Water and other volatiles on Mars.

This perspective reviews the recent advances in martian water and other volatiles and addresses the associated scientific questions for future martian exploration missions.

Ag2S-Decorated One-Dimensional CdS Nanorods for Rapid Detection and Effective Discrimination of n-Butanol.

N-butanol (C4H9OH) is a volatile organic compound (VOC) that is susceptible to industrial explosions. It has become imperative to develop n-butanol sensors with high selectivity and fast response and recovery kinetics. CdS/Ag2S composite nanomaterials were designed and prepared by the solvothermal method. The incorporation of Ag2S engendered a notable augmentation in specific surface area and a consequential narrow band gap. The CdS/Ag2S-based sensor with 3% molar ratio of Ag2S, operating at 200 °C, demonstrated a remarkably elevated response (S = Ra/Rg = 24.5) when exposed to 100 ppm n-butanol, surpassing the pristine CdS by a factor of approximately four. Furthermore, this sensor exhibited notably shortened response and recovery times, at a mere 4 s and 1 s, respectively. These improvements were ascribed to the one-dimensional single-crystal nanorod structure of CdS, which provided an effective path for expedited electron transport along its axial dimension. Additionally, the electron and chemical sensitization effects resulting from the modification with precious metal sulfides Ag2S were the primary reasons for enhancing the sensor response. This work can contribute to mitigating the safety risks associated with the use of n-butanol in industrial processes.

Exploring the spatiotemporal pattern evolution of carbon emissions and air pollution in Chinese cities.

Based on data from 335 cities in China, this study employs the standard deviation ellipse method to portray unbalanced and differential spatiotemporal evolution patterns of environmental emissions and socioeconomic elements. A logarithmic mean Divisia index analysis and in-depth discussion are carried out to disclose the main driving factors and underlying reasons for the differences. Decoupling trends exist among carbon emissions, gross domestic product (GDP) and population in terms of their gravity center migrations. The standard deviation ellipse direction of carbon emissions gradually changed from 'northeast‒southwest' to 'northwest‒southeast', and the standard deviation ellipse areas of carbon emissions and air pollution continuously expanded over time; at the same time, that of GDP contracted. Economic growth has always been the main driver of carbon emissions and air pollution nationally, but its role has weakened. Moreover, decreases in the energy intensity and carbon and pollution intensities are the main factors contributing to emissions reductions. Differentiated spatiotemporal economic structure evolution, regional heterogeneities in the energy intensity and efficiency, and cross-region power energy transmissions are identified as the underlying reasons for the unbalanced spatiotemporal patterns of the environmental emissions and socioeconomic elements. Based on these findings, policy suggestions can be made to address the imbalances and promote carbon mitigation, air quality improvement and high-quality social-economic development at the city level.

Impact of the emergency response to COVID-19 on air quality and its policy implications: Evidence from 290 cities in China.

The emergency response to the COVID-19 pandemic had an extreme exogenous impact on society and the economy. This paper aims to explore the impacts of the national emergency response and the subsequent emergency response termination on air quality and its policy implications through regression discontinuity design (RDD) estimation by employing panel data on daily air quality from January 1, 2019, to July 31, 2020, for 290 cities in China. The empirical results showed that the emergency response resulted in a significant decrease in most of the major pollutant concentrations within a short time frame, and the average air quality index (AQI) decreased by approximately 11.0%. The concentrations of PM2.5, PM10, SO2, NO2, and CO decreased by approximately 18.8%, 13.1%, 13.5%, 11.1% and 6.7%, respectively, while the O3 concentration did not change significantly. Further causal analysis found that mandatory traffic restrictions and the shutdown of industries were two important factors that contributed greatly to air quality improvement. Moreover, since the process of returning to normal daily activities and promoting the economy were gradual, the results showed that air pollution did not rebound immediately after the government called for the "resumption of production and work" and announced the "termination of the emergency response". Our findings suggest that to achieve a substantial and sustainable improvement in air quality, it is necessary to continuously implement strict emission control routines and take co-control measures for various VOCs precursors of ozone.

Strata behaviour and stability control of the automatic roadway formation by roof cutting below a fault influenced longwall goaf.

Automatic roadway formation by roof cutting (ARFRC) is a novel nonpillar mining method that has the potential to dramatically increase coal recovery while reducing the roadway excavation ratio. When this method is used below a fault influenced longwall goaf, large deformation and support failure occur in the roadway using conventional roadway formation techniques. In the study, the ARFRC method was tested in the Liliu mining area of China, which is characterized by goafs and faults. Field experiments and numerical modelling were used to evaluate the stability of the roadway by analysing the behaviour of overlying strata under the special geological condition. The results show that the surroundings of the formed roadway were greatly affected by the fault and the overlying coal pillar in the goaf. In the fault- and coal pillar-affected areas, the loads on the roadway roof increased by approximately 35% and 15%, respectively. According to the strata behaviour of the formed roadway surroundings, targeted support techniques for ARFRC were proposed, and the reliability of the support techniques were demonstrated by field practice.

Cadmium sulfide in-situ derived heterostructure hybrids with tunable component ratio for highly sensitive and selective detection of ppb-level H2S.

Herein, we reported cadmium sulfide derivatives pine needles-like CdS/CdO heterostructure hybrids synthesized by hydrothermal treatment and subsequent self-template oxidation approach. The component ratio of the CdS/CdO hybrids can be controlled specifically via tuning the annealing treatment protocol, and thereby giving rise to the optimization of morphology, electrical characteristics, and gas sensing properties of derived hybrids. As proof of concept, the pine needles-like CdS/CdO, which obtained after different annealing temperatures and durations, as sensitive material was employed to manufacture H2S gas sensors. The sensor based on CdS/CdO hybrids (400 °C & 1 h) exhibited high sensitivity (73.5 to 5 ppm), ppb-level limit of detection (10 ppb), and excellent selectivity regardless of the interference of other gases at optimal working temperature of 200 °C. Due to the abnormal resistance variation of n-type cadmium sulfide derived hybrids while contacting with H2S, the sensing mechanism mainly depends on the surface chemical conversion from oxide to sulfide. The pine needles-like hierarchical morphology provided an excellent scaffold for the carriers transportation and the growth of the CdO, which played a key role in resistance modulation both in air and target gas, resulting in the enhanced H2S sensing performance ultimately.

Lower coordination Co3O4 mesoporous hierarchical microspheres for comprehensive sensitization of triethylamine vapor sensor.

Monitoring and detecting triethylamine (TEA) vapor are essential in the organic synthesis industry. Two-dimensional Co3O4 nanosheets with large surface areas and multiple active sites are ideal for fabricating chemiresistive gas sensors. However, the face-to-face stacking owing to the high surface energy of nanosheets, would cover up the active sites, obstruct gas diffusion, raise contact resistance, which all hinder its utilization for TEA detection. Herein, the Co3O4 mesoporous nanosheets were assembled into hierarchical microspheres by adding the structure-directing agent PVP K30 and combined with a proper annealing temperature, which optimized their grain size, specific surface area, pores structure, oxygen vacancies, and the atomic ratio of Co2+ to Co3+. And these ultimately improved the detection capability of TEA. The sensor based on Co3O4 sphere-300 exhibits the highest sensor response of 34.1-100 ppm TEA and a low detection limit (0.5 ppm) at a low working temperature of 150 °C. The promising properties are mainly due to the combination of several advantages that facilitate simultaneous chemical and electronic sensitization. This work prepared a high-performance TEA gas sensor and verified the improvement of comprehensive sensitization on the gas-sensing performance of two-dimensional metal oxide semiconductors.

ATTfold: RNA Secondary Structure Prediction With Pseudoknots Based on Attention Mechanism.

Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.