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Metal ion extraction from brines

A material includes a porous particle that includes a metal ion imprinted polymer. The metal ion imprinted polymer is formed from a hydrophilic co-monomer, a metal containing polymerizable compound, and a cross-linking agent. The metal containing polymerizable compound includes at least one metal chelating ligand. The metal ion imprinted polymer includes a plurality of metal ion selective binding sites. A method includes flowing brine containing a metal ion through a reactor that includes the material. The method further includes discharging the brine from the reactor, contacting the porous particles with water, and pressurizing the reactor with carbon dioxide. The carbon dioxide reacts with the adsorbed metal ions to form a metal carbonate solution. The method further includes depressurizing the reactor to precipitate metal carbonate from the metal carbonate solution and discharging the metal carbonate solution from the reactor.

36 MATERIALS SCIENCE↗

Thermoelectric power generation and mineral extraction from brines

Disclosed herein is a method and apparatus that uses a brine from a well that is used to both generate electricity and recover valuable minerals present in the brine. The method and apparatus uses a hydrophobic membrane to separate water vapor from the brine to concentrate the brine that is then used to recover the minerals.

15 GEOTHERMAL ENERGY↗

Extract useful information from building permits data to profile a city’s building retrofit history

Building retrofit is one of the key strategies for cities to reduce energy use and GHG emissions. The historical information about changes to buildings is crucial to infer the buildings’ current energy system efficiency levels and to identify candidate buildings for retrofit. In general, a building permit is required before the start of any construction activity of a building, such as changing building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Therefore, the permits are a potentially good resource for mining information on the city’s retrofit history. In this study, we use the permit dataset from the city of San Francisco as a case study. Location and time information from the dataset is also used to depict the retrofit timeline of each building and the whole building stock. The type of work of the permit is inferred from the descriptive text by a machine learning model. At last, the limitations of the current permit dataset and potential improvements on the permit data management are discussed to better utilize the information in the future.

Zhang, Wanni↗