2 Open Studio for Marine Corrosion and Protection, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China;
3 Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China;
4 University of Chinese Academy of Sciences, Beijing 100049, China;
5 Shanghai Bainite Chain Material Tech Co. Ltd., Shanghai 200439, China
Seawater is a rather harsh electrolyte for metal corrosion. Additional to the high salt concentration, diverse microorganisms survived play quite an important role in the corrosivity. Corrosion affected by microorganisms is known as microbially influenced corrosion (MIC), a destructive type of corrosion. Approximately 20%-30% of all corrosion failure is related to MIC, with a direct financial cost of ＄30-50 billion per year (Javaherdashti, 2008). There are numerous cases for the marine corrosion deterioration due to MIC like a rapid perforation of welded stainless steel pipes in a yacht served in Auckland with a corrosion rate of 40 mm/a (Liu, 2014), accelerated low water corrosion of steel pilings in some harbours around the world (Melchers and Jeffrey, 2013), and severe pitting corrosion of mooring chains served in West Africa (Fontaine et al., 2012). MIC is normally achieved by the biofilm from the attachment of microorganisms onto the substrate, and two kinds of mechanisms are involved in metal materials, i.e., extracellular electron transfer-MIC and metaboliteMIC (Xu et al., 2016; Li et al, 2018; Gu et al., 2019; Jia et al., 2019). It is expected to be suppressed via the inhibition on biofilm formation and development. Several methods have been proposed and utilized for biofilm inhibition, which can be divided into four categories: modification in chemical compositions and structures of the substrate metals, surface modification with coatings or functional films, cathodic protection, and introduction of bactericides into bulk solutions. Among them, substrate metal modification by the addition of alloying elements has attracted extensive attention due to its simplicity and flexibility.
Copper is a common alloying element in steel manufacture to promote mechanical performance (Kim et al., 2011), and its addition can also improve the atmospheric corrosion resistance of high strength low alloy (HSLA) steels owing to the formation of adherent and protective rust layers (Lins et al., 2017). A typical copper-bearing HSLA steel is ASTM A690 steel. Because copper has long been utilized as a disinfectant for bacteria, the introduction of copper into steels might favor their resistance against MIC. There are extensive reports on MIC inhibition by copper addition into stainless steels, especially the work of Xu and Yang et al. Copper has been added into stainless steels with the designations like 304, 304L, 317L, and 2205 to resist corrosion from Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa, etc. (Hong and Koo, 2005; Nan et al., 2015; Xia et al., 2015; Sun et al., 2016). The MIC inhibition of copper-bearing stainless steels is closely related to the release of copper ions, which kill bacteria via damaging cytoderm and cytomembrane, generating the reactive oxygen species, and other mechanisms (Espírito et al., 2011; Warnes et al., 2012; Sun et al., 2016). Although these reports are significant for the development of copper-bearing steels, two points ought to be stressed before their practical applications in seawater.
Firstly, how about the impact of copper introduction on MIC of HSLA steels? Unlike the case for stainless steels, there are hardly any reports on HSLA steels that are widely utilized in marine structural construction, except for the very recent work by Shi et al. (2018). They found that the as-aged copper-bearing X80 pipeline steel (1.06% copper) with Cu-rich precipitates inhibited the attachment of sulfatereducing bacteria (SRB) and Pseudomonas aeruginosa, and therefore had better corrosion resistance than bare X80 steel. Consequently, further studies are highly desirable on this topic.
Secondly, how copper introduction affects microbial communities and what is the relationship between microbial communities and corrosion of copper-bearing steels in seawater? Normally, the efficiency of copper-bearing steels against MIC was evaluated with specific isolated bacterial strains in laboratories, and to the best of our knowledge, there has been no reports focused on the impact of copper introduction on microbial communities in seawater. Microbial communities seem to be sensitive to the variation in material surface features and the environments, and they vary with substrate material types, surface microscale topography of organic films, organic biocides in antifouling coatings, chemical composition of solutions, and environmental factors like temperature. It is expected that microbial communities will be affected by copper introduction due to its release from the steel surface, but the accurate changes need further investigation. Although there are several reports on the impact of copper addition on the corrosion rate of HSLA alloys in seawater by field experiments, the role of copper is still in controversy: some reports demonstrate that it is strongly beneficial (Petersen, 1977), while others indicate deleterious or no effects (Forgeson et al., 1960; Hou et al., 2000). These conflicts are closely related to the difference in copper contents, exposure time, and seawater features. Melchers(2003, 2004) gave a new comparative analysis of these reported contradictory data for copper-bearing steels based on a multi-phase phenomenological corrosion-time model, and proposed that copper had relatively little effect on corrosion under aerobic conditions, inhibited the development of anaerobic corrosion, and increased the rate in the fully developed anaerobic corrosion. Although he did not give much explanation for the different impacts of copper at different stages, SRB were assumed to have a greater influence on the corrosion behavior of copper-bearing steels than that of copper-free steels. Unfortunately, no efforts have been made to analyze the influence of copper addition on microbial communities, which is in urgent need for the comprehension of corrosion of copper-bearing steels in seawater.
In this study, mooring chain steels, typical HSLA steels, were selected as research subjects, and the effect of copper addition on corrosion rate in seawater was investigated by field experiments. Furthermore, microbial communities on different steels were investigated with the aid of fluorescence microscope observation and high-throughput Illumina Miseq sequencing, and the relationship between bacterial communities and corrosion rate of steels was analyzed. The present work is meaningful for the comprehension on the corrosion mechanism of copper-bearing HSLA steels and their potential applications in seawater.2 MATERIAL AND METHOD 2.1 Steel coupon preparation and sampling
Different amounts of high purity copper were added together with other raw materials during melting to give mooring chain steels with different copper contents. The chemical compositions of different steels were analyzed by the optical emission spectroscopy method, and are reported in Table 1. All steels gave a phase structure of composite bainite (Fig. 1), and copper introduction had little impact on the grain size, which was around 6.7 μm measured by employing the linear intercept procedure according to ASTM E112. Flat tensile specimens were fabricated according to SATM D3039-76, and the copper alloying led to a slight enhancement in strength (Table 2).
Coupons with the size of 200 mm×100 mm×2 mm were cut from steel plates, abraded sequentially with a series of silicon carbide papers with grit sizes of 400, 600, and 800, cleaned by ethanol in an ultrasonication bath, dried by nitrogen, and weighed with a microbalance. Coupons were fixed to a coated frame and hung about 1 m below the lowest tide level at a seawater corrosion station in Qingdao, China (36°03′N; 120°25′E) from 27 July 2017 to 27 October 2017. The average seawater temperature, dissolved oxygen concentration, pH, and salinity during this period were 22.1℃, 8.3 mg/L, 8.2, and 31%, respectively. Three duplicates were done for each steel.
At the end of the immersion experiments, all samples were removed from seawater, photographed, carried to the laboratory in sealed sterile Whirl-Pak bags within 1 h, and processed immediately. Corrosion product layers were scraped randomly at different sites of all the corroded coupons, placed into two beakers holding 50 mL seawater sterilized via autoclaving for each steel, and weighed. The weight of corrosion products (Δm) in each beaker was obtained from the difference between values before and after product introduction. The beakers were shaken vigorously to give uniform suspensions, which were then subjected to DNA extraction. Half of the suspension was fixed with 0.1% formaldehyde for bacterial cell enumeration later.2.2 DNA extraction, amplication, sequencing, and data processing
Corrosion product suspensions for each steel in different beakers were utilized as independent duplicates for DNA extraction after filtration with 0.22 μm membrane, which was carried out with a Power Soil DNA Extraction Kit (MO BIO, Carlsbad, CA, USA) according to the manufacturer's instructions. The purity of the DNA extracts was checked using a Nanodrop ND-1000 spectrophotometer (Nanodrop Thermo Scientific, Wilmington, DE, USA), and quantified with a Qubit 2.0 fluorimeter (Qubit, Invitrogen, Carlsbad, CA, USA) using the dsDNA BR Assay Kit (Invitrogen) according to the manual.
The V3-V4 region of 16S rRNA gene was amplified by polymerase chain reaction (PCR) with the primer pair 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) following the protocol described by Li et al. (2017). After amplification, PCR products were analyzed on 2% agarose gel via electrophoresis to determine the relative intensity of bands, and then purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). The purified amplicons were subjected to quantitative PCR using Quant-iTPicoGreen dsDNA Assay Kit (Invitrogen) at a microplate reader (BioTek, FLx800), and pooled accordingly. The final PCR products were used to prepare the DNA library by following the Illumina TruSeq DNA library preparation protocol, and then paired-end Illumina MiSeq sequencing was performed.
Raw sequences were demultiplexed and qualityfiltered using the default parameters in QIIME software package to obtain high-quality clean tags. Chimeras were detected and removed using the de novo mode of UCHIME, and the remaining sequences were subsequently clustered into operational taxonomical units (OTUs) at 97% similarity. Representative sequences with the highest abundance were chosen from each OTU, and aligned against the Greengenes database using PyNAST.
A Venn diagram was created using Mothur v.1.30.1 to identify the common and unique OTUs among microbial communities formed on different steels. Rarefaction curves were plotted based on the aligned OTU table using QIIME, and then alpha diversity analysis including Chao1, ACE, Shannon, and Simpson indexes was evaluated. The relative abundance of OTUs at different taxonomic levels was plotted as bar graphs with the aid of R program, and a heatmap at the genus level was also drawn.2.3 Microbial cell enumeration
The corrosion product suspension fixed by formaldehyde was stained with acridine orange with a final concentration of 0.1% for 5 min in the dark, and then 1 mL suspension was syringe filtered through Whatman polycarbonate filters (pore size: 0.2 μm) previously stained with Sundan Blank. The filters were mounted on microscopic glass slides, and examined under a fluorescence microscope (Axio Imager A2; Carl Zeiss Microimaging GmbH; Jena, Germany). For each filter, at least 15 fields were selected and counted to give an average value (Na). The cell number per g of corrosion products was 50NaS/(SfΔm), in which S and Sf were the area of field and filter, respectively.2.4 Corrosion product characterization, weight loss measurement, and morphology observation
Corrosion products scraped were subjected to X-ray diffraction (XRD) for chemical composition analysis. XRD spectra were recorded with Cu Kα radiation at 40 kV and 40 mA, and scans were stepped from 10° to 80° at a rate of 10°/min. After the surface was scrubbed carefully with a brush, the coupons were cleaned with the Clark's solution containing 20 g antimony trioxide and 50 g stannous chloride in 1 000 mL concentrated hydrochloric acid (ASTM G1-03). Then they were rinsed with distilled water, cleaned in absolute ethanol, dried with nitrogen, and weighed. Corrosion rate in the unit of μm/a was calculated from the weight loss divided by the product of steel density, exposure time, and surface area. Corrosion morphology after corrosion product removal was recorded with a digital camera.3 RESULT 3.1 Corrosion morphology, product, and rate
After 3 months of field exposure in seawater, the surface of all steel coupons was fully covered by corrosion products with the color of brown and brick red (Fig. 2a). When the products were removed, it could be observed that samples corroded almost evenly with several sporadic shallow pits (Fig. 2b-d). XRD results shown in Fig. 3 demonstrated that all these steels gave similar corrosion products, mainly consisting of Fe3O4 and γ-FeOOH, which are common in field seawater immersion experiments. The calculated corrosion rate of different steels is shown in Table 3, which increased in the order of BR5 steel < BR5CuH steel < BR5CuL steel with the values of 0.18, 0.34, and 0.24 mm/a, respectively. The introduction of copper promoted corrosion, and the promotion efficiency was content-dependent, which was higher with the content of 0.4%.3.2 Microbial counts
Corrosion products formed on different steels gave similar microbial quantity with the magnitude of 108 cells/g (Table 3), which was much higher than the values determined by methods based on cultivation (Yang et al., 2009). Different from the case for single strains like Pseudomonas aeruginosa and Staphylococcus aureus (Sun et al., 2016; Xu et al., 2018), the introduction of copper did not lead to an obvious decrease in the quantity of attached microbial cells for natural consortia in the present study. The antibacterial activity of copper may be considered carefully when copper-bearing HSLA steels are utilized in natural environments.3.3 Diversity, richness, and composition of microbial communities
The raw paired-end reads obtained by MiSeq highthroughput sequencing were filtered to remove potential erroneous sequences. 32 453 and 38 966 effective tags were yielded for the independent duplicates of BR5 steel, giving an average of 35 710. The average counts of effective tags for BR5CuL and BR5CuH steels were 38 367 and 38 813. Tags were grouped into OTUs at a 97% similarity level, and an average of 818 OTUs was gained from BR5 steel duplicates with the numbers of 812 and 824. The average number of OTUs for BR5CuL and BR5CuH steels was 878 and 631 (Table 4), respectively. Therefore, microbial community diversity increased with the addition of 0.4% copper, and decreased with a further increase in copper content to 0.8%.
All rarefaction curves exhibited in Fig. 4 tended to approach the saturation plateau, indicating the sequencing data are sufficient to assess the complete population. The highest OTU richness was observed on BR5CuL steel, and the lowest was on BR5CuH steel. Furthermore, alpha diversity indexes were calculated and displayed in Table 4, in which Chao1 and ACE indices reflected the community richness, while Shannon and Simpson indexes were related to the community diversity (Liu et al., 2015b). The higher these indices, the more community diversity, and richness had. Consequently, microbial communities formed on BR5CuL steel possessed the highest diversity and richness, and those on BR5CuH steel gave the lowest. Similar to the case of corrosion rate, the impact of copper introduction on community diversity and richness was also dependent on its contents, in which 0.4% was beneficial and 0.8% was detrimental.
The Venn diagram was constructed to compare common and unique OTUs among different steels, and the result is displayed in Fig. 5. Total 1 184 OTUs were presented for microbial communities formed on BR5 steel, which was derived from the summation of 812 and 824 for individual duplicates with the subtraction of 452 common OTUs. Similarly, there were total 1 260 and 895 OTUs for communities on BR5CuL and BR5CuH steels, respectively. All microbial communities on 3 steels carried a total of 1 980 OTUs, but only 431 of them were shared by all steels, accounting for 21.8% of the total OTUs. The shared OTUs covered 12 phyla, and Proteobacteria dominated with the proportion of 83.1%, which is followed by Firmicutes (6.3%), Actinobacteria (3.2%), and Bacteroidetes (2.8%). The number of unique OTUs was 418, 415, and 219 for BR5, BR5CuL, and BR5CuH steels, respectively, accounting for 53.1% of the total OTUs. Therefore, the variation in microbial community composition was remarkable among different steels. Furthermore, this variation between BR5 and BR5CuH steels was larger evidenced by the lower ratio of common OTUs (32.8%) than that for BR5CuL-BR5 (38.8%) and BR5CuL-BR5CuH (37.8%) couples.
The composition of bacterial communities was further analyzed at different taxonomic levels, and the results are displayed in Fig. 6. At the phylum level, Proteobacteria dominated in microbial communities formed on all steels with a proportion of more than 90%, which was in good accordance with its predominance in shared OTUs exhibited in Fig. 5. Proteobacteria is a major phylum of gram-negative bacteria, and ubiquitous in marine biofilms (Salta et al., 2013; McBeth and Emerson, 2016). The five most abundant classes of microbial communities all belonged to the phylum Proteobacteria, but their contents varied among different steels. Class Gammaproteobacteria took a commanding leading in microbial communities formed on BR5 steel with the content of 62.9%, which was followed by Epsilon -(11.4%), Alpha -(7.6%), and Betaproteobacteria (6.8%). Superiority of Gammaproteobacteria was decreased on BR5CuL steel, and the proportion of Gamma -, Delta -, Alpha -, Epsilon -, and Betaproteobacteria was 40.8%, 27.8%, 9.2%, 7.8%, and 5.0%, respectively. For microbial communities formed on BR5CuH steel, it was Deltaproteobacteria (37.1%) that held a narrow lead over Gammaproteobacteria (35.7%), followed by Epsilon -(12.6%), Alpha -(6.0%), and Betaproteobacteria (4.4%). It was obvious that the proportion of Deltaproteobacteria increased with copper contents.
Figure 6c shows the proportion of the 20 most abundant genera in microbial communities on different steels, and there were five genera of which the proportion reduced with an increase in copper contents, i.e., Pseudomonas, Cupriavidus, Lutibacter, Erythrobacter, and Methylobacterium. Pseudomonas from the class Gammaproteobacteria dominated in biofilms formed on BR5 and BR5CuL steels with a percentage of 53.4% and 36.8%, and it was the second most abundant genus for BR5CuH steel just behind Desulfovibrio (27.0% vs. 31.5%). Species and strains of Pseudomonas are normally aerobic microbes, and a significant number of them produce extracellular polymeric substances (EPS) to favor biofilm formation, which are expected to affect metal corrosion. Corrosion of stainless steels is commonly enhanced by Pseudomonas species via mechanisms like differential aeration cells, the chemical reaction between EPS and steel substrates, the production of siderophore, and the formation of CrN and CrO3 (Abdolahi et al., 2014; Xu et al., 2017). By contrast, for carbon steel and low alloy steel materials that do not form a passive film on the surface, the presence of Pseudomonas sp. usually inhibits corrosion due to the decreased oxygen concentration by metabolism and the formation of compact biofilms (Jayaraman et al., 1998; Volkland et al., 2000). The compact films can prevent diffusion of corrosive species such as oxygen to the metal surface, thereby reducing the corrosion rate. Under extreme conditions without oxygen, some species like Pseudomonas aeruginosa promote carbon steel corrosion via biocatalytic electron transfer when they survive with nitrate as the terminal electron acceptor (Jia et al., 2017). Since nitrate concentration was low in the present seawater (< 2 μmol/L), such kind of corrosion promotion by Pseudomonas was rather limited. Cupriavidus affiliated to class Betaproteobacteria occupied 5.6%, 3.1%, and 1.8% of the bacterial communities on BR5, BR5CuL, and BR5CuH steels. Contents of the other three genera were no more than 0.6%, and the difference among different steels was smaller than 0.4%. Species and strains of these four genera are obligate aerobic microbes, and not involved in sulfur or iron cycling that is believed to play an important role in corrosion impact from microbes. So far, there has been no knowledge on the corrosion of HSLA steels affected by specific strains of these genera. Consequently, their contribution to corrosion variation among different mooring chain steels may be quite limited in the present study.
The percentage of Sulfurimonas, EscherichiaShigella, Hydrogenovibrio, and Acinetobacter decreased firstly and then increased with the increase in copper contents. The ratio for Sulfurimonas was 10.6%, 5.6%, and 12.2% among different steels, and the corresponding values for Hydrogenovibrio were 1.3%, 0.5%, and 2.5%. As an Epsilonproteobacteria, Sulfurimonas is known for reducing nitrate, oxidizing both sulfur and hydrogen, which has been reported as a significant genus in offshore water injection systems treated with nitrate (Bødtker et al., 2008).Sulfurimonas oxidizes corrosive sulfide generated by SRB to inhibit corrosion, but the produced sulfuric acid is also corrosive. Consequently, there have been some conflicts in the effectiveness of nitrate treatment, which are closely related to the specific environments (Nemati et al., 2001; Okoro et al., 2014). When reductive sulfur-containing compounds other than sulfide are utilized as electron donors, the corrosivity of Sulfurimonas is expected due to corrosive sulfuric acid produced (Huber et al., 2016). Similar to Sulfurimonas, Hydrogenovibrio from class Gammaproteobacteria is also capable of oxidizing reduced sulfur compounds and hydrogen, and similar corrosion impact is expected. Gammaproteobacterial genus Escherichia-Shigelle possessed a proportion of 5.5%, 1.3%, and 3.4% for microbial communities on BR5, BR5CuL, and BR5CuH steels, and another Gammaproteobacterial genus Acinetobacter gave values of 1.2%, 0.4%, and 1.5%. Their influence on steel corrosion is similar to other plain aerobic microorganisms like Pseudomonas (Jayaraman et al., 1998).
The proportion of genera Desulfovibrio, Desulfuromusa, Magnetovibrio, and Gallionella improved with copper contents. Desulfovibrio, a member of class Deltaproteobacteria, is a typical genus for SRB. Its content increased sharply from 3.2% to 23.1% with the addition of 0.4% copper into BR5 steel, and rose furthered to 31.5% with 0.8% copper. SRB have for long been viewed as the major corrosion-causing microbes in anoxic environments, and they constitute half of the total MIC loss (Chen et al., 2014). Extensive attention has been paid to steel corrosion affected by SRB, and several corrosion acceleration mechanisms have been proposed including cathodic depolarization by hydrogenase and iron sulfide (Von Wolzogen Kühr and Van der Vlugt, 1934; King et al., 1973), production of phosphorous compounds (Iverson, 1998), direct electron transfer from Fe0 (Dinh et al., 2004), and biocatalytic cathodic electron transfer (Li et al., 2015). The percentage of another Deltaproteobacterial genus Desulfuromusa was improved from 0.6% to 1.3% and 2.7% by copper addition. Species of Desulfuromusa cannot utilize sulfate as an electron acceptor, and they reduce elementary sulfur to sulfide. Although there have been none work on corrosion mechanisms affected by Desulfuromusa species, their role in corrosion enhancement is expected from the corrosive sulfide (Mand et al., 2016). The proportion of Magnetovibrio and Gallionella was both 0.1% in bacterial communities formed on BR5 steel, increased to 0.2% and 0.8% on BR5CuL steel, and then rose further to 1.9% and 1.8% on BR5CuH steel, respectively. Species and strains of Magnetovibrio included in class Alphaproteobacteria assimilate inorganic carbon chemolithoautotrophically with thiosulfate and sulfide as the electron donors, and are typical sulfur-oxidizing bacteria (SOB). Furthermore, they synthesize magnetosomes containing magnetite crystals via biomineralization (Bazylinski et al., 2013). Magnetovibrio can accelerate metal corrosion by the corrosive sulfuric acid produced similar to SOB genera Sulfurimonas and Hydrogenovibrio mentioned above. Gallionella, a member of class Betaproteobacteria, is a typical genus for ironoxidizing bacteria (IOB) that promote steel corrosion by accelerated transformation of Fe2+ to Fe3+ (Liu et al., 2015a).
There are another seven genera of which the proportion increased firstly and then decreased with the increase in copper contents, and they gave the highest values in microbial communities formed on BR5CuL steel. Ochrobactrum from the class Alphaproteobacteria possessed a percentage of 2.6%, 3.3%, and 0.4% for BR5, BR5CuL, and BR5CuH steels, respectively. Species from this genus are strictly aerobic, and their effect on steel corrosion has not been reported. The proportion of Desulfobacter increased from 0.1% on BR5 steel to 2.7% on BR5CuL steel, and then decreased to 1.4% on BR5CuH steel. As a typical SRB genus, Desulfobacter included in class Deltaproteobacteria affects steel corrosion similar to genus Desulfovibrio. Epsilonproteobacterial genus Arcobacter took a percentage of 0.4%, 2.2%, and 0.4% on different steels with increased copper contents, and it shows an unusually wide range of habitats. Some species are aerotolerant, while some are obligate anaerobes. Meanwhile, its role in sulfur cycling is variable, and some species oxidize reduced sulfur compounds (Voordouw, 2011), while some produce sulfide (Jyothsna et al., 2013). They are expected to result in a complex impact of Arcobacter on steel corrosion. Although the largest difference of Sphaerochaeta and Labrenzia from class Spirochaetes and Alphaproteobacteria was 2.0% (BR5 steel: 0.1%, BR5CuL steel: 2.2%, BR5CuH steel: 0.6%) and 1.0% (BR5 steel: 0.2%, BR5CuL steel: 1.2%, BR5CuH steel: 0.7%) among different steels, there has been no knowledge about their influence on steel corrosion. Zetaproteobacterial genus Mariprofundus can affect steel corrosion like other IOB, but the variation in proportion was small among different steels (0.5%, 0.9%, and 0.8% for BR5, BR5CuL, and BR5CuH steels). The proportion of Rhiobium included in class Alphaproteobacteria was also close, and the values were 0.5%, 0.8%, and 0.7%, respectively.
The variation in the relative abundance of the top 20 most abundant genera among different steels is presented more directly by different colors in a hierarchically clustered heatmap (Fig. 7). Genera are clustered according to their changes in relative abundance with copper contents, and the distance among different genera in the dendrogram reflected their similarity in those changes. Furthermore, the distance between BR5CuL and BR5CuH steels was smaller than the other two couples, and therefore the distribution of the 20 most abundant genera in these two steels is closer.4 DISCUSSION 4.1 An adverse impact of copper introduction on corrosion rate
In natural seawater, corrosion of HSLA steels occurs via the electrochemical actions of anodic iron oxidation (reaction 1) and cathodic dissolved oxygen reduction (reaction 2). Fe2+ and OH‒ are combined to form Fe(OH)2 (reaction 3), and further oxidized to Fe(OH)3 via reaction 4. As Fe(OH)3 is not stable, it transforms to γ-FeOOH via dehydration (reaction 5). When γ-FeOOH contacts with steels directly or mediates with other conductors, it is reduced (reaction 6) to favor corrosion. BR5, BR5CuL, and BR5CuH steels corroded in a similar way, evidenced by the γ-FeOOH and Fe3O4 components in corrosion products from XRD results (Fig. 3):
Copper introduction was detrimental to corrosion rate of mooring chain steels investigated in the present work, and the adverse impact was greater with 0.4% copper than that with 0.8% (Table 3). This conflicted with the report from Shi et al. (2018) that as-aged copper-bearing X80 pipeline steel gave better corrosion resistance against SRB than bare X80 steel, which might be closely associated with the variation in composition and microstructure of steels and characteristics of electrolytes. The adverse impact of copper alloying on long-term marine immersion corrosion of HSLA steels has been reported by Forgeson et al. (1960). Melchers (2004) proposed that the adverse impact of copper alloying was due to corrosion promotion at the anaerobic steady corrosion stage. Normally, it takes several years to get anaerobic corrosion developed completely, which is dependent on the average seawater temperature, and the present exposure period (three months) was too short to the arrival of that stage. Consequently, the detrimental role of copper alloying could not be attributed to its influence on anaerobic steady corrosion, and the mechanisms will be discussed below.
The dependence of corrosion rate on copper contents in the present work was different from most cases that corrosion parameters increased or decreased linearly with the increased copper contents (Southwell and Alexander, 1970; Blekkenhorst et al., 1986). Unfortunately, these references available did not explain the causes of corrosion variation among steels with different copper contents. Therefore, it is now difficult to discuss the difference between them and our results in a penetrating way, and we will just explain the variation among the present three steels.4.2 Dependence of microbial community diversity, richness, and composition on copper contents
Unlike the case for most single strains that bacterial quantity was decreased by the introduction of copper into steels (Sun et al., 2016; Xu et al., 2018), BR5CuL and BR5CuH steels held a similar quantity to copperfree BR5 steel (Table 3). This difference could be ascribed to the stronger defense capacity against copper of natural complex consortia than that of pure strains, and the number loss in copper-sensitive species could be remedied by those with high resistance against copper like species from genus Desulfovibrio. The evolution of microbial community structure with copper introduction is discussed later. Different from microbial quantity, microbial community diversity, and richness varied among different steels (Table 4, Fig. 4). BR5CuL steel provided biofilms with the highest community diversity and richness, and BR5CuH gave the lowest. Copper is an essential trace element for organisms, and more than 30 types of copper-containing proteins are known today. However, excess copper is highly toxic, and this virucidal property has been exploited by humankind for centuries. Copper ions can be released from copper-rich phases in copper-bearing steels (Sun et al., 2016; Shi et al., 2018), and their amount was expected to be higher on the surface of BR5CuH steel than that on BR5CuL steel. When the concentration of copper ions is above the threshold, they become toxic to microbes, which was the case for BR5CuH steel in the present study. This decreased microbial community diversity and richness was in good accordance with extensive reports on microbes in aquatic environments and soils affected by copper ions with high concentrations (Smit et al., 1997; Wang et al., 2007), which derived from unavailability of certain copper-sensitive bacteria. The inhibition of copper towards microbe survival can be achieved by several mechanisms like replacing other metals in the metal-binding sites to interfere with the correct functioning of proteins (De la Iglesia et al., 2012), depolarizing and impairing of receptors or transporter molecules (Webster et al., 2001), and catalyzing the production of reactive oxygen species by a Fentonlike reaction (Dupont et al., 2011).
The higher microbial community diversity and richness on BR5CuL than that on BR5 steel might not be expected, because it was common in numerous reports that microbial community diversity and richness decreased with copper contents (Cantera et al., 2016; Taylor and Walker, 2016). However, Xie (2010) pointed that the influence of copper on microbial diversity and richness was not a simple linear relationship with its concentrations, and copper could be beneficial in a certain content range. In addition, this was proved by the work of Wang et al. (2008), who found that there was a significant increase in the number of PCR-denaturing gradient gel electrophoresis bands in soil with Elsholtzia splendens incorporated with either 200 or 500 mg/kg copper, and the number of bands decreased sharply when copper concentration increased to 1 000 mg/kg. This increase in microbial diversity and richness was believed to be closely related to the stimulation of copper-adapted bacteria in response to the copper stress. According to the Venn diagram in Fig. 2, there were 415 unique OTUs in biofilms formed on BR5CuL steel, and some of these unique species are well known for their copper tolerance such as those from the genera Dethiosulfatibacter, Desulfotomaculum, and Desulfotignum (data not shown).
Additional to microbial community diversity and richness, the relative abundance of common OTUs varied among different steels (Figs. 6 & 7). This microbial community structure alternation was universal in previous reports on the microbial response to the presence of copper, which was due to species-dependent tolerance against copper (Zhao et al., 2014; Wang et al., 2015). It is important to mention that the response of individual genera or species may vary in different reports, depending on copper speciations, copper contents, and the environments. In the present study, it seemed that genera Pseudomonas, Cupriacidus, Lutibacter, Erythrobacter, and Methylobacterium were susceptible to copper stress evidenced by the decreased percentage with an increase in copper contents. Although Pseudomonas has been reported previously as one of the most copper-tolerant genera due to its capacity in the production of EPS having a high affinity for copper ions (Li and Ramakrishna, 2011; Andreazza et al., 2012), it is not the case here. There might be two reasons for the continuous reduction in its relative abundance. Firstly, unlike the case of soil or nonmetal materials, mooring chain steels corroded with the consumption of oxygen under aerobic conditions, leading to an oxygen stress to the growth of Pseudomonas. Secondly, species with higher coppertolerance like those from genus Desulfovibrio were stimulated, which competed with Pseudomonas for nutrients, and their metabolites imposed great environmental pressure on Pseudomonas. These two reasons also applied to the other 4 genera.
By contrast, genera Desulfovibrio, Desulfurumusa, Magnetovibrio, and Gallionella possessed higher copper resistance, and gave increased relative abundance with copper contents. The coppertolerance of Desulfovibrio and Desulfurumusa is closely related to their biogenic sulfide, which binds with copper ions to form highly stable copper sulfides with quite low solubility product constants (Utgikar et al., 2003). The formation of metal sulfides dramatically reduces the bioavailablity of copper ions, and therefore copper toxicity is reduced. Meanwhile, EPS produced by species of these two genera are good copper-complexing ligands, which further decreases copper ion concentration and the toxicity (Flemming and Trevors, 1989). According to references available, the copper sensitivity of genus Magnetovibrio is unclear. As a typical genus for SOB, its stimulation by copper introduction may be ascribed to the sufficient sulfide provided by the tremendous growth of SRB. Species from genus Gallionella are common in copper bioleaching industry due to its capacity in transferring Fe2+ to Fe3+, which catalyzes the transformation of zero-valence copper to copper ions (Xiang et al., 2010). In turn, the transformation of copper provides new Fe2+ for Gallionella. Consequently, it was not surprising that copper introduction in mooring chain steel stimulated the growth of Gallionella.
Unlike the linear change of above nine genera with copper contents, genera Sulfurimonas, EscherichiaShigella, Hydrogenovibrio, and Acinetobacter possessed the lowest relative abundance on BR5CuL steel. As typical genera for SOB, the percentage of Sulfurimonas and Hydrogenovibrio was expected to improve due to the stimulation of SRB by copper introduction, which was in good agreement with their highest proportion on BR5CuH steel. The lowest relative abundance of these two genera on BR5CuL steel might be due to the promotion from SRB was smaller than the dispersion effect from the highest microbial community diversity/richness. Species from genus Escherichia-Shigella were sensitive to copper, because the highest relative abundance was present on BR5. Similar to Sulfurimonas and Hydrogenovibrio, genus Acinetobacter exhibited the lowest and highest percentages on BR5CuL and BR5CuH steels, respectively. The highest relative abundance on BR5CuH steel was closely related to its well-known metal-tolerance via the expression of copper resistance proteins and the binging of EPS with metal ions (Yadav et al., 2013), and the lowest on BR5CuL steel could be ascribed to the predominant dispersion effect from the high community diversity/richness.
The other 7 genera gave the highest relative abundance on BR5CuL steel, demonstrating that they could tolerate copper and be stimulated at a low copper concentration, and the thresholds for copper toxicity ought to be lower than those of Desulfovibrio, Magnetovibrio, and Acinetobacter, etc. Among them, genus Ochrobactrum has been reported to resist copper toxicity by several mechanisms, including binging copper ions on cell wall surface, generating EPS to chelate copper ions, bio-transporting copper from intramembrane to the outer membrane, and reducing copper with the aid of enzyme-mediated biotransformation (Ozdemir et al., 2003; Peng et al., 2019). The adaptation of Desulfobacter to copper is similar to other SRB genera. The copper-tolerance mechanisms of the residual five genera are unclear due to quite limited references available.4.3 Probable involvement of well-known corrosive bacteria and microbial community diversity/richness in corrosion acceleration
Bacteria involved in sulfur and iron cycling have long been viewed to play a significant role in steel corrosion, and the relative abundance of these special categories was analyzed. As described in the results part, there were typical genera for SRB (including sulfur-reducing bacteria), SOB, and IOB among the 20 most dominant genera. The sum of typical SRB genera Desulfovibrio, Desulfuromusa, and Desulfobacter was 3.9%, 27.1%, and 35.6% on BR5, BR5CuL, and BR5CuH steels, respectively. Typical SOB also covered three genera, ieSulfurimonas, Hydrogenovibrio, and Magnetovibrio, and the total proportion was 12.0% for BR5 steel, 6.3% for BR5CuL steel, and 16.6% for BR5CuH steel. Besides, species from genus Arcobacter could behave as SRB and SOB depending on the environments, and its proportion was 0.4%, 2.7%, and 1.4% for steels in the order of BR5, BR5CuL, and BR5CuH steels. IOB consisting of genera Gallionella and Mariprofundua occupied 0.6%, 1.7%, and 2.6% of microbial communities on BR5, BR5CuL, and BR5CuH steels, respectively. It was obvious that SRB and IOB were stimulated by copper introduction, and SOB was restrained on BR5CuL steel, stimulated on BR5CuH steel. Combined with corrosion rate results, it could be found that BR5CuH steel with the lowest microbial community diversity/richness but the largest percentage in SRB, SOB, and IOB corroded more severely than BR5 steel, and consequently these typical well-known corrosive bacteria might be involved in the accelerated corrosion of BR5CuH steel. However, corrosion rate of BR5CuH steel was smaller than that of BR5CuL steel with a lower proportion of those corrosive bacteria but the highest microbial community diversity/richness. Therefore, only high relative abundance of well-known corrosive bacteria could not guarantee the high corrosion rate of BR5CuL steel, and complex microbial community structure ought to be considered in corrosion acceleration.
Extensive efforts have been made to establish the relationship between corrosion rate and the amount or relative abundance of typical well-known corrosive bacteria, especially SRB, and there are some controversies. For example, Bonifay et al. (2017) compared microbial communities formed on pipeline systems with different corrosion rate, and found that samples with high corrosion rate gave a rather high relative abundance of SRB, while Génin et al. (1994) reported that there was no significant difference in SRB quantity determined by the most-probable number technique at accelerated and normal low-water corrosion sites of steel sheet piles in a channel harbor. These conflicts may be closely related to the difference in characterization methods for microbial communities, exposure environments and time, and feature of substrates. Results from the present study seemed as a compromise for such conflicts, and a certain amount of well-known corrosive bacteria seemed to contribute to corrosion, but the corrosion rate did not rise linearly with their quantity. Furthermore, several reports have indicated that the metabolic status of SRB might be a relevant parameter to corrosion rate (Marty et al., 2014), and the metabolic activity of microbial communities will be investigated in our future work.
It is common that the corrosivity of natural microbial consortia with high diversity/richness is much stronger than that of single strains, which is associated with the synergy among different species (Rao et al., 2000). Much attention has been paid to the analysis of microbial community components and their relationship with corrosion rate, and quite few reports dealt with the role of community diversity/richness. Although the diversity/richness depended on the composition of microbial communities, it could give an overall evaluation besides the well-known corrosive bacteria. In the present study, the microbial communities with the highest diversity/richness on BR5CuL steel held a large variety of species other than SRB, SOB, and IOB, and these species might contain highly corrosive bacteria that have not been recognized. With persistent efforts in MIC research, more species are found to be highly corrosive by unique mechanisms, and a typical example was a Methanobacterium-like methanogen which captured electrons from iron via the direct electron transfer to accelerate corrosion greatly (Dinh et al., 2004). The involvement of community diversity/ richness in corrosion helps cover the impact of potential corrosive bacteria. Furthermore, the high diversity/ richness of microbial communities offers a possibility to the complicated collaboration among different species, which may also accelerate corrosion. Corrosion acceleration by the synergy of mixed species could be achieved by several modes like stimulation of anaerobes by oxygen consumption from aerobes (Wu et al., 2016), mutual stimulation of microbes metabolizing different valences of the same element (Beech and Campbell, 2008), and promotion in electron transfer of some species by redox mediators from others (Zhang et al., 2015). Consequently, diversity/ richness analysis ought to be involved together with structure in microbial communities related to corrosion.5 CONCLUSION
Corrosion rate varied among three mooring chain steels with different copper contents, increasing in the order of BR5 steel (without copper) < BR5CuH steel (0.8% copper) < BR5CuL steel (0.4% copper). The adverse impact of copper introduction on corrosion rate seemed to be closely related to the feature of microbial communities. Although copper introduction did not result in an obvious change in microbial quantity, it altered the diversity, richness, and structure of microbial communities due to species-dependence of tolerance against copper. The addition of copper stimulated the growth of some well-known corrosive bacteria like SRB and IOB, increased the community diversity/richness at a low content (0.4%), and reduced the diversity/richness sharply with the content of 0.8%. The highest corrosion rate of BR5CuL steel indicated that both well-known corrosive bacteria and microbial community diversity/ richness might be involved in the corrosion acceleration. The present work shed some light on corrosion mechanisms of copper-bearing steels from the viewpoint of microbial communities, and there are still some defects like the lack of metabolomic analysis, which will be remedied in our future work.6 DATA AVAILABILITY STATEMENT
The 16S rRNA gene sequences determined in this study have been submitted to the NCBI database with the accession number PRJNA493882.
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