CO2 Concentration In Gas Research Articles

Catalytic Gasification and Reforming of Residual Biomass in a Bench Scale System with Low Cost Catalysts.

This paper demonstrates the effectiveness of using of different catalysts for reforming tars contained in the syngas of biomass gasifiers. The conversion of the tar content allows to obtain high quality syngas and to maximize the gas fraction. A bench scale equipment consisting of an autothermal fluidized bed gasifier and a downstream packed bed reformer was used. Pine sawdust was selected as the feedstock for gasification. TGA analysis showed that the temperature must be above 350 °C to ensure the ignition of the biomass and maintain the process in an autothermal steady-state. Dolomite and pyrolysis char were used to test of the fluidized bed catalysts. In the reformer, dolomite, pyrolysis char, iron doped activated carbon and spent HDS catalyst were used. All catalysts decreased the CO2 concentration in the product gas and increased H2 , CH4 and CO. When iron doped activated carbon is used, tar contents below 60 g/Nm3 in the product gas could be obtained, reaching less than 1 g/Nm3 . The best value of LHV (lower heating value) was obtained with pyrolysis char as a catalyst (4.8 MJ/Nm3 ). The results demonstrate that catalytic biomass gasification with downstream tar reforming with low-cost catalysts is a promising solution for energy applications.

Grafting amine-functionalized ligand layer on catalyst for electrochemical CO2 capture and utilization

Direct electrochemical CO2 capture and utilization from flue gas offers appealing route to closing the carbon cycle and avoiding the cost for CO2 purification. Yet, the low CO2 concentration in flue gas and competing oxygen reduction reaction (ORR) pose severe challenges for this process. Herein, we report 4-aminobutylphosphonic acid ligand layer capped Ag nanoparticles (NH2BPA-Ag) for high-efficiency conversion of simulated flue gas (SFG). The amino group in NH2BPA is available for CO2 capture and activation, while phosphonic acid groups inhibit ORR by desorbing *OOH intermediate. NH2BPA-Ag achieves 82% CO Faradaic efficiency (FECO) in H-cell for SFG reduction and enhances FECO 2.6 times over pristine Ag nanoparticles. In a membrane electrode assembly electrolyzer, NH2BPA-Ag shows 79.6% FECO and 38.1% full-cell energy efficiency at 48 mA cm-2 for SFG conversion. This study highlights the strategy of tailoring catalytic microenvironments at molecular level for realizing a highly selective performance.

Characterization of a novel two-stage high ash coal gasifier for low tar and high calorific value gas

The high ash content of coal poses two critical challenges in conventional gasifiers: (a) ash agglomeration and (b) low-quality syngas. The ash agglomerations are caused by the interaction of alkali and alkaline earth metals with other inorganic compounds, localized temperature generation, and unoptimized process parameters. Whereas low-quality gas is due to moderate temperature operations and the mixing of syngas with pyrolysis products. Recognizing these challenges, the current work discusses the design, development, and performance characterization of a novel two-stage gasification system that strategically segregates oxidative pyrolysis in a cyclone reactor and oxy-steam char gasification in a packed bed reactor. Such segregation provides explicit control over parameters like local temperatures, particle and gas residence time, enabling the configuration to handle coal with a wide range of ash content. Thermodynamic analysis and performance characterization experiments have been carried out with varied temperatures, oxygen, and steam flow rates. While operating the system up to 950˚C, there was no indication of clinker formation. The gravimetric tar content was found 52 ± 3 mg/Nm3. The concentration of CO, CO2, CH4, and H2 in the product gas during experiments was 40 ± 5, 20 ± 5, 2 ± 1, and 45 ± 5%, respectively. The carbon conversion was greater than 77%, with further scope for improvement.

Reducing the Energy Penalty of Retrofit Decarbonization in Combined Cycle Power Plants

Abstract This study is a continuation of previous work aimed at elucidating the effect of hydrogen-cofiring and exhaust gas recirculation (EGR) on combined cycle (CC) performance. The thermodynamic analysis was expanded to include postcombustion capture (PCC) by means of mono-ethanolamine (MEA). Attention was paid to net power output and thermal efficiency. Part-load operation of the CC without carbon capture was taken as a reference. Decarbonization solutions, in ascending order of complexity, included the following: (1) adding a PCC unit; (2) combining EGR with PCC, so as to exploit the increase in the flue gas CO2 concentration while reducing the exhaust gas flow delivered to the absorber; (3) including hydrogen cofiring at the largest capability dictated by the gas turbine (GT) combustion system, with the opportunity to explore a wider range of EGR rates, while still relying on PCC of the residual CO2 in flue gas, before discharge into the environment. Scenarios were first discussed under the same GT load for consistency with the published literature, thus enabling the validation of the modeling procedure. Then, CC net power production was assumed as the basis of comparison. The third solution was found to be the most promising thus minimizing both the energy penalty due to carbon capture and CO2 emission intensity (EI).

Modeling the effects of speed limit cameras (SLCs) on air quality and traffic flow on access-controlled highways

The impact of speed limit cameras (SLCs) on speed limit enforcement and traffic safety has long been studied. However, the effects of SLCs on air quality have not been thoroughly investigated due to the lack of data at sites where both air quality and traffic flow measurements (such as speed and volume) are monitored in close proximity to SLCs. This study aims to quantify the effect of SLCs on air quality and traffic flow parameters on access-controlled highways using linear regression models (LRM) and generalized additive models (GAM). The latter allows the modeling of nonlinear functional relationships between the predictor and outcome variables. We collected experimental data, including traffic flow parameters, weather conditions, and air pollutants (CO, CO2, NO2, SO2, and VOC), on high-volume, high-speed access-controlled highways in the wider metropolitan area of Dammam, Saudi Arabia. Key parameters related to traffic, such as volume, speed, percentage of heavy-duty vehicles (HDV), and percentage of traffic exceeding the speed limit, were used to estimate air pollutant generation from vehicular exhaust emissions near highway locations. Our analysis of the collected data revealed that locations with SLCs showed higher adherence to the speed limit of 120 km/h. The 85th percentile speed at these sites was approximately 114 km/h, which was 11 percent lower than that at the no-camera sites. Additionally, field data demonstrated a significant reduction in CO and SO2 emissions at the camera locations by approximately 23.7 and 20.3 percent, respectively. However, at these locations, the CO2, NO2, and VOC gas concentrations increased slightly, ranging from 1.5 to 4.9 percent. The predictive capabilities of the GAMs for gas emissions were higher than those of the LRMs, except for CO2 emissions. The GAM results identified optimal speed values between 110 km/h and 130 km/h to minimize vehicle emissions. Furthermore, the GAM results indicated that SLC locations had significantly lower concentrations of exhaust gas emissions for CO, SO2, and VOC, but higher levels of CO2 and NO2 gases. Thus, the field data and GAM results were in agreement for all gases except VOC. These findings have implications for shaping environmental transportation policies and guidelines, highlighting the multifaceted impact of SLCs on both traffic behavior and air quality.

Implications of CO2 Sourcing on the Life-Cycle Greenhouse Gas Emissions and Costs of Algae Biofuels.

Production of algal biomass and its conversion to biofuels are important technological platforms within the larger umbrella of CO2 capture and utilization. This analysis incorporates a life-cycle assessment (LCA) with respect to global warming potential and techno-economic assessment (TEA) of algae biofuels, focusing on the sourcing and delivery of CO2. This analysis evolves past work in this area to include high-purity biogenic CO2, industrial fossil fuel use, fossil power plants, and direct air capture, and uses a Sherwood plot approach to estimate the CO2 capture energy penalty. We also show that allocation or displacement facilitates a more intuitive distinction between biogenic and fossil sources of carbon. Thus, the LCA better reflects the influence of coproduct handling strategies as compared to previous works. The TEA is also strongly influenced by the CO2 concentration in the flue gas. Currently, when CO2 is sourced from large-point sources, the price of biofuels ($4.5-6.5/GGE) may become comparable to fossil diesel. However, as DAC systems become more economical, they may deliver competitive CO2 sources for biofuels in 2050 with a total cost of <$7/GGE. Based on the net emissions and costs, algae biofuels with CO2 sourced from biogenic sources are consistent with a decarbonized economy as of now, with substantial potential for DAC with decreasing costs.

MEA-based CO2 capture: a study focuses on MEA concentrations and process parameters

CO2 capture using monoethanolamine (MEA) is one of the important decarbonization options and often considered as a benchmark, while the optimal MEA contraction and systematic process study are still lacking. In this work, firstly, the MEA concentrations between 15 and 30 wt% were studied from both process simulations with Aspen Plus and experimental measurements in the pilot-scale. 20 wt% MEA was identified as the preferable solution. Subsequently, a systematic analysis was conducted for CO2 capture using 20 wt% MEA with/without CO2 compression to study how various parameters, including gas flow rate, CO2 concentration, and CO2 removal rate, affected the energy demand and techno-economic performances quantitatively. The influence of each parameter on both energy demand and cost showed an obvious non-linear relationship, evidencing the importance of systematic analysis for further study on decarbonization. The evaluation indicated that the regeneration heat required the largest portion of energy demand. The economic analysis showed that the capital cost was more sensitive to the selected parameters than the operational cost, while the operational cost created a major change in the overall cost. In addition, the gas flow rate and CO2 concentration were the main parameters affecting the cost, rather than the CO2 removal rate. Finally, it was suggested that, for a new plant, CO2 capture showed the minimum investment cost per ton CO2 when operating the plant on a large scale, high CO2 concentration, and high CO2 removal rate; for an existing plant, the capture preferred to run with the high CO2 removal rate.

Determination and analysis of macro-featured parameters based on kilogram-level coal spontaneous combustion experiment

The quantitative characterization of coal spontaneous combustion and the accurate determination of the dangerous degree are hot and difficult in the field of coal mine safety. In the paper, the experimental test analysis of Huangling No. 2 coal was carried out by a self-designed and built kilogram-level coal spontaneous combustion experimental device, and the macro featured parameters were obtained and analyzed, such as temperature field, index gas, weight, oxygen consumption rate and exothermic intensity, and spontaneous combustion limit parameters. The results show that the experimental spontaneous combustion period of Huangling No.2 coal is 34 days. When the coal temperature was below the critical temperature of 74.59 °C, the oxidation of coal samples was relatively slow, and the oxygen consumption and the production rate of gas products did not change much. After the coal temperature exceeded the critical temperature, the oxygen consumption rate and exothermic intensity increased faster and the oxidation accelerated. After reaching the dry cracking temperature of 104.93 °C, the oxidation degree accelerated sharply. CO starts to appear at the beginning of the experiment, and the increase of CO gas concentration during the heating process is exponential and can be used as an index gas. Under the temperature condition of 40 °C, the floating coal of 0.7 m needs 25.83% oxygen concentration, and the maximum air leakage intensity of the coal is negative, and no spontaneous combustion will occur. The test data and calculation results of the experiment are important for the prediction and forecast of natural fires and the determination of the risk of spontaneous combustion on site.

Inhibition property of gasification ash to coal spontaneous combustion

In view of the problems of uncomprehensive utilization of solid waste resources and environmental pollution caused by the stacking of coal gasification cinder, the gel inhibition performance of coal gasification cinder was studied. A new kind of anti-fire gel material was prepared by crosslinking with gasification cinder as the base material, HPMC as the gelling agent, and Na2CO3 as the coagulant. The effect of coal to oil gasification ash gel on coal spontaneous combustion and activation energy was analyzed in essence and phenomenon through temperature-programmed experiments and determination of marker gases. Results showed that the concentration of CO gas and C2H4 gas of anthracite during the process of temperature oxidation decreased by 38.1% and 65.8%. The inhibition rate after gel treatment was 56.4%, and the activation energy was 6.5%. It shows that the adjunction of ash gel can validly mitigate the reaction rate of the coal spontaneous combustion process and inhibit the reaction procedure between coal and oxygen. The inhibition performance is better than the common CaCl2.

High performance triboelectric nanogenerator with needle tips discharge for gas detection applications

Nowadays, there is an increasing demand for carbon dioxide (CO2) gas detection in various domains, including industry, agriculture, and livestock, et. However, existing CO2 gas detection methods often suffer from issues such as bulkiness, heaviness, high power consumption. Thus, there is a pressing need to develop a compact, lightweight, and efficient CO2 gas detection approach. This paper introduces a self-powered CO2 gas detection method based on a rotating triboelectric nanogenerator (TENG) with a needle-like ionization structure, capable of inducing gas ionization. This paper presents a self-powered CO2 gas detection method based on a rotating triboelectric nanogenerator (TENG) with a needle-like ionisation structure that induces ionisation of the gas. After gas ionisation, the output performance of the overall TENG system is increased extremely substantially, and CO2 is characterised by its high output of distinct signals. By ionizing CO2 gas under different air pressure, concentration, and humidity conditions, the TENG generates distinct voltage outputs, enabling discrimination of CO2 gas pressure, concentration, and humidity levels. Experimental tests involving gas ionization are carried out at air pressures ranging from − 10 kPa to − 60 kPa, yielding a post-ionization voltage increase from 650 V to 1800 V, showing a linear increase. As the CO2 gas concentration in the gas mixture rises from 10% to 90%, the gas ionization gradually and linearly enhances the voltage performance of the TENG. Humidity tests demonstrate a negative correlation between voltage change and humidity when humidity is below 55 RH (%), while a positive correlation is observed when humidity exceeds 55 RH (%). Leveraging the correlation between CO2 gas pressure, concentration, humidity, and the TENG's output performance, a simple and efficient method of CO2 gas detection can be proposed. The proposed CO2 gas detection method exhibits simplicity, cost-effectiveness, and self-powered operation, offering potential applications in industrial and Internet of Things (IoT) domains.

Carbon dioxide sensing using a PEI-Cr2O3-rGO nanocomposite sensor with patterned copper clad as a substrate

ABSTRACT The focus of this work is on carbon dioxide sensing using a nanocomposite sensor composed of PEI, Cr2O3, and rGO, with a patterned copper-clad substrate. Study explores how sensors with varying weight percentages of Cr2O3 and rGO in PEI perform in terms of sensitivity when exposed to carbon dioxide gas. Furthermore, the study probed into exploring the correlation between the fluctuations in electrical resistance of the sensors and the levels of CO2 gas concentration. This examination delves into the sensitivity, repeatability, response time, and recovery time of the sensors. At 1000 ppm of CO2, the sensor with 0.25 wt% of Cr2O3-rGO in PEI demonstrated the lowest sensitivity percentage of 1.71, while the highest sensitivity of 2.13 was achieved by the sensor with 1.0 wt% of Cr2O3-rGO in PEI. The repeatability of the sensors containing 0.25, 0.50, 0.75, and 1.00 wt% of Cr2O3-rGO in PEI was found to be comparable at 8 minutes. However, the repeatability of the sensor with 1.00 wt% of Cr2O3-rGO in PEI was especially noteworthy. A PEI nanocomposite with 0.25 wt% Cr2O3-rGO demonstrated the quickest response time of 33 seconds and recovery time of 36 seconds compared to the other sensors. The sensing mechanism involves the physical absorption of CO2, the effective interactions of the amino groups with CO2 to form carbamates, and the electrical response changes of the PEI-Cr2O3-rGO nanocomposite at room temperature. This mechanism provides a reliable and efficient means of sensing CO2 gas for various applications at equal to or less than 1000 ppm.

Visualisation of [11C]CO2 storage in coal with positron emission tomography imaging

Positron Emission Tomography (PET) imaging has demonstrated its capability in providing real-time visualisation of fluids flow in geological materials. However, it has not been used for the study of CO2 injection and storage in coal for the application of CO2 geo-sequestration. To observe the processes directly, [11C]CO2 is the most optimal radiotracer, which is rarely used due to its short half-life (20.4 min) and handling safety issues as a gaseous tracer. In this work, a novel laboratory protocol is developed to use gaseous [11C]CO2 as the radiolabelled tracer to visualise and quantify dynamic processes of gas spreading, adsorption, diffusion, and advection flow in coal under in-situ conditions. The experimental setup integrates core flooding setup with PET scanning. Coal samples are pre-treated to mimic different injection conditions, including coal seam gas reservoirs in early production stage, gas depleted stage and CO2 storage stage. Due to high temporal resolution of PET imaging, time-lapse CO2 gas concentration map of each test is acquired by converting the PET intensity values to gas concentrations. Impacting factors on storage capacity and efficiency are also studied, including permeability, gas adsorption, gas exchange, and initial storage conditions. This work introduces a new laboratory protocol and analysing framework to quantify sub-core scale multiphysics CO2 flow in coal, which provides a foundation for future across-scale theoretical and experimental study of multiphase and multicomponent flow behaviours in coal for the application of CO2 geo-sequestration.

Modeling of a Rotary Adsorber for Continuous Capture of Indoor Carbon Dioxide

Removing indoor CO2 as a pollutant via solid sorbents is a promising solution to maintaining acceptable indoor air quality while minimizing the energy consumption of ventilation. Compared to fixed-bed and fluidized-bed configurations, which require at least two beds to allow for continuous operation, a rotary adsorber is more compact and suitable to be integrated into the ventilation systems of buildings. In the present study, a regenerative rotary adsorber based on temperature swing adsorption was modeled to investigate continuous CO2 capture in an indoor environment. The governing equations of heat and mass transfer processes associated with the capture were established and coded in ANSYS Fluent software. The spatiotemporal variations of CO2 concentration and temperature in gas and solid phases within the rotary adsorber were obtained. The key findings are: (1) adjusting the speed mainly affects circumferential concentration and temperature distribution, but has little impact on axial concentration and temperature; (2) Increasing desorption inlet flow rate has little impact on adsorption outlet concentration, but significantly decreases desorption outlet concentration; (3) Raising desorption inlet temperature can increase both adsorption and desorption outlet average concentrations; (4) Reducing the volume proportion of the desorption sector will slightly increase adsorption outlet concentration and slightly decrease desorption outlet concentration, but barely affects average adsorption and desorption outlet temperatures.

Enhanced single-loop control of a moving bed temperature swing adsorption CO2 capture process

Using a model in gPROMS, we study a Zeolite 13X-based moving bed temperature swing adsorption (MBTSA) process designed to capture CO2 from a coal-fired power plant. Two enhanced single-loop control strategies were compared to a proportional-integral configuration for variations in power plant load, control variable setpoints, flue gas CO2 concentration and external heat source temperature. Measurement delays were also investigated. Adaptive adjustment of controller parameters with system load gave smoother and narrower manipulated variable profiles and efficient CO2 recovery setpoint tracking. The controller gain is the most important parameter for adaptive tuning. A combined feedback and feedforward scheme showed improved control of the regenerated sorbent temperature, possibly due to better decoupling of the higher-level control loops. When delays were considered, the investigated strategies significantly outperformed the reference case for CO2 recovery control. The results demonstrate that the MBTSA process can be efficiently controlled for several disturbances and changes in operation.

Optical CO2 gas sensor based on liquid crystals in a textile grid

Given the increasing concerns about global warming, it is undeniable that measuring and controlling carbon dioxide (CO2) levels, a colorless and odorless greenhouse gas, is of great value. In this respect, liquid crystals (LCs) as an anisotropic material hold promise for fabricating such gas sensors. Here, we report a sensitive optical gas sensor for real-time monitoring of CO2 gas, exploiting a textile grid impregnated with LC and diethanolamine (DEA) as a CO2-sensitive material. The sensing mechanism relies on the reorientation of LC molecules upon the interaction of gas analytes with DEA. By tracing optical texture changes and extracting the corresponding intensities, CO2 gas concentrations ranging from 300 to 10,000 ppm were detected. The sensor exhibits a response time of 12 seconds and a recovery time of 7 seconds at 800 ppm. The sensor is simple and cost-effective.

Experimental investigation of the smoke explosion phenomenon

This study investigates the burning history of medium-density fibre (MDF) cribs in an underventilated enclosure with two vertical openings in the front wall. Such compartment fires have been explored before by other researchers and demonstrated the conditions that would lead to smoke explosions (Sutherland, 1999; Chen, 2012) [1,2]. A total of 19 experiments were completed, 5 of which led to smoke explosions. A gas conditioning system is utilised for the experiments which included a hydrocarbon analyser and an enhanced Phi-meter. Heat release rate, mass loss rate, temperatures, pressures, and O2, CO2 and CO gas concentrations within the compartment are measured for each experiment. Compartment fires with MDF crib fuel in underventilated conditions can lead to smoke explosions for both porosity-controlled and surface-controlled cribs. The smoke explosion is found to be occurring when the changes of pyrolysate gases and oxygen concentrations in the compartment form a flammable mixture. The equivalence ratio of the compartment was found between 1.5 and 2.0 before the occurrence of smoke explosions. Transformation of flaming combustion to smouldering was generally observed as a precursor for the accumulation of unburned fuel and the occurrence of smoke explosion.

Investigating the effect of surface urban heat island on the trend of temperature changes

Urbanization has led to the emergence of surface urban heat islands (SUHI) and air pollution, necessitating comprehensive investigations into the urbanization process. This study explores the impact of urbanization on temperature trends in northern Iran using remote sensing and neural networks. MODIS images from 2001, 2010, and 2019 were employed to determine climatic features and vegetation levels. SUHI indices and the Urban Thermal Field Variance Index (UTFVI) were utilized to assess ecological comfort and urban heat islands. Markov chain methods were used for forecasting future thermal pollution. The relationship between temperature, air pollutants, spectral indices, and thermal indices was investigated through regression analysis. Furthermore, multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM) neural networks were employed for predicting indicator values. The findings reveal an increase in thermal pollution indicated by elevated Land Surface Temperature (LST), SUHI, and UTFVI values, along with a decrease in Normalized Difference Vegetation Index (NDVI) values between 2001 and 2019. The Urbanization Index (UI) values demonstrate a 5% increase in urban areas during the same period. The UTFVI values suggest a decline in ecological comfort due to urbanization and reduced vegetation, with the majority of the region experiencing the poorest thermal comfort in 2019. The SUHI index peaked at 11.72 in 2019, with the highest values observed in spring and the lowest in winter. Correlation analysis highlights a significant relationship between LST and various factors, including SUHI, UTFVI, CH4, SO2, and CO2, underscoring the influence of temperature increase on pollution escalation in the study area. Air pollution assessment indicates the highest concentration of CO gas in the northern and southern parts of the region. Tehran exhibits the highest CO gas levels at 0.051 ppm, while Golestan records the lowest at 0.012 ppm. SO2 levels reach their peak in northern regions (0.0088 ppm) and are lowest in southern regions (0 ppm). The Markov and CA-Markov chain methods predict that by 2050, approximately 50% of the region will experience high temperatures. Neural network results demonstrate that the LSTM method outperforms MLP and RBF methods, with R2 values of 0.99 and 0.98, respectively, indicating higher accuracy in LST prediction during testing and validation stages. Overall, this study highlights the potential of neural networks in predicting thermal pollution and facilitating effective land management strategies in urban areas.

Global Warming Potential of Carbon dioxide and Methane Emission from Mangrove Sediment in Waiheru Coastal, Ambon Bay.

Mangroves can store carbon. However, it also produces carbon emissions by degrading organic matter in mangrove sediments. This research was conducted in a mangrove ecosystem at Waiheru, in the inner Ambon bay, to determine CO2 and CH4 emissions and their potential for global warming (GWP) from mangrove sediments. Gas is taken through a syringe mounted on the hood. Gas concentration analysis used the gas chromatography method, while the average gas concentration in each sediment was tested using ANOVA Single Factor. The results showed that the average concentration of CO2 gas was significantly different for each sediment (P-value <0.05), while the average concentration of CH4 gas was not significantly different (P-value >0.05). The largest CO2 gas emission in mangrove sediments in Waiheru Village was found in sandy mud sediments with an emission value of 136.99 mg.m−2.h−1, and the lowest was in sand sediments, namely 13.01 mg.m−2.h−1. Meanwhile, the largest emission of CH4 gas was in silty sand sediments, namely 0.32 mg.m−2.h−1, and the lowest was in sandy mud sediments, namely 0.15 mg.m−2.h−1. Fluctuations in carbon gas emissions in each sediment are inversely proportional, which indicates that the formation of CH4 gas is obtained through ethanol reduction, which utilizes CO2 gas concentrations. The total GWP in the mangrove sediments of Waiheru Village is 231.58 mg.m−2.h−1. The GWP contributed by the type of sandy silt sediment was 140.72 CO2-eq mg.m−2.h−1, muddy sand was 70.95 mg.m−2.h−1, and sand sediment was 19.91 CO2-eq mg.m−2. h−1.

Sodium Alginate-CuS Nanostructures Synthesized at the Gel-Liquid Interface: An Efficient Photocatalyst for Redox Reaction and Water Remediation.

The use of visible light to propel chemical reactions is an exciting area of study that is crucial in the current socioeconomic environment. However, various photocatalysts have been developed to harness visible light, which consume high energy during synthesis. Thus, synthesizing photocatalysts at gel-liquid interfaces in ambient conditions is of scientific importance. Herein, we report an environmentally benign sodium alginate gel being used as a biopolymer template to synthesize copper sulfide (CuS) nanostructures at the gel-liquid interface. The driving force for the synthesis of CuS nanostructures is varied by changing the pH of the reaction medium (i.e., pH 7.4, 10, and 13) to tailor the morphology of CuS nanostructures. The CuS nanoflakes obtained at pH 7.4 transform into nanocubes when the pH is raised to 10, and the nanostructures deform at the pH of 13. Fourier transform infrared spectroscopy (FTIR) confirms all the characteristic stretching of sodium alginate, whereas the CuS nanostructures are crystallized in a hexagonal crystal system, as revealed by the powder X-ray diffraction analysis. The high-resolution X-ray photoelectron spectroscopy (XPS) spectra show the +2 and -2 oxidation states of copper (Cu) and sulfur (S) ions, respectively. The CuS nanoflakes physisorbed a higher concentration of greenhouse CO2 gas. Owing to a lower band gap of CuS nanoflakes synthesized at a pH of 7.4, compared to other CuS nanostructures prepared at pH 10 and 13, CuS photocatalytically degrades 95% of crystal violet and 98% of methylene blue aqueous dye solutions in 60 and 90 min, respectively, under blue light illumination. Additionally, sodium alginate-copper sulfide (SA-CuS) nanostructures synthesized at a pH of 7.4 demonstrate excellent performance in photoredox reactions to convert ferricyanide to ferrocyanide. The current research opens the door to developing new photocatalytic pathways for a wide range of photochemical reactions involving nanoparticle-impregnated alginate composites prepared on gel interfaces.

Development of Cotton Picker Fire Monitoring System Based on GA-BP Algorithm.

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.