A PAM hydrogel surface-coated hydroponic bamboo evaporator with efficient thermal utilization for solar evaporation.

Solar-driven interfacial water purification emerges as a sustainable technology for seawater desalination and wastewater treatment to address the challenge of water scarcity. Currently, the energy losses via radiation and convection to surrounding environment minimize its energy efficiency. Therefore, it is necessary to develop strategies to minimize the heat losses for efficient water purification. Here, a novel evaporator was developed through the in situ gelation of PAM hydrogel on the surface carbonized hydroponic bamboo (PSC) to promote energy efficiency. The inherent porous and layered network structures of bamboo, in synergy with the functional hydration capacity of PAM hydrogel, facilitated adequate water transportation, while reducing evaporation enthalpy. The PAM hydrogel firmly covered on the photothermal layer surface effectively minimized the radiation and convection heat losses, while further harvesting those thermal energy that would otherwise dissipate into the surrounding environment. The reduced thermal conductivity of PSC served as a thermal insulator as well, obstructing heat transfer to bulk water and thus diminishing conduction losses. Consequently, the rational designed PSC could efficiently convert solar energy to purified water, leading to the evaporation of 2.09 kg m-2 h-1, the energy efficiency of 87.6 % under one sun irradiation, and yielding 9.6 kg m-2 fresh water over 11 h outdoor operation. Moreover, the PSC also performs excellent salt rejection, and long-term stability at outdoor experiment. These results demonstrated high and stable solar evaporation performance could be achieved if turning heat losses into a way of extra energy extraction to further enhance the evaporation performance. This strategy appears to be a promising strategy for effective thermal energy management and practical application.

Comparison of in-situ and ex-situ electrolytic H2 supply for microbial methane production from CO2.

Both in-situ and ex-situ electrolytic H2 supply have been used for biomethane production from CO2. However, the pros and cons of them have not been systematically compared. The present study makes this comparison using a 20 L continuous stirred-tank reactor equipped with external and internal electrolyzers. Compared to the ex-situ H2 supply, the in-situ electrolytic H2 bubbles were one order of magnitude smaller, which resulted in improved H2 mass transfer and biomass growth. Consequently, the methane production rate and the coulombic efficiency of the in-situ H2 supply (0.51 L·L-1·d-1, 96%) were higher than those of the ex-situ H2 supply (0.30 L·L-1·d-1, 56%). However, due to high internal resistance, the energy consumption for the in-situ electrolysis was 2.54 times higher than the ex-situ electrolysis. Therefore, the in-situ electrolytic H2 supply appears to be more promising, but reducing energy consumption is the key to the success of this technology.

Enhancement of acetate production in hydrogen-mediated microbial electrosynthesis reactors by addition of silica nanoparticles.

Microbial electrosynthesis (MES) is a promising technology for CO2 fixation and electrical energy storage. Currently, the low current density of MES limits its practical application. The H2-mediated and non-biofilm-driven MES could work under higher current density, but it is difficult to achieve high coulombic efficiency (CE) due to low H2 solubility and poor mass transfer. Here, we proposed to enhance the hydrogen mass transfer by adding silica nanoparticles to the reactor. At pH 7, 35 â„ƒ and 39 A·m- 2 current density, with the addition of 0.3wt% silica nanoparticles, the volumetric mass transfer coefficient (kLa) of H2 in the reactor increased by 32.4% (from 0.37 h- 1 to 0.49 h- 1), thereby increasing the acetate production rate and CE of the reactor by 69.8% and 69.2%, respectively. The titer of acetate in the reactor with silica nanoparticles (18.5 g·L- 1) was 56.9% higher than that of the reactor without silica nanoparticles (11.8 g·L- 1). Moreover, the average acetate production rate of the reactor with silica nanoparticles was up to 2.14 g·L- 1·d- 1 in the stable increment phase, which was much higher than the other reported reactors. These results demonstrated that the addition of silica nanoparticles is an effective approach to enhancing the performance of H2-mediated MES reactors.

Iron carbide nanoparticles supported on an N-doped carbon porous framework as a bifunctional material for electrocatalytic oxygen reduction and supercapacitors.

Highly active and durable bifunctional materials are of pivotal importance for energy conversion and storage devices, yet a comprehensive understanding of their geometric and electronic influence on electrochemical activity is urgently needed. Fe-N-C materials with physical and chemical structural merits are considered as one of the promising candidates for efficient oxygen reduction reaction electrocatalysts and supercapacitor electrodes. Herein, Fe3C nanoparticles supported on a porous N-doped carbon framework (denoted as Fe3C/PNCF) were readily prepared by one-step chemical vapor deposition under the assistance of a NaCl salt template. The experiment results revealed that the as-synthesized Fe3C/PNCF nanocomposites successfully displayed attractive electrocatalytic oxygen reduction reaction (ORR) activity comparable to that of the Pt/C catalyst (E1/2 of 0.84 V and 0.83 V, respectively), and a superior capacitance of 385.3 F g-1 under 1 A g-1 for a supercapacitor. It's proposed that the increased pyridinic and graphitic N coordination on the hydrophilic porous framework provides more electrochemical active surface area for the storage and transport of electrolyte ions. Additionally, an appropriate d-band center created by the optimized adsorption function endows Fe3C/PNCF with excellent electrochemical properties. The results confirmed that the integration strategy of porous heterogeneous structure and accessible active sites balanced the complex relationship between geometry, electronic structure, and electrochemical activity. Our research provides a facile approach for fabricating multi-functional nanomaterials applicable in both ORR and supercapacitors in the future.

Electrochemical deposition of flower-like nanostructured silver particles with a PVA modified carbon cloth cathode.

A novel electrochemical method for preparing flower-like nanostructured silver particles using polyvinyl alcohol (PVA) modified carbon cloth as a cathode is reported. The method does not involve the use of any morphological control agents in aqueous solution. The morphology of the silver nanoparticles obtained was studied using scanning electron microscopy (SEM) and X-ray diffractometry (XRD). The effects of the operating conditions on the deposited silver nanoparticles were investigated. It was found that PVA concentration for carbon cloth modification had a significant effect on the deposited silver morphology. With 1% PVA modification, current density of 10 µA cm-2 and silver nitrate concentration of 1 mM, a flower-like nanostructured silver with petal thickness of 100 nm can be prepared. With the reaction proceeding, silver nanocrystals nucleated on the cathode in a few seconds, then the nuclei grew and the rudimental flower-like silver started to form in 1 min. The perfect flower-like nanostructure of silver was formed in 20 min. However overlong reaction time led to micrometer sized blocks. The specific silver nanostructure growth might be attributed to the silver ion concentration gradient caused by reaction and diffusion rate and the effects of PVA.

Machine learning to predict dynamic changes of pathogenic Vibrio spp. abundance on microplastics in marine environment.

Microplastics are widely found in the marine environment. Recent studies have shown that pathogenic microorganisms can hitchhike on microplastics, which might act as a vector for the spread of pathogens. Vibrio spp. are known to be pathogenic to humans and can cause serious foodborne diseases. In this study, using datasets from an estuary and a mariculture zone in China, five machine learning models were established to predict the relative abundance of Vibrio spp. on microplastics. The results showed that deep neural network (DNN) model and RandomForest algorithm achieved the best predictive performance. Different data sources, data sampling, and processing methods had a little impact on the prediction performance of DNN and RandomForest models. SHapley Additive exPlanations (SHAP) indicated that salinity and temperature are the primary factors affecting the relative abundance of Vibrio spp. The prediction performances of the five machine learning models were further improved by feature selection, providing information to support future experimental research. The results of this study could help establish a long-term and dynamic monitoring system for the relative abundance of Vibrio spp. on microplastics in response to environmental factors as well as provide useful information for assessing the potential health impacts of microplastics on marine ecology and humans.

Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data.

Modeling of anaerobic digestion (AD) is crucial to better understand the process dynamics and to improve the digester performance. This is an essential yet difficult task due to the complex and unknown interactions within the system. The application of well-developed data mining technologies, such as machine learning (ML) and microbial gene sequencing techniques are promising in overcoming these challenges. In this study, we investigated the feasibility of 6 ML algorithms using genomic data and their corresponding operational parameters from 8 research groups to predict methane yield. For classification models, random forest (RF) achieved accuracies of 0.77 using operational parameters alone and 0.78 using genomic data at the bacterial phylum level alone. The combination of operational parameters and genomic data improved the prediction accuracy to 0.82 (p<0.05). For regression models, a low root mean square error of 0.04 (relative root mean square error =8.6%) was acquired by neural network using genomic data at the bacterial phylum level alone. Feature importance analysis by RF suggested that Chloroflexi, Actinobacteria, Proteobacteria, Fibrobacteres, and Spirochaeta were the top 5 most important phyla although their relative abundances were ranging only from 0.1% to 3.1%. The important features identified could provide guidance for early warning and proactive management of microbial communities. This study demonstrated the promising application of ML techniques for predicting and controlling AD performance.

Enhancement of microbiome management by machine learning for biological wastewater treatment.

Here, we propose to develop microbiome-based machine learning models to predict the response of biological wastewater treatment systems to environmental or operational disturbances or to design specific microbiomes to achieve a desired system function. These machine learning models can be used to enhance the stability of microbiome-based biological systems and warn against the failure of these systems.

Influenced CD cells and ICAM-1 by pulmonary surfactant combined with high-frequency oscillatory ventilation and its effects on immune function in children with neonatal respiratory distress syndrome.

This study aimed to explore the clinical efficacy of pulmonary surfactant combined with high-frequency oscillatory ventilation (HFOV) on neonatal respiratory distress syndrome (NRDS) and its influence on immune function in children. Children admitted to our hospital from March 2017 to March 2019 who received HFOV combined with pulmonary surfactant therapy as a research group. Sixty-two children received conventional nasal continuous positive pressure combined with pulmonary surfactant therapy as a control group. Clinical efficacy, blood gas and immune function of patients were compared between the two groups. The clinical efficacy of the research group was better than that of the control group (P< 0.050). PaO2 and PaO2/FiO2 were both higher after treatment (P< 0.050). CD3+ and NK cells in the research group were higher than those in the control group, while CD8+ cells and ICAM-1 were lower than those in the control group (P< 0.050). CD3+, CD4+ and NK cells decreased in both groups after treatment, while CD8+ cells and ICAM-1 increased (P< 0.050). HFOV combined with pulmonary surfactant has significant clinical efficacy and high safety on NRDS, and has a certain protective effect on children's immune function. Hence, it is worthy of being the first choice for the clinical treatment of NRDS in the future.

Microbial Community Predicts Functional Stability of Microbial Fuel Cells.

Stability as evaluated by functional resistance and resilience is critical to the effective operation of environmental biotechnologies. To date, limited tools have been developed that allow operators of these technologies to predict functional responses to environmental and operational disturbances. In the present study, 17 Microbial Fuel Cells (MFCs) were exposed to a low pH perturbation. MFC power dropped 52.7 ± 35.8% during the low pH disturbance. Following the disturbance, 3 MFCs did not recover while 14 took 60.7 ± 58.3 h to recover to previous current output levels. Machine learning models based on genomic data inputs were developed and evaluated on their ability to predict resistance and resilience. Resistance and resilience levels corresponding to risk of deactivation could be classified with 70.47 ± 15.88% and 65.33 ± 19.71% accuracy, respectively. Models predicting resistance and resilience coefficient values projected postperturbation current drops within 6.7-15.8% and recovery times within 5.8-8.7% of observed values. Results suggest that abundances of specific genera are better predictors of resistance while overall microbial community structure more accurately predicts resilience. This approach can be used to assess operational risk and is a first step toward the further understanding and improvement of overall stability of environmental biotechnologies.

Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells.

The complicated interactions that occur in mixed-species biotechnologies, including biosensors, hinder chemical detection specificity. This lack of specificity limits applications in which biosensors may be deployed, such as those where an unknown feed substrate must be determined. The application of genomic data and well-developed data mining technologies can overcome these limitations and advance engineering development. In the present study, 69 samples with three different substrate types (acetate, carbohydrates and wastewater) collected from various laboratory environments were evaluated to determine the ability to identify feed substrates from the resultant microbial communities. Six machine learning algorithms with four different input variables were trained and evaluated on their ability to predict feed substrate from genomic datasets. The highest accuracies of 93 ±â€¯6% and 92 ±â€¯5% were obtained using NNET trained on datasets classified at the phylum and family taxonomic level, respectively. These accuracies corresponded to kappa values of 0.87 ±â€¯0.10, 0.86 ±â€¯0.09, respectively. Four out of six of the algorithms used maintained accuracies above 80% and kappa values higher than 0.66. Different sequencing method (Roche 454 or Illumina sequencing) did not affect the accuracies of all algorithms, except SVM at the phylum level. All algorithms trained on NMDS-compressed datasets obtained accuracies over 80%, while models trained on PCoA-compressed datasets presented a 10-30% reduction in accuracy. These results suggest that incorporating microbial community data with machine learning algorithms can be used for the prediction of feed substrate and for the potential improvement of MFC-based biosensor signal specificity, providing a new use of machine learning techniques that has substantial practical applications in biotechnological fields.

The current status and effects of emergency drug shortages in China: Perceptions of emergency department physicians.

OBJECTIVES: The shortage of emergency drugs in China is severe. This study aimed to characterize emergency drug shortages in China and to measure their effects. METHODS: An online questionnaire based on a literature review was sent to emergency department physicians in Chinese secondary and tertiary hospitals from November 2016 to February 2017. The survey asked physicians questions about their experiences with emergency drug shortages. RESULTS: In total, 236 physicians from 29 provinces participated in the survey. According to their responses, 90.7% of the respondents experienced drug shortages during the last year. More than half of the physicians (65.7%) reported that drug shortages occurred at least once a month. Hospitals in the eastern and western regions of China had more emergency drugs in shortage than hospitals in central China, especially those with many inpatient beds (≥800). In addition, the shortage situation was more serious in secondary hospitals than in tertiary hospitals. More respondents agreed that original medicines, injections, essential medicines, medicines without alternative agents and cheap medicines were more susceptible to shortages than generics, oral medicines, nonessential medicines, medicines with alternative agents and expensive medicines, respectively. Most respondents thought that drug shortages always, often or sometimes affected patients [delayed therapy (62.6%), longer rescue and recovery times (58.9%) and higher costs (58.7%)] and physicians [inconvenience (81.0%), higher pressure (76.5%) and harm to patient-doctor relationships (72%)] and compromised hospital reputations (55.1%). CONCLUSIONS: The shortage of emergency drugs in China is serious, especially in secondary hospitals located in eastern and western China. Emergency drug shortages have significant effects on patients and physicians.

Disparities in out-of-pocket inpatient expenditures in rural Shaanxi Province, western China from 2011 to 2014: a time series analysis.

OBJECTIVE: To investigate the long-term trend of disparity of monthly average out-of-pocket inpatient expenditures (OOP) between areas with different developing levels since the new healthcare reform. METHODS: Time series regression was used to assess the trend of disparities of OOP and monthly average inpatient expenditures (AIE) between areas with different developing levels in rural Shaanxi Province, western China. The data of OOP and AIE in primary health institutions, secondary hospitals, tertiary hospitals and also all levels of the hospital were analysed separately covering the period 2011 through to 2014. RESULTS: The disparity of AIE at all levels of hospitals was increasing (coefficient = 0.003, P = 0.029), and only the disparity of AIE in secondary hospitals was statistical significant (coefficient = 0.003, P = 0.012) when separately considering different levels of the hospital. The disparity of OOP in all levels of the hospital was increasing (coefficient = 0.007, P = 0.001), and the OOP in primary hospitals contributed most of the disparity (coefficient = 0.019, P = 0.000), followed by OOP in secondary (coefficient = 0.008, P = 0.003) and tertiary hospitals (coefficient = 0.004, P = 0.091). CONCLUSIONS: A statistically significant absolute increase in the trend of disparities of OOP and AIE at all levels of hospital was detected after the new healthcare reform in Shaanxi Province, western China. The increase rate of disparity of OOP was bigger than that of AIE. A modified health insurance plan should be proposed to guarantee equity in the future.

Impact of the zero-markup drug policy on hospitalisation expenditure in western rural China: an interrupted time series analysis.

OBJECTIVES: To assess the long-term effects of the introduction of China's zero-markup drug policy on hospitalisation expenditure and hospitalisation expenditures after reimbursement. METHODS: An interrupted time series was used to evaluate the impact of the zero-markup drug policy on hospitalisation expenditure and hospitalisation expenditure after reimbursement at primary health institutions in Fufeng County of Shaanxi Province, western China. Two regression models were developed. Monthly average hospitalisation expenditure and monthly average hospitalisation expenditure after reimbursement in primary health institutions were analysed covering the period 2009 through to 2013. RESULTS: For the monthly average hospitalisation expenditure, the increasing trend was slowed down after the introduction of the zero-markup drug policy (coefficient = -16.49, P = 0.009). For the monthly average hospitalisation expenditure after reimbursement, the increasing trend was slowed down after the introduction of the zero-markup drug policy (coefficient = -10.84, P = 0.064), and a significant decrease in the intercept was noted after the second intervention of changes in reimbursement schemes of the new rural cooperative medical insurance (coefficient = -220.64, P < 0.001). CONCLUSIONS: A statistically significant absolute decrease in the level or trend of monthly average hospitalisation expenditure and monthly average hospitalisation expenditure after reimbursement was detected after the introduction of the zero-markup drug policy in western China. However, hospitalisation expenditure and hospitalisation expenditure after reimbursement were still increasing. More effective policies are needed to prevent these costs from continuing to rise.

Current Situation, Determinants, and Solutions to Drug Shortages in Shaanxi Province, China: A Qualitative Study.

OBJECTIVE: Drug shortages were a complex global problem. The aim of this study was to analyze, characterize, and assess the drug shortages, and identify possible solutions in Shaanxi Province, western China. METHODS: A qualitative methodological approach was conducted during May-June 2015 and December 2015-January 2016. Semi-structured interviews were performed to gather information from representatives of hospital pharmacists, wholesalers, pharmaceutical producers, and local health authorities. RESULTS: Thirty participants took part in the study. Eight traditional Chinese medicines and 87 types of biologicals and chemicals were reported to be in short supply. Most were essential medicines. Five main determinants of drug shortages were detected: too low prices, too low market demands, Good Manufacturing Practice (GMP) issues, materials issues, and approval issues for imported drugs. Five different solutions were proposed by the participants: 1) let the market decide the drug price; 2) establish an information platform; 3) establish a reserve system; 4) enhance the communication among the three parties in the supply chain; and 5) improve hospital inventory management. CONCLUSIONS: Western China was currently experiencing a serious drug shortage. Numerous reasons for the shortage were identified. Most drug shortages in China were currently because of "too low prices." To solve this problem, all of the stakeholders, especially the government, needed to participate in managing the drug shortages.

Sequential recovery of copper and nickel from wastewater without net energy input.

A novel bioelectrochemical system (BES) was designed to recover copper and nickel from wastewater sequentially. The BES has two chambers separated by a bipolar membrane and two cathodes. Firstly, the copper ions were reduced on a graphite cathode with electricity output, and then with an additional bias-potential applied, the nickel ions were recovered sequentially on a copper sheet with electricity input. In this design, nickel and copper can be recovered and separated sequentially on two cathodes. By adjusting the molar ratio of copper and nickel ions to 2.99:1 in wastewater, 1.40 mmol Cu²âº could be recovered with 143.78 J electricity outputs, while 50.68 J electricity was input for 0.32 mmol nickel reduction. The total energy output of copper recovery was far more than the electricity input of nickel reduction. The present technology provides a potential method for heavy metal ion separation and recovery.