Revisiting the thermal decomposition mechanism of MAPbI3.

The thermal stability of MAPbI3 poses a challenge for the industry. To overcome this limitation, a thorough investigation of MAPbI3 is necessary. In this work, thermal gravimetric analysis (TGA) and Fourier transform infrared (FTIR) spectroscopy were conducted to identify the thermal decomposition products of MAPbI3, which were found to be CH3I, NH3, and PbI2. In situ X-ray diffraction (XRD) measurements were then performed in the temperature range from 300 to 700 K, which revealed the significant decomposition of the (110), (220), and (310) surfaces of MAPbI3 between 550 and 600 K. Density functional theory (DFT) calculations demonstrated that the (220) surface exhibited the highest stability. Additionally, the transition states of thermal decomposition showed that the energy barrier for the decomposition of the (110) surface was 2.07 eV. Our combined experimental and theoretical results provide a better understanding of the thermal decomposition mechanism of MAPbI3, providing valuable theoretical support for the design of long-term stable devices.

Picturing the Gap Between the Performance and US-DOE's Hydrogen Storage Target: A Data-Driven Model for MgH2 Dehydrogenation.

Developing solid-state hydrogen storage materials is as pressing as ever, which requires a comprehensive understanding of the dehydrogenation chemistry of a solid-state hydride. Transition state search and kinetics calculations are essential to understanding and designing high-performance solid-state hydrogen storage materials by filling in the knowledge gap that current experimental techniques cannot measure. However, the ab initio analysis of these processes is computationally expensive and time-consuming. Searching for descriptors to accurately predict the energy barrier is urgently needed, to accelerate the prediction of hydrogen storage material properties and identify the opportunities and challenges in this field. Herein, we develop a data-driven model to describe and predict the dehydrogenation barriers of a typical solid-state hydrogen storage material, magnesium hydride (MgH2), based on the combination of the crystal Hamilton population orbital of Mg-H bond and the distance between atomic hydrogen. By deriving the distance energy ratio, this model elucidates the key chemistry of the reaction kinetics. All the parameters in this model can be directly calculated with significantly less computational cost than conventional transition state search, so that the dehydrogenation performance of hydrogen storage materials can be predicted efficiently. Finally, we found that this model leads to excellent agreement with typical experimental measurements reported to date and provides clear design guidelines on how to propel the performance of MgH2 closer to the target set by the United States Department of Energy (US-DOE).

Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm.

In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony-optimized radial basis function neural network (ABC-RBFNN) is applied to industrial two-phase flow EIT for the first time. This algorithm aims to enhance the accuracy of image reconstruction in electrical impedance tomography (EIT) technology. The EIDORS-v3.10 software platform is utilized to generate electrode data for a 16-electrode EIT system with varying numbers of bubbles. This generated data is then employed as training data to effectively train the ABC-RBFNN model. The reconstructed electrical impedance image produced from this process is evaluated using the image correlation coefficient (ICC) and root mean square error (RMSE) criteria. Tests conducted on both noisy and noiseless test set data demonstrate that the ABC-RBFNN algorithm achieves a higher ICC value and a lower RMSE value compared to the Gauss-Newton algorithm and the radial basis function neural network (RBFNN) algorithm. These results validate that the ABC-RBFNN algorithm exhibits superior noise immunity. Tests conducted on bubble models of various sizes and quantities, as well as circular bubble models, demonstrate the ABC-RBFNN algorithm's capability to accurately determine the size and shape of bubbles. This outcome confirms the algorithm's generalization ability. Moreover, when experimental data collected from a 16-electrode EIT experimental device is employed as test data, the ABC-RBFNN algorithm consistently and accurately identifies the size and position of the target. This achievement establishes a solid foundation for the practical application of the algorithm.

Exploring the Effects of Ionic Defects on the Stability of CsPbI3 with a Deep Learning Potential.

Inorganic metal halide perovskites, such as CsPbI3 , have recently drawn extensive attention due to their excellent optical properties and high photoelectric efficiencies. However, the structural instability originating from inherent ionic defects leads to a sharp drop in the photoelectric efficiency, which significantly limits their applications in solar cells. The instability induced by ionic defects remains unresolved due to its complicated reaction process. Herein, to explore the effects of ionic defects on stability, we develop a deep learning potential for a CsPbI3 ternary system based upon density functional theory (DFT) calculated data for large-scale molecular dynamics (MD) simulations. By exploring 2.4 million configurations, of which 7,730 structures are used for the training set, the deep learning potential shows an accuracy approaching DFT-level. Furthermore, MD simulations with a 5,000-atom system and a one nanosecond timeframe are performed to explore the effects of bulk and surface defects on the stability of CsPbI3 . This deep learning potential based MD simulation provides solid evidence together with the derived radial distribution functions, simulated diffraction of X-rays, instability temperature, molecular trajectory, and coordination number for revealing the instability mechanism of CsPbI3 . Among bulk defects, Cs defects have the most significant influence on the stability of CsPbI3 with a defect tolerance concentration of 0.32 %, followed by Pb and I defects. With regards to surface defects, Cs defects have the largest impact on the stability of CsPbI3 when the defect concentration is less than 15 %, whereas Pb defects act play a dominant role for defect concentrations exceeding 20 %. Most importantly, this machine-learning-based MD simulation strategy provides a new avenue to explore the ionic defect effects on the stability of perovskite-like materials, laying a theoretical foundation for the design of stable perovskite materials.

A computational study on the adsorption of arsenic pollutants on graphene-based single-atom iron adsorbents.

Integrated gasification combined cycle (IGCC) is a promising clean technology for coal power generation; however, the high volatility and toxicity of arsenic pollutants (As2, As4, AsO and AsH3) released from an IGCC coal plant cause serious damage to human health and the ecological environment. Therefore, highly efficient adsorbents for simultaneous treatment of multiple arsenic pollutants are urgently needed. In this work, the adsorption characteristics and competitive adsorption behaviors of As2, As4, AsO, and AsH3 on four kinds of graphene-based single-atom iron adsorbents (Fe/GA) were systematically investigated through density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. The results suggest that single-vacancy Fe/GA doped with three nitrogen atoms has the largest adsorption ability for As2, As4, AsO and AsH3. The adsorption energies of As2, AsO and As4 on Fe/GA depend on both charge transfer and orbital hybridization, while the adsorption energy of AsH3 is mainly decided by electronic transfer. The adsorption differences of As2, As4, AsO and AsH3 on four Fe/GA adsorbents can be explained through the obvious linear relationship between the adsorption energy and Fermi softness. As2, As4, AsO and AsH3 will compete for adsorption sites when they exist on the same adsorbent surface simultaneously, and the adsorption capacities of AsO and As2 are relatively stronger. After the competitive adsorption between AsO and As2, AsO occupies the adsorption site at 300-900 K. This theoretical work suggests that Fe/GA is a promising adsorbent for the simultaneous removal of multiple arsenic pollutants with high adsorption capacity and low cost.

High throughput screening of promising lead-free inorganic halide double perovskites via first-principles calculations.

Perovskite solar cells (PSCs) have been intensively investigated and made great progress due to their high photoelectric conversion efficiency and low production cost. However, poor stability and the toxicity of Pb limit their commercial applications. It is particularly important to search for new non-toxic, high-stability perovskite materials. In this study, 760 Cs2B2+B'2+X6 (X = F, Cl, Br, I) inorganic halide double perovskites are screened based on high-throughput first-principles calculations to obtain an ideal perovskite material. The band gaps of this type of double perovskite are mainly determined by the elements X and B2+, decreasing monotonously with the increase in the atomic number of X (from F to I). We obtain 14 optimal and unreported materials with suitable band gaps as potential alternative materials for Pb-based photovoltaic absorbers in PSCs. This theoretical investigation can provide theoretical guidance for developing novel lead-free PSC materials.

A new perspective for evaluating the photoelectric performance of organic-inorganic hybrid perovskites based on the DFT calculations of excited states.

The high efficiency of organic-inorganic hybrid perovskites has attracted the attention of many scholars all over the world, the chemical formula of which is ABX3, where A is an organic cation, B is a metal cation, and X is a halogen ion. In addition, the micro-mechanism behind the efficient photoelectric conversion needs more in-depth exploration. Therefore, in this work, based on time-dependent density functional theory (TD-DFT), the electron transfer mechanism from the ground state to the first singlet excited state was systematically investigated by electron and hole analysis and an inter-fragment charge transfer amount method (IFCT). In this work, we optimized and analyzed 99 different perovskite cluster configurations, where A sites are CH3NH3+ (MA+), NH2CHNH2+ (FA+), CH3CH2NH3+ (EA+), NH2CHOH+ (JA+), NH3OH+ (BA+), N(CH3)4+ (DA+), CH3CH2CH2NH3+ (KB+), CH3CH2CH2CH2NH3+ (KC+), C3N2H5+ (RA+), CH(CH3)2+ (TA+), and CH3NH(CH3)2+ (UA+), B sites are Ge2+, Sn2+ and Pb2+, and X sites are Cl-, Br- and I-. According to the analysis of a series of perovskite clusters of the hole-electron distribution, the distribution is mainly concentrated on BX, and electrons and holes are respectively distributed on B and X sites. The exciton binding energy decreases when the metal element changes from Ge to Pb and the halogen element changes from Cl to I. A radar chart including the exciton binding energy, excited energy, amount of net charge transfer, electron and hole overlap index, distance between the centroid of holes and electrons, and the hole and electron separation index was proposed to intuitively describe the electron transmission characteristics of perovskites. Based on that, a comprehensive score index was innovatively proposed to evaluate the photoelectric property of perovskites, providing foundational guidance for the design of high-efficiency organic-inorganic hybrid perovskites.

The effect of coordination environment on the kinetic and thermodynamic stability of single-atom iron catalysts.

The stability of a single-atom catalyst is directly related to its preparation and applications, especially for high-loading single-atom catalysts. Here, the effect of a coordination environment induced by nitrogen (N) atoms coordinated with iron on the kinetic and thermodynamic stabilities of single-atom iron catalysts supported with carbon-based substrates (FeSA/CS) was investigated by density functional theory (DFT) calculations. Five FeSA/CS with different numbers of N atoms were modelled. The kinetic stability was evaluated by analyzing the migration paths of iron atoms and energy barriers. The thermodynamic stability was studied by calculating the adsorption and formation energies. Our results indicated that the coordination environment induced by N can promote the kinetic and thermodynamic stability of FeSA/CS. N atoms on the substrate promote the kinetic stability by raising the energy barrier for iron migration and not only increase the thermodynamic stability, but also contribute to catalyst synthesis. Doping N on the substrate enhances charge transfer between the iron atoms and substrates simultaneously improving the kinetic and thermodynamic stabilities. This theoretical research provides guidance for synthesizing stable and high loading single-atom catalysts by tuning the coordination environment of single-atom elements.

Support effects on adsorption and catalytic activation of O2 in single atom iron catalysts with graphene-based substrates.

The adsorption and catalytic activation of O2 on single atom iron catalysts with graphene-based substrates were investigated systematically by density functional theory calculation. It is found that the support effects of graphene-based substrates have a significant influence on the stability of the single atom catalysts, the adsorption configuration, the electron transfer mechanism, the adsorption energy and the energy barrier. The differences in the stable adsorption configuration of O2 on single atom iron catalysts with different graphene-based substrates can be well understood by the symmetrical matching principle based on frontier molecular orbital analysis. There are two different mechanisms of electron transfer, in which the Fe atom acts as the electron donor in single vacancy graphene-based substrates while the Fe atom mainly acts as the bridge for electron transfer in double vacancy graphene-based substrates. The Fermi softness and work function are good descriptors of the adsorption energy and they can well reveal the relationship between electronic structure and adsorption energy. This single atom iron catalyst with single vacancy graphene modified by three nitrogen atoms is a promising non-noble metal single atom catalyst in the adsorption and catalytic oxidation of O2. Furthermore, the findings can lay the foundation for the further study of graphene-based support effects and provide a guideline for the development and design of new non-noble-metal single atom catalysts.

Surface states of dual-atom catalysts should be considered for analysis of electrocatalytic activity.

Experimentally well-characterized dual-atom catalysts (DACs), where two adjacent metal atoms are stably anchored on carbon defects, have shown some clear advantages in electrocatalysis compared to conventional catalysts and emerging single-atom catalysts. However, most previous theoretical studies directly used a pristine dual-atom site to analyze the electrocatalytic activity of a DAC. Herein, by analyzing 8 homonuclear and 64 heteronuclear DACs structures with ab initio calculations, our derived surface Pourbaix diagrams show that the surface states of DACs generally differ from a pristine surface at electrocatalytic operating conditions. This phenomenon suggests that the surface state of a DAC should be considered before analyzing the catalytic activity in electrocatalysis, while the electrochemistry-driven pre-adsorbed molecules generated from the liquid phase may either change the electronic properties or even block the active site of DACs. Based on these results, we provide a critical comment to the catalyst community: before analyzing the electrocatalytic activity of a DAC, its surface state should be analyzed beforehand.

Effects of intermetal distance on the electrochemistry-induced surface coverage of M-N-C dual-atom catalysts.

The often-overlooked electrocatalytic bridge-site poisoning of the emerging dual-atom catalysts (DACs) has aroused broad concerns very recently. Herein, based on surface Pourbaix analysis, we identified a significant change in the electrochemistry-induced surface coverages of DACs upon changing the intermetal distance. We found a pronounced effect of the intermetal distance on the electrochemical potential window and the type of pre-covered adsorbate, suggesting an interesting avenue to tune the electrocatalytic function of DACs.

Dual response of fibroblasts viability and Porphyromonas gingivalis adhesion on nanostructured zirconia abutment surfaces.

Soft tissue integration surrounding dental implant is considered as a biological barrier against the oral microorganisms and external stimuli. At present, successful soft tissues integration around implants remains a challenge. The aim of this study was to evaluate the effects of nanostructured zirconia abutment surfaces on fibroblasts viability and Porphyromonas gingivalis adhesion, which are the two main factors influencing the quality of peri-implant soft-tissue seal. Zirconia samples were made from three-dimensional gel deposition approach and divided into three groups: zirconia surfaces, zirconia film coated surfaces (CZr) and polished surfaces (PZr). The Surface properties were characterized by scanning electron microscopy, atomic force microscopy and water contact angle. The behavior of human gingival fibroblasts (HGFs) on samples, including of proliferation, morphology, gene expression and protein expression as well as collagen synthesis were assessed. Additionally, the adhesion of P. gingivalis on samples was analyzed by scanning electron microscopy and live/dead staining. CZr and PZr group significantly enhanced fibroblasts viability such as cell proliferation, cell spreading and extracellular matrix secretion, and impaired P. gingivalis adhesion. Therefore, our findings demonstrated that CZr and PZr surfaces with nano-grains or grinding grooves might be considered as the feasible zirconia abutments substrate for reinforcing HGFs response and minimizing P. gingivalis adhesion, and thereby enhance peri-implant soft-tissue integration.

A descriptor for the structural stability of organic-inorganic hybrid perovskites based on binding mechanism in electronic structure.

The poor stability of organic-inorganic hybrid perovskites hinders its commercial application, which motivates a need for greater theoretical insight into its binding mechanism. To date, the binding mode of organic cation and anion inside organic-inorganic hybrid perovskites is still unclear and even contradictory. Therefore, in this work based on density functional theory (DFT), the binding mechanism between organic cation and anion was systematically investigated through electronic structure analysis including an examination of the electronic localization function (ELF), electron density difference (EDD), reduced density gradient (RDG), and energy decomposition analysis (EDA). The binding strength is mainly determined by Coulomb effect and orbital polarization. Based on the above analysis, a novel 2D linear regression descriptor that Eb = - 9.75Q2/R0 + 0.00053 V∙EHL - 6.11 with coefficient of determination R2 = 0.88 was proposed to evaluate the binding strength (the units for Q, R0, V, and EHL are |e|, Å, bohr3, and eV, respectively), revealing that larger Coulomb effect (Q2/R0), smaller volume of perovskite (V), and narrower energy difference (EHL) between the lowest unoccupied molecular orbital (LUMO) of organic cation and the highest occupied molecular orbital (HOMO) of anion correspond to the stronger binding strength, which guides the design of highly stable organic-inorganic hybrid perovskites.

A novel Fe-Co double-atom catalyst with high low-temperature activity and strong water-resistant for O3 decomposition: A theoretical exploration.

Developing catalysts with high activity, durability, and water resistance for ozone decomposition is crucial to regulate the pollution of ozone in the troposphere, especially in indoor air. To overcome the shortcomings of metal oxide catalysts with respect to their durability and water resistance, Fe-Co double-atom catalyst (DAC) is proposed as a novel catalyst for ozone decomposition. Here, through a systematic study using density functional theory (DFT) calculations and microkinetic modeling, the adsorption and catalytic decomposition of O3 on Fe-Co DAC have been examined based on adsorption configuration, orbital hybridization, and electron transfer. Based on Eley-Rideal (E-R) and Langmuir-Hinshelwood (L-H) reaction mechanisms, the mechanisms of ozone decomposition on Fe-Co DAC were explored by analyzing reaction paths and energy variations. To confirm the water-resistant of Fe-Co DAC, competitive adsorption behavior between O3 and dominant environmental gases was discussed through ab initio molecular dynamic (AIMD) simulation. The dominant reaction mechanism of ozone decomposition is L-H and the rate-determining step is the desorption of the first oxygen molecule from the surface of Fe-Co DAC which has an energy barrier of 0.78 eV. Due to this relatively low energy barrier and high turnover frequency (TOF), the optimal operation window of catalytic O3 decomposition on Fe-Co DAC is <500 K suggesting that catalytic decomposition of O3 on Fe-Co DAC can occur at room temperature. This theoretical study provides new insights for designing novel catalysts for ozone decomposition and fundamental guidance for subsequent experimental research.

A field study on using soybean waste-derived superabsorbent hydrogel to enhance growth of vegetables.

Food security is critical and has become a global concern with many of our basic food crops growing in areas with high drought risk. To improve soil water holding capacity, hydrogels are a promising solution. However, the current ones are mostly derived from petroleum products and are environmental unsustainable. In this study, the main objective is to determine if bio-based hydrogel can help in the growth of leafy vegetables while minimizing water use under field conditions. To achieve this, we developed an okara-derived hydrogel (Ok-PAA; OP) from by-products of bean curd and soybean milk production. We incorporated OP into soil and assessed the growth performance of leafy vegetables. We observed that vegetables grown with 0.2% (w/v) OP in soil with a watering frequency of 7 times per week resulted in >60 % and 35 % yield increase for the common Asian leafy vegetables, choy sum (CS) and pak choi (PC), respectively, as compared to without hydrogel supplementation. Both vegetables produced larger leaf areas (20-40 % increment) in the presence of the hydrogel as compared to those without. In addition, with OP amendment, the irrigation water use efficiency improved >60 % and 30 % for CS and PC, respectively. It is estimated that with the use of the hydrogel, a reduction in watering frequency from 21 times to 7 times per week could be achieved, and based on a per hectare estimation, this would result in 196,000 L of water saving per crop cycle. Statistical analysis and modelling further confirmed vegetables grown with 0.2 % (w/v) OP and with a watering frequency of 7 times per week showed the best growth performance and water use efficiency. Such a waste-to-resource approach offers a plant-based soil supplement for crop growers, contributes to waste valorization, and enhances the growth of plants especially under water-limited conditions.

A Sulfur-Tolerant MOF-Based Single-Atom Fe Catalyst for Efficient Oxidation of NO and Hg0.

Catalytic oxidation of NO and Hg0 is a crucial step to eliminate multiple pollutants from emissions from coal-fired power plants. However, traditional catalysts exhibit low catalytic activity and poor sulfur resistance due to low activation ability and poor adsorption selectivity. Herein, a single-atom Fe decorated N-doped carbon catalyst (Fe1 -N4 -C), with abundant Fe1 -N4 sites, based on a Fe-doped metal-organic framework is developed to oxidize NO and Hg0 . The results demonstrate that the Fe1 -N4 -C has ultrahigh catalytic activity for oxidizing NO and Hg0 at low and room temperature. More importantly, Fe1 -N4 -C exhibits robust sulfur resistance as it preferably adsorbs reactants over sulfur oxides, which has never been achieved before with traditional catalysts. Furthermore, SO2 boosts the catalytic oxidation of NO over Fe1 -N4 -C through accelerating the circulation of active sites. Density functional theory calculations reveal that the Fe1 -N4 active sites result in a low energy barrier and high adsorption selectivity, providing detailed molecular-level understanding for its excellent catalytic performance. This is the first report on NO and Hg0 oxidation over single-atom catalysts with strong sulfur tolerance. The outcomes demonstrate that single-atom catalysts are promising candidates for catalytic oxidation of NO and Hg0 enabling cleaner coal-fired power plant operations.

Mechanism of hydrogen storage on Fe3B.

Recent experimental finding has indicated that Fe3B has the capacity to uptake and release hydrogen. However, the mechanism has not been clarified. Here, for the first time, the characteristics were investigated through the electronic structures and energies of H2 molecules adsorbed on Fe3B using density functional theory calculations. Most importantly, this work provides a guideline for experimental investigation of wide-source and low-cost Fe-based hydrogen storage materials.

Screening the activity of single-atom catalysts for the catalytic oxidation of sulfur dioxide with a kinetic activity model.

An accurate prediction model of catalytic activity is crucial for both structure design and activity regulation of catalysts. Here, a kinetic activity model is developed to study the activity of single-atom catalysts (SACs) in catalytic oxidation of sulfur dioxide. Using the adsorption energy of the oxygen atom as a descriptor, the catalytic activities of 132 SACs were explored. Our results indicate the highest activity when the adsorption energy of oxygen equals -0.83 eV. In detail, single-atom Pd catalyst exhibits the best catalytic activity with an energy barrier of 0.60 eV. Most importantly, this work provides a new insight for developing a highly accurate and robust prediction model for catalytic activity.

Chemical Modification of Biomass Okara Using Poly(acrylic acid) through Free Radical Graft Polymerization.

Okara (Ok) or soybean residue is produced as a byproduct from the soybean milk and soybean curd industries world wide, most of which is disposed or burned as waste. It is important to explore the possibilities to convert okara to useful materials, because okara is a naturally renewable bioresource. Here, we report the chemical modification of okara by grafting poly(acrylic acid) (PAA) onto the backbones of okara in water medium and the characterization of the Ok-PAA graft copolymers. It was found that the received okara mainly contained insoluble contents in water. The insoluble okara component Ok(Ins) was suspended in water and activated with ammonium persulfate as an initiator, followed by grafting PAA through a free radical polymerization. After the graft polymerization, the product (Ok-PAA) was separated into precipitate and supernatant, which were dried to give Ok-PAA(pre) and Ok-PAA(sup), respectively. It was found that PAA was grafted on Ok backbones and co-precipitated with the insoluble Ok. In addition, Ok-PAA(sup) was found to be translucent as a result of the grafting of PAA. Further, the successful grafting of PAA onto okara backbones was proven by Fourier transform infrared, thermogravimetric analysis, and microscopic measurements. Ok-PAA(sup) dispersed in water formed nanoparticles with an average diameter of 420 nm, while Ok-PAA(pre) was clustered coarse particles in water. The rheological data including the storage modulus, loss modulus, and viscosity indicated that the Ok-PAA product was a viscoelastic gel-like material with potential for agricultural and environmental applications.

Adsorption behavior of mercuric oxide clusters on activated carbon and the effect of SO2 on this adsorption: a theoretical investigation.

The release of mercury (Hg) species from coal-fired power plants has attracted increasing concern, and the development of an efficient and economical method to control Hg species emission from such plants is urgently required. Activated carbon is a compelling sorbent for the elimination of mercury species from flue gas, but the adsorption mechanism of mercuric oxide clusters on carbonaceous materials is still unclear. Therefore, the adsorption characteristics of mercuric oxide clusters on activated carbon were investigated systematically utilizing density functional theory in this work. It was found that mercuric oxide clusters are chemically adsorbed on activated carbon, and that the pre-adsorption of SO2 on the activated carbon leads to complicated mercuric oxide cluster adsorption behavior due to an irregular distribution of the electrostatic potential on the surface of the carbonaceous material. Thermodynamic analysis indicated that the adsorption energy of SO2 on activated carbon is lower than that of mercuric oxide clusters in the temperature range 298.15-1000 K. Competitive adsorption analysis suggested that mercuric oxide clusters are at least 108.11 times more likely than SO2 to be adsorbed on activated carbon. Graphical abstract Competitive adsorption between SO2 and HgO clusters on activated carbon surface in flue gas of coal-fired power plants.