Fabrication and characterization of shape memory polymers at small scales

The objective of this research is to thoroughly investigate the shape memory effect in polymers, characterize, and optimize these polymers for applications in information storage systems. Previous research effort in this field concentrated on shape memory metals for biomedical applications such as stents. Minimal work has been done on shape memory poly- mers; and the available work on shape memory polymers has not characterized the behaviors of this category of polymers fully. Copolymer shape memory materials based on diethylene glycol dimethacrylate (DEGDMA) crosslinker, and tert butyl acrylate (tBA) monomer are designed. The design encompasses a careful control of the backbone chemistry of the materials. Characterization methods such as dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC); and novel nanoscale techniques such as atomic force microscopy (AFM), and nanoindentation are applied to this system of materials. Designed experiments are conducted on the materials to optimize spin coating conditions for thin films. Furthermore, the recovery, a key for the use of these polymeric materials for information storage, is examined in detail with respect to temperature. In sum, the overarching objectives of the proposed research are to: (i) design shape memory polymers based on polyethylene glycol dimethacrylate (PEGDMA) and diethylene glycol dimethacrylate (DEGDMA) crosslinkers, 2-hydroxyethyl methacrylate (HEMA) and tert-butyl acrylate monomer (tBA). (ii) utilize dynamic mechanical analysis (DMA) to comprehend the thermomechanical properties of shape memory polymers based on DEGDMA and tBA. (iii) utilize nanoindentation and atomic force microscopy (AFM) to understand the nanoscale behavior of these SMPs, and explore the strain storage and recovery of the polymers from a deformed state. (iv) study spin coating conditions on thin film quality with designed experiments. (iv) apply neural networks and genetic algorithms to optimize these systems.Ph.D.Committee Chair: Gall, Ken; Committee Chair: May, Gary S; Committee Member: Brand, Oliver; Committee Member: Degertekin, F Levent; Committee Member: Milor, Linda

Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014von Robert Johann Flassi

The 2022 Plasma Roadmap: low temperature plasma science and technology

The 2022 Roadmap is the next update in the series of Plasma Roadmaps published by Journal of Physics D with the intent to identify important outstanding challenges in the field of low-temperature plasma (LTP) physics and technology. The format of the Roadmap is the same as the previous Roadmaps representing the visions of 41 leading experts representing 21 countries and five continents in the various sub-fields of LTP science and technology. In recognition of the evolution in the field, several new topics have been introduced or given more prominence. These new topics and emphasis highlight increased interests in plasma-enabled additive manufacturing, soft materials, electrification of chemical conversions, plasma propulsion, extreme plasma regimes, plasmas in hypersonics, data-driven plasma science and technology and the contribution of LTP to combat COVID-19. In the last few decades, LTP science and technology has made a tremendously positive impact on our society. It is our hope that this roadmap will help continue this excellent track record over the next 5–10 years.Peer ReviewedPostprint (published version

The 2022 Plasma Roadmap: low temperature plasma science and technology

Documento escrito por un elevado número de autores/as, solo se referencia el/la que aparece en primer lugar y los/as autores/as pertenecientes a la UC3M.The 2022 Roadmap is the next update in the series of Plasma Roadmaps published by Journal of Physics D with the intent to identify important outstanding challenges in the field of low-temperature plasma (LTP) physics and technology. The format of the Roadmap is the same as the previous Roadmaps representing the visions of 41 leading experts representing 21 countries and five continents in the various sub-fields of LTP science and technology. In recognition of the evolution in the field, several new topics have been introduced or given more prominence. These new topics and emphasis highlight increased interests in plasma-enabled additive manufacturing, soft materials, electrification of chemical conversions, plasma propulsion, extreme plasma regimes, plasmas in hypersonics and data-driven plasma science.Cristina Canal acknowledges PID2019-103892RB-I00/AEI/10.13039/501100011033 Project (AEI) and the Generalitat de Catalunya for the ICREA Academia Award and SGR2017-1165. The research by Annemie Bogaerts was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Synergy Grant 810182 SCOPE). Eduardo Ahedo was funded by Spain's Agencia Estatal de Investigación, under Grant No. PID2019-108034RB-I00 (ESPEOS Project)

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Virginia Institute of Marine Science Programs and Services

Programs and faculty, education and Institute support resources are described