Corrosion Behavior Of Materials Research Articles

Initial corrosion behavior of 10CrNi3MoV steel and Q235 steel in marine atmosphere

Exploring the corrosion behavior of materials in a marine atmosphere and studying their corrosion resistance mechanisms can provide a basis for developing new weathering steels. This paper primarily introduces the initial corrosion behavior and corrosion resistance mechanism of 10CrNi3MoV steel in a marine atmosphere and explores the effects of Ni on the corrosion resistance. The corrosion resistance of 10CrNi3MoV steel and Q235 steel was compared using the weight loss method. The results show that the corrosion rate of 10CrNi3MoV steel was approximately half that of Q235 steel. The rust layer morphology and elemental distribution of 10CrNi3MoV steel were observed via scanning electron microscopy and energy-dispersive X-ray spectroscopy. The composition of the rust layer was analyzed using X-ray diffraction and X-ray photoelectron spectroscopy, and the protective effect of the rust layer was further tested using an electrochemical test. The reason why Fe2NiO4 is beneficial for improving the corrosion resistance of the rust layer was explained using the Vienna Ab initio Simulation Package. The results reveal that the morphology, composition, element distribution, and electrochemical properties of 10CrNi3MoV steel were considerably stronger than those of Q235 steel because of the formation of Fe2NiO4 in the rust layer. Fe2NiO4 refines the rust layer particles and promotes the transformation of corrosion products to α-FeOOH. In addition, it imparts ion selectivity to the rust layer, effectively blocking the invasion of Cl−.

Research on Metal Corrosion Behaviour of Indirect Air-cooled Circulating Water System

Abstract The indirect air cooling system of an air-cooled power plant consists of a surface condenser and an aluminum radiator. The radiator is usually made of 1050A pure aluminum, and the circulating water pipe connecting the radiator is made of Q235B carbon steel. This article simulates and controls typical water quality parameters such as dissolved oxygen and pH value in indirect air cooling systems using a self-designed simulation device, and studies the influence of various water quality parameters on material corrosion behavior in intercooled circulating water systems when aluminum and carbon steel coexist. The results indicate that factors such as dissolved oxygen, conductivity, pH value, temperature, and redox potential can all affect the corrosion rate of carbon steel and aluminum materials; The increase in temperature and dissolved oxygen leads to an increase in corrosion rate; The main way carbon steel corrodes the system is by consuming dissolved oxygen; The corrosion behaviour in the water circuit will lead to an overall decrease in pH value. In the presence of multiple metals, the influence of iron ions in the solution significantly increases the corrosion rate of aluminum.

Suitability of using simulated porous‐like solutions to evaluate the steel corrosion in carbonated concrete

AbstractThe use of simulated solutions is a common practice in corrosion studies to assess easily and quickly the corrosion behaviour of materials in real situations. In this context, it is frequent to substitute tests performed in concrete or mortar with tests performed in porous‐like solutions. Particularly, to simulate carbonated concrete, different carbonated porous‐like solutions (CPS) are found in the literature. However, it is not known whether the use of these solutions accurately predicts the behaviour of steel bars embedded in carbonated concrete. In the present work, a comparison between the corrosion behaviour of carbon steel in carbonated mortar and in different CPS was performed. It was found that none of the CPS studied in the present work adequately simulate the corrosion behaviour of the steel in carbonated mortar, but the CPS containing high concentrations of carbonates/bicarbonates is the only one that provides corrosion rates close to those obtained in carbonated concrete.

Comparison and mechanism research of corrosion behavior of materials used in supercritical water-cooled reactors

In this study, the corrosion characteristics of essential materials used in supercritical water-cooled reactors (SCWRs) were investigated. Austenitic stainless steels 304 SS and 316 SS and nickel-based alloys 625 and 800 were exposed to supercritical water (SCW) at 500 °C and 25 MPa. The results showed that 304 SS, 316 SS, and Alloy 800 formed a double oxide layer, with the outer and inner layers enriched in Fe and Cr/Ni, respectively. Alloy 625 formed outer Ni-rich oxides (mainly NiO) and inner Cr-rich oxides and Ni-Cr oxides (mainly Cr2O3 and NiCr2O4, respectively). The results indicated that the difference in the protective performance of the corrosion products was mainly due to the Cr content, and the order of corrosion resistance was Alloy 625 > Alloy 800 > 304 SS > 316 SS. In addition, a corrosion mechanism model was established, which lays a good foundation for corrosion prediction and protection in SCWRs.

Corrosion behavior of different building planes of selective laser melting 316L stainless steel in 0.1 M HCl solution

This study aimed to investigate the impact of building planes on the microstructure and corrosion behaviors of 316L stainless steel (SS) manufactured using selective laser melting (SLM) when subjected to a 0.1 M HCl solution. The investigation of material corrosion behavior involved the utilization of a wide array of surface characterization techniques, as well as various electrochemical measurements. The findings were subjected to a comparison with those obtained from a commercially rolled 316L SS (R 316L SS). The analysis revealed that the XZ and YZ planes displayed a distinctive fish scale melt pool structure, while the XY plane exhibited a columnar melt pool structure. The densities of the low-angle grain boundaries for different building planes were observed as YZ < XY < XZ. SLM 316L SS showing a broader passivation region and a higher breakdown potential compared to the commercially available R 316L SS, especially the XZ and XY planes. Meanwhile, the passive film on the XZ plane was the more compact and had a lower carrier density. The observed factors contributing to these findings could be attributed to the SLM sample containing an increased proportion of O2−/OH− and Cr2O3 in the passive film. This resulted in the formation of a thick and dense passive film, which is particularly evident on the XY and XZ planes. In addition, it was observed that the nucleation mechanism of the passive films remained unchanged following the SLM process.

Assessment of in-vivo corrosion of Ti and CoCrMo joint implants by electrochemical measurements in human synovial liquids

Understanding the corrosion behavior of biomedical materials in the human body is an essential step for the development of improved materials and clinical procedures in arthroplasty. In this work, the corrosion behavior of Ti and CoCrMo alloys in human synovial fluids directly extracted from patients was investigated through an electrochemical experimental protocol, including open circuit potential (OCP), electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization measurements. The results show that the corrosion behavior of both materials largely depends on patients. The obtained corrosion rates of both materials correspond well to the metal ion release rate detected in patients with joint implants revealing the adequateness of the electrochemical experimental protocol for quantifying in-vivo corrosion of biomedical implants. Based on the results, no significant risk of galvanic corrosion of Ti/CoCrMo coupling is anticipated. The rates of wear-accelerated corrosion (one of the main degradation mechanisms of metallic artificial hip joints) were theoretically extracted from the present electrochemical measurements using an established tribocorrosion model. Those material loss rates are patient-dependent and can be up to two orders of magnitude higher than the static corrosion rates, indicating that the nature of the patient can critically affect the degradation rate of the joint implant.

Comparative Study on the Corrosion Behavior of Materials in Heat Recovery Steam Generators for Combined Cycle Power Plant, Based on Sulfuric Acid Concentrations: Carbon Steels vs. Duplex Stainless Steel (UNS S32205)

This study examined the effects of sulfuric acid concentrations on the corrosion behaviors of three types of commercial steels, conventional carbon steel (SA192), sulfuric acid dew resistant steel (S-TEN), and duplex stainless steel (S32205), considered to be potential materials for the heat recovery steam generator (HRSG) in combined cycle power plants. Based on the results of electrochemical polarization, immersion and wet-dry cycle tests, when exposed to a high sulfuric acid concentration (50%), SA192 and S-TEN exhibited higher corrosion resistance than S32205, which was due to the formation of a stable FeSO4 scale on the carbon steel materials. With prolonged exposure, S-TEN with slightly higher concentrations of Cu and Sb showed lower weight loss than SA192. Conversely, when subjected to low sulfuric acid concentrations (5 and 10%), S32205 exhibited passivation behavior, and showed significantly superior corrosion resistance (i.e., much smaller weight loss) than the other two steel samples. The examination of localized corrosion damages through cross-section observation using a scanning electron microscope revealed also that S32205 suffered less damages from the wet-dry cycling. The long-term superior corrosion resistance of duplex stainless steel in low sulfuric acid concentrations, compared to carbon steel and sulfuric acid due resistant steel, make it a promising candidate material for the HRSG in combined cycle power plants.

On the Use of Machine Learning Algorithms to Predict the Corrosion Behavior of Stainless Steels in Lactic Acid

Predicting the corrosion behavior of materials in specific environmental conditions is important for establishing a sustainable manufacturing system while reducing the need for time-consuming experimental investigations. Recent studies started to explore the application of supervised Machine Learning (ML) techniques to forecast corrosion behavior in various conditions. However, there is currently a research gap in utilizing classification ML techniques specifically for predicting the corrosion behavior of stainless steel (SS) material in lactic acid-based environments, which are extensively used in the pharmaceutical and food industry. This study presents a ML-based prediction model for corrosion behavior of SSs in different lactic acid environmental conditions, using a database that described the corrosion behavior by qualitative labels. Decision tree (DT), random forest (RF) and support vector machine (SVM) algorithms were applied for classification. Training and testing accuracies of, respectively 97.5% and 92.5% were achieved using the DT classifier. Four SS alloy composition elements (C, Cr, Ni, Mo), acid concentration, and temperature were found sufficient to consider as input data for corrosion prediction. The developed models are reliable for predicting corrosion degradation and, as such, contribute to avoiding failures and catastrophes in industry.

Improved design of available atmospheric corrosion monitoring sensor with a higher sensitivity to low NaCl deposition

• A novel sensor that monitors the corrosion behavior of material was designed. • The sensor shows good responses to surface wetting and electrolyte development. • The sensor shows high resolutions in corrosion detection under low NaCl conditions. • The sensor provides perspectives on current atmospheric corrosion sensor design. The development of electrochemistry-based sensors is central to in-situ atmospheric corrosion monitoring of materials under conditions of both outdoor and indoor exposures. In this work, an improved design with an exposed Cu teeth height in the two member electrodes of the traditional EIS-based atmospheric corrosion sensor makes the sensor more sensitive under the thin electrolyte layer. With the noble design, the improved sensor exhibits a high sensitivity in surface electrolyte generation when the NaCl deposition is higher than 20 mg/m 2 , and a good corrosion rate response to NaCl deposition as low as 2 mg/m 2 . The grooves between adjacent exposed Cu teeth facilitate the formation of electrolyte layer under the hygroscopic effect of NaCl deposition, and thus improve sensitivity of the sensor to low NaCl deposition. Therefore, the improved sensor has broad potential applications in field conditions to monitor low NaCl deposition induced thin electrolyte layer evolution and corrosivity assessment of the atmosphere.

Effects of Different Concentrations of BSA on In Vitro Corrosion Behavior of Pure Zinc in Artificial Plasma

When medical metallic materials are implanted in the body and come into contact with the body fluid environment, proteins will be rapidly adsorbed on the surface and affect the corrosion process of the material. Currently, there is no uniform understanding of the effect of protein adsorption on the corrosion behavior of materials due to the limitations of the nature of metal materials, protein concentrations, and different media environments. The effect of various bovine serum albumin (BSA) concentrations in artificial plasma (AP) on the corrosion behavior of pure Zn during 14 days of immersion was investigated in this research. The corrosion rate of pure Zn was slowed down by the addition of BSA, and the decelerating effect of lower protein concentration on the corrosion rate of Zn was more significant in the initial stage of immersion. With prolonging the immersion time, the corrosion rate of pure Zn in different media slowed down and stabilized, and the corrosion rates of pure Zn showed a decreasing trend with an increase of BSA concentration. Furthermore, the Langmuir adsorption isotherm model was utilized to study the relationship between the BSA concentration and corrosion behavior of pure Zn and to analyze the role of proteins in the degradation mechanism of pure Zn. This work could be useful for further exploration of potential clinical applications of zinc alloys.

Application of Machine Learning Algorithms to Classify and Predict Corrosion Behavior of Stainless Steels in Lactic Acid

Corrosion of metals is a critical issue, which causes a considerable amount of loss in industries. There are numerous factors that can influence corrosion, including chemical composition and multiple environmental conditions. Hence, it is difficult to determine the relation between individual environmental factors and corrosion processes based on physics-based corrosion laws or to predict the corrosion life of materials. Predicting corrosion behavior of materials in any type of environment is important since testing materials in each different environment is time-consuming and expensive. Hence, analyzing corrosion data and, subsequently, predicting corrosion behavior needs advanced data mining methods. Recently, machine learning (ML) methods have been extensively used in materials research thanks to their powerful data mining capabilities. By learning from sample data and experience, it automates the searching for knowledge without reliance on predetermined equations. In corrosion research, machine learning algorithms such as random forest, support vector regression, and artificial neural networks have been used to study the corrosion behavior.Stainless steels are widely used in corrosive environments. This research aims to develop classification methods for predicting stainless steel corrosion behavior in different concentrations of lactic acid and different temperatures. The Handbook of corrosion data was used to gather data on stainless steel corrosion in lactic acid. Outlier, repeated, and missing data were treated during a pre-processing step (Figure 1). Based on the ML results, we can properly predict the corrosion behavior of various grades of stainless steels, in different lactic acid concentration and test temperatures. The best training and testing accuracies are 98.73% and 90.00%, respectively, which are obtained by fitting the decision tree classifier. It is also concluded that the percentage of four essential elements (C, Cr, Ni, Mo) in stainless steel alloys, alongside acid concentration and temperature, can be used as the input data to predict the corrosion behavior. Receiver operating characteristic (ROC) curves indicate that stainless steels with poor corrosion behavior are correctly classified by support vector machine (SVM) multiclassification modeling. Therefore, the SVM algorithm is reliable for detecting stainless steels with poor corrosion behavior, which are highly risky for critical applications if chosen incorrectly. Figure 1

Understanding biofilm impact on electrochemical impedance spectroscopy analyses in microbial corrosion and microbial corrosion inhibition phenomena

Electrochemical impedance spectroscopy (EIS) is a non-disruptive technique that can be used to assess the corrosion behavior of materials. In this work, case studies on microbial corrosion (MIC) and microbial corrosion inhibition (MICI), drawn from the recent literature, were re-elaborated and discussed to determine which underlying information EIS analysis could or could not provide, concerning the roles of biofilms and bacteria on the corrosive and inhibitive processes. The data were suitably integrated and elaborated upon using Differential impedance analysis (DIA) to estimate the maximum number of time constants (T) that constituted the electric equivalent circuit (EEC) of each considered corrosion process. Additional analyses on the corrosion products performed by XPS and EIS showed that the bacterial biofilm had a significant influence on the faradaic charge transfer and film resistance of the corrosive process, as well as on its inhibition, at the metal-biofilm interface; however, it did not constitute an additional time constant.

Effects of cold work on the corrosion behavior of Alloy 800H exposed to aerated supercritical water

• Cold work reduces the corrosion resistance of Alloy 800H in supercritical water containing 200 ppb oxygen. • At initial corrosion stage, the corrosion behavior of as-received and cold-worked materials is similar. • The formation of a continuous chromia layer reduces the oxygen partial pressure at the oxide scale-matrix interface, which contributes to the later internal oxidation and selective oxidation of the elements. • With the increase of exposure time, the microstructure of the cold worked materials becomes more complicated and is further subdivided into Fe 3 O 4 /Ni x Fe 3- x O 4 /Ni x Fe y Cr 3- x - y O 4 /Cr 2 O 3 / NiO/Fe x Cr 2- x O 4 +pure Ni multi-layer structure. • Through observation of the oxidation process and the calculation of thermodynamic equilibria, the oxide scale differences were explained through the hypothesis that diffusion barriers were created by the prevailing oxides. Revealing the corrosion mechanism of materials in supercritical water (SCW) is a key issue for the development of a supercritical water reactor (SCWR). Considering cold work is inevitable in the construction of nuclear reactors and its effect on the corrosion behavior of materials in SCW is still unclear, the corrosion behavior of solution-annealed (as-received) and 30% cold-worked Alloy 800H were studied at 600 °C in aerated SCW for 1500 h. The microstructure of the oxide scale was studied by a variety of characterization techniques. The results indicate that cold work increases the corrosion rate of the materials, promotes selective oxidation and internal oxidation, and this was attributed to the high diffusion rate of different ions along “short-circuit paths”. The oxide scale composition and structure of the cold-worked material, which is subdivided to Fe 3 O 4 /Ni x Fe 3- x O 4 /Ni x Fe y Cr 3- x - y O 4 /Cr 2 O 3 /NiO/ Fe x Cr 2- x O 4 + pure Ni multi-layer structure, are much more complicated than the as-received material after 1500 h exposure. Combined with the thermo-dynamic equilibrium calculation, the oxidation processes of the as-received and cold-worked materials were analyzed to reveal the effect of cold work on the corrosion behavior of Alloy 800H.

Corrosion resistances of metallic materials in environments containing chloride ions: A review

Corrosion, more specifically, pitting corrosion happening extremely in marine environments, leads to lifespan of materials drastically decreasing in service, which causes enormous economic loss and even environmental disaster and casualties. In the past decade, increasing efforts have been made to study the corrosion behaviors of materials in chloride-containing aqueous environments. Herein, this work provides an overview of recent progress in understanding the degradation mechanism and improving the corrosion resistance and corrosion-wear resistance of materials from bulk metal to surface treatment involving organic coating, metal and its alloy or compound coating. The particular emphasis is given to the periodic layered structures (PLSs), whose anti-corrosion properties outperformed others to some extent, wherever in terms of bulk metal or surface treatment, regardless of aggressive environment (corrosion or corrosion-wear conditions). Numerical simulation based on kinds of models at different scales is introduced to deeply understand the process of corrosion and/or corrosion-wear in chloride-containing aqueous environment. Combined experimental result with numerical simulation, the micro-galvanic corrosion dominated degradation mechanism of PLSs is critically analyzed. Types of setups to realize corrosion-wear in laboratory are also summarized. At last, future research and development are prospected, offering to develop a basic application of PLSs designed by corrosion protection methodology in the near future.

Image Deep Learning Assisted Prediction of Mechanical and Corrosion Behavior for Al-Zn-Mg Alloys

The use of metallographic images to predict the mechanical properties of materials and their corrosion behavior is helpful in achieving nondestructive detection and quality control. However, after a long-term attempt, the traditional methods cannot accurately correlate the mechanical properties and corrosion behavior of materials with the corresponding microstructure images. In this study, we propose a deep learning strategy to predict the mechanical property and corrosion behavior of large-scale extruded aluminum profiles using surface optical microstructure images. The proposed models with remarkable properties were established through experimental dataset collection, dataset preprocessing, deep learning network modification, and key parameter screening. Taking extruded Al-Zn-Mg alloys with different surface microstructures as example materials, 4,800 sets of “metallographic image – hardness (HV) – corrosion potential (E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corr</sub> )” data were experimentally collected to establish the HV and E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corr</sub> models with prediction accuracies of 90% and 82%, respectively. The proposed HV and E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corr</sub> models exhibit great generalization ability with mean average errors of 1.8 HV and 7.0 mV on experimental validation sets, respectively. The proposed model can accurately correlate the metallographic images, mechanical property, and corrosion behavior, which can provide theoretical support for intelligent and nondestructive testing methods to further prevent unexpected material failure.

Effect of surface finish on corrosion behavior of polished Al6061 alloy in simulated acid rain environment

Aluminium and its alloys are considered as most favorable material in aerospace, automobile and marine industries due to its light weight and high strength to weight ratio. Outer body of the airplanes, ships, automobile etc. are usually made of aluminium and its alloys. The tensile and fatigue failure of a component is highly affected by the corrosion behavior of the material in different environments. Surface roughness of the specimens plays an important role in determining the corrosion behavior of material. In the situations like Covid-19 or while waiting for repairing, airplanes, cars, ships etc. stand idle for months in the rainy environments. As rain is acidic in nature, metal starts corroding when get in contact with it. In the present work, corrosion due to acid rain on the polished surface of as-cast Al6061 alloy is analyzed through simulated acid rain environment (pH = 4). The effect of surface roughness on the corrosion rate was also investigated. It was observed that corrosion rate decreases as the surface finish of the specimen increases.

Electrochemical Corrosion Behavior of Pure Mg Processed by Powder Metallurgy

Pure Mg samples were prepared by powder metallurgy using the cold and hot compacting methods. Cold compacted pure Mg (500 MPa/RT) was characterized by 5% porosity and the mechanical bonding of powder particles. Hot compacted samples (100 MPa/400 °C and 500 MPa/400 °C) exhibited porosity below 0.5%, and diffusion bonding combined with mechanical bonding played a role in material compaction. The prepared pure Mg samples and wrought pure Mg were subjected to corrosion tests using electrochemical impedance spectroscopy. Similar material corrosion behavior was observed for the samples compacted at 500 MPa/RT and 100 MPa/400 °C; however, hot compacted samples processed at 500 MPa/400 °C exhibited longer corrosion resistance in 0.9% NaCl solution. The difference in corrosion behavior was mainly related to the different binding mechanisms of the powder particles. Cold compacted samples were characterized by a more pronounced corrosion attack and the creation of a porous layer of corrosion products. Hot compacted samples prepared at 500 MPa/400 °C were characterized by uniform corrosion and the absence of a layer of corrosion products on the specimen surface. Powder-based cold compacted samples exhibited lower corrosion resistance compared to the wrought pure Mg, while the corrosion behavior of the hot compacted samples prepared at 500 MPa/400 °C was similar to that of wrought material.

Corrosion Behavior of Materials Al5083 Alloy, 316L Stainless Steel and A681 Carbon Steel in Seawater

In this paper the corrosion behavior of different materials has been evaluated based on exposure in seawater. The laboratory immersion test technique has been applied to evaluate the effect of seawater on the corrosion behavior of different materials. In three sets of experiments, carbon steels (A681 Type O7), austenitic stainless steels (316L) and aluminium alloys (Al5083) were utilized. The specimens were fixed fully submerged in seawater. The corrosion process was evaluated using weight loss method, open-circuit potential measurements (OCP) and polarization techniques. To determine gravimetric index and the rate of penetration, samples were immersed in corrosive environment for 89 days and weighed periodically. The electrochemical experiments were conducted with a Potentiostat/Galvanostat (PGP 201) analyzer. It was connected to a PC. The Voltamaster software was used for electrochemical data analysis. A three-electrode cell composed of a specimen as a working electrode, Pt as counter electrode, and saturated calomel electrode (SCE) (Hg (l)/ Hg2Cl2 (s)) as a reference electrode were used for the tests. The weight loss tests revealed the lowest corrosion rate values for stainless steel and aluminium alloys, indicating a beneficial use for these materials in marine environments. The potentiodynamic method shows that the lowest corrosion rate in seawater (2.8 μm /year) was obtained for the Al5083 alloy, and the highest value of the corrosion rate (41.67 μm/year) for A681 carbon steel.&#x0D;

Microbial corrosion of DSS 2205 in an acidic chloride environment under continuous flow.

Corrosion under flow conditions is a major problem in the transportation industry. Various studies have shown the direct impact of different flow rates on bacteria biofilm formation, mass transfer and resulting different corrosion behaviour of materials in neutral environments. However, little is understood on corrosion under acidic flow conditions. This study investigated the impact of an acidic artificial seawater environment containing Desulfovibrio vulgaris on DSS 2205 microbial corrosion under different velocities (0.25 m.s-1 and 0.61 m.s-1). Experiments containing no bacteria were performed as controls. Bacterial attachment was observed by optical and scanning electron microscope (SEM). Materials corrosion was assessed using open circuit potential (OCP), electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization. Pits formed after potentiodynamic test were observed under SEM. The largest area of bacterial attachment was found on coupons immersed at a velocity of 0.25 m.s-1; however, the corrosion rate was lower than at higher velocity. Shallow pits occurred in the metal coupons when bacteria were present, while deep pits occurred in the controls. The study indicates the positive impact of biofilm formation in corrosion prevention of materials under acidic condition. The nature of corrosion behaviour of duplex stainless is discussed.

WITHDRAWN: Corrosion behavior of materials in gasifier components and coating influence in improving corrosion resistance – Review

Abstract The need for an alternate source of energy is increasing with industrialization and development of technologies all over the world. Developed countries are focusing on renewable energies and waste to energy method to cope with an increase in demand. One of the promising sectors for energy requirement is syn gas production from gasification. Gasification is one of the oldest processes to use biomass for the energy production has merits and demerits to it. This paper focuses on the corrosion behavior of the gasifier components under various simulated/Accelerated/gasifier power plants and recent steps taken in minimizing the same.