Thermal Energy Storage for Solar Power Production

Solar energy is the most abundant persistent energy resource. It is also an intermittent one available for only a fraction of each day while the demand for electric power never ceases. To produce a significant amount of power at the utility scale, electricity generated from solar energy must be dispatchable and able to be supplied in response to variations in demand. This requires energy storage that serves to decouple the intermittent solar resource from the load and enables around-the-clock power production from solar energy. Practically, solar energy storage technologies must be efficient as any energy loss results in an increase in the amount of required collection hardware, the largest cost in a solar electric power system. Storing solar energy as heat has been shown to be an efficient, scalable, and relatively low-cost approach to providing dispatchable solar electricity. Concentrating solar power systems that include thermal energy storage (TES) use mirrors to focus sunlight onto a heat exchanger where it is converted to thermal energy that is carried away by a heat transfer fluid and used to drive a conventional thermal power cycle (e.g., steam power plant), or stored for later use. Several approaches to TES have been developed and can generally be categorized as either thermophysical (wherein energy is stored in a hot fluid or solid medium or by causing a phase change that can later be reversed to release heat) or thermochemical (in which energy is stored in chemical bonds requiring two or more reversible chemical reactions).

On Ribosome Load, Codon Bias and Protein Abundance

Different codons encoding the same amino acid are not used equally in protein-coding sequences. In bacteria, there is a bias towards codons with high translation rates. This bias is most pronounced in highly expressed proteins, but a recent study of synthetic GFP-coding sequences did not find a correlation between codon usage and GFP expression, suggesting that such correlation in natural sequences is not a simple property of translational mechanisms. Here, we investigate the effect of evolutionary forces on codon usage. The relation between codon bias and protein abundance is quantitatively analyzed based on the hypothesis that codon bias evolved to ensure the efficient usage of ribosomes, a precious commodity for fast growing cells. An explicit fitness landscape is formulated based on bacterial growth laws to relate protein abundance and ribosomal load. The model leads to a quantitative relation between codon bias and protein abundance, which accounts for a substantial part of the observed bias for E. coli. Moreover, by providing an evolutionary link, the ribosome load model resolves the apparent conflict between the observed relation of protein abundance and codon bias in natural sequences and the lack of such dependence in a synthetic gfp library. Finally, we show that the relation between codon usage and protein abundance can be used to predict protein abundance from genomic sequence data alone without adjustable parameters.

On Ribosome Load, Codon Bias and Protein Abundance

Different codons encoding the same amino acid are not used equally in protein-coding sequences. In bacteria, there is a bias towards codons with high translation rates. This bias is most pronounced in highly expressed proteins, but a recent study of synthetic GFP-coding sequences did not find a correlation between codon usage and GFP expression, suggesting that such correlation in natural sequences is not a simple property of translational mechanisms. Here, we investigate the effect of evolutionary forces on codon usage. The relation between codon bias and protein abundance is quantitatively analyzed based on the hypothesis that codon bias evolved to ensure the efficient usage of ribosomes, a precious commodity for fast growing cells. An explicit fitness landscape is formulated based on bacterial growth laws to relate protein abundance and ribosomal load. The model leads to a quantitative relation between codon bias and protein abundance, which accounts for a substantial part of the observed bias for E. coli. Moreover, by providing an evolutionary link, the ribosome load model resolves the apparent conflict between the observed relation of protein abundance and codon bias in natural sequences and the lack of such dependence in a synthetic gfp library. Finally, we show that the relation between codon usage and protein abundance can be used to predict protein abundance from genomic sequence data alone without adjustable parameters.