Antagonistic effects of surfactants and CeO2 nanoparticles co-occurrence on the sludge fermentation process: Novel insights of interaction mechanisms and microbial networks

Luo, Jingyang; Cao, Wangbei; Guo, Wen; Fang, Shiyu; Huang, Wenxuan; Wang, Rui; Cheng, Xiaoshi; Du, Wei; Cao, Jiashun; Feng, Qian; Wu, Yang

doi:10.1016/j.jhazmat.2022.129556

Cited by 36 publications

(23 citation statements)

References 32 publications

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“…As depicted in Figure A–C, the WAS surface after surfactant addition was rougher with bounteous cavities compared to the control group. Previous studies have confirmed that EPS could efficiently protect microorganisms from the adverse environment . Hence, the disintegration of EPS in the surfactant-conditioned WAS would enable Fe 2 O 3 NPs to react with the previously encapsulated anaerobes and thus aggravate the biotoxicity of Fe 2 O 3 NPs in the WAS fermentation systems.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Previous studies have confirmed that EPS could efficiently protect microorganisms from the adverse environment. 44 Hence, the disintegration of EPS in the surfactant-conditioned WAS would enable Fe 2 O 3 NPs to react with the previously encapsulated anaerobes and thus aggravate the biotoxicity of Fe 2 O 3 NPs in the WAS fermentation systems. In fact, the variations in LDH release and the ATP level, which are typically used to assess cell membrane disruption and general microbial activity, 45 confirmed this hypothesis.…”

Section: Influences Of Surfactants' and Fe 2 O 3 Nps' Co-occurrence O...mentioning

confidence: 99%

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Surfactants Aggravated the Biotoxicity of Fe₂O₃ Nanoparticles in the Volatile Fatty Acids’ Biosynthesis during Sludge Anaerobic Fermentation

Fang

Guo²,

Feng

et al. 2022

ACS EST Water

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Exogenous contaminants often co-occur in waste-activated sludge (WAS), but their potential mutual interactions and joint effects on the WAS treatment are less concerning. This work revealed that surfactants induced and aggravated the toxicity of Fe 2 O 3 nanoparticles (NPs) in volatile fatty acids (VFAs)' biosynthesis during WAS anaerobic fermentation. The VFA production exhibited a 40.1 and 20.3% inhibition compared to the individual LAS and CTAB reactors, respectively. A mechanistic exploration implied that the WAS solubilization, hydrolysis, and acidification steps during VFA generation were restrained. Moreover, the Fe 2 O 3 NPs' and surfactants' coexistence suppressed the acid-forming bacteria (i.e., Saprospiraceae_uncultured, Bacteroidetes vadinHA17_norank) and the homologous metabolic functions responsible for substrate hydrolysis (e.g., malZ and pepD), intracellular metabolism (e.g., pf kA and gdhA), VFAs' biosynthesis (e.g., bkdA and ackA), and ATP generation (e.g., atpE and atpH), resulting ultimately in VFAs' reduction. Furthermore, the surfactants contributed to the dispersibility of coexisting Fe 2 O 3 NPs to maintain the high reactivity of NPs. Besides, the WAS disintegration triggered by surfactants increased the interactions between Fe 2 O 3 NPs and microorganisms, thereby aggravating toxicity. Our study provides novel insights into environmental behaviors of co-occurring contaminants in the anaerobic environment and provides in-depth guidance for practical WAS treatment.

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Section: Results and Discussionmentioning

confidence: 99%

Section: Influences Of Surfactants' and Fe 2 O 3 Nps' Co-occurrence O...mentioning

confidence: 99%

Surfactants Aggravated the Biotoxicity of Fe₂O₃ Nanoparticles in the Volatile Fatty Acids’ Biosynthesis during Sludge Anaerobic Fermentation

Fang

Guo²,

Feng

et al. 2022

ACS EST Water

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“…36 This assumption was consolidated by the LDH release analysis (Figure 5F), which is a direct indicator of membrane permeability. 17 The LDH release was improved by 52.1 and 164.3% in R1 and R3, respectively, compared with that in R0. Since the c-type cytochromes and e-pili were basically presented in the cell membrane and mainly consisted of proteins, 37 the damaging effects of PHMG on the cell membranes would damage these riboflavin, c-Cyts, and phenazine, resulting in the reduction of IET.…”

Section: Acs Esandt Engineeringmentioning

confidence: 89%

Unveiling Polyhexamethylene Guanidine Regulating Behaviors on Methane Generation in a Sludge Anaerobic Digester: Interactive Process, Microbial Metabolic Trait Interference, and Adaptive Mechanisms

Wang,

Luo,

Huang

et al. 2023

ACS EST Eng.

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Extracellular polymeric substances (EPSs) in waste activated sludge (WAS) are important guardians of active microorganisms against toxic emerging contaminants (ECs). However, how ECs interact with EPSs and microorganisms and further interfere with microbial functions during WAS anaerobic digestion has not been systemically disclosed. This work reported the dose-dependent effects of polyhexamethylene guanidine (PHMG, as a potential EC) on methane production during WAS digestion and comprehensively revealed the underlying mechanisms. PHMG at low dose showed an evident lag period for methane production (from 0.09 d in control to 8.23 d) but produced dramatic inhibition at high dose. Although PHMG promoted solubilization, hydrolysis, and acidification via enriching hydrolytic and acidogenic bacteria (i.e., Acidaminobacter and Acidibacter), methanogenesis was inhibited due to the reduction of methanogens (i.e., Methanosaeta and Syntrophomonas) and methanogenic activities related to acetoclastic and hydrogenotrophic methanogenesis (i.e., yhdR, pdhB, f wdB, and f rhD). Meanwhile, PHMG inhibited interspecies electron transfer between acidogens and methanogens via decreasing the biosynthesis of electron shuttles and e-pili (i.e., ribA, yigB, ribBA, and MtrD) and reducing mutualistic microorganisms (i.e., Desulfobulbus and Methanospirillum), which resulted in a decrease of methane formation. Further analysis found that PHMG disrupted EPSs (mainly proteins) and improved cell permeability. Then, it might enter the microbial cells to induce the formation of septal invaginations to disturb microbial replication as well as metabolic functions involved in methane biosynthesis. However, functional microorganisms could adapt to the low PHMG stress and significantly recover metabolic activities for methane production via upregulating functional genes related to microbial growth (e.g., FEN1 and f tsQ) and activating the quorum sensing system (LuxR/I system).

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“…Subsequently, this trained model was employed to label the previous data set (PD) with pseudolabels. The PD derived from previously published works that focused on the effect of diverse contaminants (i.e., surfactants and NPs (CeO 2 and Fe 2 O 3 ) on the anaerobic fermentation efficiency. , Combined Training: This work randomly divided the OD with labels into the training data set and testing data set. Then, this work augmented the training data set by incorporating the pseudolabeled PD.…”

Section: Methodsmentioning

confidence: 99%

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning

Cheng,

Xu,

et al. 2023

ACS EST Eng.

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Accurate prediction of methane production in anaerobic digestion with various pretreatment strategies is of the utmost importance for efficient sludge treatment and resource recovery. Traditional machine learning (ML) algorithms have shown limited prediction accuracy due to challenges in optimizing complex parameters and the scarcity of data. This work proposed a novel integrated system that employed an ensemble semisupervised learning (SSL)-automated ML (AutoML) model with limited variable inputs to reveal the effects of different pretreatments on methane production during sludge digestion with explainable analysis. Considering the direct correlations of the pretreatment type and digestion substrates, the pretreatment type is considered as a hidden variable. Results demonstrated that the AutoML model outperformed the conventional ML models (i.e., support vector regression (SVR), extreme gradient boosting (XGB), etc.), as evidenced by its higher R 2 value. Moreover, the integration of SSL further enhanced the prediction accuracy by effectively leveraging unlabeled data, leading to a reduction in the mean squared error from 11.3 to 9.7. Explainable analysis results revealed the significance of different variables and the utmost importance of operating time, followed by proteins, carbohydrates, chemical oxygen demand, and volatile fatty acids. Furthermore, principal component and correlation analysis unveiled the interconnected relationships among substrate concentration, microbial communities, and metabolic functions for methane production and found that the increasing substrate concentration promoted the enrichment of functional microbial and metabolic functions. These insights shed light on the advantages of SSL-AutoML in predicting methane production in anaerobic digestion systems and elucidate the dependence relationships with key variables, offering valuable guidance for effective sludge pretreatment with enhanced resource recovery capabilities.

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Antagonistic effects of surfactants and CeO2 nanoparticles co-occurrence on the sludge fermentation process: Novel insights of interaction mechanisms and microbial networks

Cited by 36 publications

References 32 publications

Surfactants Aggravated the Biotoxicity of Fe₂O₃ Nanoparticles in the Volatile Fatty Acids’ Biosynthesis during Sludge Anaerobic Fermentation

Surfactants Aggravated the Biotoxicity of Fe₂O₃ Nanoparticles in the Volatile Fatty Acids’ Biosynthesis during Sludge Anaerobic Fermentation

Unveiling Polyhexamethylene Guanidine Regulating Behaviors on Methane Generation in a Sludge Anaerobic Digester: Interactive Process, Microbial Metabolic Trait Interference, and Adaptive Mechanisms

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning

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Antagonistic effects of surfactants and CeO2 nanoparticles co-occurrence on the sludge fermentation process: Novel insights of interaction mechanisms and microbial networks

Cited by 36 publications

References 32 publications

Surfactants Aggravated the Biotoxicity of Fe2O3 Nanoparticles in the Volatile Fatty Acids’ Biosynthesis during Sludge Anaerobic Fermentation

Surfactants Aggravated the Biotoxicity of Fe2O3 Nanoparticles in the Volatile Fatty Acids’ Biosynthesis during Sludge Anaerobic Fermentation

Unveiling Polyhexamethylene Guanidine Regulating Behaviors on Methane Generation in a Sludge Anaerobic Digester: Interactive Process, Microbial Metabolic Trait Interference, and Adaptive Mechanisms

Predicting and Evaluating Different Pretreatment Methods on Methane Production from Sludge Anaerobic Digestion via Automated Machine Learning with Ensembled Semisupervised Learning

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Surfactants Aggravated the Biotoxicity of Fe₂O₃ Nanoparticles in the Volatile Fatty Acids’ Biosynthesis during Sludge Anaerobic Fermentation

Surfactants Aggravated the Biotoxicity of Fe₂O₃ Nanoparticles in the Volatile Fatty Acids’ Biosynthesis during Sludge Anaerobic Fermentation