Carbon Nanotube Solar Cells

We present proof-of-concept all-carbon solar cells. They are made of a photoactive side of predominantly semiconducting nanotubes for photoconversion and a counter electrode made of a natural mixture of carbon nanotubes or graphite, connected by a liquid electrolyte through a redox reaction. The cells do not require rare source materials such as In or Pt, nor high-grade semiconductor processing equipment, do not rely on dye for photoconversion and therefore do not bleach, and are easy to fabricate using a spray-paint technique. We observe that cells with a lower concentration of carbon nanotubes on the active semiconducting electrode perform better than cells with a higher concentration of nanotubes. This effect is contrary to the expectation that a larger number of nanotubes would lead to more photoconversion and therefore more power generation. We attribute this to the presence of metallic nanotubes that provide a short for photo-excited electrons, bypassing the load. We demonstrate optimization strategies that improve cell efficiency by orders of magnitude. Once it is possible to make semiconducting-only carbon nanotube films, that may provide the greatest efficiency improvement

Efficiently simulating personal vehicle energy consumption in mesoscopic transport models

Mesoscopic transport models can efficiently simulate complex travel behavior and traffic patterns over large networks, but simulating energy consumption in these models is difficult with traditional methods. As mesoscopic transport models rely on a simplified handling of traffic flow, they cannot provide the second-by-second measurement of vehicle speeds and accelerations that are required for accurately estimating energy consumption. Here we present extensions to the TripEnergy model that fill in the gaps of low-resolution trajectories with realistic, contextual driving behavior. TripEnergy also includes a vehicle energy model capable of simulating the impact of traffic conditions on energy consumption and CO emissions, with inputs in the form of widely available calibration data, allowing it to simulate thousands of different real-world vehicle makes and models. This design allows TripEnergy to integrate with mesoscopic transport models and to be fast enough to run on a large network with minimal additional computation time. We expect it to benefit from and enable advances in transport simulation, including optimizing traffic network controls to minimize energy, evaluating the performance of different vehicle technologies under wide-scale adoption, and better understanding the energy and climate impacts of new infrastructure and policies.

Historical Costs of Coal-Fired Electricity and Implications for the Future

We study the costs of coal-fired electricity in the United States between 1882 and 2006 by decomposing it in terms of the price of coal, transportation costs, energy density, thermal efficiency, plant construction cost, interest rate, capacity factor, and operations and maintenance cost. The dominant determinants of costs have been the price of coal and plant construction cost. The price of coal appears to fluctuate more or less randomly while the construction cost follows long-term trends, decreasing from 1902 - 1970, increasing from 1970 - 1990, and leveling off since then. Our analysis emphasizes the importance of using long time series and comparing electricity generation technologies using decomposed total costs, rather than costs of single components like capital. By taking this approach we find that the history of coal-fired electricity costs suggests there is a fluctuating floor to its future costs, which is determined by coal prices. Even if construction costs resumed a decreasing trend, the cost of coal-based electricity would drop for a while but eventually be determined by the price of coal, which fluctuates while showing no long-term trend

Nanostructural physical and chemical information derived from the unit cell scattering amplitudes of a spider dragline silk

Characterizing the nanostructures of spider major ampullate (dragline) silks is an important step in understanding the origin of their high mean strength and toughness, and for producing polymeric analogs that mimic these properties. Here we present transmission electron microscopy (TEM) diffraction patterns and an accompanying structure factor analysis for the dragline silk of Latrodectus hesperus (black widow spider). The chemical and physical composition of crystalline regions in this silk fiber was studied by manipulating the positions and size of amino acid side groups in a theoretical model, and comparing the expected unit cell scattering amplitudes with experimental electron diffraction patterns. The results suggest that - in addition to the smaller amino acid side groups such as alanine, glycine and serine - some of the bulkier amino acid side groups such as tyrosine and leucine are included in the crystalline fraction of the major ampullate silk. The structure factor analysis also demonstrates a marked sensitivity of the respective diffraction spot intensities to a slight change in both side group position and side group bulkiness. These observations point to a unique function for TEM in characterizing silk and other polymers. © 2005 Elsevier Ltd. All rights reserved

Potential for widespread electrification of personal vehicle travel in the United States

Electric vehicles can contribute to climate change mitigation if coupled with decarbonized electricity, but only if vehicle range matches travellers' needs. Evaluating electric vehicle range against a population's needs is challenging because detailed driving behaviour must be taken into account. Here we develop a model to combine information from coarse-grained but expansive travel surveys with high-resolution GPS data to estimate the energy requirements of personal vehicle trips across the US. We find that the energy requirements of 87% of vehicle-days could be met by an existing, affordable electric vehicle. This percentage is markedly similar across diverse cities, even when per capita gasoline consumption differs significantly. We also find that for the highest-energy days, other vehicle technologies are likely to be needed even as batteries improve and charging infrastructure expands. Car sharing or other means to serve this small number of high-energy days could play an important role in the electrification and decarbonization of transportation

Superexponential long-term trends in information technology

Moore's Law has created a popular perception of exponential progress in information technology. But is the progress of IT really exponential? In this paper we examine long time series of data documenting progress in information technology gathered by [1]. We analyze six different historical trends of progress for several technologies grouped into the following three functional tasks: information storage, information transportation (bandwidth), and information transformation (speed of computation). Five of the six datasets extend back to the nineteenth century. We perform statistical analyses and show that in all six cases one can reject the exponential hypothesis at statistically significant levels. In contrast, one cannot reject the hypothesis of superexponential growth with decreasing doubling times. This raises questions about whether past trends in the improvement of information technology are sustainable. © 2011

Estimating personal vehicle energy consumption given limited travel survey data

Estimating personal vehicle energy consumption is important for nationwide climate policy, local and statewide environmental policy, and technology planning. Transportation energy use is complex, depending on vehicle performance and the driving behavior of individuals, as well as on travel patterns of cities and regions. Previous studies combine large samples of travel behavior with fixed estimates of per mile fuel economy or use detailed models of vehicles with limited samples of travel behavior. This paper presents a model for estimating privately operated vehicle energy consumption—TripEnergy—that accurately reconstructs detailed driving behavior across the United States and simulates vehicle performance for different driving conditions. The accuracy of this reconstruction was tested by using out-of-sample predictions, and the vehicle model was tested against microsimulation. TripEnergy consists of a demand model, linking GPS drive cycles to travel survey trips, and a vehicle model, efficiently simulating energy consumption across different types of driving. Because of its ability to link small-scale variation in vehicle technology and driver behavior with large-scale variation in travel patterns, it is expected to be useful for a variety of applications, including technology assessment, cost and energy savings from ecodriving, and the integration of electric vehicle technologies into the grid