Development of biocatalytic microbial ecosystem (FPUS@RODMs@In-PAOREs) for rapid and sustainable degradation of various refractory organics

Engineered Probiotic Bio-Heterojunction with Robust Antibiofilm Modality via "Eating" Extracellular Polymeric Substances for Wound Regeneration

The compact three-dimensional (3D) structure of extracellular polymeric substances (EPS) within biofilms significantly hinders the penetration of antimicrobial agents, making biofilm eradication challenging and resulting in persistent biofilm-associated infections. To address this challenge, a solution is proposed: a probiotic bio-heterojunction (P-bioHJ) combining Lactobacillus rhamnosus with MXene (Ti3C2) quantum dots (MQDs)/FeS heterojunction. This innovation aims to break down the saccharides in EPS, enabling effective combat against biofilm-associated infections. Initially, the P-bioHJ targets saccharides through metabolic processes, causing the collapse of EPS and allowing infiltration into bacterial colonies. Simultaneously, upon exposure to near-infrared (NIR) irradiation, the P-bioHJ produces reactive oxygen species (ROS) and thermal energy, deploying physical mechanisms to combat bacterial biofilms effectively. Following antibiofilm treatment, the P-bioHJ adjusts the oxidative environment, reduces wound inflammation by scavenging ROS, boosts antioxidant enzyme activity, and mitigates the NF-κB inflammatory pathway, thereby accelerating wound healing. In vitro and in vivo experiments confirm the exceptional antibiofilm, antioxidant/anti-inflammatory, and wound-regeneration properties of P-bioHJ. In conclusion, this study provides a promising approach for treating biofilm-related infections.

Robust polymer hybrid and assembly materials from structure tailoring to efficient catalytic remediation of emerging pollutants

A massive amount of toxic substances and harmful chemicals are released every day into the outer environment, imposing serious environmental impacts on both land and aquatic animals. To date, research is constantly in progress to determine the best catalytic material for the effective remediation of these harmful pollutants. Hybrid nanomaterials prepared by combining functional polymers with inorganic nanostructures got attention as a promising area of research owing to their remarkable multifunctional properties deriving from their entire nanocomposite structure. The versatility of the existing nanomaterials' design in polymer-inorganic hybrids, with respect to their structure, composition, and architecture, opens new prospects for catalytic applications in environmental remediation. This review article provides comprehensive detail on catalytic polymer nanocomposites and highlights how they might act as a catalyst in the remediation of toxic pollutants. Additionally, it provides a detailed clarification of the processing of design and synthetic ways for manufacturing polymer nanocomposites and explores further into the concepts of precise design methodologies. Polymer nanocomposites are used for treating pollutants (electrocatalytic, biocatalytic, catalytic, and redox degradation). The three catalytic techniques that are frequently used are thoroughly illustrated. Furthermore, significant improvements in the method through which the aforementioned catalytic process and pollutants are extensively discussed. The final section summarizes challenges in research and the potential of catalytic polymer nanocomposites for environmental remediation.

A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement

Deep learning methods exhibited significant advantages in mapping highly nonlinear relationships with acceptable computational speed, and have been widely used to predict water quality. However, various model selection and construction methods resulted in differences in prediction accuracy and performance. Hence, a unified deep learning framework for water quality prediction was established in the paper, including data processing module, feature enhancement module, and data prediction module. In the established model, the data processing module based on wavelet transform method was applied to decomposing complex nonlinear meteorology, hydrology, and water quality data into multiple frequency domain signals for extracting self characteristics of data cyclic and fluctuations. The feature enhancement module based on Informer Encoder was used to enhance feature encoding of time series data in different frequency domains to discover global time dependent features of variables. Finally, the data prediction module based on the stacked bidirectional long and short term memory network (SBiLSTM) method was employed to strengthen the local correlation of feature sequences and predict the water quality. The established model framework was applied in Lijiang River in Guilin, China. The maximum relative errors between the predicted and observed values for dissolved oxygen (DO), chemical oxygen demand (CODMn) were 12.4% and 20.7%, suggesting a satisfactory prediction performance of the established model. The validation results showed that the established model was superior to all other models in terms of prediction accuracy with RMSE values 0.329, 0.121, MAE values 0.217, 0.057, SMAPE values 0.022, 0.063 for DO and CODMn, respectively. Ablation tests confirmed the necessity and rationality of each module for the established model framework. The established method provided a unified deep learning framework for water quality prediction.

Modelling future climate effects on N2O emission and soil carbon storage in maize fields under controlled-release urea and straw incorporation

Controlled-release urea application and straw incorporation have been conducted in recent years as environmental-friendly and sustainable farming strategies, but the long-term effects of controlled-release urea application and combination with straw on the dryland maize yield, soil fertility and the environment under future climate scenarios remain unclear. Hence, based on a six-year field experiment, four treatments were used to calibrate and validate the DeNitrification-DeComposition (DNDC) model, including non-nitrogen (CK), split applications of conventional urea (UR), single basal application of conventional urea and controlled-release urea at a ratio of 2:1 (CU), and CU combined with straw (CUS). Subsequently, coupled the well-validated model with future climate to evaluate suitable agricultural production practices under two shared socioeconomic pathways (SSP)-SSP245 and SSP585. The validation results indicated a good fit between the simulated and observed data of greenhouse gas emissions, soil organic carbon (SOC) contents and maize yields. With the anticipation of warmer temperatures and increased precipitation in the future, the yields of UR, CU, and CUS treatment significantly rose. Under SSP585 scenario, the positive impacts of CU treatment on maize yields reduced after the 2050s, exhibiting an average decline of 12.03%. Compared with the UR treatment, the CU treatment markedly reduced cumulative N2O emissions, and both treatments maintained the original state of SOC storages in the 2030s, furthermore, the CUS treatment reduced N2O emissions by 47.10%, 35.07%, 23.80% and 10.04% in the 2030s, 2050s, 2070s and 2090s, respectively. SOC storages for the CUS treatment gradually increased with an average of 464.58, 350.22, 250.87 and 177.75 kg C ha-1 y-1 for two SSP scenarios in the 2030s, 2050s, 2070s and 2090s, which excellently offset the CO2 equivalent of emissions caused by N2O emissions. Therefore, in dryland maize production, combined controlled-release urea with straw incorporation could achieve the best comprehensive effect among increase of yield, improvement of SOC storages and alleviation of greenhouse gas emissions under future climate scenario.

Hyperthermophilic pretreatment significantly accelerates thermophilic composting humification through improving bacterial communities and promoting microbial cooperation

Thermophilic composting (TC) can effectively shorten maturity period with satisfactory sanitation. However, the higher energy consumption and lower composts quality limited its widespread application. In this study, hyperthermophilic pretreatment (HP) was introduced as a novel approach within TC, and its effects on humification process and bacterial community during food waste TC was investigated from multiple perspectives. Results showed that a 4-hour pretreatment at 90 °C increased the germination index and humic acid/fulvic acid by 25.52% and 83.08%. Microbial analysis demonstrated that HP stimulated the potential functional thermophilic microbes, and significantly up-regulated the genes related to amino acid biosynthesis. Further network and correlation analysis suggested that pH was the key factor affecting bacterial communities, and higher HP temperatures help to restore bacterial cooperation and showed higher humification degree. In summary, this study contributed to a better understanding of the mechanism towards the accelerated humification by HP.