Greenhouse gas emission inventory of drinking water treatment plants and case studies in China.

The Chinese government claimed to reach carbon dioxide emissions peaking by 2030 and achieve carbon neutralization by 2060. In this context, it's meaningful and urgent to estimate GHG emissions amount in every sectors. The growing concern about reducing GHG emissions has been shared by many water companies. This work aims to identify and estimate GHG emissions from the activities of drinking water treatment plants (DWTPs). According to the GHG protocol, the GHG emission inventory of DWTPs covers the sources of fossil fuel combustion, reservoir emissions, electricity and heat supply, use of chemicals and additives, disposal of waste, transportation, operation and maintenance. The tool was tested by nine DWTPs, which had an average GHG emission intensity of 0.225 kg CO2-eq/m3. The GHG emission intensities range from 0.167 kg CO2-eq/m3 to 0.272 kg CO2-eq/m3. The main source of GHG emissions is electricity supply, followed by the use of chemicals and additives. According to the average emission intensity, the estimated total amount of GHG emissions from DWTPs in China is about 1.82 × 107 t/a, corresponding to 0.15 % of the total GHG emission in China. The proposed GHG sources and emissions help decision-makers and DWTPs companies estimate GHG emissions more accurately and undertake GHG reduction measures.

A novel sustainable N recycling process: Upcycling ammonia to ammonium fertilizer from dilute wastewater and simultaneously realizing phenol degradation via a visible solar-driven PECMA system with efficient Ag2S-BiVO4 photoanodes.

The selective recovery of NH4+ as N fertilizers from dilution wastewater is a promising but challenging topic. Herein, a novel visible-light driven photo-electrochemical membrane stripping cell (designated "PECMA") with Ag2S-BiVO4 heterojunction photoanode was proposed to recover ammonium from dilute wastewater, which comprised an anode chamber for organics treatment, intermediate chamber for separating ammonium, cathode chamber for upcycling NH4+ into NH3, and recovery chamber for converting NH3 into (NH4)2SO4. The NH4+ is concentrated by 21.5 times and recovered as (NH4)2SO4 with a concentration of 7103 mg L-1 after 10 cycles. At a current density of 3.86 A m-2, PECMA system achieves excellent NH4+ removal and recovery rates of 97.5 and 37.2 g N m-2 d-1 in 100 mgN L-1 wastewater. Moreover, PECMA degrades refractory organic pollutants through ClO· generated by Ag2S-BiVO4 photoanode, which effectively decompose phenol to CO2 with a degradation rate of 93 %. Although tested as a proof-of-concept, the hybrid system opens up a novel field involving a sunlight-water-energy nexus, promising high efficiency NH4+ recovery and wastewater remediation.

Chemoproteomic Profiling Reveals the Mechanism of Bile Acid Tolerance in Bacteria.

Bile acids (BAs) are a class of endogenous metabolites with important functions. As amphipathic molecules, BAs have strong antibacterial effects, preventing overgrowth of the gut microbiota and defending the invasion of pathogens. However, some disease-causing pathogens can survive the BA stress and knowledge is limited about how they develop BA tolerance. In this work, we applied a quantitative chemoproteomic strategy to profile BA-interacting proteins in bacteria, aiming to discover the sensing pathway of BAs. Using a clickable and photo-affinity BA probe with quantitative mass spectrometry, we identified a list of histidine kinases (HKs) of the two-component systems (TCS) in bacteria as the novel binding targets of BA. Genetic screening revealed that knocking out one specific HK, EnvZ, renders bacteria with significant sensitivity to BA. Further biochemical and genetic experiments demonstrated that BA binds to a specific pocket in EnvZ and activates a downstream signaling pathway to help efflux of BA from bacteria, resulting in BA tolerance. Collectively, our data revealed that EnvZ is a novel sensor of BA in bacteria and its associated TCS signaling pathway plays a critical role in mediating bacterial BA tolerance, which opens new opportunities to combat BA-tolerating pathogens.

A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma.

BACKGROUND: Clinical occult lymph node metastasis (cOLNM) means that the lymph node is negatively diagnosed by preoperative computed tomography (CT), but has been proven to be positive by postoperative pathology. The aim of this study was to establish and validate a nomogram based on radiomics features for the preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients. METHODS: A total of 244 patients with clinical T1-2N0M0 solid lung adenocarcinoma who underwent preoperative contrast-enhanced chest CT were divided into a primary group (n = 160) and an independent validation group from another hospital (n = 84). The records of 851 radiomics features of each primary tumor were extracted. LASSO analysis was used to reduce the data dimensionality and select features. Multivariable logistic regression was utilized to identify independent predictors of cOLNM and develop a predictive nomogram. The performance of the predictive model was assessed by its calibration and discrimination. Decision curve analysis (DCA) was performed to estimate the clinical usefulness of the nomogram. RESULTS: The predictive model consisted of a clinical factor (CT-reported tumor size) and a radiomics feature (Rad-score). The nomogram presented good discrimination, with a C-index of 0.782 (95% CI, 0.768-0.796) in the primary cohort and 0.813 (95% CI, 0.787-0.839) in the validation cohort, and good calibration. DCA showed that the radiomics nomogram was clinically useful. CONCLUSION: This study develops and validates a nomogram that incorporates clinical and radiomics factors. It can be tailored for the individualized preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Enhancing the Thermo-Mechanical Property of Polymer by Weaving and Mixing High Length-Diameter Ratio Filler.

Improving thermo-mechanical characteristics of polymers can efficiently promote their applications in heat exchangers and thermal management. However, a feasible way to enhance the thermo-mechanical property of bulk polymers at low filler content still remains to be explored. Here, we propose mixing high length-diameter ratio filler such as carbon nanotube (CNT), boron nitride (BN) nanotube, and copper (Cu) nanowire, in the woven polymer matrix to meet the purpose. Through molecular dynamics (MD) simulation, the thermal properties of three woven polymers including woven polyethylene (PE), woven poly (p-phenylene) (PPP), and woven polyacetylene (PA) are investigated. Besides, using woven PE as a polymer matrix, three polymer nanocomposites, namely PE-CNT, PE-BN, and PE-Cu, are constructed by mixing CNT, BN nanotube, and Cu nanowire respectively, whose thermo-mechanical characteristics are compared via MD simulation. Morphology and phonons spectra analysis are conducted to reveal the underlying mechanisms. Furthermore, impacts of electron-phonon coupling and electrical field on the thermal conductivity of PE-Cu are uncovered via two temperature model MD simulation. Classical theoretical models are modified to predict the effects of filler and matrix on the thermal conductivity of polymer nanocomposites. This work can provide useful guidelines for designing thermally conductive bulk polymers and polymer nanocomposites.