Dataset Information

Alloy information helps prioritize material criticality lists.

ABSTRACT: Materials scientists employ metals and alloys that involve most of the periodic table. Nonetheless, materials scientists rarely take material criticality and reuse potential into account. In this work, we expand upon lists of "critical materials" generated by national and regional governments by showing that many materials are employed predominantly as alloying elements, which can be a deterrent to recovery and reuse at end of product life and, likely as a consequence, have low functional end-of-life recycling rates, among other problematic characteristics. We thereby single out six metals for enhanced concern: dysprosium, samarium, vanadium, niobium, tellurium, and gallium. From that perspective, the use of critical metals in low concentrations in alloys unlikely to be routinely recycled should be avoided if possible. If not, provision should be made for better identification and more efficient recycling so that materials designated as critical can have increased potential for more than a single functional use.

SUBMITTER: Graedel TE

PROVIDER: S-EPMC8748655 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Alloy information helps prioritize material criticality lists.

Graedel T E TE Reck Barbara K BK Miatto Alessio A

Nature communications 20220110 1

Materials scientists employ metals and alloys that involve most of the periodic table. Nonetheless, materials scientists rarely take material criticality and reuse potential into account. In this work, we expand upon lists of "critical materials" generated by national and regional governments by showing that many materials are employed predominantly as alloying elements, which can be a deterrent to recovery and reuse at end of product life and, likely as a consequence, have low functional end-of ...[more]

PMID: 35013288

Similar Datasets

Project description:Genome-wide association studies (GWAS) evaluate associations between genetic variants and a trait or disease of interest free of prior biological hypotheses. GWAS require stringent correction for multiple testing, with genome-wide significance typically defined as association p-value <5*10-8. This study presents a new tool that uses external information about genes to prioritize SNP associations (GenToS). For a given list of candidate genes, GenToS calculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS. It thereby allows for identifying trait-associated genetic variants that do not meet genome-wide significance. The program additionally tests for enrichment of significant candidate gene associations in the human GWAS data compared to the number expected by chance. As proof of principle, this report used external information from a comprehensive resource of genetically manipulated and systematically phenotyped mice. Based on selected murine phenotypes for which human GWAS data for corresponding traits were publicly available, several candidate gene input lists were derived. Using GenToS for the investigation of candidate genes underlying murine skeletal phenotypes in data from a large human discovery GWAS meta-analysis of bone mineral density resulted in the identification of significantly associated variants in 29 genes. Index variants in 28 of these loci were subsequently replicated in an independent GWAS replication step, highlighting that they are true positive associations. One signal, COL11A1, has not been discovered through GWAS so far and represents a novel human candidate gene for altered bone mineral density. The number of observed genes that contained significant SNP associations in human GWAS based on murine candidate gene input lists was much greater than the number expected by chance across several complex human traits (enrichment p-value as low as 10-10). GenToS can be used with any candidate gene list, any GWAS summary file, runs on a desktop computer and is freely available.

Dataset Information

Alloy information helps prioritize material criticality lists.

Publications

Alloy information helps prioritize material criticality lists.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets