include fabrication-tolerance analysis, production of the multilayer structure, selective surface functionalization, long-term stability evaluation and testing with real water samples.

For experimental development, the authors propose conventional thin-film deposition for aluminum, controlled formation of the aluminum oxide layer and solution-based deposition of graphene oxide. Compact optical or microfluidic components could eventually be investigated for portable sensing formats, but neither approach was tested in the present study. The central result is a computationally optimized four-layer SPR design whose predicted resonance shifts remained sensitive under three individual metal-ion conditions and two selected mercury-containing mixtures, while graphene oxide offered a better balance of resonance displacement and linewidth than the other tested carbon nanomaterials. Whether this sensitivity can be made selective, stable and useful in environmental water remains an experimental question, and the work is best characterized as a promising sensor blueprint rather than a deployable method for monitoring metal contamination.

Subject of Research: Computational optimization of a graphene-oxide surface plasmon resonance sensor for metal-ion-associated refractive-index changes in water

Article Title: Light and nanomaterials could offer a new route for detecting metal contamination in water

Article References: Light and nanomaterials could offer a new route for detecting metal contamination in water. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: water sensors, metal contamination, graphene oxide, surface plasmon resonance, mercury, lead, zinc, nanomaterials, optical sensing, computational modeling, Light, offer