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In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype–phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners—even those with minimal knowledge in computer science—to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. [ABSTRACT FROM AUTHOR]

Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain reactions. However, its practical adoption is hindered by issues related to weak signals, complex spectra, limited datasets, and a lack of adaptability for detection and characterization of bacterial pathogens. This review focuses on addressing these issues with recent Raman spectroscopy breakthroughs enabled by machine learning (ML), particularly deep learning methods. Given the regulatory requirements, consumer demand for safe food products, and growing awareness of risks with environmental pathogens, this study emphasizes addressing pathogen detection in clinical, food safety, and environmental settings. Here, we highlight the use of convolutional neural networks for analyzing complex clinical data and surface enhanced Raman spectroscopy for sensitizing early and rapid detection of pathogens and analyzing food safety and potential environmental risks. Deep learning methods can tackle issues with the lack of adequate Raman datasets and adaptability across diverse bacterial samples. We highlight pending issues and future research directions needed for accelerating real-world impacts of ML-enabled Raman diagnostics for rapid and accurate diagnosis and surveillance of pathogens across critical fields. [ABSTRACT FROM AUTHOR]

Characterizing defects in 2D materials, such as cracks in chemical vapor deposited (CVD)-grown hexagonal boron nitride (hBN), is essential for evaluating material quality and reliability. Traditional characterization methods are often time-consuming and subjective and can be hindered by the limited optical contrast of hBN. To address this, we utilized a YOLOv8n deep learning model for automated crack detection in transferred CVD-grown hBN films, using MATLAB's Image Labeler and Supervisely for meticulous annotation and training. The model demonstrates promising crack-detection capabilities, accurately identifying cracks of varying sizes and complexities, with loss curve analysis revealing progressive learning. However, a trade-off between precision and recall highlights the need for further refinement, particularly in distinguishing fine cracks from multilayer hBN regions. This study demonstrates the potential of ML-based approaches to streamline 2D material characterization and accelerate their integration into advanced devices. [ABSTRACT FROM AUTHOR]

Designing minimally invasive, defect-free coatings based on conformal graphene layers to shield metals from both abiotic and biotic forms of corrosion is a persistent challenge. Single-layer graphene (SLG) grown on polycrystalline copper (PC-Cu) surfaces often have inherent defects, particularly at Cu grain boundaries, which weaken their barrier properties and worsen corrosion through grain-dependent mechanisms. Here, we report that an SLG grown via chemical vapor deposition (CVD) on Cu (111) single crystal serves as a high-performance coating to lower corrosion by nearly 4–6 times (lower than bare Cu (111)) in abiotic (sulfuric acid) and microbiologically influenced corrosion (MIC) environments. For example, the charge transfer resistance for SLG/Cu (111) (3.95 kΩ cm2) was 2.5-fold higher than for bare Cu (111) (1.71 kΩ cm2). Tafel analysis corroborated a reduced corrosion current (42 ± 3 µA cm−2) for SLG/Cu (111) compared to bare Cu (111) (115 ± 7 µA cm−2). These findings are consistent with the results based on biofilm measurements. The SLG/Cu (111) reduced biofilm formation by 3-fold compared to bare Cu (111), increasing corrosion resistance, and effectively mitigating pitting corrosion. The average depths of the pits (3.4 ± 0.6 µm) for SLG/Cu (111) were notably shallower than those of bare Cu (111) (6.5 ± 1.2 µm). Surface analysis of the corrosion products corroborated these findings, with copper sulfide identified as a major component across both surfaces. The absence of grain boundaries in Cu (111) resulted in high-quality SLG manifesting higher barrier properties compared to SLG on PC-Cu. Our findings show promise for using the presented strategy for developing durable graphene coatings against diverse forms of corrosion. [ABSTRACT FROM AUTHOR]

Mine tailings are the discarded materials resulting from mining processes after minerals have been extracted. They consist of leftover mineral fragments, excavated land masses, and disrupted ecosystems. The uncontrolled handling or discharge of tailings from abandoned mine lands (AMLs) poses a threat to the surrounding environment. Numerous untreated mine tailings have been abandoned globally, necessitating immediate reclamation and restoration efforts. The limited feasibility of conventional reclamation methods, such as cost and acceptability, presents challenges in reclaiming tailings around AMLs. This study focuses on phytorestoration as a sustainable method for treating mine tailings. Phytorestoration utilizes existing native plants on the mine sites while applying advanced principles of environmental biotechnology. These approaches can remediate toxic elements and simultaneously improve soil quality. The current study provides a global overview of phytorestoration methods, emphasizing the specifics of mine tailings and the research on native plant species to enhance restoration ecosystem services. [ABSTRACT FROM AUTHOR]

Human society annually produces nearly 100 billion gallons of wastewater, containing approximately 3600 GWh of energy. This study introduces a proof of concept utilizing graphene materials to extract and instantly store this energy. A hybrid device, mimicking a microbial fuel cell, acts as both a battery and supercapacitor. Wastewater serves as the electrolyte, with indigenous microorganisms on the graphene electrode acting as biocatalysts. The device features a capacitive electrode using a 3D nickel foam modified with a plasma-exfoliated graphene mixture. Compared to controls, the Gr/Ni configuration shows a 150-fold increase in power output (2.58 W/m2) and a 48-fold increase in current density (12 A/m2). The Gr/Ni/biofilm interface demonstrates outstanding charge storage capability (19,400 F/m2) as confirmed by electrochemical impedance spectroscopy. Microscopy, spectroscopy, and electrochemical tests were employed to elucidate the superior performance of Gr/Ni electrodes. Ultimately, the capacitive energy extracted from wastewater can power small electrical equipment in water infrastructure, addressing energy needs in remote regions without access to a typical power grid. [ABSTRACT FROM AUTHOR]

Over the past decade, copper (Cu) has been recognized as a crucial metal in the differential expression of soluble (sMMO) and particulate (pMMO) forms of methane monooxygenase (MMO) through a mechanism referred to as the "Cu switch". In this study, we used Methylosinus trichosporium OB3b as a model bacterium to investigate the range of Cu concentrations that trigger the expression of sMMO to pMMO and its effect on growth and methane oxidation. The Cu switch was found to be regulated within Cu concentrations from 3 to 5 µM, with a strict increase in the methane consumption rates from 3.09 to 3.85 µM occurring on the 6th day. Our findings indicate that there was a decrease in the fold changes in the expression of methanobactin (Mbn) synthesis gene (mbnA) with a higher Cu concentration, whereas the Ton-B siderophore receptor gene (mbnT) showed upregulation at all Cu concentrations. Furthermore, the upregulation of the di-heme enzyme at concentrations above 5 µM Cu may play a crucial role in the copper switch by increasing oxygen consumption; however, the role has yet not been elucidated. We developed a quantitative assay based on the naphthalene–Molisch principle to distinguish between the sMMO- and pMMO-expressing cells, which coincided with the regulation profile of the sMMO and pMMO genes. At 0 and 3 µM Cu, the naphthol concentration was higher (8.1 and 4.2 µM, respectively) and gradually decreased to 0 µM naphthol when pMMO was expressed and acted as the sole methane oxidizer at concentrations above 5 µM Cu. Using physical protein–protein interaction, we identified seven transporters, three cell wall biosynthesis or degradation proteins, Cu resistance operon proteins, and 18 hypothetical proteins that may be involved in Cu toxicity and homeostasis. These findings shed light on the key regulatory genes of the Cu switch that will have potential implications for bioremediation and biotechnology applications. [ABSTRACT FROM AUTHOR]

Sulfate-reducing bacteria (SRB) are terminal members of any anaerobic food chain. For example, they critically influence the biogeochemical cycling of carbon, nitrogen, sulfur, and metals (natural environment) as well as the corrosion of civil infrastructure (built environment). The United States alone spends nearly $4 billion to address the biocorrosion challenges of SRB. It is important to analyze the genetic mechanisms of these organisms under environmental stresses. The current study uses complementary methodologies, viz., transcriptome-wide marker gene panel mapping and gene clustering analysis to decipher the stress mechanisms in four SRB. Here, the accessible RNA-sequencing data from the public domains were mined to identify the key transcriptional signatures. Crucial transcriptional candidate genes of Desulfovibrio spp. were accomplished and validated the gene cluster prediction. In addition, the unique transcriptional signatures of Oleidesulfovibrio alaskensis (OA-G20) at graphene and copper interfaces were discussed using in-house RNA-sequencing data. Furthermore, the comparative genomic analysis revealed 12,821 genes with translation, among which 10,178 genes were in homolog families and 2643 genes were in singleton families were observed among the 4 genomes studied. The current study paves a path for developing predictive deep learning tools for interpretable and mechanistic learning analysis of the SRB gene regulation. [ABSTRACT FROM AUTHOR]

Sulfate-reducing bacteria (SRB) are terminal members of any anaerobic food chain. For example, they critically influence the biogeochemical cycling of carbon, nitrogen, sulfur, and metals (natural environment) as well as the corrosion of civil infrastructure (built environment). The United States alone spends nearly $4 billion to address the biocorrosion challenges of SRB. It is important to analyze the genetic mechanisms of these organisms under environmental stresses. The current study uses complementary methodologies, viz., transcriptome-wide marker gene panel mapping and gene clustering analysis to decipher the stress mechanisms in four SRB. Here, the accessible RNA-sequencing data from the public domains were mined to identify the key transcriptional signatures. Crucial transcriptional candidate genes of Desulfovibrio spp. were accomplished and validated the gene cluster prediction. In addition, the unique transcriptional signatures of Oleidesulfovibrio alaskensis (OA-G20) at graphene and copper interfaces were discussed using in-house RNA-sequencing data. Furthermore, the comparative genomic analysis revealed 12,821 genes with translation, among which 10,178 genes were in homolog families and 2643 genes were in singleton families were observed among the 4 genomes studied. The current study paves a path for developing predictive deep learning tools for interpretable and mechanistic learning analysis of the SRB gene regulation. [ABSTRACT FROM AUTHOR]

The transportation industry has led efforts to fight climate change and reduce air pollution. Autonomous electric vehicles (A-EVs) that use artificial intelligence, next-generation batteries, etc., are predicted to replace conventional internal combustion engine vehicles (ICEVs) and electric vehicles (EVs) in the coming years. In this study, we performed a life cycle assessment to analyze A-EVs and compare their impacts with those from EV and ICEV systems. The scope of the analysis consists of the manufacturing and use phases, and a functional unit of 150,000 miles·passenger was chosen for the assessment. Our results on the impacts from the manufacturing phase of the analyzed systems show that the A-EV systems have higher impacts than other transportation systems in the majority of the impacts categories analyzed (e.g., global warming potential, ozone depletion, human toxicity-cancer) and, on average, EV systems were found to be the slightly more environmentally friendly than ICEV systems. The high impacts in A-EV are due to additional components such as cameras, sonar, and radar. In comparing the impacts from the use phase, we also analyzed the impact of automation and found that the use phase impacts of A-EVs outperform EV and ICEV in many aspects, including global warming potential, acidification, and smog formation. To interpret the results better, we also investigated the impacts of electricity grids on the use phase impact of alternative transportation options for three representative countries with different combinations of renewable and conventional primary energy resources such as hydroelectric, nuclear, and coal. The results revealed that A-EVs used in regions that have hydropower-based electric mix become the most environmentally friendly transportation option than others. [ABSTRACT FROM AUTHOR]

Microbially induced corrosion (MIC) of metal surfaces caused by biofilms has wide-ranging consequences. Analysis of biofilm images to understand the distribution of morphological components in images such as microbial cells, MIC byproducts, and metal surfaces non-occluded by cells can provide insights into assessing the performance of coatings and developing new strategies for corrosion prevention. We present an automated approach based on self-supervised deep learning methods to analyze Scanning Electron Microscope (SEM) images and detect cells and MIC byproducts. The proposed approach develops models that can successfully detect cells, MIC byproducts, and non-occluded surface areas in SEM images with a high degree of accuracy using a low volume of data while requiring minimal expert manual effort for annotating images. We develop deep learning network pipelines involving both contrastive (Barlow Twins) and non-contrastive (MoCoV2) self-learning methods and generate models to classify image patches containing three labels--cells, MIC byproducts, and non-occluded surface areas. Our experimental results based on a dataset containing seven grayscale SEM images show that both Barlow Twin and MoCoV2 models outperform the state-of-the-art supervised learning models achieving prediction accuracy increases of approximately 8 and 6%, respectively. The self-supervised pipelines achieved this superior performance by requiring experts to annotate only ~10% of the input data. We also conducted a qualitative assessment of the proposed approach using experts and validated the classification outputs generated by the self-supervised models. This is perhaps the first attempt toward the application of self-supervised learning to classify biofilm image components and our results show that self-supervised learning methods are highly effective for this task while minimizing the expert annotation effort. [ABSTRACT FROM AUTHOR]

The use of diverse metal nanoparticles (MNPs) in a wide range of commercial products has led to their co-existence in the aqueous environment. The current study explores the dispersion and aggregation fate of five prominent MNPs (silver, copper, iron, nickel, and titanium), in both their individual and co-existing forms. We address a knowledge gap regarding their environmental fate under turbulent condition akin to flowing rivers. We present tandem analytical techniques based on dynamic light scattering, ultraviolet-visible spectroscopy, and inductively coupled plasma atomic emission spectroscopy for discerning their dispersion behavior under residence times of turbulence, ranging from 0.25 to 4 h. The MNPs displayed a multimodal trend for dispersion and aggregation behavior with suspension time in aqueous samples. The extent of dispersion was variable and depended upon intrinsic properties of MNPs. However, the co-existing MNPs displayed a dominant hetero-aggregation effect, independent of the residence times. Further research with use of real-world environmental samples can provide additional insights on the effects of sample chemistry on MNPs fate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Hollow Mn3O4 nanoparticles (diameter=31 nm, cavity diameter =16 nm, and shell thickness=7 nm) were attached to the surface of multiwall carbon nanotubes (MWCNT). A suspension of hollow Mn3O4/MWCNT with Nafion™ was dropcast onto a glassy carbon electrode, and the electrochemical reduction of oxygen in aqueous solution was investigated with this electrode. We assess the role of MWCNT, hollow Mn3O4, and Nafion™ in the performance of the electrode, and investigate the kinetics of the oxygen reduction reaction. The electrode exhibits outstanding performance in measures of cathodic current density and onset potential, and performed similarly well in acidic, neutral, and alkaline conditions. [ABSTRACT FROM AUTHOR]

The use of biochar for removal of heavy metals from stormwater is limited due to large area requirements and inadequate removal of nutrients and heavy metals at higher initial concentrations. In this study, biochar-supported nanoscale zerovalent iron (BC-nZVI) was effectively utilized for removing heavy metals from synthetic stormwater. We performed batch adsorption and laboratory-scale column experiments to demonstrate the exceptional ability of BC-nZVI to remove heavy metals (Cu, Cd, and Zn) at varying higher initial concentration range (2.5 to 60 mg L−1) compared with typical urban stormwater runoff. The batch experiment results suggested that the metal removal efficiency of BC-nZVI compared with biochar was enhanced by 43% and 57% in individual metal solution and 50% and 42% in the mixed metal solution for Cd and Zn, respectively. The maximum adsorption capacities of BC-nZVI for individual metal ions increased by 97% and 40% for Cd2+ and Zn2+, respectively, compared with original biochar. A series of characterization studies based on scanning electron microscopy, Fourier transform infrared spectroscopy, and Brunauer–Emmett–Teller revealed the chemical and morphological features of BC-nZVI, which are responsible for the enhanced metal removal. A laboratory-scale column study mimicking the field scale revealed the metal removal efficiencies of BC-nZVI increased by 115% and 123% for Cd2+ and Zn2+, respectively, compared with unmodified biochar. The higher removal efficiencies and adsorption capacities demonstrate the potential use of BC-nZVI as a media for attenuating heavy metals in current stormwater management practices. [ABSTRACT FROM AUTHOR]

Marginalized communities lack full participation in social, economic, and political life, and they disproportionately bear the burden of environmental and health risks. This special issue of Environmental Engineering Science, the official journal of the Association of Environmental Engineering and Science Professors (AEESP), reports research on the unique environmental challenges faced by historically marginalized communities around the world. The results of community-based participatory research with an Afro-descendant community in Columbia, Native American communities in Alaska, United States, villagers in the Philippines, disadvantaged communities in California, United States, rural communities in Mexico and Costa Rica, homeless encampments in the San Diego River (United States) watershed entrepreneurs in Durban, South Africa, and remote communities in the island nation of Fiji are presented. The research reported in this special issue is transdisciplinary, bringing engineers together with anthropologists, sociologists, economists, and public health experts. In the 13 articles in this special issue, some of the topics covered include inexpensive technologies for water treatment, novel agricultural strategies for reversing biodiversity losses, and strategies for climate change adaptation. In addition, one article covered educational strategies for teaching ethics to prepare students for humanitarian engineering, including topics of poverty, sustainability, social justice, and engineering decisions under uncertainty. Finally, an article presented ways that environmental engineering professors can engage and promote the success of underrepresented minority students and enable faculty engaged in community-based participatory research. [ABSTRACT FROM AUTHOR]

Biodiesel is a widely used fuel that meets the renewable fuel standards developed under the Energy Policy Act of 2005. However, biodiesel is known to pose a series of abiotic and biotic corrosion risks to storage tanks. A typical practice (incumbent system) used to protect the tanks from these risks include (i) coating the interior surface of the tank with a solvent-free epoxy (SFE) liner, and (ii) adding a biocide to the tank. Herein, we present a screening-level life-cycle assessment study to compare the environmental performance of a graphene oxide (GO)-epoxy (GOE) liner with the incumbent system. TRACI was used as an impact assessment tool to model the midpoint environmental impacts in ten categories: global warming potential (GWP, kg CO2 eq.); acidification potential (AP, kg SO2 eq.); potential human health damage impacts due to carcinogens (HH-CP, CTUh) and non-carcinogens (HH-NCP, CTUh); potential respiratory effects (REP, kg PM2.5 eq.); eutrophication potential (EP, kg N eq.); ozone depletion potential (ODP kg CFC-11 eq.); ecotoxicity potential (ETXP, CTUe); smog formation potential (SFP kg O3 eq.) and fossil fuel depletion potential (FFDP MJ surplus). The equivalent functional unit of the LCA study was designed to protect 30 m2 of the interior surface (unalloyed steel sheet) of a 10 000 liter biodiesel tank against abiotic and biotic corrosion during its service life of 20 years. Overall, this LCA study highlights the improved environmental performance for the GOE liner compared to the incumbent system, whereby the GOE liner showed 91% lower impacts in ODP impact category, 59% smaller in REP, 62% smaller in AP, 67–69% smaller in GWP and HH-CP, 72–76% smaller in EP, SFP, and FFDP, and 81–83% smaller ETXP and HH-NCP category results. The scenario analysis study revealed that these potential impacts change by less than 15% when the GOE liners are functionalized with silanized-GO nanosheets or GO-reinforced polyvinyl carbazole to improve the antimicrobial properties. The results from an uncertainty analysis indicated that the impacts for the incumbent system were more sensitive to changes in the key modeling parameters compared to that for the GOE liner system. [ABSTRACT FROM AUTHOR]

The National Academy of Engineering has called for the reinvention of engineering education by exposing students to the iterative process of designing, predicting performance, building, and testing; incorporating research into engineering education; and introducing interdisciplinary learning in the undergraduate environment. Here we describe a novel effort to integrate an undergraduate research project into the problem-based design environment of a second-year introduction to engineering design course at Rensselaer Polytechnic Institute, providing a design and research experience early in the curriculum. The project-based environment allows students to learn technical communication (technical writing and oral presentations) and teamwork (including conflict management and team coordination) in parallel. Approximately 600 sophomores from different science, technology, engineering, and mathematics (STEM) disciplines take the course, working in multidisciplinary teams to address a complex challenge facing modern society. We describe a pedagogical approach that involves designing, building, and testing a microbial fuel cell over the course of a 15-week semester. We also show that the course addresses eight different outcomes required by ABET. The benefits of incorporating research into a design course include high student engagement, while creating opportunities for students to participate in professional meetings, compete in regional and national competitions, and contribute to the peer-reviewed literature. [ABSTRACT FROM AUTHOR]

Research on hydrogen production by photofermentation has gained renewed interest in recent times because of its potential to generate hydrogen from renewable sources in a sustainable manner for use as an alternate energy carrier. Photofermentative bacteria use nitrogenase enzyme in the presence of adenosine triphosphate (ATP) and reducing power for hydrogen production in photobioreactors (PBRs). Depending on the pigments present in the bacteria, an optimal combination of photoparameters such as light source, intensity, duration, and wavelength have to be maintained in PBRs for efficient light-to-hydrogen conversion. In this article, over 130 literature reports on photoparameters are reviewed to aid in optimal design and operation of photobioreactors. This review includes a discussion of mathematical models reported in the literature to predict light attenuation and photochemical efficiencies of photobioreactors. As part of this study, models for predicting hydrogen evolution and predicting photochemical efficiency as a function of light wavelength and quantum requirement were developed and validated using a range of experimental data compiled from the literature. A case study is presented to illustrate how literature data could be used to size solar-based PBRs for hydrogen production. Based on this case study, it is concluded that major technological breakthroughs are required to reduce the current cost for biohydrogen production by solar-powered PBRs. [ABSTRACT FROM AUTHOR]

The "spotlight" column draws attention to selected articles in I Environmental Engineering Science i , the official journal of the Association of Environmental Engineering and Science Professors (AEESP). Dixon I et al. i ([2]) assessed the use of a high solids anaerobic digestion (HS-AD) method for treating food waste, yard waste, and biosolids individually as well as through codigestion. Chen I et al. i ([1]) examined an ozonation step for enhancing the feasibility of biodegradation processes designed to treat total petroleum hydrocarbons (TPHs) and the associated total organic carbon. [Extracted from the article]

The "spotlight" column draws attention to selected articles in I Environmental Engineering Science i ( I EES i ), the official journal of the I Association of Environmental Engineering and Science Professors i ( I AEESP i ). These nanomaterials include hematite, magnetite, ferrihydrite, goethite, hematite-alpha, hydroxyapatite (HAP), brucite, and four different titanium dioxides. As expected, the chemical oxygen demand exerted by all the fluids decreased over time; however, a significant recalcitrant fraction was observed for four of the six amended fluids. [Extracted from the article]

Given our vast lignocellulosic biomass reserves and the difficulty in bioprocessing them without expensive pretreatment and fuel separation steps, the conversion of lignocellulosic biomass directly into electricity would be beneficial. Here we report the previously unexplored capabilities of thermophilic Geobacillus sp. strain WSUCF1 to generate electricity directly from such complex substrates in microbial fuel cells. This process obviates the need for exogenous enzymes and redox mediator supplements. Cyclic voltammetry and chromatography studies revealed the electrochemical signatures of riboflavin molecules that reflect mediated electron transfer capabilities of strain WSUCF1. Proteomics and genomics analysis corroborated that WSUCF1 biofilms uses type-II NADH dehydrogenase and demethylmenaquinone methyltransferase to transfer the electrons to conducting anode via the redox active pheromone lipoproteins localized at the cell membrane. [ABSTRACT FROM AUTHOR]

Prevention of microbially induced corrosion (MIC) is of great significance in many environmental applications. Here, we report the use of an ultra-thin, graphene skin (Gr) as a superior anti-MIC coating over two commercial polymeric coatings, Parylene-C (PA) and Polyurethane (PU). We find that Nickel (Ni) dissolution in a corrosion cell with Gr-coated Ni is an order of magnitude lower than that of PA and PU coated electrodes. Electrochemical analysis reveals that the Gr coating offers ~10 and ~100 fold improvement in MIC resistance over PU and PA coatings respectively. This finding is remarkable considering that the Gr coating (1-2 nm) is ~25 and ~4000 times thinner than the PA (40-50 nm), and PU coatings (20-80 μm), respectively. Conventional polymer coatings are either non-conformal when deposited or degrade under the action of microbial processes, while the electro-chemically inert graphene coating is both resistant to microbial attack and is extremely conformal and defect-free. Finally, we provide a brief discussion regarding the effectiveness of as-grown vs. transferred graphene films for anti-MIC applications. While the as-grown graphene films are devoid of major defects, wet transfer of graphene is shown to introduce large scale defects that make it less suitable for the current application. [ABSTRACT FROM AUTHOR]