Advanced oxidation processes (AOPs) are effective technologies for addressing the growing threat of water pollution to the eco-environment and human health. In spite of continuous improvements in decontamination achieved through laboratory-scale research over decades, converting these scientific achievements into reliable, large-scale engineering solutions remains challenging. This gap primarily stems from the mismatch between research priorities and practical engineering requirements, specifically reflected in significant differences in catalyst application, reactor configuration, byproduct management, treatment objects, and operating conditions. Accordingly, we reveal the engineering understanding and research achievements in these areas and, meanwhile, propose insights to shift research priorities based on engineering bottlenecks to accelerate the implementation of AOPs. By evaluating emerging strategies such as precise oxidation, sustainable system design, machine-learning-assisted optimization, and resource recovery, this perspective provides future development directions for AOPs. Shifting from the pursuit of isolated academic indicators to a comprehensive and engineering-oriented scientific research paradigm is further proposed. Effective dialogue and collaboration between fundamental research and engineering practice are conducive to accelerating the deployment of AOPs in real-world water remediation.

In this study, a novel silica-supported sorbent functionalized with 1,4-bis[(4-methoxybenzyl) oxy]anthracene-9,10-dione was prepared and applied for the first time for the selective adsorption and preconcentration of Pb(II) and Cd(II) ions from brackish aquaculture water. The sorbent was characterized by FT-IR, ¹H NMR, and SEM-EDX, while kinetic modeling revealed that the adsorption process follows a pseudo-second-order kinetic model (R² > 0.999), indicating chemisorption-like kinetics for the rate-controlling step. Experimental parameters (pH, sorbent dosage, contact time, and temperature) were optimized using the one-factor-at-a-time (OFAT) approach and statistically validated via one-way ANOVA. Under optimal conditions (pH 6.0, 4 mg, 15 min), the method achieved high recoveries (96–99%), low detection limits (Pb: 1.5 µg/L; Cd: 1.6 µg/L), and excellent precision (RSD <2%). Matrix effect studies demonstrated the robustness and reliability of the method, showing <10% signal suppression and maintaining strong selectivity even in the presence of common coexisting ions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Fe³⁺, Zn²⁺). The sorbent preserved more than 90% of its adsorption capacity after five reuse cycles, indicating excellent stability and reusability. DFT analysis supported experimental findings, revealing a reduced HOMO–LUMO energy gap and stronger Pb(II)–ligand interactions, confirming the sorbent’s superior affinity toward Pb(II). Green analytical assessment (Eco-Scale: 82, AGREE: 0.87, GAPI: predominantly green profile) verified the method’s environmental compatibility, highlighting its minimal solvent use, low energy demand, and short analysis time. Overall, this study presents a selective, robust, reusable, and environmentally sustainable analytical method, introducing a new silica-based sorbent that effectively integrates experimental performance and theoretical validation for trace-level determination of Pb(II) and Cd(II).

The incorporation of recycled concrete aggregates (RCAs) into self-compacting concrete (SCC) represents a critical sustainable construction strategy addressing both construction waste management and natural resource conservation. However, predicting the compressive strength of recycled aggregate self-compacting concrete (RASCC) remains challenging due to complex nonlinear interactions among mixture parameters. This study develops a robust predictive framework using ensemble machine learning algorithms to accurately estimate RASCC compressive strength across diverse mixture compositions. A comprehensive database comprising 301 experimental specimens with 18 input variables—including curing age, binder components, water-to-binder ratio, recycled aggregate properties, and supplementary cementitious materials—was systematically analyzed. Four advanced modeling approaches were evaluated: Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Stacked Generalization with Ridge regression meta-learner, and Voting ensemble with Non-Negative Least Squares optimization. The Stacking ensemble model demonstrated superior predictive performance on the independent test set, with R2 = 0.963, RMSE = 3.321 MPa, and MAE = 2.506 MPa. Rigorous residual analysis confirmed model validity through satisfaction of normality, homoscedasticity, and independence assumptions. SHAP interpretability analysis identified specimen age as the dominant predictor, followed by recycled aggregate density and water-to-binder ratio, while elucidating the complex nonlinear contributions of supplementary cementitious materials including fly ash and ground granulated blast furnace slag. The developed framework demonstrates practical applicability for predicting RASCC compressive strength across conventional to high-performance grades, facilitating sustainable mix design optimization while maintaining structural performance requirements, and advancing circular economy principles through confident integration of recycled aggregates in SCC applications.

Building energy conservation is a hot topic of concern for the whole society. Numerous governments have introduced related policies. However, specific practice is rare. Therefore, a green hotel design study is developed whose core target is to improve buildings' cooling and heating energy efficiency. First, this article is based on the hot and humid tropical climate in Merida, Mexico. Second, the study is based on 3D modeling and energy simulation by Revit, comparing the influence on cooling and heating energy consumption of three kinds of building volumes, three types of glass and different orientations. The simulation conclusion indicates that the low and enclosed building, combined with double glass with high performance, low-E coating, high visible light transmittance and low solar heat gain coefficient, which is south-facing, performs at best comprehensively. However, this proposal is just a design conception. In the future, the real building needs to be monitored, including the court microclimate, indoor environment parameters and energy consumption in order to observe the actual effect of the energy conservation.

Brønsted acid sites in MCM-22 zeolite as molecular "Grippers" to NH3 and potassium for boosting NH3-SCR performance and resistance to alkali.

Sustainable wind farm layout design for maximizing power output and reducing environmental impact

A system dynamics model for agricultural water management in Shapour River basin using sustainable irrigation policy design under water scarcity and salinity

Sustainable environmental design using circular economy in the plastic manufacturing industry for decarbonization

Assessing the impact of agri-environmental schemes on input use in Hungary's wine sector: Implications for sustainability and policy design

Pyrene aided dual mode fluorophore sensor for trace hydrazine detection in real environmental samples: Design, mechanism and DFT insights.

A risk-averse multi-objective analytics framework for green supply chain design under uncertainty

Study on the dissolution and recovery mechanism of bacterial cellulose by green ionic liquid designed based on COSMO-RS.

The influence of green roof pot designs, openings, and spacing on the heat flow in the growth medium by CFD simulations

Heteroatom-free cyclic ether enables synergistic optimization of solvation and hydrogen-bonding in aqueous zinc batteries.

Sustainable design of broadband radar absorbers using metallurgical waste and multi-objective optimization

Data-driven compressive strength analysis of nano/micro cellulose-modified cement composites for sustainable design

Sustainable Design of a Supply Chain for the Revalorization of CO2 in Mexico Implementing a Game Theory Model

Decoupling surface area from function: isolating the dominant role of surface chemistry in superior chlortetracycline adsorption on a low-porous biochar.

This study presents an integrated framework for seismic performance assessment and multi-objective optimization of a G+12 reinforced concrete (RC) high-rise residential building using STAAD.pro and the NSGA-III algorithm. This research contributes a reproducible, automation-based framework for sustainable and code-compliant seismic design, facilitating performance-driven decisions for civil engineers, planners, and stakeholders. Future work may extend toward lifecycle cost modeling, nonlinear time-history analysis, and soil-structure interaction.

Concrete compressive strength prediction presents a fundamental challenge in sustainable construction material design due to the intricate nonlinear interactions among mixture components, admixtures, and curing conditions. This study introduces a hybrid framework that integrates the cognitively inspired iHow Optimization Algorithm (iHowOA) with Spatio-Temporal Graph Convolutional Networks (STGCN) to enhance predictive accuracy for concrete compressive strength estimation. A large language model (LLM)-driven preprocessing pipeline is employed to improve data quality through semantic validation, feature harmonization, and intelligent handling of inconsistencies, leading to robust input representations. The iHowOA optimizes the STGCN architecture by leveraging hierarchical knowledge acquisition, balanced exploration-exploitation, and adaptive decision-making mechanisms. The graph-based model captures spatial dependencies among compositional variables and temporal strength evolution during curing. Extensive benchmarking against ten established metaheuristic optimizers demonstrates that the proposed iHowOA-STGCN framework achieves superior predictive performance on the evaluated public dataset, yielding lower prediction errors and higher correlation coefficients compared to baseline models. Exploratory data analysis further highlights key cement-strength relationships, age-dependent strength gain patterns, and physicochemical interactions relevant to feature engineering. While the experimental results indicate that the proposed framework is a promising data-driven decision-support approach for concrete strength prediction, further validation on diverse datasets and real-world scenarios is required to assess its generalizability and practical applicability.