Oceanic facilities and equipment corrosion present considerable economic and safety concerns, predominantly due to microbial corrosion. Early detection of corrosive microbes is pivotal for effective monitoring and prevention. Yet, traditional detection methods often lack specificity, require extensive processing time, and yield inaccurate results. Hence, the need for an efficient real-time corrosive microbe monitoring technology is evident. Pseudomonas aeruginosa, a widely distributed microorganism in aquatic environments, utilizes its production of quinone-like compounds, specifically pyocyanin (PYO), to corrode metals. Here, we report a novel fiber optic surface plasmon resonance (SPR) sensor modified by the C-terminal of BrlR protein (BrlR-C), which is a specific receptor of PYO molecule, to detect P. aeruginosa in aquatic environments. The results showed that the sensor had a good ability to recognize PYO in the concentration range of 0-1 μg/mL, and showed excellent sensing performance in real-time monitoring the growth status of P. aeruginosa. With a strong selectivity of PYO, the sensor could clearly detect P. aeruginosa against other bacteria in seawater environment, and exhibited excellent anti-interference ability against variations in pH, temperature and pressure and other interfering substances. This study provides a useful tool for monitoring corrosive P. aeruginosa biofilm in aquatic environments, which is a first of its kind example that serves as a laboratory model for the application of fiber optic technology in real-world scenarios to monitoring biofilms in microbial corrosion and biofouling.

The corrosion behaviors of four pure metals (Fe, Ni, Mo and Cr) in the presence of sulfate reducing bacteria (SRB) were investigated in enriched artificial seawater (EASW) after 14-day incubation. Metal Fe and metal Ni experienced weight losses of 1.96 mg cm-2 and 1.26 mg cm-2, respectively. In contrast, metal Mo and metal Cr exhibited minimal weight losses, with values of only 0.05 mg cm-2 and 0.03 mg cm-2, respectively. In comparison to Mo (2.2 × 106 cells cm-2) or Cr (1.4 × 106 cells cm-2) surface, the sessile cell counts on Fe (4.0 × 107 cells cm-2) or Ni (3.1 × 107 cells cm-2) surface was higher.

Biocorrosion, also called microbiologically influenced corrosion (MIC), is a common operational threat to many industrial processes. It threatens carbon steel, stainless steel and many other metals. In the bioprocessing industry, reactor vessels in biomass processing and bioleaching are prone to MIC. MIC is caused by biofilms. The formation and morphology of biofilms can be impacted by fluid flow. Fluid velocity affects biocide distribution and MIC. Thus, assessing the efficacy of a biocide for the mitigation of MIC under flow condition is desired before a field trial. In this work, a benchtop closed flow loop bioreactor design was used to investigate the biocide mitigation of MIC of C1018 carbon steel at 25 °C for 7 days using enriched artificial seawater. An oilfield biofilm consortium was analyzed using metagenomics. The biofilm consortium was grown anaerobically in the flow loop which had a holding vessel for the culture medium and a chamber to hold C1018 carbon steel coupons. Peptide A (codename) was a chemically synthesized cyclic 14-mer (cys-ser-val-pro-tyr-asp-tyr-asn-trp-tyr-ser-asn-trp-cys) with its core 12-mer sequence originated from a biofilm dispersing protein secreted by a sea anemone which possesses a biofilm-free exterior. It was used as a biocide enhancer. The combination of 50 ppm (w/w) THPS (tetrakis hydroxymethyl phosphonium sulfate) biocide + 100 nM (180 ppb by mass) Peptide A resulted in extra 1-log reduction in the sulfate reducing bacteria (SRB) sessile cell count and the acid producing bacteria (APB) sessile cell count compared to 50 ppm THPS alone treatment. Furthermore, with the enhancement of 100 nM Peptide A, extra 44% reduction in weight loss and 36% abatement in corrosion pit depth were achieved compared to 50 ppm THPS alone treatment.

The primary objective of this study is to elucidate the synergistic effect of an exogenous redox mediator and carbon starvation on the microbiologically influenced corrosion (MIC) of metal nickel (Ni) by nitrate reducing Pseudomonas aeruginosa. Carbon source (CS) starvation markedly accelerates Ni MIC by P. aeruginosa. Moreover, the addition of exogenous riboflavin significantly decreases the corrosion resistance of Ni. The MIC rate of Ni (based on corrosion loss volume) is ranked as: 10 % CS level + riboflavin > 100 % CS level + riboflavin > 10 % CS level > 100 % CS level. Notably, starved P. aeruginosa biofilm demonstrates greater aggressiveness in contributing to the initiation of surface pitting on Ni. Under CS deficiency (10 % CS level) in the presence of riboflavin, the deepest Ni pits reach a maximum depth of 11.2 μm, and the corrosion current density (icorr) peak at approximately 1.35 × 10-5 A·cm-2, representing a 2.6-fold increase compared to the full-strength media (5.25 × 10-6 A·cm-2). For the 10 % CS and 100 % CS media, the addition of exogenous riboflavin increases the Ni MIC rate by 3.5-fold and 2.9-fold, respectively. Riboflavin has been found to significantly accelerate corrosion, with its augmentation effect on Ni MIC increasing as the CS level decreases. Overall, riboflavin promotes electron transfer from Ni to P. aeruginosa, thus accelerating Ni MIC.

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Underlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause-effect scenarios with minimal manual interpretation. This study applies natural language processing and text-mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion-related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain-specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.

Microbiologically influenced corrosion of pipeline steel in seawater has long been concerned by scholars all over the world, but there were few reports on the microorganism effect on marine tidal corrosion of steels. In this work, the effect of Pseudomonas sp. on static tidal corrosion of X80 pipeline steel were systematically studied using weight-loss, Fourier transform infrared spectroscopy (FTIR), electrochemical measurements, scanning electron microscopy (SEM) and ultra-deep field 3D microscope. The results manifested that after 720 h exposure to the marine tidal environment, the sessile Pseudomonas sp. counts multiplied with the elevation increase. The corrosion style of the steel in the inoculated environment was mainly localized corrosion. As a consequence of the higher bacteria number, the corrosion rate, pit depth and corrosion product thickness collectively enhanced. Pseudomonas sp. significantly accelerated uniform and localized corrosion of the steel in the marine tidal zone, and the acceleration role enhanced with the steel elevation in the tidal zones.

Desulfovibrio vulgaris biofilm was pre-grown on Ti coupons for 7 d and then the biofilm covered coupons were incubated again with fresh culture media with 10 % (reduced) and 100 % (normal) carbon source levels, respectively. After the pre-growth, sessile D. vulgaris cell count reached 107 cells/cm2. The sessile cell counts were 2 × 107 and 4.2 × 107 cells/cm2 for 10 % and 100 % carbon sources, respectively after the subsequent 7 d starvation test. The maximum pit depth after the 7 d pre-growth was 4.7 µm. After the additional 7 d of the starvation test, the maximum pit depth increased to 5.1 µm for 100 % carbon source vs 6.2 µm for 10 % carbon source. Corrosion current density (icorr) from potentiodynamic polarization data at the end of the 7 d starvation test for 10 % carbon source was more than 3 times of that for 100 % carbon source, despite a reduced sessile cell count with 10 % carbon source. The polarization resistance (Rp) started to decrease within minutes after 20 ppm (w/w) riboflavin (electron mediator) injection. The carbon starvation data and riboflavin corrosion acceleration data both suggested that D. vulgaris utilized elemental Ti as an electron source to replace carbon source as the electron donor during carbon source starvation.

In this study, the biofilms at different ages formed on galvanized steel coupons in a simulating cooling tower water system were exposed to the Ag-Cu ions over 504 h and the changes in the structure of the biofilms were investigated using electrochemical, microbiological, and surface analyses. The effect of the Ag-Cu ions on the structure of the natural biofilm during the maturation process was evaluated for the first time in this study. Exposure to Ag-Cu ions changed the structure of the biofilm, reducing the concentration of carbohydrates in EPS, causing the shedding from the biofilm by disrupting/making weakening the integrity of the biofilm. After exposure to Ag-Cu ions, the biofilm turned into a heterogeneous, fissured-porous and sandy structure. In addition, in the absence of the ions and after exposure to the ions, the MIC behavior of galvanized steel with natural biofilm at different ages was evaluated using electrochemical and gravimetric tests. It was determined that the galvanized steel suffered to MIC and exposure to Ag-Cu ions increased the corrosion rate of it. Therefore, using of Ag-Cu ions at maximum concentration values (0.1 ppm Ag and 1.3 ppm Cu) suggested by EPA is not recommended to prevent MIC problem in cooling tower systems.

Steel is one of the mainly used materials in the oil and gas industry. However, it is susceptible to the marine corrosion, which 20% of the total marine corrosion is caused by microbiologically influenced corrosion (MIC). The economic and environmental impacts of corrosion are significant, and it is crucial to fight against corrosion in a proper sustainability context and environmental-friendly methods. In this study, the graphene oxide/silver nanostructure (GO-Ag) inhibitory effect on the corrosion of steel in the presence of sulfate reducing bacteria (SRB) was investigated, via weight loss (WL) and Tafel polarization measurements. Moreover, molecular dynamic (MD) simulations were performed to obtain a deep understanding of the corrosion inhibition effect of GO-Ag. GO-Ag showed a significant antibacterial effect at 80 ppm. Moreover, WL and Tafel polarization measurements illustrated a great inhibition efficiency, which reached up to 84% reduction of WL and 98% reduction of corrosion current density (Icorr) after 7 days of incubation with GO-Ag. Based on MD simulations, bonding energy reached to the larger value in the presence of GO-Ag, which indicated the ability of graphene oxide nanosheets to be adsorbed on the steel surface and prevent the access of corrosive agents to the steel surface. The radial distribution function (RDF) results implied distance between corrosive agent (water and SRB) and steel surface (Fe atoms), which indicated protection of the steel surface due to the effective adsorption of GO nanosheets through the active sites of the steel surface. The mean square displacement (MSD) result showed smaller displacement of the corrosive particles on the surface of steel, resulting that the GO-Ag molecules bonded with Fe molecules on the surface of steel.