142 research outputs found
Biomimetic Soft Polymer Actuator based on Polymeric Organic Framework-derived Porous Carbon Nanostructures
No full textNitrogen and Sulfur Co-doped Holey Graphene Aerogel For Highperformance Compression-Tolerant All Solid-State Supercapacitors
No full textHighly Bendable Ionic Soft Actuator Based on Nitrogen-Enriched 3D Hetero-Nanostructure Electrode
No full textElectrically responsive ionic soft actuators that can exhibit large bending strain under low electrical input power are promising candidates for future soft electronics and wearable devices. However, some drawbacks such as low blocking force, slow response time, and poor durability should be overcome for practical engineering applications. Herein, this study reports defect-engineered 3D graphitic carbon nitride (GCN) and nitrogen-doped graphene (NG) hetero-nanostructure that were developed by one-pot hydrothermal method in order to design functionally antagonistic hybrid electrodes for superior ionic soft actuators. While NG facilitates rapid electron transfer in 3D networked nanoarchitectures, the enriched-nitrogen content in GCN provides good wettability and mechanical resiliency with poly(3,4 ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). The 3D hybrid nanostructures generate unimpeded ion channels and sufficient contact area with the electrolyte membrane to provide higher capacitance and mechanical integrity, which are critical prerequisites for high-performance actuation. The developed soft actuator based on the nitrogen-enriched 3D hetero-nanostructure is found to exhibit large bending strain (0.52%), wide frequency response, 5 h durability (93% retention), 2.4 times higher bending displacement, and twofold higher electromechanical efficiency compared to PEDOT:PSS under +/- 0.5 V input voltage. Such 3D functionally antagonistic hybrid electrodes offer hitherto unavailable opportunities in developing ultralow voltage-driven ionic actuators for the next-generation soft electronics.
Artificial intelligence and finite element analysis: applications in implant dentistry
Get PDFArtificial intelligence (AI) has shown great potential across scientific disciplines, including implant dentistry. This review investigates the applications of AI in
Finite Element Analysis (FEA) of dental implants, examining implications, limitations,
and future directions. By following Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA) guidelines, relevant articles were obtained from PubMed, Scopus, Web of Science, and Google Scholar databases. Six articles were included,
covering topics such as osseointegration assessment, implant design optimization, and
bone healing prediction. Integrating AI and FEA can improve parameter optimization,
computational efficiency, and analysis time. FEA simulations were consistently used to
train AI models, which were then validated against FEA-calculated data. While AI in
dental implantology is still in its early stages, opportunities for innovation and refinement are apparent. Challenges, such as algorithmic misconduct and interpretation of
AI outputs, need to be addressed through collaborative efforts between clinicians and
computer engineers. Future research should explore incorporating factors like bone
homeostasis and multiscale analysis to enhance understanding of peri-implant bone
response. Long-term clinical studies are necessary to validate AI model predictions in
real-world scenarios
Statistical Inference and Computational Methods for Large High-Dimensional Data with Network Structure.
Get PDFNew technological advancements have allowed collection of datasets of large volume and different levels of complexity. Many of these datasets have an underlying network structure. Networks are capable of capturing dependence relationship among a group of entities and hence analyzing these datasets unearth the underlying structural dependence among the individuals. Examples include gene regulatory networks, understanding stock markets, protein-protein interaction within the cell, online social networks etc. The thesis addresses two important aspects of large high-dimensional data with network structure. The first one focuses on a high-dimensional data with network structure that evolves over time. Examples of such data sets include time course gene expression data, voting records of legislative bodies etc. The main task is to estimate the change-point as well as the network structures prior and post it. The network structures are obtained by penalized optimization method and we establish a finite sample estimation error bound for the change-point in the high-dimensional regime. The other aspect that we examine is about parameter estimation in large heterogeneous data with network structure. Our primary goal is to develop efficient computational techniques based on random subsampling and parallelization to estimate the parameters. We provide an analysis of rate of decay of bias and variance of our parallel implementation with a single round of communication after every iteration. We further show two applications of our methodology in the case of Gaussian Mixture Model (GMM) and Stochastic Block Model (SBM).The emphasis is placed on developing new theoretical techniques and computational tools for network problems and applying the corresponding methodology in many fields, including biomedical and social science research, where network modeling and analysis plays an exceedingly important role.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113602/1/sandipan_1.pd
Heat transfer model and finite element formulation for simulation of selective laser melting additive manufacturing
No full textAugust 2017School of EngineeringSelective laser melting (SLM) is a type of additive manufacturing (AM) process that involves direct manufacturing by locally melting and binding of metallic powder using a guided laser beam. Rapid manufacturing processes like SLM provides the ability to produce low-volume, customized meal parts with complex geometries relatively quickly at a moderate cost. The barrier to the adoption of SLM is the inability to predict material properties for any given process and thus it is required to develop an automated simulation tool for modeling SLM. In this work, a novel approach and finite element formulation for modeling the melting, consolidation and re-solidification that occurs in SLM is presented. Two state variables to track the phase and the amount of consolidation is introduced. The material properties and the heating of the material naturally varies due to the densification of the powdered metal. A Lagrangian finite element formulation is derived, which solves for the governing equations on the unconsolidated reference configuration. A transient fully implicit integration algorithm is used allowing for relatively larger time steps without loss of accuracy. The finite element model is implemented into a general-purpose parallel finite element solver Albany developed by the Computational Mechanics Department at Sandia National Laboratory integrated with Simmetrix meshing and capable of running on parallel high-performance computing platforms. Results are presented compared to experimental results in the literature for a linear laser track and a single layer of powder bed and are found to be in good agreement.M
Synthesis of Methylene Blue Analogues as Multifunctional Radical Quenchers, Synthesis of Unnatural Amino Acids and Their Ribosomal Incorporation into Proteins
Get PDFabstract: The energy required in a eukaryotic cell is provided by mitochondria. Mitochondrial electron transport chain (ETC) coupled with oxidative phosphorylation generates ATP. During electron transport, electron leakage from the ETC produces reactive oxygen species (ROS). In healthy cells, there are preventive and defense mechanisms in place to manage ROS. Maintaining a steady balance of ROS is very important because overproduction of ROS can lead to several pathological conditions. There are several strategies to prevent ROS production. Addition of external antioxidants is widely used among them. Discussed in the first part of Chapter 1 is the mitochondrial ETC, ROS production and antioxidant strategies.
The second part of Chapter 1 is concerned with ribosomal protein synthesis in bacteria. Ribosome, the organelle that synthesizes proteins with exceptional fidelity, has a strong bias for α-L-amino acids. It has been demonstrated that reengineering of the peptidyltransferase center (PTC) of the ribosome could enable the incorporation of both α-D-amino acids and β-amino acids into full length protein.
Oxidative stress is a common cause of various neurological disorders such as Alzheimer’s disease and Parkinson’s disease. Antioxidative strategies are used widely for the treatment of these disorders. Although several antioxidants demonstrated positive results in vitro as well as in in vivo models, none of them have been effective in clinical settings. Hence, there is an ongoing search for effective neuroprotective drugs. Described in Chapter 2 is the synthesis and biological evaluation of several methylene blue analogues as potentially effective antioxidants for the treatment of pathologies related to oxidative stress.
In Chapter 3, the synthesis and ribosomal incorporation of several rationally designed dipeptidomimetic analogues are discussed. The dipeptidomimetic analogues are structurally similar to the GFP chromophore and, therefore, highly fluorescent. In addition, the backbone of the dipeptidomimetic analogues resemble the peptide backbone of a dipeptide, due to which they can be incorporated into protein by modified ribosomes selected for the incorporation of dipeptides.
Discussed in Chapter 4 is the synthesis of the pdCpA derivatives of several β-amino acids. The pdCpA derivatives were ligated to tRNA-COH and were used as probes for studying the regio- and stereoselectivity of modified ribosomes.Dissertation/ThesisDoctoral Dissertation Chemistry 201
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