Carbon nanotube based systems for high energy efficient applications

In the current age of fast-depleting conventional energy sources, top priority is given to exploring non-conventional energy sources, designing highly efficient energy storage systems and converting existing machines/instruments/devices into energy-efficient ones. ‘Energy efficiency’ is one of the important challenges for today’s scientific and research community, worldwide. In line with this demand, the current research was focused on developing two highly energy-efficient devices – field emitters and Li-ion batteries, using beneficial properties of carbon nanotubes (CNT). Interface-engineered, directly grown CNTs were used as cathode in field emitters, while similar structure was applied as anode in Li-ion batteries. Interface engineering was found to offer minimum resistance to electron flow and strong bonding with the substrate. Both field emitters and Li-ion battery anodes were benefitted from these advantages, demonstrating high energy efficiency. Field emitter, developed during this research, could be characterized by low turn-on field, high emission current, very high field enhancement factor and extremely good stability during long-run. Further, application of 3-dimensional design to these field emitters resulted in achieving one of the highest emission current densities reported so far. The 3-D field emitter registered 27 times increase in current density, as compared to their 2-D counterparts. These achievements were further followed by adding new functionalities, transparency and flexibility, to field emitters, keeping in view of current demand for flexible displays. A CNT-graphene hybrid structure showed appreciable emission, along with very good transparency and flexibility. Li-ion battery anodes, prepared using the interface-engineered CNTs, have offered 140% increment in capacity, as compared to conventional graphite anodes. Further, it has shown very good rate capability and an exceptional ‘zero capacity degradation’ during long cycle operation. Enhanced safety and charge transfer mechanism of this novel anode structure could be explained from structural characterization. In an attempt to progress further, CNTs were coated with ultrathin alumina by atomic layer deposition technique. These alumina-coated CNT anodes offered much higher capacity and an exceptional rate capability, with very low capacity degradation in higher current densities. These highly energy efficient CNT based anodes are expected to enhance capacities of future Li-ion batteries.

High pressure studies on group VI metal hexacarbonyl molecular solids

Group VI metal hexacarbonyls, M(CO)6 (M = Cr, Mo and W), are of extreme importance as catalysts in industry and also of fundamental interest due to the established charge transfer mechanism between the carbon monoxide and the metal. They condense to molecular solids at ambient conditions retaining the octahedral (Oh) symmetry of gas phase and have been extensively investigated by previous workers to understand their fundamental chemical bonding and possible industrial applications. However little is known about their behavior at high pressures which is the focus of this dissertation. Metal hexacarbonyls were subjected to high pressures in Diamond-Anvil cells to understand the pressure effect on chemical bonding using Raman scattering in situ. The high-pressure results on each of the three metal hexacarbonyls are presented and are followed by a critical analysis of the entire family. The Raman study was conducted at pressures up to 45 GPa and X-ray up to 58 GPa. This is followed by a discussion on infra red spectra in conjunction with Raman and X-ray analysis to provide a rationale for polymerization. Finally the probable synthesis of extremely reactive species under high-pressures and as identified via Raman is discussed. The high-pressure Raman scattering, up to 30 GPa, demonstrated the absence of Π-backbonding. The disappearance of parental Raman spectra for (M = Cr, Mo and W) at 29.6, 23.3 and 22.2 GPa respectively was attributed to the total collapse of the Oh symmetry. This collapse under high-pressure lead to metal-mediated polymeric phase characterized by Raman active δ(OCO) feature, originating from intermolecular vibrational coupling in the parent sample. Further increase in pressures up to 45 GPa, did not affect this feature. The pressure quenched Raman spectra, revealed various chemical groups non-characteristic of the parent sample and adsorption of CO in addition to the characteristic δ(OCO) feature. The thus recorded Raman, complemented with the far and mid-infrared pressure quenched spectra, reveal the formation of novel metal-mediated polymers. The X-ray diffraction on W(CO)6 up to 58 GPa revealed the generation of amorphous polymeric pattern which was retained back to ambient conditions.

Multimode Analysis of Nanoscale Biomolecular Interactions

Biomolecular interactions, including protein-protein, protein-DNA, and protein-ligand interactions, are of special importance in all biological systems. These interactions may occer during the loading of biomolecules to interfaces, the translocation of biomolecules through transmembrane protein pores, and the movement of biomolecules in a crowded intracellular environment. The molecular interaction of a protein with its binding partners is crucial in fundamental biological processes such as electron transfer, intracellular signal transmission and regulation, neuroprotective mechanisms, and regulation of DNA topology. In this dissertation, a customized surface plasmon resonance (SPR) has been optimized and new theoretical and label free experimental methods with related analytical calculations have been developed for the analysis of biomolecular interactions. Human neuroglobin (hNgb) and cytochrome c from equine heart (Cyt c) proteins have been used to optimize the customized SPR instrument. The obtained Kd value (~13 µM), from SPR results, for Cyt c-hNgb molecular interactions is in general agreement with a previously published result. The SPR results also confirmed no significant impact of the internal disulfide bridge between Cys 46 and Cys 55 on hNgb binding to Cyt c. Using SPR, E. coli topoisomerase I enzyme turnover during plasmid DNA relaxation was found to be enhanced in the presence of Mg2+. In addition, a new theoretical approach of analyzing biphasic SPR data has been introduced based on analytical solutions of the biphasic rate equations. In order to develop a new label free method to quantitatively study protein-protein interactions, quartz nanopipettes were chemically modified. The derived Kd (~20 µM) value for the Cyt c-hNgb complex formations matched very well with SPR measurements (Kd ~16 µM). The finite element numerical simulation results were similar to the nanopipette experimental results. These results demonstrate that nanopipettes can potentially be used as a new class of a label-free analytical method to quantitatively characterize protein-protein interactions in attoliter sensing volumes, based on a charge sensing mechanism. Moreover, the molecule-based selective nature of hydrophobic and nanometer sized carbon nanotube (CNT) pores was observed. This result might be helpful to understand the selective nature of cellular transport through transmembrane protein pores.

Improving storage performance through layout optimizations

Disk drives are the bottleneck in the processing of large amounts of data used in almost all common applications. File systems attempt to reduce this by storing data sequentially on the disk drives, thereby reducing the access latencies. Although this strategy is useful when data is retrieved sequentially, the access patterns in real world workloads is not necessarily sequential and this mismatch results in storage I/O performance degradation. This thesis demonstrates that one way to improve the storage performance is to reorganize data on disk drives in the same way in which it is mostly accessed. We identify two classes of accesses: static, where access patterns do not change over the lifetime of the data and dynamic, where access patterns frequently change over short durations of time, and propose, implement and evaluate layout strategies for each of these. Our strategies are implemented in a way that they can be seamlessly integrated or removed from the system as desired. We evaluate our layout strategies for static policies using tree-structured XML data where accesses to the storage device are mostly of two kinds—parent-to-child or child-to-sibling. Our results show that for a specific class of deep-focused queries, the existing file system layout policy performs better by 5–54X. For the non-deep-focused queries, our native layout mechanism shows an improvement of 3–127X. To improve performance of the dynamic access patterns, we implement a self-optimizing storage system that performs rearranges popular block accesses on a dedicated partition based on the observed workload characteristics. Our evaluation shows an improvement of over 80% in the disk busy times over a range of workloads. These results show that applying the knowledge of data access patterns for allocation decisions can substantially improve the I/O performance.

Efficient storage and domain-specific information discovery on semistructured documents

The increasing amount of available semistructured data demands efficient mechanisms to store, process, and search an enormous corpus of data to encourage its global adoption. Current techniques to store semistructured documents either map them to relational databases, or use a combination of flat files and indexes. These two approaches result in a mismatch between the tree-structure of semistructured data and the access characteristics of the underlying storage devices. Furthermore, the inefficiency of XML parsing methods has slowed down the large-scale adoption of XML into actual system implementations. The recent development of lazy parsing techniques is a major step towards improving this situation, but lazy parsers still have significant drawbacks that undermine the massive adoption of XML. ^ Once the processing (storage and parsing) issues for semistructured data have been addressed, another key challenge to leverage semistructured data is to perform effective information discovery on such data. Previous works have addressed this problem in a generic (i.e. domain independent) way, but this process can be improved if knowledge about the specific domain is taken into consideration. ^ This dissertation had two general goals: The first goal was to devise novel techniques to efficiently store and process semistructured documents. This goal had two specific aims: We proposed a method for storing semistructured documents that maps the physical characteristics of the documents to the geometrical layout of hard drives. We developed a Double-Lazy Parser for semistructured documents which introduces lazy behavior in both the pre-parsing and progressive parsing phases of the standard Document Object Model’s parsing mechanism. ^ The second goal was to construct a user-friendly and efficient engine for performing Information Discovery over domain-specific semistructured documents. This goal also had two aims: We presented a framework that exploits the domain-specific knowledge to improve the quality of the information discovery process by incorporating domain ontologies. We also proposed meaningful evaluation metrics to compare the results of search systems over semistructured documents. ^

Optimizing storage and memory systems for energy and performance

Electrical energy is an essential resource for the modern world. Unfortunately, its price has almost doubled in the last decade. Furthermore, energy production is also currently one of the primary sources of pollution. These concerns are becoming more important in data-centers. As more computational power is required to serve hundreds of millions of users, bigger data-centers are becoming necessary. This results in higher electrical energy consumption. Of all the energy used in data-centers, including power distribution units, lights, and cooling, computer hardware consumes as much as 80%. Consequently, there is opportunity to make data-centers more energy efficient by designing systems with lower energy footprint. Consuming less energy is critical not only in data-centers. It is also important in mobile devices where battery-based energy is a scarce resource. Reducing the energy consumption of these devices will allow them to last longer and re-charge less frequently. Saving energy in computer systems is a challenging problem. Improving a system's energy efficiency usually comes at the cost of compromises in other areas such as performance or reliability. In the case of secondary storage, for example, spinning-down the disks to save energy can incur high latencies if they are accessed while in this state. The challenge is to be able to increase the energy efficiency while keeping the system as reliable and responsive as before. This thesis tackles the problem of improving energy efficiency in existing systems while reducing the impact on performance. First, we propose a new technique to achieve fine grained energy proportionality in multi-disk systems; Second, we design and implement an energy-efficient cache system using flash memory that increases disk idleness to save energy; Finally, we identify and explore solutions for the page fetch-before-update problem in caching systems that can: (a) control better I/O traffic to secondary storage and (b) provide critical performance improvement for energy efficient systems.

Improving Storage Performance Through Layout Optimizations

Disk drives are the bottleneck in the processing of large amounts of data used in almost all common applications. File systems attempt to reduce this by storing data sequentially on the disk drives, thereby reducing the access latencies. Although this strategy is useful when data is retrieved sequentially, the access patterns in real world workloads is not necessarily sequential and this mismatch results in storage I/O performance degradation. This thesis demonstrates that one way to improve the storage performance is to reorganize data on disk drives in the same way in which it is mostly accessed. We identify two classes of accesses: static, where access patterns do not change over the lifetime of the data and dynamic, where access patterns frequently change over short durations of time, and propose, implement and evaluate layout strategies for each of these. Our strategies are implemented in a way that they can be seamlessly integrated or removed from the system as desired. We evaluate our layout strategies for static policies using tree-structured XML data where accesses to the storage device are mostly of two kinds - parent-tochild or child-to-sibling. Our results show that for a specific class of deep-focused queries, the existing file system layout policy performs better by 5-54X. For the non-deep-focused queries, our native layout mechanism shows an improvement of 3-127X. To improve performance of the dynamic access patterns, we implement a self-optimizing storage system that performs rearranges popular block accesses on a dedicated partition based on the observed workload characteristics. Our evaluation shows an improvement of over 80% in the disk busy times over a range of workloads. These results show that applying the knowledge of data access patterns for allocation decisions can substantially improve the I/O performance.

Efficient Storage and Domain-Specific Information Discovery on Semistructured Documents

The increasing amount of available semistructured data demands efficient mechanisms to store, process, and search an enormous corpus of data to encourage its global adoption. Current techniques to store semistructured documents either map them to relational databases, or use a combination of flat files and indexes. These two approaches result in a mismatch between the tree-structure of semistructured data and the access characteristics of the underlying storage devices. Furthermore, the inefficiency of XML parsing methods has slowed down the large-scale adoption of XML into actual system implementations. The recent development of lazy parsing techniques is a major step towards improving this situation, but lazy parsers still have significant drawbacks that undermine the massive adoption of XML. Once the processing (storage and parsing) issues for semistructured data have been addressed, another key challenge to leverage semistructured data is to perform effective information discovery on such data. Previous works have addressed this problem in a generic (i.e. domain independent) way, but this process can be improved if knowledge about the specific domain is taken into consideration. This dissertation had two general goals: The first goal was to devise novel techniques to efficiently store and process semistructured documents. This goal had two specific aims: We proposed a method for storing semistructured documents that maps the physical characteristics of the documents to the geometrical layout of hard drives. We developed a Double-Lazy Parser for semistructured documents which introduces lazy behavior in both the pre-parsing and progressive parsing phases of the standard Document Object Model's parsing mechanism. The second goal was to construct a user-friendly and efficient engine for performing Information Discovery over domain-specific semistructured documents. This goal also had two aims: We presented a framework that exploits the domain-specific knowledge to improve the quality of the information discovery process by incorporating domain ontologies. We also proposed meaningful evaluation metrics to compare the results of search systems over semistructured documents.