Generating Bulk-Scale Ordered Optical Materials Using Shear-Assembly in Viscoelastic Media

We review recent advances in the generation of photonics materials over large areas and volumes, using the paradigm of shear-induced ordering of composite polymer nanoparticles. The hard-core/soft-shell design of these particles produces quasi-solid “gum-like” media, with a viscoelastic ensemble response to applied shear, in marked contrast to the behavior seen in colloidal and granular systems. Applying an oscillatory shearing method to sub-micron spherical nanoparticles gives elastomeric photonic crystals (or “polymer opals”) with intense tunable structural color. The further engineering of this shear-ordering using a controllable “roll-to-roll” process known as Bending Induced Oscillatory Shear (BIOS), together with the interchangeable nature of the base composite particles, opens potentially transformative possibilities for mass manufacture of nano-ordered materials, including advances in optical materials, photonics, and metamaterials/plasmonics

Iron-Catalyzed Belousov-Zhabotinsky Hydrogels and Liquid Crystals

Reducing stress is an important goal in poultry production. The Saccharomyces cerevisiae-derived yeast fermentation product Original XPC (XPC, Diamond V Mills, Cedar Rapids, IA, United States) has been shown to reduce the severity of enteric infection and reduce measures of stress in poultry exposed to acute or chronic stress. However, the effect of dietary supplementation of yeast fermentate on other physiological parameters and its mode of action in reducing stress remains unclear. This work aimed to investigate the effects of supplementing XPC or its liquid equivalent, AviCare (Diamond V Mills), on measures of stress susceptibility, health and well-being in poultry exposed to acute and chronic stressors. Three consecutive experiments were conducted to evaluate the effects of yeast fermentate supplementation on measures of stress, growth and feed efficiency in Cobb 500 male broilers exposed to acute and rearing stressors. Both XPC and AviCare consistently and equally reduced measures of short- and long-term stress across all 3 experiments, although trends in body weight gain and feed efficiency were inconsistent. A fourth experiment investigated the effects of XPC and AviCare on measures of stress, plasma biochemistry, cecal microbiome and expression of stress- and immune-related genes in Cobb 500 male broilers. Both XPC and AviCare reduced stress by reducing expression of the ACTH receptor, and modulated immune activity by reducing IL10 and CYP1A2 gene expression as well as plasma IL- The Belousov-Zhabotinsky (BZ) reaction is one of the most studied nonlinear dynamic chemical systems due to its autonomous periodic oscillations. It represents a suitable model for various oscillatory phenomena in Nature such as neuron synapsis, cardiac muscle beating and/or tachycardia, cellular formation cycle in molds, and other types of live-organism morphogenesis. The complexity of the BZ reaction chemical mechanism led to the creation of the Fields-Koros-Noyes model (FKN) that allows for studies via theoretical and mathematical models. Thus, experimental studies of this reaction are necessary to create 3D and life-like models. To bring these models into a more naturalistic setting, we researched the BZ reaction through hydrogels containing iron because of its natural occurrence and relevance. Chemically, the BZ reaction requires a catalyst based on iron (Fe), ruthenium (Ru) or cerium (Ce), and most of the current reports employ Ru. Alternatively, we employed Fe complexes as the catalyst due to their lower toxicity compared to Ru. The Fe-based catalyst was incorporated into polymer matrices (PNIPAM-co-PAAm, gelatin + kappa-carrageenan, and gelatin) to obtain hydrogels that exhibited pattern-rich, self-oscillatory response. Hence, the hydrogels served as models to investigate the effect of liquid crystalline structures on oscillations, the effect of geometry on the wave pattern of 3D-printed hydrogels, and the autonomous motion of hydrogels. Overall, these results open the door for future research on BZ reaction systems with low-toxicity. Furthermore, they contribute to the creation of new 3D locomotive hydrogels and to the development of realistic 3D models that could mimic Nature more efficiently.. However, cecal microbiome and antioxidative capacity were not affected after 42 d. Finally, 2 consecutive experiments were conducted to evaluate the effect of XPC and AviCare on measures of intestinal health in Cobb 500 male broilers and mixed-sex Pekin ducks exposed to cyclic heat stress during the last 14 d of growth. In both experiments yeast fermentate attenuated the negative effects of heat stress on villus length and villus/crypt ratio but not goblet cell density. Yeast fermentate also affected metabolism but did not improve electrolyte balance. In conclusion, adding yeast fermentate to the feed or drinking water reduced stress susceptibility by reducing glucocorticoid production, supported intestinal cell survival during cyclic heat stress, and modulated inflammatory processes in poultry exposed to rearing stress but not cyclic heat stress

MECHANICAL ENERGY HARVESTER FOR POWERING RFID SYSTEMS COMPONENTS: MODELING, ANALYSIS, OPTIMIZATION AND DESIGN

Finding alternative power sources has been an important topic of study worldwide. It is vital to find substitutes for finite fossil fuels. Such substitutes may be termed renewable energy sources and infinite supplies. Such limitless sources are derived from ambient energy like wind energy, solar energy, sea waves energy; on the other hand, smart cities megaprojects have been receiving enormous amounts of funding to transition our lives into smart lives. Smart cities heavily rely on smart devices and electronics, which utilize small amounts of energy to run. Using batteries as the power source for such smart devices imposes environmental and labor cost issues. Moreover, in many cases, smart devices are in hard-to-access places, making accessibility for disposal and replacement difficult. Finally, battery waste harms the environment. To overcome these issues, vibration-based energy harvesters have been proposed and implemented. Vibration-based energy harvesters convert the dynamic or kinetic energy which is generated due to the motion of an object into electric energy. Energy transduction mechanisms can be delivered based on piezoelectric, electromagnetic, or electrostatic methods; the piezoelectric method is generally preferred to the other methods, particularly if the frequency fluctuations are considerable. In response, piezoelectric vibration-based energy harvesters (PVEHs), have been modeled and analyzed widely. However, there are two challenges with PVEH: the maximum amount of extractable voltage and the effective (operational) frequency bandwidth are often insufficient. In this dissertation, a new type of integrated multiple system comprised of a cantilever and spring-oscillator is proposed to improve and develop the performance of the energy harvester in terms of extractable voltage and effective frequency bandwidth. The new energy harvester model is proposed to supply sufficient energy to power low-power electronic devices like RFID components. Due to the temperature fluctuations, the thermal effect over the performance of the harvester is initially studied. To alter the resonance frequency of the harvester structure, a rotating element system is considered and analyzed. In the analytical-numerical analysis, Hamilton’s principle along with Galerkin’s decomposition approach are adopted to derive the governing equations of the harvester motion and corresponding electric circuit. It is observed that integration of the spring-oscillator subsystem alters the boundary condition of the cantilever and subsequently reforms the resulting characteristic equation into a more complicated nonlinear transcendental equation. To find the resonance frequencies, this equation is solved numerically in MATLAB. It is observed that the inertial effects of the oscillator rendered to the cantilever via the restoring force effects of the spring significantly alter vibrational features of the harvester. Finally, the voltage frequency response function is analytically and numerically derived in a closed-from expression. Variations in parameter values enable the designer to mutate resonance frequencies and mode shape functions as desired. This is particularly important, since the generated energy from a PVEH is significant only if the excitation frequency coming from an external source matches the resonance (natural) frequency of the harvester structure. In subsequent sections of this work, the oscillator mass and spring stiffness are considered as the design parameters to maximize the harvestable voltage and effective frequency bandwidth, respectively. For the optimization, a genetic algorithm is adopted to find the optimal values. Since the voltage frequency response function cannot be implemented in a computer algorithm script, a suitable function approximator (regressor) is designed using fuzzy logic and neural networks. The voltage function requires manual assistance to find the resonance frequency and cannot be done automatically using computer algorithms. Specifically, to apply the numerical root-solver, one needs to manually provide the solver with an initial guess. Such an estimation is accomplished using a plot of the characteristic equation along with human visual inference. Thus, the entire process cannot be automated. Moreover, the voltage function encompasses several coefficients making the process computationally expensive. Thus, training a supervised machine learning regressor is essential. The trained regressor using adaptive-neuro-fuzzy-inference-system (ANFIS) is utilized in the genetic optimization procedure. The optimization problem is implemented, first to find the maximum voltage and second to find the maximum widened effective frequency bandwidth, which yields the optimal oscillator mass value along with the optimal spring stiffness value. As there is often no control over the external excitation frequency, it is helpful to design an adaptive energy harvester. This means that, considering a specific given value of the excitation frequency, energy harvester system parameters (oscillator mass and spring stiffness) need to be adjusted so that the resulting natural (resonance) frequency of the system aligns with the given excitation frequency. To do so, the given excitation frequency value is considered as the input and the system parameters are assumed as outputs which are estimated via the neural network fuzzy logic regressor. Finally, an experimental setup is implemented for a simple pure cantilever energy harvester triggered by impact excitations. Unlike the theoretical section, the experimental excitation is considered to be an impact excitation, which is a random process. The rationale for this is that, in the real world, the external source is a random trigger. Harmonic base excitations used in the theoretical chapters are to assess the performance of the energy harvester per standard criteria. To evaluate the performance of a proposed energy harvester model, the input excitation type consists of harmonic base triggers. In summary, this dissertation discusses several case studies and addresses key issues in the design of optimized piezoelectric vibration-based energy harvesters (PVEHs). First, an advanced model of the integrated systems is presented with equation derivations. Second, the proposed model is decomposed and analyzed in terms of mechanical and electrical frequency response functions. To do so, analytic-numeric methods are adopted. Later, influential parameters of the integrated system are detected. Then the proposed model is optimized with respect to the two vital criteria of maximum amount of extractable voltage and widened effective (operational) frequency bandwidth. Corresponding design (influential) parameters are found using neural network fuzzy logic along with genetic optimization algorithms, i.e., a soft computing method. The accuracy of the trained integrated algorithms is verified using the analytical-numerical closed-form expression of the voltage function. Then, an adaptive piezoelectric vibration-based energy harvester (PVEH) is designed. This final design pertains to the cases where the excitation (driving) frequency is given and constant, so the desired goal is to match the natural frequency of the system with the given driving frequency. In this response, a regressor using neural network fuzzy logic is designed to find the proper design parameters. Finally, the experimental setup is implemented and tested to report the maximum voltage harvested in each test execution

Dynamic nanostructured scaffolds as advanced biomaterials

Growing replacement tissues and organs in the laboratory will revolutionise healthcare; however, the maturation of cells into functional tissue constructs requires the controlled presentation of biochemical factors within a mechanically suitable scaffold. In nature, the presentation of such signals is provided through factors and structures existent within the nanoarchitecture of the extracellular matrix (ECM); therefore, in tissue engineering there is significant need to develop dynamic advanced artificial tissue constructs capable of mimicking the complexities of the native ECM. The requirement for bioactive, innervated constructs that contain biologically relevant signals delivered through tuneable mechanisms has yet to be achieved. One approach to address this key-challenge is offered through bioprinting, which allows for the controlled spatial distribution of bioinks containing cells, structures and signals within a single printed construct. However, currently bioprinting applications are severely limited by bioink function - with the majority of bioinks either lacking sufficient mechanical properties or biochemical signalling. Therefore, there is a key need to develop bioinks which adequately mimic the native ECM on a nanostructured, chemical level - particularly in establishing effective control over cell fate and tissue innervation. Tissue composition and extracellular signalling varies substantially between tissue-types, and therefore, advanced approaches that allow for ease of mechanical and biological tuneability through modular mechanisms would provide a practical avenue for bioink development. Self-assembling peptides (SAPs) are a unique class of biomaterials capable of spontaneously forming simple biomimetic structures which entangle to form highly hydrated, bioactive networks with favourable conditions for cell maturation. These biomaterials are easily tuned through modification of amino acid sequence, enabling tailored control over biochemical signalling between cells and scaffold. This provides the ability to artificially replicate natural signalling in a controlled manner - bringing about desired cell behaviour. Using these peptides, a variety of synergistic ECM-protein analogues have been developed, including Fmoc-FRGDF containing fibronectin's attachment motif RGD, and Fmoc-DIKAV, containing laminin's attachment motif IKVAV. Fmoc-SAPs possess the ability to be further functionalised through macromolecule addition, allowing for the presentation of charged, developmentally or structurally-important macromolecules on the surface of peptide fibrils. These macromolecules can integrate with the peptide networks, facilitating additional signalling and allowing for mechanical tunability. Here, we take advantage of these properties to develop an advanced and dynamic bioink for bioprinting applications. Initially, material enhancement is investigated through development of multi-sequence scaffolds. Specifically, Fmoc-FRGDF is combined with a synergistic cell attachment motif PHSRN, either through sequence engineering (Fmoc-FRGSFPHSRN) or through control over assembly properties (Fmoc-FRGDF/Fmoc-PHSRN coassembly). Here, the coassembled (Fmoc-FRGDF/Fmoc-PHSRN) system forms a synergistic network which promotes the attachment, proliferation and migration of muscle cells in vitro. The potential of Fmoc-SAP multi-sequence scaffolds is further investigated through the development of an artificial tumour microenvironment for cancer-cell studies. Here, Fmoc-FRGDF is combined with Fmoc-DIKVAV and used as a spheroid (LLC, NOR-10, LLC + NOR-10) micro-environment. The coassembled Fmoc-FRGDF/Fmoc-DIKVAV microenvironment enhances cancer-cell growth and progression compared to 2D cultures, non-encapsulate spheroids, and spheroids encapsulated in agarose. Agarose was selected as a control owing to the similar physical properties yet lack of biofunctionalisation. Results from this study reinforce the potential of Fmoc-SAPs as advanced microenvironments, and further support the ease of biological functionalisation inherent with this material. Further scaffold functionalisation is investigated through macromolecule addition. Here, one of two macromolecules are coassembled into a Fmoc-FRGDF network. The first macromolecule is fucoidan, a seaweed-derived polysaccharide with known anti-inflammatory properties, while the second is versican, a developmentally important proteoglycan which plays a variety of roles in muscle development. Versican was selected owing to its charge similarity to fucoidan, yet vastly different biological function. Fucoidan addition was found to increase fibre bundling and alter hydrogel mechanical properties, while versican addition had no substantial effect on hydrogel mechanics when compared to an Fmoc-FRGDF empty-vector control. Cell morphology was substantially altered by macromolecule addition, with fucoidan samples resulting in smaller, rounder cells with fewer multinucleated syncytia compared to an Fmoc-FRGDF control, while versican hydrogels showed an initial decrease in cell-size and multinucleation after 24h and a comparable cell-size and multinucleation following 72h. Here, it is possible that macromolecule addition perturbs cells attachment, and therefore, macromolecule selection is a key consideration. Interestingly, the regain of cell morphological characteristics in versican-containing hydrogels following 72h indicates the ability of cells to break-down versican, while the maintenance of small, round cells in the fucoidan hydrogels shows an inability for cells to break down fucoidan. The ability of Fmoc-SAPs to form components in bioinks is investigated through assembly with gelatin methacryloyl (GelMA) macromolecules. Initially, GelMA nanostructure and mechanical properties are investigated in response to increased degree of methacrylation or increased control. Here, structure-function relationships are drawn, and 18% methacryloyl Gelma (LM-GelMA) is selected for further bioink development owing to favourable thermoresponsive viscoelastic properties and improved strain tolerance. LM-GelMA assembly with coassembled Fmoc-FRGDF/Fmoc-PHSRN is investigated as a potential avenue to develop biologically and mechanically tuneable hydrogels. The incorporation of Fmoc-SAPs allows for control over sequence selection, while control over mechanical properties is offered through GelMA inclusion. LM-GelMA/Fmoc-FRGDF/Fmoc-PHSRN (FPG-Hybrid) bioinks demonstrate enhanced printability and are shown to support primary myoblast differentiation. The potential of Fmoc-SAP/GelMA bioinks to act as a modular bioink toolkit is further investigated through Fmoc-FRGDF/Fmoc-PHSRN substitution with Fmoc-DIKVAV, to develop a neural-suitable bioink (DIKVAV-Hybrid). This DIKVAV-Hybrid bioink demonstrated unique mechanical morphological properties and is shown to support rat cortical neurosphere viability. Throughout this project, the networks have been vigorously characterised through various analytical techniques, including micro/nanoimaging (Transmission electron microscopy, Atomic force microscopy, Cryo-scanning electron microscopy), Small-angle X-ray scattering, Small-angle neutron scattering, rheology, and spectroscopy; while the overall effectiveness of these systems have been analysed through in vitro muscle and neural cultures. Work detailed through this thesis aims to vigorously characterise Fmoc-SAP hydrogels and bioinks, providing the foundations for further biological studies and material optimisation

Nanomaterials-Based Bioinspired Next Generation Wearable Sensors: A State-of-the-Art Review

With a constantly growing percentage of the population having access to high-quality healthcare facilities, preventable pathogenic illnesses have been nearly eradicated in the developed parts of the world, which has led to a significant rise in the average human life expectancy over the last few decades. In such a highly developed world, age-related illnesses will lead to an immense burden on healthcare providers. Remote health monitoring enabled by wearable sensors will play a significant role in the growth and evolution of Health 3.0 by providing intimate and valuable information to healthcare providers regarding the progression of disease in patients with critical life-altering conditions. Especially, in the case of people suffering from neurodegenerative disorders, inexpensive and user-friendly wearable sensors can enable physiotherapists monitor real-time physiological parameters to design patient-specific treatment plans. This review provides a comprehensive overview of the recent advances and emerging trends at the convergence of biomimicry and nanomaterial sensors, with a specific focus on wearable skin-inspired mechanical sensors for applications in IoT-enabled human physiological parameters monitoring. Skin-inspired wearable mechanical sensors with relevance to the most common types of sensing mechanisms including piezoresistive, piezocapacitive, and triboelectric sensing are discussed along with their current challenges and possible future opportunities

Nanomaterials-Based Bioinspired Next Generation Wearable Sensors: A State-of-the-Art Review

With a constantly growing percentage of the population having access to high-quality healthcare facilities, preventable pathogenic illnesses have been nearly eradicated in the developed parts of the world, which has led to a significant rise in the average human life expectancy over the last few decades. In such a highly developed world, age-related illnesses will lead to an immense burden on healthcare providers. Remote health monitoring enabled by wearable sensors will play a significant role in the growth and evolution of Health 3.0 by providing intimate and valuable information to healthcare providers regarding the progression of disease in patients with critical life-altering conditions. Especially, in the case of people suffering from neurodegenerative disorders, inexpensive and user-friendly wearable sensors can enable physiotherapists monitor real-time physiological parameters to design patient-specific treatment plans. This review provides a comprehensive overview of the recent advances and emerging trends at the convergence of biomimicry and nanomaterial sensors, with a specific focus on wearable skin-inspired mechanical sensors for applications in IoT-enabled human physiological parameters monitoring. Skin-inspired wearable mechanical sensors with relevance to the most common types of sensing mechanisms including piezoresistive, piezocapacitive, and triboelectric sensing are discussed along with their current challenges and possible future opportunities

Advances in modeling and characterization of human neuromagnetic oscillations

Intracranial electrophysiological measurements as well as electromagnetic recordings from the scalp have shown that oscillatory activity in the human brain plays an important role in sensory and cognitive processing. Communication between distant brain regions seems to be mediated by oscillatory coherence and synchrony. Our brain is both reactive and reflexive: it reacts to changes in the external environment, but it is also influenced by its past and present internal state. On the one hand, task-related or induced modulations of oscillatory activity provide an important marker for cortical excitability and information processing of the reactive brain. On the other hand, spontaneous oscillatory dynamics subserves information processing of the reflexive brain. In this thesis, methods were developed to model and characterize task-related oscillatory changes, as well as spontaneous oscillatory activity measured using magnetoencephalography (MEG). In Publication I, we developed a predictive model to capture the suppression-rebound reactivity of the ~20 Hz mu rhythm originating in the sensorimotor cortex and applied this model to locate the cortical generators of the rhythm from independent measurements. In Publications II and III, we developed temporal and spatial variants of a data-driven method to characterize spatial, temporal, and spectral aspects of spontaneous MEG oscillations. Analysis of complex-valued Fourier coefficients identified well-known rhythms, such as the parieto-occipital ~10-Hz and the rolandic ~20-Hz rhythms consistently across subjects. In Publication IV, we applied independent component analysis to time-frequency representations of cortical current estimates computed from simulated as well as resting-state and naturalistic stimulation data. Group-level analysis of Fourier envelopes also identified the ~20-Hz bilateral sensorimotor network, a subset of the default-mode network at ~8 and ~15 Hz, and lateralized temporal-lobe sources at ~8 Hz. The methods developed here represent important advances in the modeling and characterization of the brain's oscillatory activity measured using non-invasive electrophysiological methods in healthy humans