Thermal responses of dissolved organic matter under global change

Harnessing the power of microbial fuel cells as pioneering green technology: advancing sustainable energy and wastewater treatment through innovative nanotechnology

The purpose of this review is to gain attention about intro the advanced and green technology that has dual action for both clean wastewater and produce energy. Water scarcity and the continuous energy crisis have arisen as major worldwide concerns, requiring the creation of ecologically friendly and sustainable energy alternatives. The rapid exhaustion of fossil resources needs the development of alternative energy sources that reduce carbon emissions while maintaining ecological balance. Microbial fuel cells (MFCs) provide a viable option by producing power from the oxidation of organic and biodegradable chemicals using microorganisms as natural catalysts. This technology has sparked widespread attention due to its combined potential to cleanse wastewater and recover energy. The review presents a complete examination of current advances in MFCs technology, with a focus on the crucial role of anode materials in improving their performance. Moreover, different anode materials and their nanoscale modifications are being studied to boost MFC efficiency. This current review also focused on the effects of surface modifications and different anode compositions on power generation and system stability. It also investigates the electrochemical principles behind these enhancements, providing insights into the economic potential of MFCs. MFCs provide a long-term solution to energy and environmental issues by addressing both wastewater treatment and energy production.

Electroactive bacteria-established long-distance electron transfer to oxygen facilitates bio-transformation of dissolved organic matter for sediment remediation

Electroactive bacteria (EAB) in sediment commonly establish long-distance electron transfer (LDET) to access O2, facilitating the degradation of organic contaminants, which we hypothesize is mediated by the bio-transformation of dissolved organic matter (DOM). This study confirmed that EAB-established LDET to O2 via a microbial electrochemical snorkel raised the electric potential of sediment by increasing HCl-extracted Fe(III) and NO3- concentrations while reducing DOM concentrations, which further modified microbial diversity and composition, notably reduced the relative abundance of fermentative bacteria. As a result, DOM showed the highest SUVA254 value (3.88) and SUVA280 value (1.61), preliminarily suggesting their enhanced aromaticity, humification and average molecular weight. Additionally, these DOM exhibited the highest electron transfer capacity (174.14±3.62 μmol e- /g C) and redox current. Based on these findings, we propose four possible avenues through which EAB-established LDET to O2 facilitates sediment remediation, mainly including DOM involved affinity, direct and indirect electron transfer, and induced photochemical reaction in degradation or humification process of organic contaminants. Although these proposed avenues require further verification, this work sheds light on deciphering the mechanisms underlying the augmented degradation of organic contaminants facilitated by EAB-established LDET to O2, offering fresh insights into sediment remediation.

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes

The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting the processes, structures and environmental effects of NOM self-assembly. This review seeks to provide a tutorial-like compilation of data source determination, algorithm selection, model construction, interpretability analyses, applications and challenges for big-data-based ML aiming at elucidating NOM self-assembly mechanisms in environments. The results from advanced nano-submicron-scale spatial chemical analytical technologies are suggested as input data which provide the combined information of molecular interactions and structural visualization. The existing ML algorithms need to handle multi-scale and multi-modal data, necessitating the development of new algorithmic frameworks. Interpretable supervised models are crucial owing to their strong capacity of quantifying the structure-property-effect relationships and bridging the gap between simply data-driven ML and complicated NOM assembly practice. Then, the necessity and challenges are discussed and emphasized on adopting ML to understand the geochemical behaviors and bioavailability of pollutants as well as the elemental cycling processes in environments resulting from the NOM self-assembly patterns. Finally, a research framework integrating ML, experiments and theoretical simulation is proposed for comprehensively and efficiently understanding the NOM self-assembly-involved environmental issues.

Interface mechanism of Cd bioavailability by porewater prediction in paddy field system

Cadmium (Cd) contamination poses a significant threat to human health. Predicting the risk of Cd in rice grains is challenging due to the heterogeneity and complexity of bioavailable Cd in paddy soils. We proposed that porewater during the grain-filling period can effectively predict Cd bioavailability in rice (R2 > 0.5, p < 0.05). The prediction mechanism was elucidated through soil-porewater interface characterization analysis and DFT calculations. Key factors determining Cd bioavailability included Cd2+ and SO42- concentration, pH, and ORP of porewater, with pH showing the highest correlation. As porewater pH increased from 5 to 9, typical mineral surfaces gradually deprotonated and formed complexation bonds {SOCd+} instead of {SOH} or {SOH2+}. Additionally, the complexation energy between montmorillonite and Cd was 1.57-1.78 eV higher than that between goethite and Cd, while the protonation activation energy barrier on montmorillonite was 1.33-1.52 eV lower than on goethite. Therefore, {H+} concentration in porewater had the potential to quantify {SOCd+} content and binding capacity, aiding Cd bioavailability prediction. This study outlines the mechanism of predicting heavy metal health risks in rice grains through porewater and offers a potential regulation approach for agricultural product safety.

Soil bacteria and fungi on tropical mountainsides: Joint effects of weathering, climate, and chemical factors

Contemporary environmental factors such as temperature and pH are generally identified as primary influences on microbial diversity, while the role of geological processes remain understudied. Here, we investigated the diversity and community composition of bacteria and fungi along an elevational gradient from703 to 4514 m on Mt. Kilimanjaro, East Africa. We further examined the effects of contemporary environment and geological processes such as weathering on microbial communities and diversities. For community composition, bacteria and fungi showed clear differentiation along elevations and their community dissimilarities increased with elevational distance indicating elevational distance-decay relationships. Multiple variables such as weathering, climate and chemical factors were significantly associated with microbial communities and showed greater effects on bacterial than fungal communities. Specifically, soil pH mainly shaped bacterial communities, while mean annual temperature for fungi, followed by other variables such as weathering processes. For Shannon diversity, bacteria and fungi showed significant hump-shaped elevational patterns with the peak values at 1857 and 1436 m, respectively. Shannon diversity was mainly affected by soil weathering accounting for 8.9% of the total variance for bacteria, while jointly by weathering and climate accounted for 14.3% of fungi. For the community uniqueness, represented by local contribution to beta diversity (LCBD), there were U-shaped patterns for both taxonomic groups. LCBD was mainly explained by the joint effects of chemical and climate variables which accounted for 51.1% and 33.4% for bacteria and fungi, respectively. Our results highlight the effects of soil weathering processes on diversity and community composition for bacteria and fungi. Thus, the integration of weathering with contemporary environments could provide new insights into microbial elevational diversity patterns.

Light-driven differences in bacterial networks and organic matter decomposition: Insights from an analysis of the harmful cyanobacterium Microcystis aeruginosa PCC 7806

Freshwater systems are critical yet often underestimated components of global carbon cycling, functioning both as carbon sinks and sources. Cyanobacteria play a key role in this cycle by capturing atmospheric carbon dioxide through photosynthesis. The captured carbon is either released back into the atmosphere or sequestered in sediments following organismal decay. This study examines the pivotal role of cyanobacteria, specifically Microcystis aeruginosa PCC 7806, in the biogeochemical cycling of carbon in freshwater ecosystems, with a focus on how light influences the degradation of cyanobacteria-derived organic matter. Using a combination of 16S rDNA sequencing and excitation-emission matrix coupled with parallel factor (EEM-PARAFAC) analysis, we conducted a 50-day experiment to investigate the dynamics of dissolved organic matter (DOM) and lysate organic matter (LOM) derived from M. aeruginosa PCC 7806 under light and dark conditions. Our results demonstrate that light significantly impacts bacterial community composition, gene functionality, and the decomposition of organic matter. The findings emphasize the crucial role of light in facilitating microbial adaptation, stabilizing microbial networks and driving organic substrate transformation. These insights underscore the influence of light on microbial community dynamics and organic matter degradation, revealing shifts in microbial populations under varying light conditions. This suggests a strong link between photochemical processes and microbial activity, with significant ecological implications.

Characterization of Low-Molecular-Weight Dissolved Organic Matter Using Optional Dialysis and Orbitrap Mass Spectrometry

Low-molecular-weight (LMW, <1000 Da) dissolved organic matter (DOM) plays a significant role in metal/organic pollutant complexation, as well as photochemical/microbiological processes in freshwater ecosystems. The micro size and high reactivity of LMW-DOM hinder its precise characterization. In this study, Suwannee River fulvic acid (SRFA), a commonly used reference material for aquatic DOM, was applied to examine the optical features and molecular composition of LMW-DOM by combining membrane separation, ultraviolet-visible absorption and Orbitrap mass spectrometry (MS) characterization. The 100-500 Da molecular weight cut-off (MWCO) membrane had a better performance in regard to separating the tested LMW-DOM relative to the 500-1000 Da MWCO membrane. The ultraviolet-visible absorbance decreased dramatically for the retentates, whereas it increased for the dialysates. Specifically, carbohydrates, lipids and peptides exhibited high selectivity to the 100-500 Da MWCO membrane in early dialysis. Lignins, tannins and condensed aromatic molecules displayed high permeability to the 500-1000 Da MWCO membrane in late dialysis. Overall, the retentates were dominated by aromatic rings and phenolic hydroxyls with high O/Cwa (weighted average of O/C) and low H/Cwa. Conversely, such dialysates had numerous aliphatic chains with high H/Cwa and low O/Cwa compared to SRFA. In particular, LMW-DOM below 200 Da was identified by Orbitrap MS. This work provides an operational program for identifying LMW-DOM based on the SRFA standard and MS analysis.

Network Complexity and Stability of Microbes Enhanced by Microplastic Diversity

Microplastic mixtures are ubiquitously distributed in global ecosystems and include varying types. However, it remains unknown how microplastic diversity affects the biotic interactions of microbes. Here, we developed novel experiments of 600 microcosms with microplastic diversity ranging from 1 to 6 types and examined ecological networks for microbial communities in lake sediments after 2 months of incubation at 15 and 20 °C. We found that microplastic diversity generally enhanced the complexity of microbial networks at both temperatures, such as increasing network connectance and reducing average path length. This phenomenon was further confirmed by strengthened species interactions toward high microplastic diversity except for the negative interactions at 15 °C. Interestingly, increasing temperatures further exaggerated the effects of microplastic diversity on network structures, resulting in higher network connectivity and species interactions. Consistently, using species extinction simulations, we found that higher microplastic diversity and temperature led to more robust networks, and their effects were additionally and positively mediated by the presence of biodegradable microplastics. Our findings provide the first evidence that increasing microplastic diversity could unexpectedly promote the complexity and stability of microbial networks and that future warming could amplify this effect.