| Title | Cobalt recycling patents dataset selected using 'green' classification codes: Focus on the nickel manganese cobalt (NMC) batteries recycling |
|---|---|
| ID_Doc | 23238 |
| Authors | Priore, R |
| Title | Cobalt recycling patents dataset selected using 'green' classification codes: Focus on the nickel manganese cobalt (NMC) batteries recycling |
| Year | 2024 |
| Published | |
| Abstract | The Critical Raw Materials Act has been emanated to address perceived risks around supply chains for critical raw materials in light of expected demand growth [1] . Cobalt is one of the critical raw materials for the EU: it plays a central role in transition to a low-carbon economy and is crucial for batteries powering electric vehicles. The cobalt industry is important not only because of the green transition, it also creates employment opportunities as Europe is the third largest region for total global cobalt value chain employment [2] . The global market for battery electric vehicles (BEVs) is continuously increasing which results in higher material demand for the production of Li-ion batteries (LIBs). Especially Lithium nickel manganese cobalt (NMC) batteries are one of the leading types of batteries deployed on BEVs and recovering of materials from used batteries for producing new battery materials may mitigate the material supply risk [3 , 4] . The present survey concerns the creation and comparison of two datasets of patents concerning Cobalt recycling, respectively achieved without (dataset 1) and with (dataset 2) the addition of one specific 'green' CPC classification code to a huge list of classification codes concerning recycling. Namely, the CPC Y02P10/20 determines a significant improvement on the exhaustivity of the search regarding Cobalt recycling in dataset 2, on the other hand it might also generate issues evidenced by a subsequent selection of those records in prin ciple focused on the recovery of NMC batteries and characterized by several false positives. Therefore, a refinement strategy is recommended, to make the accuracy of the NMC dataset comparable to that characterizing the NMC results from dataset 1 achieved without any refinement. (c) 2024 Published by Elsevier Inc. |
| https://doi.org/10.1016/j.dib.2024.110320 |
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