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Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset.

Elias Arbash^1,2, Ahmed Jamal Afifi³, Ymane Belahsen^3,4

¹Helmholtz-Zentrum Dresden-Rossendorf (HZDR) - Helmholtz Institute Freiberg for Resource Technology (HIF), Freiberg, Germany. e.arbash@hzdr.de.

Scientific Data

|November 19, 2025

Summary

This summary is machine-generated.

This study introduces Electrolyzers-HSI, a new dataset for automated identification of electrolyzer materials, enhancing critical raw material recovery for sustainable recycling. The dataset and code accelerate AI-driven circular economy solutions.

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Area of Science:

Materials Science
Computer Science
Environmental Science

Background:

Sustainable recycling is a global challenge requiring advanced material detection.
Integrating AI and advanced technologies is crucial for a circular economy and Green Deal ambitions.
Current waste analysis is often isolated, hindering scalable industrial practice.

Purpose of the Study:

Introduce Electrolyzers-HSI, a multimodal benchmark dataset for accurate electrolyzer material classification.
Accelerate the recovery of critical raw materials from electronic waste.
Promote reproducible research and adoption of smart recycling technologies.

Main Methods:

Collected 55 co-registered RGB images and hyperspectral imaging (HSI) data cubes (400-2500 nm).
Enabled non-invasive analysis of shredded electrolyzer samples for quantitative material classification.
Evaluated state-of-the-art Transformer-based deep learning (DL) architectures for material identification.

Main Results:

The Electrolyzers-HSI dataset facilitates accurate classification of electrolyzer materials.
Demonstrated the effectiveness of DL models for robust electrolyzer identification.
Validated the dataset for advancing automated E-waste recycling.

Conclusions:

Electrolyzers-HSI supports AI-driven sustainability in recycling.
The dataset and codebase accelerate critical raw material recovery.
Facilitates scalable industrial practice for smart and sustainable E-waste recycling.