Zeyu Tao

Despite the proliferation of CO2 adsorbents, a critical disconnect between material chemistry and process-level indicators metrics hinders their industrial adoption. Furthermore, the profoundly inadequate understanding of CO2/H2O co-adsorption under real-world conditions hinders deployment readiness. To bridge these gaps, this study proposes a framework to systematically evaluate existing adsorbents across three scenarios: post-combustion capture (PCC), direct air capture (DAC), and natural gas purification (NGP). By integrating reported adsorption and desorption data, the framework predicts critical process indicators, the specific CO2 emission (SCE). We further evaluate two distinct strategies: Dehyd-Cap (pre-dehydration followed by dry-gas CO2 capture) and Humid-Cap (humid-gas capture with downstream dehydration) to provide a direct performance comparison for the PCC and DAC scenarios. Our analysis demonstrates that since industrial condensation reduces the flue gas moisture in the PCC scenario, pre-dehydration only accounts for 3–8% of the total carbon footprint across the adsorbents studied. This suggests that adsorbent screening and development should prioritize dry-gas efficiency. For the DAC scenario, however, the carbon footprint of dehydration is 16 to 120 times greater than that of the dry CO2 removal process itself. Consequently, moisture-robustness must be treated as a non-negotiable criterion for DAC development. For the NGP scenario, results show that CH4 recovery is strictly dictated by vacuum requirements, with the SCE escalating sharply once CO2 working capacity drops below 50%. In addition, sustaining a high working capacity ratio (WCR) for CO2 to CH4 across varying feeding pressures is essential to prevent disproportionate methane co-adsorption from undermining separation efficiency. Overall, this research bridges the lab-to-industry gap, ensuring future adsorbent development is optimized for practical, large-scale CO2 capture.

A stepwise-cluster inference (SI) model was established through introducing stepwise-cluster analysis (SCA) into the phenanthrene immobilization process at the aqueous/modified palygorskite interface. SCA has the advantages of tackling the nonlinear relationships among environmental factors and the phenanthrene sorption amount in the immobilization process. The essence of SCA is to form a tree-based classification on a series of cutting or mergence procedures under given statistical criteria. The results indicated that SI could help develop a statistical relationship between environmental variables and the phenanthrene sorption amount, where discrete and nonlinear complexities exist. During the experiment, data were randomly sampled 10 times for model calibration and verification. The R² (close to one) and root mean squared error (RMSE) (close to zero) values guaranteed the prediction accuracy of the model. Compared to other statistical methods, the calculation of R² and RMSEs showed that SI was more straightforward for describing the nonlinear relationships and precisely fitting and predicting the immobilization of phenanthrene. Through the calculation of the input effects on the output in the SI model, the influence of environmental factors on phenanthrene immobilization were ranged in descending order as: initial phenanthrene concentration, ionic strength, pH, added humic acid dose, and temperature. It is revealed that SCA can be used to map the nonlinear and discrete relationships and elucidate the transport patterns of phenanthrene at the aqueous/modified palygorskite interface.

In nature, microorganisms mainly live in biofilms rather than planktonically to defend against various environmental stimuli. Understanding and predicting the dynamics and mechanisms of biofilms is of grand importance to human life. Besides experimental approaches, modeling provides a powerful tool to describe biofilms mathematically, and the in silico simulation offers a deep insight into the underlying functional machineries of biofilms. In this review, we briefly summarized the main advances in biofilm modeling, including the 1D model, multidimensional model, as well as the incorporation of extracellular polymeric substance and quorum sensing signals. Specifically, we focus on recent experimental advances in biofilms, which may challenge the current modeling systems via newly discovered properties and novel participants in natural water systems. We hope this article could build a bridge between in silico modeling and new experimental discoveries, and, in particular, we wish to attract more attention to biofilm-related-environmental issues at various scales.

The present study investigated the effect of the food-to-microorganism (F/M) ratio on nitrous oxide (N₂O) emissions in aerobic granular sludge sequencing batch airlift reactors. Three identical sequencing batch airlift reactors were fed with sodium acetate-based wastewater at different chemical oxygen demand (COD) concentrations, resulting in F/M ratios from 0.2 to 0.67 g COD/g SS. The results indicated that N₂O emissions increased with an increase of the F/M ratio. N₂O emissions at the high F/M ratio of 0.67 g COD/g SS were the highest (4.4 ± 0.94 mg/d). The main source of the high N₂O emissions at the F/M ratio of 0.67 g COD/g SS was nitrifier denitrification, rather than heterotrophic denitrification, confirmed by the qPCR (quantitative real-time PCR) results. The heterotrophic denitrification was destroyed by the DO (dissolved oxygen) diffusing into the sludge particles with porous structures. This study offers theoretical support to study the N₂O emissions in aerobic granular sludge, and can provide guidance for conducting risk assessment and enhancing our ability to predict N₂O production in aerobic granular sludge at different F/M ratios.