The study, published in Clean Technologies and Environmental Policy, is notable not just for the headline number but for the depth of the physical analysis behind it. Before any adsorption tests began, the researchers subjected their quarry dust to a battery of characterization techniques: Fourier transform infrared spectroscopy to identify surface functional groups, scanning electron microscopy to map the particle morphology, energy-dispersive X-ray spectroscopy to confirm elemental composition, zeta potential measurements to track surface charge, Brunauer–Emmett–Teller analysis to quantify surface area, thermogravimetric analysis to probe thermal behavior, and X-ray diffraction to confirm the mineralogical structure. Together, these measurements painted a picture of a silicate-rich, mineralogically stable powder whose surface chemistry is well suited to capturing cationic dye molecules through electrostatic attraction, ion exchange, and hydrogen bonding interactions.

The experimental design was deliberately systematic. The team ran single-parameter batch experiments, varying one factor at a time across wide ranges: solution pH from 1 to 10, adsorbent dose from 0.5 to 5 grams per liter, initial dye concentration from 10 to 80 milligrams per liter, contact time from 20 to 160 minutes, and temperature from 20 to 55 degrees Celsius. Out of this parametric sweep emerged a clear optimum: a mildly acidic pH of 4, an adsorbent dose of 3 grams per liter, an initial dye concentration of 50 milligrams per liter, a contact time of just 80 minutes, and a temperature of 20 degrees Celsius. Under these conditions, the dust removed 96.45 percent of the dye. The fact that the optimum sits at room temperature and a moderately acidic pH is practically significant, because real textile effluents are often acidic and heating water is expensive.

Why does pH matter so much for a cationic dye like malachite green? Zeta potential measurements reveal the point at which the adsorbent surface carries no net charge. Below that point, in acidic conditions, the basalt surface develops the right electrostatic character to attract the positively charged dye ions, while at very low pH, abundant hydrogen ions compete with dye molecules for the same surface sites. The optimum at pH 4 represents the sweet spot where the surface is attractively charged but not yet saturated with competing protons. Similarly, the increase in removal with adsorbent dose reflects the growing number of available binding sites, while the decline in efficiency at higher dye concentrations shows the finite capacity of those sites, which is exactly what isotherm analysis is designed to quantify.

Single-parameter experiments, however, cannot capture how factors interact in the real world, where pH, dose, concentration, and time all fluctuate simultaneously. To handle that complexity, the researchers turned to two complementary modeling frameworks. The first was response surface methodology, a statistical technique that fits a polynomial surface across the factor space and identifies optimal operating regions while revealing interaction effects. The second was an artificial neural network, a machine learning model that learns nonlinear relationships between the operating variables and the removal efficiency from the data itself. Both models performed impressively. The RSM model achieved a coefficient of determination of 0.9993, while the ANN reached 0.9750, indicating that nearly all of the variance in dye removal could be predicted from the input conditions. This dual-modeling approach is increasingly popular in adsorption research because it bridges classical experimental design with modern data-driven prediction, and here it demonstrates that a humble mineral waste can be operated as a predictable, engineerable treatment process rather than a black box.

Kinetic modeling addressed a different question: how fast does the dye bind, and what governs the rate? The pseudo-first-order model described the adsorption kinetics adequately, suggesting that the uptake rate is primarily controlled by the dye concentration in solution as binding sites fill over time. Equilibrium data, by contrast, were best represented by the Sips isotherm, a hybrid model that combines features of the Langmuir and Freundlich equations and can capture heterogeneous surface binding. The Sips fit yielded a maximum adsorption capacity of 20.41 milligrams of dye per gram of dust. While activated carbons can exceed that capacity, they carry a far higher production cost and environmental footprint; quarry dust, by contrast, is essentially free at the point of generation and requires no activation chemistry, making the per-removal economics compelling for low-resource treatment scenarios.

Thermodynamics added a further layer of insight. The analysis indicated that the adsorption of malachite green onto the basalt dust is spontaneous and exothermic, meaning the process releases heat and becomes less favorable as temperature rises. This is consistent with a physical adsorption mechanism dominated by electrostatic attraction and surface complexation rather than energy-intensive chemical reaction, and it reinforces the practical preference for ambient-temperature operation. For treatment plants, an exothermic, spontaneous process at room temperature translates directly into lower operating costs and simpler equipment, since no thermal management is required to keep the process efficient.

Perhaps the most important finding for real-world deployment is reusability. An adsorbent that works once but cannot be regenerated simply converts a water problem into a solid waste problem. The team ran five consecutive adsorption–desorption cycles, using 0.5 molar hydrochloric acid to strip the bound dye from the dust between cycles. After five cycles, the material still removed 86.58 percent of the dye, a remarkably modest decline that suggests the basalt structure tolerates acid regeneration without collapsing or losing its active surface chemistry. This durability, combined with the dust’s abundance and zero material cost, is what elevates the work from a laboratory curiosity to a credible candidate for circular-economy water treatment, where an industrial byproduct solves an industrial pollution problem.

The broader significance of the study lies in the convergence of waste valorization, environmental remediation, and computational modeling. Quarry operators gain a potential outlet for a stockpiled byproduct; water utilities gain a cheap, robust, regenerable adsorbent; and the modeling framework provides a transferable template for optimizing any adsorption process with minimal experimental iterations. Malachite green itself is a serious target: it is toxic to aquatic organisms, suspected of health effects in humans, and widely detected in effluents and even farmed fish, so cheap removal technologies carry real public health value. The work also opens obvious follow-up questions that the research community will now pursue, including performance in real textile effluents with competing ions and organics, continuous-flow column testing, and scaling the regeneration process. If those steps succeed, the fine grey dust accumulating beside basalt crushers may soon find its highest and best use: quietly cleaning the water that industry has fouled.

Subject of Research: Use of waste basalt quarry dust as a low-cost adsorbent for removing malachite green dye from aqueous solutions through experimental and RSM–ANN modeling studies.

Article Title: Waste basalt quarry dust for efficient adsorptive removal of malachite green dye: experimental and modeling studies

Article References: Chaware, C. Y., Nayak, A. K., & Khobragade, M. U. (2026). Waste basalt quarry dust for efficient adsorptive removal of malachite green dye: experimental and modeling studies. Clean Technologies and Environmental Policy, 28(10), Article 259. https://doi.org/10.1007/s10098-026-03603-8

Image Credits: AI Generated

DOI: 10.1007/s10098-026-03603-8

Keywords: basalt quarry dust, malachite green, adsorption, dye removal, wastewater treatment, response surface methodology, artificial neural network, Sips isotherm, adsorption kinetics, thermodynamics, waste valorization, water remediation