Nitrogen Dioxide Research Articles

Assessing Tunisia's urban air quality using combined lichens and Sentinel-5 satellite integration.

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia's rapidly expanding cities.

Accounting for NOx emissions from biomass burning and urbanization doubles existing inventories over South, Southeast and East Asia

Rapid urbanization and broad use of biomass burning have led to important changes in NOx [sum of nitrogen dioxide and nitrous oxide] emissions across South, Southeast, and East Asia, frequently occurring on day-to-day time scales and over areas not identified by existing emissions databases. Here we compute NOx emissions using remotely sensed NO2 [nitrogen dioxide] and a model-free mass-conserving inverse method, resulting respectively in 61 kt d−1 and 40 kt d−1 from biomass burning in Northern and Southern Continental Southeast Asia, and 14.3 kt d−1 and 3.7 kt d−1 from urbanization in China and Eastern South Asia, a net increase more than double existing inventories. Three observationally based physical constraints consistent with theory are found which current chemical transport models cannot match: more NO2 per unit of NOx emissions, longer and more variable in-situ lifetime, and longer-range transport. This result provides quantitative support for mitigation efforts targeting specific events, processes, or geographies.

Relationship Between Air Pollutants and the Incidence of Epistaxis in Yangzhou.

Objectives: This project aims to explore the relationship between the air quality index (AQI), the concentration of 6 air pollutants, and the incidence of epistaxis in Yangzhou. Also, to provide reference information for the prevention and treatment of epistaxis. Methods: Data of patients with epistaxis admitted to the Northern Jiangsu People's Hospital Affiliated to Yangzhou University from January 2017 to December 2021 were collected. In addition, the local AQI and the concentrations of 6 air pollutants, namely particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), were analyzed at the time of onset. Furthermore, the correlation with the incidence of epistaxis has been analyzed. Results: From 2017 to 2021, there were 24,721 patients with epistaxis aged from 0 to 17 years old while male patients were more than females. The incidence was higher in April, May, and June. There was a statistically significant difference in the number of daily epistaxis in different months and under AQI conditions (P < .05). Spearman's correlation analysis showed that there was a positive correlation between the number of daily epistaxis and the concentrations of AQI, CO, NO2, O3, PM2.5, PM10, and SO2 in Yangzhou, in which O3, PM10, and SO2 were highly correlated with the average number of daily epistaxis, and there was no obvious time lag effect of air pollutants on epistaxis. Conclusion: Epistaxis in the Yangzhou area is more common in males, mostly occurs in 0 to 17 years old, with seasonal. There was also a positive correlation between the incidence of epistaxis and air pollutants in Yangzhou. Therefore, by reducing the AQI index in daily life, and reducing the concentration of environmental pollutants in the air, the occurrence of epistaxis could be prevented and reduced to a certain extent.

A Review of the Current Status of Disease-Modifying Therapies and Prevention of Alzheimer's Disease.

Alzheimer's disease is the most common form of dementia and includes cognitive, personality, and behavioral changes. The 2024 report from the Alzheimer's Association estimated that 6.9 million adults >65 years in the US are currently living with Alzheimer's disease. Modeling studies predict that this number will double by 2050, and associated healthcare costs will reach $1 trillion. In June 2021, regulatory approval of aducanumab, a humanized recombinant monoclonal antibody to amyloid ß, initially raised expectations for improved disease-modifying therapy. However, in February 2024, production of aducanumab and a post-marketing clinical trial ceased in the US due to the costs and limitations of aducanumab therapy. In March 2024, biobank data identified significant modifiable risk factors for Alzheimer's disease, including diabetes mellitus, exposure to nitrogen dioxide (a proxy for air pollution), and the frequency of alcohol intake. Therefore, modification of identifiable risk factors, combined with testing for disease-susceptibility genes, could be the most effective approach to reduce the incidence. This article aims to review the current status of disease-modifying therapies and prevention of Alzheimer's disease.

Enhanced performance of 1D rod shaped Zirconium (Zr) decorated over spherical shaped Zinc oxide (ZnO) nanostructures for NO2 gas

1D rod shaped Zirconium (Zr) was decorated over spherical shaped Zinc (ZnO) nanostructures, using hydrothermal technique. The as-synthesized nanocomposites were discovered to be discriminatory in terms of sensing nitrogen dioxide (NO2) gas. The impact of doping on structural parameters, optical properties, thermal stability, morphology, and shape were explored using HR-TEM, XRD, SAED, UV–Visible, FE-SEM, FT-IR, and EDS. The doping concentration was optimized for maximal reactivity with gas. ZZ@5 nanocomposites have the highest response (70.9 %) when compared to pure ZnO, ZZ@2, ZZ@7, ZZ@5, and ZZ@10 nanocomposites. NO2 gas reaction and recovery times were 24.5 and 27.8 s, respectively. As the concentration of NO2 gas climbs from 20 to 200 ppm, the reaction to gas sensors increases. NO2 gas has higher selectivity for ZZ@5 nanocomposite than other gases (ethanol, ammonia, hydrogen, carbon monoxide, and liquefied petroleum gas). The repeatability of ZZ@5 nanocomposite was researched for 50 days and discovered to be impressive.

Sex-specific associations between air pollutants and asthma prevalence in Japanese adults: a population-based study

ABSTRACT This study investigated the association between air pollutants and asthma prevalence in male and female Japanese adults. In this retrospective cross-sectional analysis, annual mean exposure levels of air pollutants, specifically nitrogen dioxide (NO2) and particulate matter with a median aerodynamic diameter ≤2.5 μm (PM2.5), were assessed at a local monitoring site. Multivariable logistic regression models, adjusted for genetic and/or lifestyle factors, were used to explore the association between air pollutants and asthma, with stratification by sex. A total of 1,497 participants aged ≥40 years were included. Their mean age was 65.9 ± 12.4 years, with 847 being women. Overall, 91 participants were diagnosed with asthma. In the multivariable model, ambient exposure levels of NO2 and PM2.5 were significantly associated with asthma in women but not in men. This study highlights sex as a significant determinant of the link between air pollutants and asthma exacerbation, particularly among female Japanese adults.

Associations between exposure to various air pollutants and risk of metabolic syndrome: a systematic review and meta-analysis.

Metabolic Syndrome (MetS) is a widely observed metabolic disorder that is increasingly prevalent worldwide, leading to substantial societal consequences. Previous studies have conducted two separate meta-analyses to investigate the relationship between MetS and air pollutants. However, these studies yielded conflicting results, necessitating a thorough systematic review and meta-analysis to reassess the link between different air pollutants and the risk of developing MetS. We conducted a comprehensive search of relevant literature in databases including PubMed, Embase, Cochrane Library, and Web of Science up to October 9, 2023. The search was specifically restricted to publications in the English language. Following the screening of studies investigating the correlation between air pollution and MetS, we utilized random-effects models to calculate pooled effect sizes along with their respective 95% confidence intervals (CIs). We would like to highlight that this study has been registered with PROSPERO, and it can be identified by the registration number CRD42023484421. The study included twenty-four eligible studies. The results revealed that an increase of 10μg/m3 in annual concentrations of PM1, PM2.5, PM10, NO2, SO2, and O3 was associated with a 29% increase in metabolic syndrome (MetS) risk for PM1 (OR = 1.29 [CI 1.07-1.54]), an 8% increase for PM2.5 (OR = 1.08 [CI 1.06-1.10]), a 17% increase for PM10 (OR = 1.17 [CI 1.08-1.27]), a 24% increase for NO2 (OR = 1.24 [CI 1.01-1.51]), a 19% increase for SO2 (OR = 1.19 [CI 1.04-1.36]), and a 10% increase for O3 (OR = 1.10 [CI 1.07-1.13]). The findings of this study demonstrate a significant association between exposure to fine particulate matter (PM1, PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and the incidence of metabolic syndrome (MetS). Moreover, the results suggest that air pollution exposure could potentially contribute to the development of MetS in humans.

Short-term effects of nitrogen dioxide on inpatient acute myocardial infarction in Lanzhou, China

ABSTRACT Nitrogen dioxide (NO2) represents a deleterious effect on acute myocardial infarction (AMI), but few relevant studies have been conducted in China. We aim to evaluate the acute effects of NO2 exposure on hospitalization for AMI in Lanzhou, China. In this study, we applied a distributional lag nonlinear model (DLNM) to assess the association between NO2 exposure and AMI hospitalization. We explored the sensitivity of various groups through stratified analysis by gender, age, and season. The daily average concentration of NO2 is 47.50 ± 17.38 µg/m3. We observed a significant exposure-response relationship between NO2 concentration and AMI hospitalization. The single pollutant model analysis shows that NO2 is positively correlated with AMI hospitalization at lag1, lag01, lag02, and lag03. The greatest lag effect estimate occurs at lag01, where a 10 µg/m3 increase in NO2 concentrations is significantly associated with a relative risk (RR) of hospitalization due to AMI of 1.027 [95% confidence interval (CI): 1.013, 1.042]. The results of the stratified analysis by gender, age, and season indicate that males, those aged ≥65 years, and the cold season are more sensitive to the deleterious effects caused by NO2 exposure. Short-term exposure to NO2 can enhance the risk of AMI hospitalization in urban Lanzhou.

Hourly near-ground NO2 concentration retrieval from geostationary satellite observations

Abstract. Nitrogen dioxide (NO2) is an important contributor to the formation of acid rain, photochemical smog and aerosol particles, which seriously endangers public health. At present, remote sensing of polar-orbiting satellites is a conventional means to obtain large-scale NO2 distribution, but it cannot capture the rapid change because of long revisit periods. The Advanced Himawari Imager (AHI) on the Himawari-8 geostationary satellite has the advantage of high time resolution, which makes it possible to realize near-real-time atmospheric monitoring. Here, based on the absorption characteristics of NO2 in infrared radiation, hourly near-surface NO2 concentrations are retrieved based on the brightness temperature from AHI and auxiliary information such as meteorology and aerosol. The results of 10-fold cross-validation show that NO2 estimations from satellite are in good agreement with in-situ measurements, and their determination coefficient (R2) can reach 0.79. Due to different emission and atmospheric diffusion conditions at different time, the model performance presents a diurnal variation of high accuracy in the noon and afternoon but low accuracy in the morning. Based on the retrieval dataset, it is found that high NO2 concentrations are mainly concentrated in the densely populated and industrial areas such as the North China area. In addition, NO2 pollution mainly occurs in autumn and winter, and the average NO2 concentration in winter is about 1.63 times that in summer in 2021. This study provides a new insight for satellite retrieval of NO2, which is of great significance for real-time pollution monitoring and public health protection.

Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways.

This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.

Catalytic performance of modified carbon black on methane decomposition for hydrogen production.

Carbon-based catalysts catalyze methane decomposition to produce hydrogen is a very attractive technical route. Carbon black in carbon-based catalysts has the advantages of high catalytic activity, good stability and better tolerance to toxic impurities such as sulphur in the feedstock, which has become a hot topic of research for many scientists. In this work, the effect of heat treatment on the structural and surface properties of carbon blacks and their catalytic performance in hydrogen production from methane decomposition was investigated. A commercial carbon blacks N110 was selected to heat treatment with nitrogen or carbon dioxide atmosphere at 850 °C, respectively. The Raman spectrums indicated that the graphitization degree of modified carbon under two atmospheres were promoted with the treatment time increasing. BET results revealed that the specific surface area of the carbon black treated under carbon dioxide increased, while the specific surface area was unchanged for that of the carbon black treated under nitrogen. The catalytic test of the two modified carbon blacks for methane decomposition exhibited the almost same activity, which meant that the graphitization degree of carbon black is the key factor for methane decomposition rather than the specific surface area. It was suggested that highly graphitized carbon black could be used as the potential catalysts for hydrogen production from methane decomposition.

Do air pollutants levels during the previous 48 hours predict out-of-hospital cardiac arrest incidence compared to same-day pollutants levels?

Abstract Funding Acknowledgements None. Introduction Association between air pollutants (APs) levels and out-of-hospital cardiac arrest (OHCA) at the same day of the measurements have been previously described. Whether APs levels measured during the previous 48 hours may predict OHCA occurrence is not known. Purpose To analyse whether same-day APs levels compared to APs levels during the previous 48 hours are associated with OHCA incidence. Methods OHCA admissions on five Cardiac Intensive Care Units of our city were assessed during September-2020 to December-2021. Free access data regarding particulate matter (PM10), carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) levels during the same period were also registered. APs measures occurred each hour daily at three different spots, delivering more than 31000 measurements. APs levels during the previous 48 hours and during the index day were compared between OHCA-free days (OHCA-Fd) and OHCA-days. Results 179 patients were included. Main demographic characteristics are shown on table 1. Daily OHCA incidence and APs mean levels/month are shown in Table 2. As shown at table 3 same-day higher NO2 and lower O3 levels were found on OHCA-days compared to OHCA-fd: 41 ±10 vs. 36 ±9 µg/m3, p=0.016 for NO2 and 45 ±13 vs. 51 ±11 µg/m3, p= 0.003 for O3. As shown on Table 2 same day mean O3 levels were also higher from April to September compared to the rest of the year (p&amp;lt;0.001). OHCA incidence was lower in this same period (p&amp;lt;0.001). There wasn’t any association between OHCA incidence and APs (NO2, O3, PM10 and CO) measured at any moment during the event's prior 48 hours. Conclusions Same-day higher NO2 and lower O3 levels were associated to a higher OHCA incidence in our city. On the other hand, association between OHCA and NO2, O3, PM10 or CO measurements during the previous 48 hours was not found in our cohort. Reducing production of NO2 (i.e., reducing fossil-fuel consumption) may reduce OHCA risk during that same day.

Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response

Breathing clean air is crucial for maintaining good human health. The air we inhale can significantly impact our physical and mental well-being, influenced by parameters such as particulate matter and gases (e.g. carbon dioxide, carbon monoxide, and nitrogen dioxide). Building on previous research that explored the effects of particulate matter (PM) in specific environments, analyzed using biometric indicators and machine learning models; this work focuses on the effects and estimation of inhaled nitrogen dioxide (NO&lt;sub&gt;2&lt;/sub&gt;). This study involved a cyclist equipped with sensors to monitor various biometric parameters. In addition, an electric car following the cyclist measured the ambient NO&lt;sub&gt;2&lt;/sub&gt; levels using an onboard sensor. A total of 329 biometric variables have been taken into account, of which 320 biometric variables are cognitive responses extracted using an electroencephalogram (EEG) and 9 biometric variables are physiological responses extracted using several sensors. Inhaled NO&lt;sub&gt;2&lt;/sub&gt; levels are first estimated initially by making use of all 329 variables, then using 9 physiological responses and finally using only 6 of the 9 physiological responses. The study also uses a ranking method to pinpoint which biometric variables most significantly estimate inhaled NO&lt;sub&gt;2&lt;/sub&gt; levels. Furthermore, it investigates the linear and non-linear relationship between certain variables and inhaled NO&lt;sub&gt;2&lt;/sub&gt;. The general precision of the prediction for the data set was moderate, as indicated by the coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) and the root mean square error (RMSE) between the true and estimated values of NO&lt;sub&gt;2&lt;/sub&gt; to be 0.35 and 5.41 ppb, respectively, in the test set. A higher accuracy in the prediction of lower values of NO&lt;sub&gt;2&lt;/sub&gt; levels was qualitatively observed using a scatter diagram and a Quantile-Quantile plot where the data were more plentiful. For more robust conclusions, additional data and refined machine learning models are necessary.

Effect analysis on passive and active regeneration performance of catalytic diesel particulate filters at the equilibrium point in the presence of ash layer

AbstractCatalytic diesel particulate filters (CDPF) form the main post‐processing devices for the diesel engines. Particulates trapped by CDPF can be oxidized by nitrogen dioxide (NO2) and oxygen (O2). The CDPF passive and active regeneration model on platinum‐based catalyst in the presence of ash layer, including the mass, momentum, and enthalpy balances of the gas and the solid phase, the gas diffusion, and the kinetic of the soot oxidation reaction is performed and validated by the bench test. The effects of different temperatures on CDPF passive and active regeneration at the equilibrium point are analyzed. Further, the effective analysis of ash loading and ash layer distribution on the equilibrium state is conducted. The research on CDPF passive and active regeneration at the equilibrium point in the presence of ash layer provides a theoretical basis for CDPF application.

Long-term health effects of outdoor air pollution on asthma and respiratory symptoms among adults in low-and middle-income countries (LMICs): a systematic review and meta-analysis

BackgroundFew studies have investigated the association between long-term exposure to outdoor air pollution and the risk of asthma and respiratory symptoms in low-and middle-income countries (LMICs).ObjectiveTo systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to outdoor air pollution and respiratory symptoms in LMICs.MethodsWe searched for literature published between January 1946 and September 2022 in Embase (Ovid), Medline (Ovid), and Web of Science (Core Collection). The air and gaseous pollutants studied included particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and black carbon (BC), and exposure was 1-year duration or more. We conducted a systematic review and meta-analysis with a random-effects model to calculate the relative risk (RR) estimates. A rerun of the databases was conducted in November 2023 with no eligible studies found.ResultsOf the 1,346 studies identified, only six met our inclusion criteria, and these six reported PM2.5, PM10, and NO2 with asthma as the main outcome. Three of these included studies were further included in the meta-analysis because they had data on the same exposure and outcome (PM2.5 and asthma). The main result of our study showed a borderline significant association between a 10 µg/m3 increase in exposure to PM2.5 and an increased risk of asthma (RR 1.21, 95% CI 0.93, 1.50). There was evidence of considerable heterogeneity (I2 = 75.87%). The regression-based Egger test for small-study effects showed no significant publication bias among these three studies.ConclusionOur results indicate that long-term exposure to PM2.5 increases the risk of asthma in LMICs, but studies are scarce and there is a large need for more research in LMICs in this field.Systematic Review Registrationhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0288667, PROSPERO (CRD42022311326).

The effect of coal-fired power plants on ambient air quality in Mpumalanga province, South Africa, 2014–2018

ABSTRACT Several coal-fired power plants (CFPPs) were built in South Africa, mainly in the central Mpumalanga Province, due to an increase in the demand for Eskom, the national power utility, to keep up with socio-economic growth. The CFPPs, of which 90% are owned by Eskom, generate a significant share of the country’s electricity but contribute to the air pollution experienced in the country. The paper discusses sulphur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter of size less than 10 micrometre (μm) in diameter (PM10), using data from 2014 to 2018. The statistics revealed higher PM10 concentrations during winter than in summer and spring at the Kriel and Komati sites; associated with the higher contribution of domestic burning. The study’s results could influence legislation and policies and help to understand the source of poor ambient air quality by assessing the three pollutants within the area of the selected power plants.

AIR QUALITY MONITORING USING MACHINE LEARNING

Air pollution is a critical environmental issue with significant implications for public health and the well-being of ecosystems. This project focuses on developing an innovative solution for air pollution monitoring utilizing machine learning (ML) techniques. The primary objective is to design a system that can accurately predict, analyze, and monitor air quality in real time, providing valuable insights for effective pollution control and management. The proposed system incorporates a network of sensors strategically placed in various locations to capture diverse air quality parameters such as particulate matter, nitrogen dioxide, sulfur dioxide, and more. The collected data is then processed through ML algorithms to identify patterns, correlations, and trends, enabling the system to accurately predict air quality levels. The project aims to address traditional monitoring systems' limitations by leveraging ML models' adaptability and self-learning capabilities. By continuously updating and refining the models based on incoming data, the system becomes more adept at providing precise and timely information on air quality fluctuations. This endeavour contributes to a deeper understanding of local air pollution dynamics and empowers decision-makers with actionable insights for implementing targeted interventions. The integration of ML in air pollution monitoring represents a significant step towards creating sustainable and data-driven strategies for mitigating the adverse effects of pollution on human health and the environment. Index terms Air pollution, Machine Learning, Environmental Monitoring, Sustainability, Predictive Modeling, Sensor Network, Data Analysis.

Long-term exposure to ambient air pollution and measures of central hemodynamics and arterial stiffness among multiethnic Chicago residents

ObjectivesTo examine whether long-term air pollution exposure is associated with central hemodynamic and brachial artery stiffness parameters.MethodsWe assessed central hemodynamic parameters including central blood pressure, cardiac parameters, systemic vascular compliance and resistance, and brachial artery stiffness measures [including brachial artery distensibility (BAD), compliance (BAC), and resistance (BAR)] using waveform analysis of the arterial pressure signals obtained from a standard cuff sphygmomanometer (DynaPulse2000A, San Diego, CA). The long-term exposures to particles with an aerodynamic diameter < 2.5 μm (PM2.5) and nitrogen dioxide (NO2) for the 3-year periods prior to enrollment were estimated at residential addresses using fine-scale intra-urban spatiotemporal models. Linear mixed models adjusted for potential confounders were used to examine associations between air pollution exposures and health outcomes.ResultsThe cross-sectional study included 2,387 Chicago residents (76% African Americans) enrolled in the ChicagO Multiethnic Prevention And Surveillance Study (COMPASS) during 2013–2018 with validated address information, PM2.5 or NO2, key covariates, and hemodynamics measurements. We observed long-term concentrations of PM2.5 and NO2 to be positively associated with central systolic, pulse pressure and BAR, and negatively associated with BAD, and BAC after adjusting for relevant covariates. A 1-µg/m3 increment in preceding 3-year exposures to PM2.5 was associated with 1.8 mmHg higher central systolic (95% CI: 0.98, 4.16), 1.0 mmHg higher central pulse pressure (95% CI: 0.42, 2.87), a 0.56%mmHg lower BAD (95% CI: -0.81, -0.30), and a 0.009 mL/mmHg lower BAC (95% CI: -0.01, -0.01).ConclusionThis population-based study provides evidence that long-term exposures to PM2.5 and NO2 is related to central BP and arterial stiffness parameters, especially among African Americans.

Microwave-Solvothermal Synthesis of Mesoporous CeO2/CNCs Nanocomposite for Enhanced Room Temperature NO2 Detection

Nitrogen dioxide (NO2) gas sensors are pivotal in upholding environmental integrity and human health, necessitating heightened sensitivity and exceptional selectivity. Despite the prevalent use of metal oxide semiconductors (MOSs) for NO2 detection, extant solutions exhibit shortcomings in meeting practical application criteria, specifically in response, selectivity, and operational temperatures. Here, we successfully employed a facile microwave-solvothermal method to synthesize a mesoporous CeO2/CNCs nanocomposite. This methodology entails the rapid and comprehensive dispersion of CeO2 nanoparticles onto helical carbon nanocoils (CNCs), resulting in augmented electronic conductivity and an abundance of active sites within the composite. Consequently, the gas-sensing sensitivity of the nanocomposite at room temperature experienced a notable enhancement. Moreover, the presence of cerium oxide and the conversion of Ce3+ and Ce4+ ions facilitated the generation of oxygen vacancies in the composites, thereby further amplifying the sensing performance. Experimental outcomes demonstrate that the nanocomposite exhibited an approximate 9-fold increase in response to 50 ppm NO2 in comparison to pure CNCs at room temperature. Additionally, the CeO2/CNCs sensor displayed remarkable selectivity towards NO2 when exposed to gases such as NH3, CO, SO2, CO2, and C2H5OH. This straightforward microwave-solvothermal method presents an appealing strategy for the research and development of intelligent sensors based on CNCs nanomaterials.

Synthesis and characterisation of K2CO3-activated carbon produced from distilled spent grains for the adsorption of CO2.

Activated carbon was prepared from distilled spent grains (DSG) using K2CO3 activation and chitosan modification. The effects of activator dosage, activation temperature, and the incorporation of chitosan as a nitrogen source on the adsorption performance were studied in this paper. The activated carbons were characterised by scanning electron microscopy, X-ray photoelectron spectroscopy, and nitrogen and carbon dioxide gas adsorption. Under the optimal conditions, the BET-specific surface area, total pore volume, and microporous volume of the activated carbon were as high as 1142 m2/g, 0.62 cm3/g, and 0.40 cm3/g, respectively. Chitosan was used as the nitrogen source, and surface modification was carried out concurrently with the K2CO3 activation process. The results revealed a carbon dioxide adsorption capacity of 5.2 mmol/g at 273.15 K and 1 bar without a nitrogen source, which increased to 5.76 mmol/g after chitosan modification. The isosteric heat of adsorption of CO2 all exceed 20 kJ/mol, hinting at the coexistence of both physisorption and chemisorption. The adsorption behaviour of the DSG-based activated carbon can be well-described by the Freundlich model.