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Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, integrating key parameters such as pH, pHpzc, surface area, temperature, and PFAS molecular properties. RSM was employed to model adsorption behavior, while gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGB) were used to enhance predictive performance. Hybrid models—linear, RMSE-based, multiplicative, and meta-learning—were developed and evaluated. The meta-learning HOP-RSM-GB model achieved near-perfect accuracy (R² = 1.00, RMSE = 10.59), outperforming all other models. Surface plots revealed that low pH and high pHpzc maximized the adsorption while increasing log Kow consistently enhanced PFAS adsorption. These findings establish hybrid RSM-ML modeling as a powerful framework for optimizing PFAS remediation strategies. The integration of statistical and machine learning approaches significantly improves predictive accuracy, reduces experimental costs, and provides deeper insights into adsorption mechanisms. This study underscores the importance of data-driven approaches in environmental engineering and highlights future opportunities for integrating ML-driven modeling with experimental adsorption research. 

Abstract The next radical change in the thermal management of data centers is to shift from conventional cooling methods like air-cooling to direct liquid cooling to enable high thermal mass and corresponding superior cooling. There has been in the past few years a limited adoption of direct liquid cooling in data centers because of its simplicity and high heat dissipation capacity. Single-phase engineered fluid immersion cooling has several other benefits like better server performance, even temperature profile, and higher rack densities and the ability to cool all components in a server without the need for electrical isolation. The reliability aspect of such cooling technology has not been well addressed in the open literature. This paper presents the performance of a fully single-phase dielectric fluid immersed server over wide temperature ranges in an environmental chamber. The server was placed in an environmental chamber and applied extreme temperatures ranging from −20 °C to 10 °C at 100% relative humidity and from 20 to 55 °C at constant 50% relative humidity for extended durations. This work is a first attempt of measuring the performance of a server and other components like pump including flow rate drop, starting trouble, and other potential issues under extreme climatic conditions for a completely liquid-submerged system. Pumping power consumption is directly proportional to the operating cost of a data center. The experiment was carried out until the core temperature reached the maximum junction temperature. This experiment helps to determine the threshold capacity and the robustness of the server for its applications in extreme climatic conditions.