The purpose of this study was to investigate the removal mechanism of phosphate by rape straw biochar (RSBC) supported nanoscale zero-valent iron (nZVI). BET, TEM, FTIR and XPS characterizations of the composite material (nZVI-RSBC) indicated that nZVI was successfully supported on the RSBC, and nZVI-RSBC had a high specific surface area and abundant oxygen-containing functional groups. Batch experiments showed that the adsorption data could be fitted well with the Sips isotherm model and pseudo-second-order kinetic model, suggesting that phosphate adsorption onto RSBC and nZVI-RSBC was due to surface and chemical processes. The maximum adsorption capacities of RSBC and nZVI-RSBC for phosphate obtained by the Sips isotherm model fitting were 3.49 mg g⁻¹ and 12.14 mg g⁻¹, respectively. The pH value of the solution greatly affected the adsorption capacity of nZVI-RSBC for phosphate. The combined results of batch experiments and characterizations revealed that the possible mechanism was the complexation of oxygen-containing functional groups on the surface of nZVI-RSBC with phosphate, hydrogen bonding, and electrostatic attraction between phosphate and the positively charged adsorption sites under acidic conditions. Such a strong adsorption capacity, as well as the characteristics of easy availability, excellent recyclability and low cost, make nZVI-RSBC potentially suitable for the treatment of phosphate-rich water.

The contamination of aquatic environments with hexavalent chromium (Cr(VI)) poses significant environmental and public health risks, necessitating the development of high-performance adsorbents for its efficient removal. This study evaluates the potential of green-synthesized nanoscale zero-valent iron-modified sludge biochar (TP-nZVI/BC) as an effective adsorbent for Cr(VI) removal through isothermal adsorption experiments, fixed-bed column studies, and artificial neural network (ANN) modeling. Fixed-bed experiments demonstrated that breakthrough time, exhaustion time, and unit adsorption capacity increased with bed height. Conversely, these parameters decreased with higher influent concentrations and flow rates. Breakthrough curve analysis revealed that the Thomas model provided the best fit for the experimental data (R² = 0.992–0.998). An ANN model, developed using the Levenberg–Marquardt algorithm, employed a single hidden layer with six neurons and exhibited excellent predictive performance (R² = 0.996, MSE = 0.520). The ANN model was validated for its ability to predict adsorption behavior under untested conditions, demonstrating its applicability for process optimization. This study highlights the superior performance of TP-nZVI/BC as an adsorbent for Cr(VI) and establishes a theoretical basis for optimizing and scaling up fixed-bed adsorption systems using ANN modeling. The findings provide valuable insights into the practical application of sustainable materials in environmental remediation.

This work provides systematic insights into CIP adsorption onto pristine and aged MPs, offering a foundation for understanding MPs–antibiotic interactions and assessing environmental risks.