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1. Adsorption energy data and relative energy dataFirstly, the above energy data were calculated by Materials Studio Molecular Simulation (MS) software (structure optimisation). Secondly, the adsorption energy data were calculated by calculating the energy difference between the reactants before and after adsorption of carbon nanotubes/sulfur-doped carbon nanotubes, which needs to be noted as the construction of the model, the energy comparison of the adsorption configurations, and the distance of the adsorption (3 Å). The relative energy data are then compared by calculating the difference between the energies of the products in the reaction and the intermediate state of the substance with the energy of the substance before the reaction, and attention needs to be paid to the energies of the transition state, from which the activation energy barrier for the absorption-desorption reaction can be calculated.The column labels in the table mainly include the name of the reactant/adsorption configuration, geometrically optimised model, E-energy, adsorption energy, and relative energy. Reactants are the intermediate states and products in the reaction between monoethanolamine and CO2. The adsorption configuration refers to the configuration of the reactants adsorbed on carbon nanotubes/sulfur-doped carbon nanotubes. Geometry optimised model refers to the first two obtained after geometry optimisation by MS software. Adsorption energy refers to the energy generated during adsorption of adsorbate A on substrate B. The relative energy is the difference between the energy of the products and intermediate states of the reaction and the energy of the substance before the reaction, and the whole reaction path allows us to see whether the reaction is easy to carry out or not, and to see the key steps in the reaction.The units of measurement have been converted, 1hartree = 627.51kcal/mol 1ev = 23.0605kcal/mol 1kcal = 4.1858 kJ2, Origin plots the dataThe energy variation plots of the MEA-CO2 and MEA-CO2- CNT/ S-CNT catalytic reaction paths were plotted using the energy data derived above, which in turn led to a more visualised reaction path. The horizontal coordinate reaction path in the plot refers to the path in the catalytic reaction, and the vertical coordinate relative energy refers to the energy difference between the energy of the products and intermediate states in the reaction and the substance before the reaction, in kcal/mol.The PDOS diagrams of C, S and other atoms in the CNT/S-CNT structure are drawn by using the data calculated (property analysis) by MS software.PDOS, which means partial wave state density in Chinese, can help us to understand the distribution of the electrons in the material and the law of motion in depth, so as to provide a theoretical basis for the design and optimisation of the material. And we can understand the distribution of electrons in different orbitals in different energy ranges. S P in the figure refers to the orbitals, the horizontal coordinate is the energy (kcal/mol), and the vertical coordinate is the PDOS (eV).The meaning of the PDOS plots for atoms such as C, N, and O in the structure of the CNT/S-CNT_R1 catalytic reaction is plotted in the same procedure as described above, with the difference being that different atoms are analysed. (The fractional wave state densities of the important atoms C, N, and O in the reactants on carbon nanotubes and sulphur-doped carbon nanotubes were analysed and compared)PDOS plots of C, N and O atoms in the catalytic reaction structure of CNT/S-CNT_IM1 (the fractional wave state densities of the important atoms C, N and O in the intermediate states on carbon and sulphur doped carbon nanotubes were analysed and compared)PDOS plots of C, N and O atoms in the catalytic reaction structure of CNT/S-CNT_P1 (the fractional wave state densities of important atoms C, N and O in the desorption products on carbon and sulphur doped carbon nanotubes were analysed and compared)PDOS plots of C, S, O atoms in the structure of the MEA-CO2- S-CNT catalytic reaction (the fractional wave state densities of different atoms in the whole reaction pathway were analysed)3. PPT plot of the dataFig. 1. Schematic diagram of the structure of S-CNTS The basic model diagram was drawn by model construction in MS software, and then by shape, combination and other functions in PPT.Fig. 2. (a) Schematic diagram of the corresponding structures of MEA-CO2 and MEA-CO2-CNTS/ S-CNTS catalytic reaction pathways with energy changes (b)Subfigure (a) was plotted by Origin and then pasted as an image (the specific data is from the relative energy data) Subfigure (b) was plotted by model construction in MS software and after geometrical optimisation of the model.Fig. 3. (a) Electrostatic potential map and (b) charge density map of the catalytic reaction path structure of MEA-CO2-CNTS/S-CNTS (c) Differential charge density mapSubplot (a) is calculated by MS software (property analysis), the surface electrostatic potential is the distribution of electrostatic potential on a surface somewhere around the molecule, usually an equivalent surface of electron density will be used. Subplot (b) is calculated by MS software (nature analysis), the charge density is the distribution of charge over a certain spatial area. The charge density can be used to explain the properties of substances, as well as processes such as chemical reactions and intermolecular interactions.The subplot (c) is calculated by MS software (nature analysis) Differential charge density is the difference in charge density between two different states. By calculating the difference in charge densities before and after a reaction, it is possible to understand which regions of the reaction have changed in charge and to further deduce changes in the structure of molecules or ions. (All used the Dmol3 module in the MS software)Figure 4.(a) PDOS plot of C, S and other atoms in the structure of CNTS/S-CNTS (b) PDOS plot of C, N, O and other atoms in the structure of the catalytic reaction of CNTS/S-CNTS_R1 Copy the Origin data plots as pictures and then combine them via PPT.Fig. 5. PDOS plots of C, S, and O atoms in the catalytic reaction path structure of MEA-CO2- S-CNTS and Accurate molecular modelling system Space-filled model (Corey-Pauling-koltun, CPk) model The PDOS plots were copied from Origin data plots as pictures and then combined via PPT. The molecular models were plotted by MS software and then screenshotted.Translated with DeepL.com (free version) 1. Adsorption energy data and relative energy dataFirstly, the above energy data were calculated by Materials Studio Molecular Simulation (MS) software (structure optimisation). Secondly, the adsorption energy data were calculated by calculating the energy difference between the reactants before and after adsorption of carbon nanotubes/sulfur-doped carbon nanotubes, which needs to be noted as the construction of the model, the energy comparison of the adsorption configurations, and the distance of the adsorption (3 Å). The relative energy data are then compared by calculating the difference between the energies of the products in the reaction and the intermediate state of the substance with the energy of the substance before the reaction, and attention needs to be paid to the energies of the transition state, from which the activation energy barrier for the absorption-desorption reaction can be calculated.The column labels in the table mainly include the name of the reactant/adsorption configuration, geometrically optimised model, E-energy, adsorption energy, and relative energy. Reactants are the intermediate states and products in the reaction between monoethanolamine and CO2. The adsorption configuration refers to the configuration of the reactants adsorbed on carbon nanotubes/sulfur-doped carbon nanotubes. Geometry optimised model refers to the first two obtained after geometry optimisation by MS software. Adsorption energy refers to the energy generated during adsorption of adsorbate A on substrate B. The relative energy is the difference between the energy of the products and intermediate states of the reaction and the energy of the substance before the reaction, and the whole reaction path allows us to see whether the reaction is easy to carry out or not, and to see the key steps in the reaction.The units of measurement have been converted, 1hartree = 627.51kcal/mol 1ev = 23.0605kcal/mol 1kcal = 4.1858 kJ2, Origin plots the dataThe energy variation plots of the MEA-CO2 and MEA-CO2- CNT/ S-CNT catalytic reaction paths were plotted using the energy data derived above, which in turn led to a more visualised reaction path. The horizontal coordinate reaction path in the plot refers to the path in the catalytic reaction, and the vertical coordinate relative energy refers to the energy difference between the energy of the products and intermediate states in the reaction and the substance before the reaction, in kcal/mol.The PDOS diagrams of C, S and other atoms in the CNT/S-CNT structure are drawn by using the data calculated (property analysis) by MS software.PDOS, which means partial wave state density in Chinese, can help us to understand the distribution of the electrons in the material and the law of motion in depth, so as to provide a theoretical basis for the design and optimisation of the material. And we can understand the distribution of electrons in different orbitals in different energy ranges. S P in the figure refers to the orbitals, the horizontal coordinate is the energy (kcal/mol), and the vertical coordinate is the PDOS (eV).The meaning of the PDOS plots for atoms such as C, N, and O in the structure of the CNT/S-CNT_R1 catalytic reaction is plotted in the same procedure as described above, with the difference being that different atoms are analysed. (The fractional wave state densities of the important atoms C, N, and O in the reactants on carbon nanotubes and sulphur-doped carbon nanotubes were analysed and compared)PDOS plots of C, N and O atoms in the catalytic reaction structure of CNT/S-CNT_IM1 (the fractional wave state densities of the important atoms C, N and O in the intermediate states on carbon and sulphur doped carbon nanotubes were analysed and compared)PDOS plots of C, N and O atoms in the catalytic reaction structure of CNT/S-CNT_P1 (the fractional wave state densities of important atoms C, N and O in the desorption products on carbon and sulphur doped carbon nanotubes were analysed and compared)PDOS plots of C, S, O atoms in the structure of the MEA-CO2- S-CNT catalytic reaction (the fractional wave state densities of different atoms in the whole reaction pathway were analysed)3. PPT plot of the dataFig. 1. Schematic diagram of the structure of S-CNTS The basic model diagram was drawn by model construction in MS software, and then by shape, combination and other functions in PPT.Fig. 2. (a) Schematic diagram of the corresponding structures of MEA-CO2 and MEA-CO2-CNTS/ S-CNTS catalytic reaction pathways with energy changes (b)Subfigure (a) was plotted by Origin and then pasted as an image (the specific data is from the relative energy data) Subfigure (b) was plotted by model construction in MS software and after geometrical optimisation of the model.Fig. 3. (a) Electrostatic potential map and (b) charge density map of the catalytic reaction path structure of MEA-CO2-CNTS/S-CNTS (c) Differential charge density mapSubplot (a) is calculated by MS software (property analysis), the surface electrostatic potential is the distribution of electrostatic potential on a surface somewhere around the molecule, usually an equivalent surface of electron density will be used. Subplot (b) is calculated by MS software (nature analysis), the charge density is the distribution of charge over a certain spatial area. The charge density can be used to explain the properties of substances, as well as processes such as chemical reactions and intermolecular interactions.The subplot (c) is calculated by MS software (nature analysis) Differential charge density is the difference in charge density between two different states. By calculating the difference in charge densities before and after a reaction, it is possible to understand which regions of the reaction have changed in charge and to further deduce changes in the structure of molecules or ions. (All used the Dmol3 module in the MS software)Figure 4.(a) PDOS plot of C, S and other atoms in the structure of CNTS/S-CNTS (b) PDOS plot of C, N, O and other atoms in the structure of the catalytic reaction of CNTS/S-CNTS_R1 Copy the Origin data plots as pictures and then combine them via PPT.Fig. 5. PDOS plots of C, S, and O atoms in the catalytic reaction path structure of MEA-CO2- S-CNTS and Accurate molecular modelling system Space-filled model (Corey-Pauling-koltun, CPk) model The PDOS plots were copied from Origin data plots as pictures and then combined via PPT. The molecular models were plotted by MS software and then screenshotted.Translated with DeepL.com (free version)

This paper focuses on the influence of key components in biomass ash on the gasification reactivity of coal by using simulated biomass ash. The migration patterns of typical biomass ash components and the structural evolution characteristics of coal during gasification process were deeply investigated. The results indicate that gasification temperature and Si element content are the key factors affecting gasification reactivity of coal. When the Si/K mass ratio is 0.5 and 1.0, gasification reactivity of the composite coal sample is stronger than that of raw coal, while the Si/K mass ratio is 1.5, gasification reactivity is weaker than that of raw coal. Under the experimental conditions, the composite coal sample with a Si/K mass ratio of 0.5 and a Ca/K mass ratio of 0.4 shows the strongest reactivity. The gasification reactivity index is 1.35 times higher than that of raw coal. Compared to potassium-containing minerals, calcium-containing minerals have stronger reactivity and are more likely to react with silicates to form calcium-containing silicates, such as calcium zeolites (CaO·Al2O3·2SiO2·4H2O), thereby avoiding the reaction between potassium-containing minerals and silicates to form non-catalytic minerals, which allows potassium to fully exert its catalytic effects. Dynamic analysis implies that shrinking core model well describes the gasification process of deashing coal catalyzed by simulated biomass ash. When the Si/K mass ratio is 0.5 and the Ca/K mass ratio is 0.4, the activation energy of composite coal sample is reduced to 174.39 kJ/mol, which is 14.32% lower than that of raw coal. This paper focuses on the influence of key components in biomass ash on the gasification reactivity of coal by using simulated biomass ash. The migration patterns of typical biomass ash components and the structural evolution characteristics of coal during gasification process were deeply investigated. The results indicate that gasification temperature and Si element content are the key factors affecting gasification reactivity of coal. When the Si/K mass ratio is 0.5 and 1.0, gasification reactivity of the composite coal sample is stronger than that of raw coal, while the Si/K mass ratio is 1.5, gasification reactivity is weaker than that of raw coal. Under the experimental conditions, the composite coal sample with a Si/K mass ratio of 0.5 and a Ca/K mass ratio of 0.4 shows the strongest reactivity. The gasification reactivity index is 1.35 times higher than that of raw coal. Compared to potassium-containing minerals, calcium-containing minerals have stronger reactivity and are more likely to react with silicates to form calcium-containing silicates, such as calcium zeolites (CaO·Al2O3·2SiO2·4H2O), thereby avoiding the reaction between potassium-containing minerals and silicates to form non-catalytic minerals, which allows potassium to fully exert its catalytic effects. Dynamic analysis implies that shrinking core model well describes the gasification process of deashing coal catalyzed by simulated biomass ash. When the Si/K mass ratio is 0.5 and the Ca/K mass ratio is 0.4, the activation energy of composite coal sample is reduced to 174.39 kJ/mol, which is 14.32% lower than that of raw coal.

Perfluoride glass ceramics (FGCs) with SrF2/CaF2 mixed crystals were prepared using a concurrent rapid-quenching-crystallization process. The FGCs contained dense, homogeneous, round crystals of sizes less than 1 μm with high transmittance (exceeding 80% at 3-8 μm) and low phonon energy (508 cm-1). The fluorescence properties of the FGCs doped with Er and Dy were investigated. The energy transfer efficiency between Er and Dy reached 93.81% and 98.13%, corresponding to Er: 4I11/2 to Dy: 6H5/2 and Er: 4I13/2 to Dy: 6H11/2, respectively. This enhancement is attributed to the enrichment of rare-earth ions in the crystals induced by phase-separation and low phonon energy of the host material, FGC. Moreover, the average lifetime of Er: 4I11/2 in FGC reached 10.13 ms, which is the highest reported value for glass ceramics to our knowledge. This study enriches the research theory of FGC and provides guidance for expanding the properties of materials for mid-infrared photonics. Perfluoride glass ceramics (FGCs) with SrF2/CaF2 mixed crystals were prepared using a concurrent rapid-quenching-crystallization process. The FGCs contained dense, homogeneous, round crystals of sizes less than 1 μm with high transmittance (exceeding 80% at 3-8 μm) and low phonon energy (508 cm-1). The fluorescence properties of the FGCs doped with Er and Dy were investigated. The energy transfer efficiency between Er and Dy reached 93.81% and 98.13%, corresponding to Er: 4I11/2 to Dy: 6H5/2 and Er: 4I13/2 to Dy: 6H11/2, respectively. This enhancement is attributed to the enrichment of rare-earth ions in the crystals induced by phase-separation and low phonon energy of the host material, FGC. Moreover, the average lifetime of Er: 4I11/2 in FGC reached 10.13 ms, which is the highest reported value for glass ceramics to our knowledge. This study enriches the research theory of FGC and provides guidance for expanding the properties of materials for mid-infrared photonics.

Segmentation of prostate magnetic resonance images is of great significance in the interventional diagnosis and treatment of prostate diseases. In this work, the conventional distance regularized level set evolution (DRLSE) model is improved and applied to prostate segmentation. In magnetic resonance image, the prostate boundary near the bladder is often blurred, while that near the urethra is clear, resulting in a poor performance for the traditional gradient information indicator function. In this study, two indicator functions were used to control the evolution of boundary in the clear segment and blurred segment, respectively, to achieve better segmentation. In addition, an energy check term was added to the external energy function to prevent evolution from stopping at a false boundary. This modification could drive the level set to move to regions with large gray level fluctuation and stop evolution at a blurred boundary. Experimental results demonstrated that the performance of prostate segmentation was satisfactory, judging from the Dice similarity coefficient (DSC) which reached an average of 96%.