Search

Blast furnace gas (BFG) is an important by-product energy for the iron and steel industry and has been widely used for heating or electricity generation. However, the undesirable contaminants in BFG (especially H2S) generate harmful environmental emissions. The desulfurization of BFG is urgent for integrated steel plants due to the stringent ultra-low emission standards. Compared with other desulfurization materials, zeolite-based adsorbents represent a viable option with low costs and long service life. In this study, an ammonia-induced CuO modified 13X adsorbent (NH3–CuO/13X) was prepared for H2S removal from simulated BFG at low temperature. The XRD, H2-TPR and TEM analysis proved that smaller CuO particles were formed and the dispersion of Cu on the surface of 13X zeolite was improved via the induction of ammonia. Evaluation on H2S adsorption performance of the adsorbent was carried out using simulated BFG, and the results showed that NH3–CuO/13X-3 has better breakthrough sulfur capacity, which was more than twice the sulfur capacity of CuO/13X. It is proposed that the enhanced desulfurization performance of NH3–CuO/13X is attributed to an abundant pore of 13X, and combined action of 13X and CuO. This work provided an effective way to improve the sulfur capacity of zeolite-based adsorbents via impregnation method by ammonia induction.

Abstract Developing high-performance Pt-based catalysts with low Pt loading is crucial but challenging for CO oxidation at temperatures below 100 °C. Herein, we report a Pt-based catalyst with only a 0.15 wt% Pt loading, which consists of Pt–Ti intermetallic single-atom alloy (ISAA) and Pt nanoparticles (NP) co-supported on a defective TiO2 support, achieving a record high turnover frequency of 11.59 s–1 at 80 °C and complete conversion of CO at 120 °C. This is because the coexistence of Pt–Ti ISAA and Pt NP significantly alleviates the competitive adsorption of CO and O2, enhancing the activation of O2. Furthermore, Pt single atom sites are stabilized by Pt–Ti ISAA, resulting in distortion of the TiO2 lattice within Pt–Ti ISAA. This distortion activates the neighboring surface lattice oxygen, allowing for the simultaneous occurrence of the Mars-van Krevelen and Langmuir–Hinshelwood reaction paths at low temperatures.

Vacuum pressure swing adsorption has a high potential to reduce CO2 from blast furnace gas, while there are also H2S and COS exist in the blast furnace gas, and the sulfur deposition effect on CO2 adsorption over zeolite was quite necessary to be investigated. In this work, laboratory fixed bed evaluation and two-tower pressure swing adsorption apparatus were employed and it has been found that sulfur deposition occurs only in the presence of coexisting H2S and O2 without water and the highest sulfur accumulation is 13.21 % for the adsorbent under CO2+H2S+COS+O2 atmosphere. Cyclic evaluation of H2S and COS on CO2 cyclic adsorption was first reported, and the sulfur is ultimately converted into S monomers and sulfate, which can be deposited inside the pore channel, with the specific surface area reduced 82.26 % of the adsorbent. Furthermore, sulfur deposition gradually diffused with the upward shift of the adsorption mass transfer zone, and the sulfur deposition densities are calculated to be approximately 0.34 g/cm3. These mechanisms and data demonstrate the significant impact of sulfur deposition on sustainable CO₂ capture in industrial processes, and provide important guidance for the design of CO₂ capture technologies, which is of great importance for carbon reduction.

Hyperspectral imaging technology has emerged as a prominent research area for quantitatively estimating soil nutrient content owing to its non-destructive, rapid, and convenient features. Our work collected the data from soil samples using the hyperspectrometer. Then, the data were processed. The competitive adaptive reweighted sampling (CARS) algorithm reduced the original 148 bands to 13, which accounted for 8.8% of the total bands. These selected bands possess a certain level of interpretability. Based on the modelling results, it can be concluded that the prediction model constructed by the least squares support vector machine (LSSVM) exhibited the highest accuracy. The coefficient determination, root mean square error, and ratio performance deviation were 0.8295, 2.95, and 2.42, respectively. These findings can provide theoretical support for the application of hyperspectral technology in detecting the content of the AHN in soil. Moreover, they can also serve as a reference for the rapid detection of other soil components.

As the largest steel-producing country, China’s steel industry has experienced rapid development in terms of production level and quality. Owing to the high consumption of coal in the iron and steel industry, air pollutants and carbon dioxide (CO2) show similar emission properties in flue gas. In view of the collaborative reduction of pollution and carbon emissions, the emission standards for pollutants and carbon were first analyzed, suggesting that carbon emission standards for the iron and steel industry should be accelerated. A collaborative technology system for the reduction of pollution and carbon emissions from the iron and steel industry in China is demonstrated, consisting of ① optimization of present ultra-low emission technology, ② low-carbon innovation for present production processes, ③ steel production process reengineering, and ④ carbon capture, utilization, and storage (CCUS). Finally, the technical prospect for collaborative reduction of pollution and carbon emissions from the iron and steel industry in China is suggested to support high-quality green development in this industry.

Tetrahedral copper(I) clusters [Cu4(MBIZ)4(PPh3)2] (2), [Cu4(MBOZ)4(PPh3)4] (6) (MBIZ = 2-mercaptobenzimidazole, MBOZ = 2-mercaptobenzoxazole) were prepared by regulation of the copper-thiolate clusters [Cu6(MBIZ)6] (1) and [Cu8(MBOZ)8I]− (5) with PPh3. With the presence of iodide anion, the regulation provided the iodide-containing clusters [CuI4(MBIZ)3(PPh3)3I] (3) and [CuI4(MBOZ)3(PPh3)3I] (7). The cyclic voltammogram of 3 in MeCN (0.1 M nBu4NPF6, 298 K) at a scan rate of 100 mV s−1 shows two oxidation processes at Epa = +0.11 and +0.45 V with return waves observed at Epc = +0.25 V (vs. Fc+/Fc). Complex 3 has a higher capability to lose and gain electrons in the redox processes than complexes 2, 4, 4′, 6, and 7. Its thermal stability was confirmed by thermogravimetric analysis. The catalytic performance of 3 was demonstrated by the catalytic transformation of iodobenzenes to benzonitriles using AIBN as the cyanide source. The nitrile products show potential applications in the preparation of 1,3,5-triazine compounds for organic fluorescence materials.

A base-induced synthesis of 2-(4-(2-(phenylthio)ethyl)piperazinyl) acetonitriles by reaction of disulfides, 1-(chloromethyl)-4-aza-1-azonia bicyclo[2.2.2]octane chloride and trimethylsilyl cyanide is reported. The scope of the method is demonstrated with 30 examples. The reaction mechanism research indicates that the three-component reaction would be a SN2 reaction. The products exhibit good activities towards advanced synthesis of aqueous soluble acyl-CoA: cholesterol O-acyltransferase-1 (ACAT-1) inhibitors. Our work is superior as it uses less-odor disulfides as carbon sources and EtOH as solvent in a water and dioxygen insensitive reaction system, followed by a simple purification process.

The daylily (Hemerocallis citrina Baroni) is rich in not only nutrition ingredients but also functional components, and the edible part is the flower, not containing its pedicel. The primary challenge in developing a robotic daylily harvester is recognizing mature bud in the unstructured and uncertain environment. The objective of this study is to propose an accurate detection model. Hemerocallis citrina cv. ‘DatongHuanghua’ variety is used in this study. We initially adopt VGG16, VGG19, ResNet50, ResNet101 and ResNet152 as the backbones of Faster R-CNN respectively to build different detection models. The experimental results show that VGG19 and ResNet50 are two best-performing models in the corresponding VGGNet and ResNet, and the Average Precision (AP) of VGG19 is 90.18%, while ResNet50 is 88.35%. Based on these, we further integrate Convolutional Block Attention Module (CBAM) in Faster R-CNN with three different integration modes: plugging CBAM behind Conv5_x of VGG19 and ResNet50 respectively, as well as between every two “bottleneck” blocks of ResNet50. The comparison demonstrate plugging CBAM between every two blocks of ResNet50 is the best integration mode, and the corresponding detection model has a 2.22% highest increase in AP. Therefore, we empirically validate the performance of detection model for daylily mature bud based on Faster R-CNN integrated with CBAM.

Abstract According to previous studies of theory of mind (ToM), social environment and cultural background affect individuals’ cognitive ability to understand other people’s minds. There are cross-group differences in ToM. The present study aimed to examine whether social environment and culture affect the ToM in Uygur and Han groups and whether the individual’s cognitive ToM and affective ToM show in-group advantages. Han and Uygur college students were recruited as participants. The “self/other differentiation task” was used to measure cognitive ToM (Study 1), and the “Yoni task” was used to measure both cognitive and affective ToM (Study 2). We found that Han participants processed the cognitive and affective states of others faster and more accurately than Uygur ones. Uygur and Han participants processed in-group members’ cognitive and affective states faster and more accurately. Furthermore, Uygur participants were more accurate in the cognitive ToM processing of in-group members, while Han participants were faster in the affective ToM processing of in-group members. The findings indicated that ethnic culture and group identify might influence ToM processing. Strengthening exchanges between ethnic groups may enable individuals to better process out-group members’ psychological states.

Background: While extant evidence supports the link between mother phubbing (Mphubbing) and harsh mothering, the current understanding of factors that may affect this relationship is limited. Methods: Hierarchical regression analyses were conducted to examine the relation between Mphubbing and harsh mothering, as well as to explore whether mothers' irritability and adolescents' gender would moderate this relationship. The participants included 482 middle school students (51.7 % girls) and their mothers from China. Results: The results revealed a significant positive association between Mphubbing as reported by adolescents and their perception of harsh mothering. However, the predictive power of Mphubbing for harsh mothering varied based on mothers' irritability and adolescents' gender. Specifically, the association between Mphubbing and harsh mothering was perceived more strongly in girls than in boys, but this gender difference was only observed among adolescents whose mothers rated themselves as high in irritability. Conclusions: The current study offers a preliminary understanding of the association between Mphubbing and harsh mothering through mothers' irritability and adolescents' gender as moderators, which has certain theoretical and practical implications for comprehending harsh mothering in the digital age.

Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from leaves of tomato seedlings cultured at different nitrogen concentrations. The visible/near-infrared(VIS/NIR) hyperspectral imaging (HSI) non-destructive measurement technology, 430-900 nm and 950-1650 nm, with total variables of 794, was used to obtain the reflection spectra of leaves. An improved strategy of the sparrow search algorithm (SSA) based on Logistic chaotic mapping was proposed and optimized the back propagation (BP) neural network to predict the pigment content of leaves. Different pretreatment methods were used to effectively improve the prediction accuracy of the model. The results showed that when the nitrogen concentration in the nutrient solution was 302.84 mg·L-1, the pigment content of leaves reached the maximum. Meanwhile, the inhibition effect of high concentration was much stronger than that of low concentration. To address the problem that the SSA is prone to get in premature convergence due to the reduction of population diversity at the end of the iteration, the initialization of the SSA population by Logistic chaotic mapping improves the initial solution quality, convergence speed and search capacity. The root mean squared error (RMSE), coefficient of determination (R2) and relative percent deviation (RPD) of chlorophyll a were 0.77, 0.77 and 2.08, respectively. The RMSE, R2 and RPD of chlorophyll b were 0.30, 0.66 and 1.71, respectively. The RMSE, R2 and RPD of chlorophyll were 0.88, 0.81 and 2.28, respectively. The RMSE, R2 and RPD of carotenoid were 0.14, 0.75 and 2.00, respectively. The HSI technology combined with machine learning algorithms can achieve rapid and accurate prediction of crop physiological information, providing data support for the precise management of fertilization in facility agriculture, which is conducive to improving the quality and output of tomatoes.

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to rapidly and accurately obtain the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophylls (Chls) and total carotenoids (Cars) as indicators to study the variations in the leaf positions of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR–HSI). In this study, a “coarse–fine” screening strategy was proposed using competitive adaptive reweighted sampling (CARS) and the iteratively retained informative variable (IRIV) algorithm to extract the characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS–IRIV–PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE) and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43 and 2.38 for Chla; 0.8391, 0.53 and 2.49 for Chlb; 0.7899, 2.24 and 2.18 for Chls; and 0.7577, 0.27 and 2.03 for Cars, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of the pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis and fertilization decisions in plant growth management.

Tetraspanins (TETs) function as key molecular scaffolds for surface signal recognition and transduction via the assembly of tetraspanin-enriched microdomains. TETs’ function in mammalian has been intensively investigated for the organization of multimolecular membrane complexes, regulation of cell migration and cellular adhesion, whereas plant TET studies lag far behind. Animal and plant TETs share similar topologies, despite the hallmark of “CCG” in the large extracellular loop of animal TETs, plant TETs contain a plant specific GCCK/RP motif and more conserved cysteine residues. Here, we showed that the GCCK/RP motif is responsible for TET protein association with the plasma membrane. Moreover, the conserved cysteine residues located within or neighboring the GCCK/RP motif are both crucial for TET anchoring to membrane. During virus infection, the intact TET3 protein enhanced but GCCK/RP motif or cysteine residues-deficient TET3 variants abolished the cell-to-cell movement capability of virus. This study provides cellular evidence that the GCCK/RP motif and the conserved cysteine residues are the primary determinants for the distribution and function of TET proteins in Arabidopsis.

Blast furnace gas (BFG) is an important by-product energy for the iron and steel industry and has been widely used for heating or electricity generation. However, the undesirable contaminants in BFG (especially H2S) generate harmful environmental emissions. The desulfurization of BFG is urgent for integrated steel plants due to the stringent ultra-low emission standards. Compared with other desulfurization materials, zeolite-based adsorbents represent a viable option with low costs and long service life. In this study, an ammonia-induced CuO modified 13X adsorbent (NH3–CuO/13X) was prepared for H2S removal from simulated BFG at low temperature. The XRD, H2-TPR and TEM analysis proved that smaller CuO particles were formed and the dispersion of Cu on the surface of 13X zeolite was improved via the induction of ammonia. Evaluation on H2S adsorption performance of the adsorbent was carried out using simulated BFG, and the results showed that NH3–CuO/13X-3 has better breakthrough sulfur capacity, which was more than twice the sulfur capacity of CuO/13X. It is proposed that the enhanced desulfurization performance of NH3–CuO/13X is attributed to an abundant pore of 13X, and combined action of 13X and CuO. This work provided an effective way to improve the sulfur capacity of zeolite-based adsorbents via impregnation method by ammonia induction. [ABSTRACT FROM AUTHOR]