No description available.

No description available.

No description available.

No description available.

The typical catalyst layer structure for proton exchange membrane (PEM) fuel cells has changed little over the last two decades. A new electrode design with improved control over factors such as ionic and electrical pathways, porosity, and catalyst placement, could allow the application of less expensive catalyst alternatives. In this work, a novel electrode design based on ionomer-coated carbon nanofibers is proposed and studied. Governing equations for this design were established, and a mathematical model was created and solved using MATLAB to predict the performance of the new electrode design. A parametric study was performed to identify the design variables that had the most significant effect on performance. The best performing catalyst layer design studied with this model produces a current density of 1.1 A cm-2 at 600 mV which is better than state-of-the-art cathode designs. The results offer insight into the performance of ionomer-coated carbon nanofiber catalyst layers and can guide the fabrication and testing of these promising catalyst layer structures. / Master of Science / Proton exchange membrane (PEM) fuel cells have the potential to replace traditional energy conversion systems in many applications, however their widespread adoption is currently limited by their high cost and insufficient durability. PEM fuel cells are expensive because they require the use of platinum as a catalyst. Currently, less expensive non-platinum catalysts, must be used in much higher amounts in the catalyst layer to achieve similar electrochemical activity, creating very thick catalyst layers. Traditional fuel cell catalyst layer structures are designed to be thin and perform poorly when thick enough to accommodate non-platinum catalysts. This work proposes a novel catalyst layer design based on ionomer-coated carbon nanofibers that can allow for thicker catalyst layers and much higher catalyst loadings. A mathematical model was developed for the novel catalyst layer based on first principles. The model was solved using MATLAB to predict the performance of the new catalyst layer design. A parametric study was performed to identify the critical design variables and their effect on catalyst layer performance. The best performing catalyst layer design studied with this model produced a current density of 1.1 A cm-2 at 600mV, which is better than state-of-the-art fuel cell designs. This work is meant to offer insight into the performance of an ionomer-coated nanofiber catalyst layer and to guide future research in the fabrication of high performance fuel cells based on this novel catalyst layer architecture.

Piezoelectric materials produce an electric potential in response to a mechanical strain. They are, therefore, capable of converting ambient waste mechanical energy into useful electrical energy which, in turn, may be harnessed and used as a supplemental source of power in a variety of applications. Engineered piezoelectric materials may be deployed to improve treatment efficiency during the production of potable water, which is both chemically and energetically intensive. Ambient mechanical energy is prevalent in municipal water treatment. Vibrations induced by water treatment plant pumps (such as High Service Pumps), turbulence resulting from cross-flow or dead-end membrane filtration, or agitation from mechanical mixing (paddle or impeller) may provide sufficient input mechanical input energy to activate a piezoelectric response. The objective of this work was to fabricate and characterize a range of nanofiber-based piezoelectric materials and demonstrate their application as an alternative energy supply for driving environmental treatment (e.g., pollutant degradation) via simple mechanical agitation. To achieve this objective, we fabricated a variety of piezoelectric nanofiber composite mats consisting of barium titanate (BTO) nanocrystals grown via an alkaline hydrothermal method atop an electrospun carbon nanofiber (CNF) support. We hypothesized that the greatest degree of piezoelectric activity (as measured by the voltage produced as a function of mechanical strain) would be achieved for nanofiber composites containing BTO with the largest fraction of tetragonal crystal structure, known to be piezoelectrically active. A systematic study on the impacts of hydrothermal treatment time, temperature, as well as the influence of ethylene glycol as an organic co-solvent on BTO crystal size and morphology was performed. For example, ethylene glycol was found to disrupt the dissolution-precipitation mechanism of BTO crystal growth and instead spurred the growth of BTO nanorods and nanosheets on the CNF support. After characterization, the strength and electromechanical properties of various BTO-CNF composites was assessed. In some cases, output voltages have been measured on the order of 2.0 V/cm2 in response to surface bending strain induced by a custom cantilever-oscillometer apparatus. Optimal fractions of BTO loading in the composites were assessed through mass-loading electromechanical studies. As a proof of concept application, BTO nanoheterostructures were shown to utilize ultrasonic vibrations to degrade sodium orange II salt (4-(2-Hydroxy-1-naphthylazo)benzenesulfonic acid sodium salt) via piezocatalysis. Ongoing and future work will continue to develop optimized piezocatalytic nanoheterostructures able to harvest the electrochemical potential generated from mechanical agitation and structural deformation for the production of oxidizing and reducing equivalents for degradation of persistent and emerging organic contaminants and disinfection in water treatment.

Research in nanoparticle-reinforced composites is predicated by the promise for exceptional properties. However, to date the performance of nanocomposites has not reached its potential due to processing challenges such as inadequate dispersion and patterning of nanoparticles, and poor bonding and weak interfaces. The main objective of this dissertation is to improve the physical properties of polymer nanocomposites at low nanoparticle loading. The first step towards improving the physical properties is to achieve a good homogenous dispersion of carbon nanofibers (CNFs) and single wall carbon nanotubes (SWNTs) in the polymer matrix; the second step is to manipulate the well-dispersed CNFs and SWNTs in polymers by using an AC electric field. Different techniques are explored to achieve homogenous dispersion of CNFs and SWNTs in three polymer matrices (epoxy, polyimide and acrylate) without detrimentally affecting the nanoparticle morphology. The three main factors that influence CNF and SWNT dispersion are: use of solvent, sonication time, and type of mixing. Once a dispersion procedure is optimized for each polymer system, the study moves to the next step. Low concentrations of well dispersed CNFs and SWNTs are successfully manipulated by means of an AC electric field in acrylate and epoxy polymer solutions. To monitor the change in microstructure, alignment is observed under an optical microscope, which identifies a two-step process: rotation of CNFs and SWNTs in the direction of electric field and chaining of CNFs and SWNTs. In the final step, the aligned microstructure is preserved by curing the polymer medium, either thermally (epoxy) or chemically (acrylate). The conductivity and dielectric constant in the parallel and perpendicular direction increased with increase in alignment frequency. The values in the parallel direction are greater than the values in the perpendicular direction and anisotropy in conductivity increased with increase in AC electric field frequency. There is an 11 orders magnitude increase in electrical conductivity of 0.1 wt% CNF-epoxy nanocomposite that is aligned at 100 V/mm and 1 kHz frequency for 90 minutes. Electric field magnitude, frequency and time are tuned to improve and achieve desired physical properties at very low nanoparticle loadings.

A data analytic materials informatics methodology is proposed after applying different data mining techniques on some datasets of particular domain in order to discover and model certain patterns, trends and behavior related to that domain. In essence, it is proposed to develop an information mining tool for vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites as a case study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs or responses. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. A clustering technique, i.e., fuzzy C-means (FCM) algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis (PCA) as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens in separate clusters. In addition, an artificial neural network (ANN) model was used to explore the VGCNF/VE dataset. The ANN was able to predict/model the VGCNF/VE responses with minimal mean square error (MSE) using the resubstitution and 3olds cross validation (CV) techniques. Furthermore, the proposed methodology was employed to acquire new information and mechanical and physical patterns and trends about not only viscoelastic VGCNF/VE nanocomposites, but also about flexural and impact strengths properties for VGCNF/ VE nanocomposites. Formulation and processing factors (curing environment, use or absence of dispersing agent, mixing method, VGCNF fiber loading, VGCNF type, high shear mixing time, sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics.

The use of nanoreinforcements in automotive structural composites has provided promising improvements in their mechanical properties. For the first time, a robust statistical design of experiments approach was undertaken to demonstrate how key formulation and processing factors (nanofiber type, use of dispersing agent, mixing method, nanofiber weight fraction, and temperature) affected the dynamic mechanical properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites. Statistical response surface models were developed to predict nanocomposite storage and loss moduli as functions of significant factors. Only ~0.50 parts of nanofiber per hundred parts resin produced a roughly 20% increase in the storage modulus versus that of the neat VE at room temperature. Optimized nanocomposite properties were predicted as a function of design factors employing this methodology. For example, the use of highshear mixing (one of the mixing methods in the design) with the oxidized VGCNFs in the absence of dispersing agent or arbitrarily with pristine VGCNFs in the presence of dispersing agent was found to maximize the predicted storage modulus over the entire temperature range (30-120 °C). To study the key concept of interphase in thermoset nanocomposites, molecular dynamics simulations were performed to investigate liquid VE resin monomer interactions with the surface of a pristine VGCNF. A liquid resin having a mole ratio of styrene to bisphenol A-diglycidyl dimethacrylate monomers consistent with a 33 wt% styrene VE resin was placed in contact with both sides of pristine graphene sheets, overlapped like shingles, to represent the outer surface of a pristine VGCNF. The relative monomer concentrations were calculated in a direction progressively away from the surface of the graphene sheets. At equilibrium, the styrene/VE monomer ratio was higher in a 5 Å thick region adjacent to the nanofiber surface than in the remaining liquid volume. The elevated styrene concentration near the nanofiber surface suggests that a styrene-rich interphase region, with a lower crosslink density than the bulk matrix, could be formed upon curing. Furthermore, styrene accumulation in the immediate vicinity of the nanofiber surface might, after curing, improve the nanofiber-matrix interfacial adhesion compared to the case where the monomers were uniformly distributed throughout the matrix.

Molecular dynamics (MD) simulations have been performed to investigate the system equilibrium through the atomic/molecular interactions of a liquid vinyl ester (VE) thermoset resin with the idealized surfaces of both pristine vapor-grown carbon nanofibers (VGCNFs) and oxidized VGCNFs. The VE resin has a mole ratio of styrene to bisphenol-A-diglycidyl dimethacrylate VE monomers consistent with a commercially available 33 wt% styrene VE resin (Derakane 441-400). The VGCNF-VE resin interactions may influence the distribution of the liquid VE monomers in the system and the formation of an interphase region. Such an interphase may possess a different mole ratio of VE resin monomers at the vicinity of the VGCNF surfaces compared to the rest of the system after resin curing. Bulk nano-reinforced material properties are highly dependent on the interphase features because of the high surface area to volume ratio of nano-reinforcements. For example, higher length scale micromechanical calculations suggest that the volume fraction and properties of the interphase can have a profound effect on bulk material properties. Interphase formation, microstructure, geometries, and properties in VGCNF-reinforced polymeric composites have not been well characterized experimentally, largely due to the small size of typical nano-reinforcements and interphases. Therefore, MD simulations offer an alternative means to probe the nano-sized formation of the interphase and to determine its properties, without having to perform fine-scale experiments. A robust crosslinking algorithm for VE resin was then developed as a key element of this research. VE resins are crosslinked via free radical copolymerization account for regioselectivity and monomer reactivity ratios. After the VE crosslinked network was created, the constitutive properties of the resin were calculated. This algorithm will be used to crosslink equilibrated VE resin systems containing both pristine and oxidized VGCNFs. An understanding of formation and kinematics of a crosslinked network obtained via MD simulations can facilitate nanomaterials design and can reduce the amount of nanocomposite experiments required. VGCNF pull-out simulations will then be performed to determine the interfacial shear strength between VGCNFs and the matrix. Interphase formation, thickness and interfacial shear strength can directly feed into higher length scale micromechanical models within a global multiscale analysis framework.