MKOPSC Theses and Dissertationshttps://hdl.handle.net/1969.1/1926682024-03-29T05:26:49Z2024-03-29T05:26:49ZInvestigating Microbiologically Influenced Corrosion Using Co-culture Biofilmshttps://hdl.handle.net/1969.1/1920462021-05-26T19:09:38Z2018-11-19T00:00:00ZInvestigating Microbiologically Influenced Corrosion Using Co-culture Biofilms
A holistic understanding of microbiologically influenced corrosion (MIC) requires investigation of the underlying microbiological, metallurgical and electrochemical mechanisms. MIC studies are typically conducted using batch reactors or large scale flow loops that are nutrient-limited and have buildup of waste products. To overcome these disadvantages, we developed a continuous-flow, microfluidic microbiologically influenced corrosion model, M-MIC-1, comprising of carbon steel coated glass slide bonded to a microchannel imprinted in a polymer. Using M-MIC1, we investigated the effect of two biocides on short-term and long-term, single-species and co-culture biofilms of Shewanella oneidensis and Vibrio natriegens. We found that biocide resistance was impacted by biofilm type, biofilm growth time and the type of biocide. These results show the importance of conducting biocide screening studies with process fluids for effective MIC mitigation. Our studies illustrate that M-MIC1 flow model provides an ideal platform.
To effectively comprehend MIC mechanisms, we developed M-MIC2 flow model that is amenable to dynamic and integrated measurements of biofilm dynamics and electrochemical impedance. M-MIC2 comprises of a two-metal electrode system with carbon steel and titanium bonded to a microchannel. Preliminary static and continuous-flow studies with single-species and co-culture biofilms of S. oneidensis and V. natriegens in M-MIC2 indicated some correlation of the variations in biofilm biomass to impedance spectra for S. oneidensis biofilms.
We also hypothesized that a systems-level understanding of the microbial community and their metabolism can enhance the understanding of underlying mechanisms. Produced water from a MIC-impacted oil field was exposed to carbon steel coupons to mimic the corrosion environment in the laboratory. We observed an increased abundance (using 16S rRNA sequencing) of the genera, Nitratireductor and Desulfovibrio and unclassified genera of Rhodobacteraceae, Deltaproteobacteria, and Desulfobacteraceace. Based on the metagenome, we predicted an increased abundance of several genes related to energy, carbohydrate, lipid, and xenobiotic metabolism. This correlated to the increased abundance of measured metabolites (using untargeted metabolomics analysis) such as carboxylic acids, fatty acids, and amino acids that were earlier associated with MIC. These analyses can be repeated for multiple MIC-impacted field locations to delineate common features and identify metabolite biomarkers for MIC detection.
2018-11-19T00:00:00ZSurface-Active Nanoplate for Oil Recoveryhttps://hdl.handle.net/1969.1/1919392021-05-31T23:14:46Z2020-03-23T00:00:00ZSurface-Active Nanoplate for Oil Recovery
Janus colloidal surfactants with opposing wettability on two structural parts are receiving attention for their intriguing structural and practical application in various industries. Combining the advantages of molecular surfactants and particle-stabilized Pickering emulsions, Janus colloidal surfactants generate remarkably stable emulsions. This dissertation developed a straightforward and cost-efficient strategy to develop Janus nanoplate surfactants (JNPS) from aluminosilicate nanoclays materials, including Kaoinite and Halloysite, by stepwise surface modifications, including an innovative selective surface modification step. Such colloidal surfactants are found to be able to stabilize Pickering emulsions of different oil/water systems. The microstructural characterization of solidified polystyrene emulsions indicates that the emulsion interface is evenly covered by JNPS. The phase behaviors of water/oil emulsion generated by these novel platelet surfactants were also investigated. Furthermore, this dissertation demonstrated the application of JNPS for enhanced oil recovery with a microfluidic flooding test, showing a dramatic increase of oil recovery ratio. This research provides important insights for the design and synthesis of two-dimensional Janus colloidal surfactants, which could be utilized in biomedical, food and mining industries, especially for circumstances where high salinity and high temperature are involved.
2020-03-23T00:00:00ZThe Effects of Advice-Giving and Advice-Taking on Safety Behavior: A Social Network Perspectivehttps://hdl.handle.net/1969.1/1916702022-05-01T07:13:37Z2020-04-16T00:00:00ZThe Effects of Advice-Giving and Advice-Taking on Safety Behavior: A Social Network Perspective
Previous research suggests that safety behavior is an important antecedent of workplace safety that can be influenced by coworkers within the employee’s network. Drawing on the social network perspective, both advice and trust network structures (centrality and density) are proposed to influence resource exchanges among employees and thus impact their safety behaviors. The objective of the current study is to examine the extent to which: 1) advice-giving (indegree centrality) and advice-taking (outdegree centrality) impact safety behavior, 2) cognitive and affective trustworthiness (indegree centrality) and trust in coworkers (outdegree centrality) are related to advice-giving and advice-taking behaviors which in turn are expected to be related to safety behavior, and 3) advice network density relates to safety behavior. Four hundred sixteen nurses in 42 workgroups and their respective supervisors from a hospital in China each completed a survey. Data were analyzed using social network analysis and hierarchical linear modeling. Advice-giving was positively associated with safety behavior and this relationship was stronger when corresponding group members reported more advice-giving and advice-taking behaviors within the group. Further, cognitive and affective trustworthiness were positively related to advice-giving and subsequent safety behavior; whereas cognitive trust in coworkers was positively related to advice-taking. Both cognitive and affective trust in coworkers were not related to safety behavior. These findings highlight the relevance of the advice social network to safety behavior revealing in part the value of measuring social network variables to understanding workplace safety.
2020-04-16T00:00:00ZDevelop a Hazard Index Using Machine Learning Approach for the Hazard Identification of Chemical Logistic Warehouseshttps://hdl.handle.net/1969.1/1892472021-12-01T08:43:32Z2019-11-04T00:00:00ZDevelop a Hazard Index Using Machine Learning Approach for the Hazard Identification of Chemical Logistic Warehouses
With the rapid development of chemical process plants, the safe storage of hazardous chemicals become an essential topic. Several chemical warehouse incidents related to fire and explosion have been reported recently. Therefore, an accurate hazard identification method for the logistic warehouse is needed not only for the facility to develop a proper emergency response plan but also for the residents who live near the facility to have an effective hazard communication. Furthermore, the government can better allocate the resources for first responders to make fire protection strategies, and the stakeholders can lead to improved risk management.
The storage of hazardous chemicals in a warehouse is a complex problem. The potential hazards include flammability, reactivity, and interaction among different types of hazardous chemicals. Hazard index is a helpful tool to identify and quantify the hazard in a facility or a process unit. Various hazard indices are developed in history. However, the challenge for this research is to improve the current method with the novel technique to implement our purpose.
The first objective of this research is to develop a “Storage Hazard Factor” (SHF) to evaluate and rank the inherent hazards of chemicals stored in logistic warehouses. In the factor calculation, the inherent hazard of chemicals is determined by various parameters (e.g., the NFPA rating, the flammability limit, and the protective action criteria values, etc.) and validated by the comparison with other indices. The current criteria for flammable hazard ratings are based on flash point, which is proved to be insufficient. Two machine learning based methods will be used for the classification of liquid flammability considering aerosolization based on DIPPR 801 database. Subsequently, SHF and other warehouse safety penalty factors (e.g., the quantity of the chemicals, the distance to the nearest fire department, etc.) are utilized to identify the Logistic Warehouse Hazard Index (LWHI) of the facilities. In the last chapter, LWHI is applied to an actual case from Houston Chronicle, and several statistical analyses are used to prove that the LWHI is helpful for hazard identification to emergency responders and hazard communication to the public.
2019-11-04T00:00:00Z