Atomic Layers of Graphene for Microbial Corrosion Prevention

Chilkoor, Govind; Shrestha, Namita; Kutana, Alex; Hernández, Francisco C. Robles; Yakobson, Boris I.; Meyyappan, M.; Dalton, Alan B.; Ajayan, Pulickel M.; Rahman, Muhammad M.; Gadhamshetty, Venkataramana

doi:10.1021/acsnano.0c03987

Cited by 27 publications

(20 citation statements)

References 38 publications

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“…The drop shape contact angles using water for bare Cu and GL-Cu (Figure 1e) were measured as 92°± 2.0°and 97°± 1.0°, respectively. 41,47 These differences in hydrophobicity were further validated through high-resolution force−distance spectroscopy in terms of the pull-out force against the hydrophilic tip apex. The higher the pull-out force (nN) value, the higher the adhesion force (see Figure S3c).…”

Section: Resultsmentioning

confidence: 97%

“…For characterizing the layers, the graphene film from the Cu foil was transferred onto SiO 2 /Si substrate using a poly(methyl methacrylate) (PMMA) transfer method described in our earlier studies. 40,41 Noncontact mode AFM techniques were used to study the surface morphology of the graphene films on SiO 2 /Si (Figure 1b). The thickness of the film was ∼2.8 nm, which translates to eighr GLs based on the layer thickness of 0.34 nm as calculated using empirical force-constant models.…”

Section: Resultsmentioning

confidence: 99%

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Graphene as Thinnest Coating on Copper Electrodes in Microbial Methanol Fuel Cells

et al. 2022

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Dehydrogenation of methanol (CH3OH) into direct current (DC) in fuel cells can be a potential energy conversion technology. However, their development is currently hampered by the high cost of electrocatalysts based on platinum and palladium, slow kinetics, the formation of carbon monoxide intermediates, and the requirement for high temperatures. Here, we report the use of graphene layers (GL) for generating DC electricity from microbially driven methanol dehydrogenation on underlying copper (Cu) surfaces. Genetically tractable Rhodobacter sphaeroides 2.4.1 (Rsp), a nonarchetypical methylotroph, was used for dehydrogenating methanol at the GL-Cu surfaces. We use electrochemical methods, microscopy, and spectroscopy methods to assess the effects of GL on methanol dehydrogenation by Rsp cells. The GL-Cu offers a 5-fold higher power density and 4-fold higher current density compared to bare Cu. The GL lowers charge transfer resistance to methanol dehydrogenation by 4 orders of magnitude by mitigating issues related to pitting corrosion of underlying Cu surfaces. The presented approach for catalyst-free methanol dehydrogenation on copper electrodes can improve the overall sustainability of fuel cell technologies.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Graphene as Thinnest Coating on Copper Electrodes in Microbial Methanol Fuel Cells

et al. 2022

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“…Gr coating showed ∼10and ∼100-fold improvement in MIC resistance compared to PA and PU coated. In a more recent study, Chilkoor et al (2021) found that multilayered Gr coating on Cu and Ni is more suited for MIC prevention when compared to singlelayer Gr. Multilayered graphene (MLG) on Cu restricted the MIC by 10-and 1.4-fold compared to single-layer Gr-Cu and bare Cu, respectively.…”

Section: Protective Coatings Based On 2d Materials For Preventing Microbiologically Influenced Corrosionmentioning

confidence: 97%

Gene Sets and Mechanisms of Sulfate-Reducing Bacteria Biofilm Formation and Quorum Sensing With Impact on Corrosion

et al. 2021

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Sulfate-reducing bacteria (SRB) have a unique ability to respire under anaerobic conditions using sulfate as a terminal electron acceptor, reducing it to hydrogen sulfide. SRB thrives in many natural environments (freshwater sediments and salty marshes), deep subsurface environments (oil wells and hydrothermal vents), and processing facilities in an industrial setting. Owing to their ability to alter the physicochemical properties of underlying metals, SRB can induce fouling, corrosion, and pipeline clogging challenges. Indigenous SRB causes oil souring and associated product loss and, subsequently, the abandonment of impacted oil wells. The sessile cells in biofilms are 1,000 times more resistant to biocides and induce 100-fold greater corrosion than their planktonic counterparts. To effectively combat the challenges posed by SRB, it is essential to understand their molecular mechanisms of biofilm formation and corrosion. Here, we examine the critical genes involved in biofilm formation and microbiologically influenced corrosion and categorize them into various functional categories. The current effort also discusses chemical and biological methods for controlling the SRB biofilms. Finally, we highlight the importance of surface engineering approaches for controlling biofilm formation on underlying metal surfaces.

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“…Owing to its high ductility, weldability, and low cost, mild steel remains a popular choice of metal in civil infrastructure, transportation and oil and gas industry applications, and routine applications. However, under aqueous conditions, mild steel is susceptible to microbially infused corrosion caused by microorganisms, including SRB [50]. The goal of segmentation of the biofilm dataset is to identify the shape and size of each bacterial cell or a cluster of cells to detect metal corrosion.…”

Section: Biofilm Datasetmentioning

confidence: 99%

Semantic Image Segmentation Using Scant Pixel Annotations

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Abeyrathna

Subramaniam

et al. 2022

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The success of deep networks for the semantic segmentation of images is limited by the availability of annotated training data. The manual annotation of images for segmentation is a tedious and time-consuming task that often requires sophisticated users with significant domain expertise to create high-quality annotations over hundreds of images. In this paper, we propose the segmentation with scant pixel annotations (SSPA) approach to generate high-performing segmentation models using a scant set of expert annotated images. The models are generated by training them on images with automatically generated pseudo-labels along with a scant set of expert annotated images selected using an entropy-based algorithm. For each chosen image, experts are directed to assign labels to a particular group of pixels, while a set of replacement rules that leverage the patterns learned by the model is used to automatically assign labels to the remaining pixels. The SSPA approach integrates active learning and semi-supervised learning with pseudo-labels, where expert annotations are not essential but generated on demand. Extensive experiments on bio-medical and biofilm datasets show that the SSPA approach achieves state-of-the-art performance with less than 5% cumulative annotation of the pixels of the training data by the experts.

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Atomic Layers of Graphene for Microbial Corrosion Prevention

Cited by 27 publications

References 38 publications

Graphene as Thinnest Coating on Copper Electrodes in Microbial Methanol Fuel Cells

Graphene as Thinnest Coating on Copper Electrodes in Microbial Methanol Fuel Cells

Gene Sets and Mechanisms of Sulfate-Reducing Bacteria Biofilm Formation and Quorum Sensing With Impact on Corrosion

Semantic Image Segmentation Using Scant Pixel Annotations

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