Back

Nondestructive Detection and Quantification of Dysprosium in Plant Tissues

Hernandez-Pagan, E.; Laosuntisuk, K.; Harris, A. T.; Haynes, A. N.; Buitrago, D.; Rajabu, C.; Kudenov, M. W.; Doherty, C. J.

2025-01-08 plant biology
10.1101/2025.01.07.631589 bioRxiv
Show abstract

BackgroundThe growing demand for rare-earth elements (REEs), particularly dysprosium (Dy), in part driven by clean energy technologies, underscores the need for sustainable extraction methods. Recovery of Dy, particularly from geographically distributed waste sources is challenging. This gap positions phytomining--a technique using plants to accumulate metals-- as a promising alternative. However, plant species differ in their ability to accumulate metals in high concentrations, necessitating efficient screening methods. In this study, we developed a high-throughput fluorescence-based assay to detect and quantify Dy uptake in plant tissues. ResultsOur Dy detection method exploits Dys unique spectroscopic properties for sensitive and efficient analysis, enabling detection of concentrations as low as 0.3 {micro}M. By incorporating sodium tungstate (Na WO) as a fluorescence enhancer, we achieved robust emissions at 480 and 580 nm, facilitating Dy quantification in complex plant matrices. Additionally, time-resolved fluorescence techniques reduced background autofluorescence from plant tissues, enhancing signal specificity. Validation against Inductively Coupled Plasma Mass Spectrometry (ICP-MS) demonstrated strong correlation. Greenhouse trials confirmed the methods utility for screening Dy accumulation in living plants and highlight the potential for rapid standoff detection. ConclusionsThis fluorescence-based approach offers a scalable, efficient tool for identifying Dy-accumulating plants, advancing phytomining as a sustainable strategy for REE recovery.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.