Feedback of chain elongation microorganisms on iron-based conductive materials: Enhanced microbial functions and biotoxicity adaptation mechanisms.

Despite the increased research efforts aimed at understanding iron-based conductive materials (CMs) for facilitating chain elongation (CE) to produce medium chain fatty acids (MCFAs), the impact of these materials on microbial community functions and the adaptation mechanisms to their biotoxicity remain unclear. This study found that the supply of zero-valent iron (ZVI) and magnetite enhanced the MCFAs carbon-flow distribution by 26 % and 52 %, respectively. Metagenomic analysis revealed the upregulation of fatty acid metabolism, pyruvate metabolism and ABC transporters with ZVI and magnetite. The predominant functional microorganisms were Massilibacterium and Tidjanibacter with ZVI, and were Petrimonas and Candidatus_Microthrix with magnetite. Furthermore, it was demonstrated that CE microorganisms respond and adapt to the biotoxicity of iron-based CMs by adjusting Two-component system and Quorum sensing for the first time. In summary, this study provided a new deep-insight on the feedback mechanisms of CE microorganisms on iron-based CMs.

Upgrading soybean dreg to caproate via intermediate of lactate and mediator of biochar.

To address the environmental hazards posed by high-yield soybean dreg (SD), a high-value strategy is firstly proposed by synthesizing caproate through chain elongation (CE). Optimized conditions for lactate-rich broth as intermediate, utilizing 50 % inoculum ratio, 40 g/L substrate concentration, and pH 5, resulting in 2.05 g/L caproate from direct fermentation. Leveraging lactate-rich broth supplemented with ethanol, caproate was optimized to 2.76 g/L under a refined electron donor to acceptor of 2:1. Furthermore, incorporating 20 g/L biochar elevated caproate production to 3.05 g/L and significantly shortened the lag phase. Mechanistic insights revealed that biochar's surface-existed quinone and hydroquinone groups exhibit potent redox characteristics, thereby facilitating electron transfer. Moreover, biochar up-regulated the abundance of key genes involved in CE process (especially fatty acids biosynthesis pathway), also enriching Lysinibacillus and Pseudomonas as an unrecognized cooperation to CE. This study paves a way for sustainable development of SD by upgrading to caproate.

Attenuation effects of ZVI/PDS pretreatment on propagation of antibiotic resistance genes in bioreactors: Driven by antibiotic residues and sulfate assimilation.

Sulfate radical-based advanced oxidation processes (AOPs) combined biological system was a promising technology for treating antibiotic wastewater. However, how pretreatment influence antibiotic resistance genes (ARGs) propagation remains largely elusive, especially the produced by-products (antibiotic residues and sulfate) are often ignored. Herein, we investigated the effects of zero valent iron/persulfate pretreatment on ARGs in bioreactors treating sulfadiazine wastewater. Results showed absolute and relative abundance of ARGs reduced by 59.8%- 81.9% and 9.1%- 52.9% after pretreatments. The effect of 90-min pretreatment was better than that of the 30-min. The ARGs reduction was due to decreased antibiotic residues and stimulated sulfate assimilation. Reduced antibiotic residues was a major factor in ARGs attenuation, which could suppress oxidative stress, inhibit mobile genetic elements emergence and resistant strains proliferation. The presence of sulfate in influent supplemented microbial sulfur sources and facilitated the in-situ synthesis of antioxidant cysteine through sulfate assimilation, which drove ARGs attenuation by alleviating oxidative stress. This is the first detailed analysis about the regulatory mechanism of how sulfate radical-based AOPs mediate in ARGs attenuation, which is expected to provide theoretical basis for solving concerns about by-products and developing practical methods to hinder ARGs propagation.

Machine learning approaches to predict the apparent rate constants for aqueous organic compounds by ferrate.

The apparent second-order rate constant with hexavalent ferrate (Fe(VI)) (kFe(VI)) is a key indicator to evaluate the removal efficiency of a molecule by Fe(VI) oxidation. kFe(VI) is often determined by experiment, but such measurements can hardly catch up with the rapid growth of organic compounds (OCs). To address this issue, in this study, a total of 437 experimental second-order kFe(VI) rate constants at a range of conditions (pH and temperature) were used to train four machine learning (ML) algorithms (lasso regression (LR), ridge regression (RR), extreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM)). Using the Morgan fingerprint (MF)) of a range of organic compounds (OCs) as the input, the performance of the four algorithms was comprehensively compared with respect to the coefficient of determination (R2) and root-mean-square error (RMSE). It is shown that the RR, XGBoost, and LightGBM models displayed generally acceptable performance kFe(VI) (R2test > 0.7). In addition, the shapely additive explanation (SHAP) and feature importance methods were employed to interpret the XGBoost/LightGBM and RR models, respectively. The results showed that the XGBoost/LightGBM and RR models suggestd pH as the most important predictor and the tree-based models elucidate how electron-donating and electron-withdrawing groups influence the reactivity of the Fe(VI) species. In addition, the RR model share eight common features, including pH, with the two tree-based models. This work provides a fast and acceptable method for predicting kFe(VI) values and can help researchers better understand the degradation behavior of OCs by Fe(VI) oxidation from the perspective of molecular structure.