Dietary survey of poultry egg intake among residents in Kunming city, southwest China.

Background: For almost all people, eggs can be a wholesome addition to the diet. However, there is insufficient applicable data to evaluate the poultry egg intake of people in the city of Kunming located in southwest China. Objectives: To understand the situation of egg consumption among local residents in Kunming via a dietary survey. Methods: Residents living in three places of Kunming were chosen using a multi-stage random sampling method. The recall methods of 3-day food intake and 1-month food intake frequency were used to assess the quantity and frequency of poultry egg dietary intake of local people. Results: Of the 1,118 respondents, 565 (50.54%) were female and 553 (49.46%) were male with age range 0.5-91 years old. Egg consumption was widespread among the survey respondents with 88.01% reporting hen egg ingestion, but the dietary intake of other poultry eggs such as duck, quail, and goose eggs were much less frequent. The medium daily intake of hen eggs was 20.00 g/d with greater amount of hen egg consumption in older age groups. However, when calculated on a body-weight basis, the median amount of hen eggs consumed daily per kilogram of body weight for all survey respondents was 0.47 g/kg/d whereas this indicator for children was 1.33 g/kg/d, becoming the greatest among all age groups. Conclusions: Our study obtained a better understanding of poultry egg intake among residents in Kunming city and calculated the egg intake kilogram of body weight that can be a useful reference to inform the development of more accurate dietary recommendation. These results also provide basic data for nutrition monitoring and dietary exposure risk assessment of poultry egg intake.

Rapid Detection of Trace Nitro-Explosives under UV Irradiation by Electronic Nose with Neural Networks.

The development of an electronic nose (E-nose) for rapid explosive trace detection (ETD) has been extensively studied. However, the extremely low saturated vapor pressure of explosives becomes the major obstacle for E-nose to be applied in practical environments. In this work, we innovatively combine the decomposition characteristics of nitro explosives when exposed to ultraviolet light into gas sensors for detecting explosives, and an E-nose consisting of a SnO2/WO3 nanocomposite-based chemiresistive sensor array with an artificial neural network is utilized to identify trace nitro-explosives by detecting their photolysis gas products, rather than the explosive molecules themselves or their saturated vapor. The ultralow detection limits for nitro-explosives can be achieved, and the detection limits toward three representative nitro-explosives of trinitrotoluene, pentaerythritol tetranitrate, and cyclotetramethylene tetranitroamine are as low as 500, 100, and 50 ng, respectively. Moreover, by extracting the features of sensor responses within 15 s, a classification system based on convolutional neural network (CNN) and long short-term memory network (LSTM) is introduced to realize fast and accurate classification. The 5-fold cross-validation results demonstrate that the CNN-LSTM model exhibits the highest classification accuracy of 97.7% compared with those of common classification models. This work realizes the detection of explosives photolysis gases using sensor technology, which provides a unique insight for the classification of trace explosives.

Dual Improvement in Sensitivity and Humidity Tolerance of a NO2 Sensor Based on 3-Aminopropyltriethoxysilane Self-Assembled Monolayer-Functionalized SnSe2 for Explosive Photolysis Gas Detection.

Explosives can be analyzed for their content by detecting the photolytic gaseous byproducts. However, to prevent electrostatic sparking, explosives are frequently preserved in conditions with low temperatures and high humidity, impeding the performance of gas detection. Thus, it has become a research priority to develop gas sensors that operate at ambient temperature and high humidity levels in the realm of explosive breakdown gas-phase detection. In this work, 3-aminopropyltriethoxysilane (APTES) self-assembled monolayer-functionalized tin diselenide (APTES-SnSe2) nanosheets were synthesized via a facile solution stirring strategy, resulting in a room-temperature NO2 sensor with improved sensitivity and humidity tolerance. The APTES-SnSe2 sensor with moderate functionalization time outperforms the pure SnSe2 sensor in terms of the response value (317.51 vs 110.98%) and response deviation (3.11 vs 24.13%) under humidity interference to 500 ppb NO2. According to density functional theory simulations, the stronger adsorption of terminal amino groups of the APTES molecules to NO2 molecules and stable adsorption energy in the presence of H2O are the causes of the improved sensing capabilities. Practically, the APTES-SnSe2 sensor achieves accurate detection of photolysis gases from trace nitro explosives octogen, pentaerythritol tetranitrate, and trinitrotoluene at room temperature and various humidity levels. This study provides a potential strategy for the construction of gas sensors with high responsiveness and antihumidity capabilities to identify explosive content in harsh environments.

Topological insulator Bi2Se3 for highly sensitive, selective and anti-humidity gas sensors.

Chemiresistive gas sensors generally surfer from low selectivity, inferior anti-humidity, low response signal or signal-to-noise ratio, severely limiting the precise detection of chemical agents. Herein, we exploit high-performance gas sensors based on topological insulator Bi2Se3 that is distinguished from conventional materials by robust metallic surface states protected by time-reversal symmetry. In the presence of Se vacancies, Bi2Se3 nanosheets exhibit excellent gas sensing capability toward NO2, with a high response of 93% for 50 ppm and an ultralow theoretical limit of detection concentration about 0.06 ppb at room temperature. Remarkably, Bi2Se3 demonstrates ultrahigh anti-humidity interference characteristics, as the response with standard deviation of only 3.63% can be achieved in relative humidity range of 0-80%. These findings are supported by first-principles calculations, with analyses on adsorption energy and charge transfer directly revealing the anti-humidity and selectivity. This work may pave the way for implementation of exotic quantum states for intelligent applications.

Physical Activity among Rural Residents in Eastern, Central, and Western Provinces of China: A Cross-Sectional Survey.

Physical activity (PA) in which physical exercise (PE) is an important component is probably the most important intervention for preventing noncommunicable diseases (NCDs). However, few studies on PA and PE of rural residents in China were reported. This study conducted the first population-based cross-sectional survey in three provinces of China in 2021 that examined both PA and PE as well as the associated factors of rural residents. The International Physical Activity Questionnaire Short Form (IPAQ-S) was used, and a total of 3780 rural residents were surveyed. The result showed that 22.2% of the rural residents were physical inactivity and rural residents reporting practice of PE was 54.4%. Binary logistic regression analyses showed that being female, people aged between 15 to 34 years or 60 years old and above, employees of governmental departments/retirees, school students, the unemployed, and people with NCDs were risk factors of PA while ethnic minority groups, smoking, and alcohol consumption were risk factors of PE. Health promotion programme aiming at increasing people's PA in rural China is urgently needed, and it should focus on the population groups of the female, people aged 60 years and above, school students, the unemployed, and people with NCDs.

Improving Anti-Humidity Property of a SnO2-Based Chemiresistive Hydrogen Sensor by a Breathable and Hydrophobic Fluoropolymer Coating.

Metal-oxide-based chemiresistive hydrogen sensors exhibit high sensitivity, long-term stability, and low cost and have been extensively applied in safety monitoring of H2. However, the sensing performances are dramatically affected by the water vapor, resulting in reduced response value and increased response/recovery time. To improve the anti-humidity property of sensors, coating the breathable and hydrophobic membrane on the surface of the sensing film is an effective strategy. In this work, the poly[4,5-difluoro-2,2-bis(trifluoromethyl)-1,3-dioxole-co-tetrafluoroethylene] (Teflon AF-2400) was dip-coated on the surface of SnO2 in a commercial hydrogen sensor (TGS2615) as a breathable and hydrophobic membrane. For safety, He instead of H2 was used to test the gas permeability of membranes. The Teflon membrane shows a high He permeability of up to 40,700 Barrer and an excellent He/H2O selectivity of 99. Moreover, Teflon shows high processability to form a defect-free coating on the rough surface of the sensing film and high chemical stability under the operando condition of the sensor. Thus, the Teflon-modified sensor possesses excellent selectivity with a value of 5, and the resistance is stable at 10,554 ± 3% Ω for 20 days in 80% RH. The modified sensor shows an improved anti-humidity property with a 75% response to 200 ppm H2 at 80% RH and has a low coefficient of variation value of 7.23% that shows advances than other reported sensors modified by coatings. The commercially available Teflon and the simple coating technology make the strategy easily scale up and show promising applications.

2D SnSe2 nanoflakes decorated with 1D ZnO nanowires for ppb-level NO2 detection at room temperature.

The detection of air pollutant nitrogen dioxide (NO2) is of great importance arising from its great harm to the ecological environment and human health. However, the detection range of most NO2 sensors is ppm-level, and it is still challenging to achieve lower concentration (ppb-level) NO2 detection. Herein, 2D tin diselenide nanoflakes decorated with 1D zinc oxide nanowires (SnSe2/ZnO) heterojunctions were first reported by facile hydrothermal and ultra-sonication methods. The response of the fabricated SnSe2/ZnO sensor enhances 3.41 times on average compared with that of pure SnSe2 sensor to 50-150 ppb NO2 with a high detection sensitivity (22.57 ppm-1) at room temperature. In addition, the SnSe2/ZnO sensor has complete recovery, negligible cross-sensitivity, and small relative standard deviation (6.98%) during the 1 month sensing test, which can meet the requirements for NO2 detection in environmental monitoring. The enhanced NO2 sensing performance can be attributed to the n-n heterojunction constructed between SnSe2 and ZnO. The as-prepared sensor based on SnSe2/ZnO hybrid significantly promotes the development of the low detection limit of the NO2 sensor at room temperature.

Mask-Wearing and Handwashing Behaviors of Chinese Rural Residents during the Pandemic of COVID-19: A Cross-Sectional Survey.

OBJECTIVE: To understand mask-wearing and handwashing behaviors of Chinese rural residents during the COVID-19 pandemic and to analyze the associated factors. METHODS: This study used a multi-stage random sampling method to conduct a cross-sectional questionnaire survey during the period of July to December of 2021, in six counties located in Shandong, Shanxi, and Yunnan provinces representing the eastern, central, and western regions of China, respectively. A total of 3864 villagers were surveyed with a questionnaire, and 3832 valid questionnaires were finally analyzed. Descriptive statistics and logistic regression analysis were used for statistical analysis. RESULTS: Around ninety-four percent (93.6%) of rural residents reported mask-wearing during the COVID-19 pandemic, but only 44.5% of them could replace masks in time. Multivariate logistic regression analysis showed that those who were female, aged 15-59, had an education level of high school and above, were divorced/widowed, worked as farmers (workers), or were rural residents in Shandong Province were more likely to wear masks. Furthermore, those who were female, aged 15-59, had an education level of high school and above, were unmarried and married, were business and service workers, or were rural residents in Shandong and Shanxi Province replaced masks more timely. Around seventy percent (69.7%) of rural residents reported using soap when washing their hands, but only 38.0% of rural residents could wash their hands properly. Multivariate logistic regression analysis showed that rural residents who were aged 35-59, had an education of high school and above, or lived in Shandong Province and Shanxi Province were more likely to wash their hands with soap. Those who were aged 15-59, had an education of high school and above, worked as farmers (workers), were employees of governmental departments and retirees, were business and service workers, or were students had higher proper handwashing rates. CONCLUSION: During the COVID-19 pandemic, the proportion of Chinese rural residents wearing masks reached 93.6%, but only 44.5% were able to replace masks in time, gender, age, education level, marital status, occupation, and living place had an impact on mask-wearing. The proportion of Chinese rural residents who could wash hands with soap reached 69.7%, but only 38.0% could wash their hands properly. Age and education level were influencing factors for both washing-hand with soap and proper handwashing.

Facile hydrothermal synthesis of Ti3C2Tx-TiO2 nanocomposites for gaseous volatile organic compounds detection at room temperature.

The gaseous volatile organic compounds (VOCs) sensors with high-selectivity and low-power consumption have been expected for practical applications in environmental monitoring and disease diagnosis. Herein, we demonstrate a room-temperature VOCs gas sensor with enhanced performance based on Ti3C2Tx-TiO2 nanocomposites. The Ti3C2Tx-TiO2 nanocomposites with regular morphology are successfully synthesized via a facile one-step hydrothermal synthesis strategy by using Ti3C2Tx itself as titanium source. Attributed to the formation of interfacial heterojunctions and the modulation of carrier density, the Ti3C2Tx-TiO2 sensor exhibits about 1.5-12.6 times enhanced responses for the detection of various VOCs at room temperature than pure MXene sensor. Moreover, the nanocomposite sensor has better response to hexanal, both an air pollutant and a typical lung cancer biomarker. The gas response of the Ti3C2Tx-TiO2 sensor towards 10 ppm hexanal is about 3.4%. The hexanal gas sensing results display that the nanocomposite sensor maintains a high signal-to-noise ratio and the lower detection limit to hexanal gas is as low as 217 ppb. Due to the low power consumption and easy fabrication process, the Ti3C2Tx-TiO2 nanocomposite sensor is promising for application in IoT environmental monitoring as well as real-time health monitoring.

Enhanced ammonia sensing performance based on MXene-Ti3C2Tx multilayer nanoflakes functionalized by tungsten trioxide nanoparticles.

Low-power consumption and high sensitivity are highly desirable for a vast range of NH3 sensing applications. As a new type of two-dimension (2D) material, Ti3C2Tx is extensively studied for room temperature NH3 sensors recently. However, the Ti3C2Tx MXene based gas sensors suffer mainly from low sensitivity. Herein, we report a sensitive Ti3C2Tx/WO3 composite resistive sensor for NH3 detection. The Ti3C2Tx/WO3 composite consisting of WO3 nanoparticles anchored on Ti3C2Tx nanoflakes were synthesized successfully with a facile ultra-sonication technique. The composite sensor with optimized components exhibits a high sensitivity of 22.3% for 1 ppm NH3 at room temperature, which is 15.4 times higher than the pure Ti3C2Tx sensor. Furthermore, the composite sensor has excellent reproducibility, good long-term stability, and high selectivity to NH3. The relative humidity influence on NH3 gas sensing properties of the sensors was systematically studied. This research provides an efficient route for the preparation of novel MXene-based sensitive materials for high-performance NH3 sensors.

Surface Ligand Engineering for a Lead-Free Cs3Cu2Br5 Microcrystal-Based Humidity Sensor with a Giant Response.

Halide perovskites are potential humidity-detection materials due to their sensitivity to water, but the instability of traditional lead-based halide perovskites and the toxicity of Pb hinder further application in humidity sensing. Here, lead-free Cs3Cu2Br5 perovskite microcrystals passivated by surface ligands (OLA and OAm) are used to prepare an environmentally friendly humidity sensor. The humidity sensing performance of the prepared sensors was tested, and the effect of surface ligands of perovskites on the performance of humidity sensors was analyzed. The results show that the impedance variations of the manufactured humidity sensors at 12 to 95% relative humidity are 106Ω (OLA) and 105Ω (OAm), respectively. Besides, the sensors demonstrated excellent repeatability, low hysteresis, and considerable stability at different RH values. Furthermore, the analysis of the different ligands attests that short-chain OLA is more conducive to the formation of porous films with stronger water absorption capacity, further improving the responsiveness of the sensor. By contrast, and long-chain OAm is more conducive to the formation of dense films, improving the response ability at low humidity. Additionally, the more hydrophilic OLA contributes to greater responsiveness, while the more hydrophobic OAm helps to shorten the response and recovery time.

An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack.

In this paper, a support vector machine (SVM) model which can be used to predict the compressive strength of mortars exposed to sulfate attack was established. An accelerated corrosion test was applied to collect compressive strength data. For predicting the compressive strength of mortars, a total of 638 data samples obtained from experiment was chosen as a dataset to establish a SVM model. The values of the coefficient of determination, the mean absolute error, the mean absolute percentage error and the root mean square error were used for evaluating the predictive accuracy. The main factors affecting the predicted compressive strength were obtained by sensitivity analysis. A SVM model was calibrated, validated, and finally established. Moreover, the performance of the SVM model was compared to an artificial neural network (ANN) model. Results show that the prediction values from the SVM model were close to the experimental values; the main factors sensitive to concrete compressive strength were exposure time, water-cement ratio and sulfate ions; the performance of the SVM model was better than the ANN model. The SVM model developed in this study can be potentially used for predicting the compressive strength of cement-based materials servicing in harsh environments.