Supply and Demand Drivers of Global Hydrogen Deployment in the Transition toward a Decarbonized Energy System.

The role of hydrogen in energy system decarbonization is being actively examined by the research and policy communities. We evaluate the potential "hydrogen economy" in global climate change mitigation scenarios using the Global Change Analysis Model (GCAM). We consider major hydrogen production methods in conjunction with delivery options to understand how hydrogen infrastructure affects its deployment. We also consider a rich set of hydrogen end-use technologies and vary their costs to understand how demand technologies affect deployment. We find that the availability of hydrogen transmission and distribution infrastructure primarily affects the hydrogen production mix, particularly the share produced centrally versus on-site, whereas assumptions about end-use technology primarily affect the scale of hydrogen deployment. In effect, hydrogen can be a source of distributed energy, enabled by on-site renewable electrolysis and, to a lesser extent, by on-site production at industrial facilities using natural gas with carbon capture and storage (CCS). While the share of hydrogen in final energy is small relative to the share of other major energy carriers in our scenarios, hydrogen enables decarbonization in difficult-to-electrify end uses, such as industrial high-temperature heat. Hydrogen deployment, and in turn its contribution to greenhouse gas mitigation, increases as the climate objective is tightened.

Determining the composition and microstructure of ethylene-propylene copolymers by pyrolysis-gas chromatography.

The average composition and detailed microstructure of copolymers of ethylene and propylene have been studied by pyrolysis-gas chromatography (Py-GC), using a statistical modeling approach to analyze the data. The trimer distribution obtained from Py-GC is used to infer monomer arrangement information, which is quantified in terms of a number-average sequence length for each monomer. These values are used to define the microstructure and to calculate the average composition. Compared with other available techniques, Py-GC provides a simple, quick and reliable approach to study the microstructure and composition of polyolefin copolymers. Details of this Py-GC method are discussed, including an examination of its advantages and disadvantages, and a summary of the qualitative and quantitative analysis aspects of this approach is presented. The combination of a statistical modeling approach with Py-GC to study copolymer composition and microstructure allows one to investigate the complex problem of monomer arrangement in copolymers using a widely available analytical technique. We expect that with further advances in separation technology, especially two-dimensional gas chromatography (GC x GC), research of this type will be become increasingly accurate and reproducible in the near future.