Sebastian Wicklein
Sensor-based XRF separating non-magnetic high-alloy ferrous fractions.
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Electronic waste recovery covers identifying, separating and recovering the metals and materials in end-of-life electronics and batteries. On Halo, the solutions cover sorting by spectroscopy, sorting by vision and learning, recovering critical materials, and collecting devices from consumers. Sign up to search the full network and post your specific need.
Sebastian Wicklein
Sensor-based XRF separating non-magnetic high-alloy ferrous fractions.
PS
ZORTENET PC · Supplier
Laser-induced breakdown spectroscopy identifying scrap and grading it.
Meng Zhou
Lawrence Technical University
Powder X-ray diffraction reading zinc content in real time.
Eco Mercantile · Startup
Machine learning applied to spectroscopy for metal identification.
Maastricht University
Optical and spectral imaging identifying material in bulk scrap.
UHV Technologies · Supplier
AI-driven identification and sorting, running on the scrap line itself.
Robert Baudinet
Matvision SRL · Startup
RGB vision, hyperspectral and X-ray transmission on one robot.
Geometric Data Analytics · Supplier
Actively learned segmentation, so the model improves on the operator's stream.

SRJX RESEARCH AND INNOVATION LAB LLP · Startup
An automated system separating ferrous from non-ferrous material.
Primobius · Startup
Hydrometallurgical and mechanical processing that recycles spent electric-vehicle batteries so their materials return to industry.
tozero · Startup
A water-based, acid-free, low-temperature process recovering lithium and graphite from battery waste for new cathodes and anodes.
CU
Cornell University
Engineered microbes extract rare earth elements from electronic waste, spent catalysts and coal ash, then separate them element by element.
Vendy · Startup
AI kiosks that value and pay for used devices on the spot, raising formal collection and recycling feedstock quality.
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Universities, startups, and suppliers with solutions in electronic waste recovery.
E-waste recycling organizationsMost of the work on Halo sorts a mixed scrap stream, identifying material by its spectrum through XRF, laser breakdown, X-ray diffraction or hyperspectral imaging, or by vision and machine learning. A smaller set recovers the material itself, from spent battery packs and from the rare earths held in electronic scrap. On Halo it spans four groups, including sorting by spectroscopy, sorting by vision and learning, and recovering critical materials.
Stage of development. Work on electronic waste recovery on Halo comes mostly from companies. 35% of the solutions are in market. Co-development and licensing are the usual partnering routes, and about 25% of the solutions that state terms offer sponsored research.
Recovery chemistry for batteries and rare earths. Recovery work is moving past sorting to getting the material back. Hydrometallurgical processing returns the metals in spent electric-vehicle batteries to industry (Primobius), and an acid-free, low-temperature route recovers lithium and graphite for new electrodes (tozero). Engineered microbes extract rare earths from electronic scrap and separate them element by element (Cornell University).
Every physics gets tried. X-ray fluorescence, laser-induced breakdown spectroscopy, powder X-ray diffraction, hyperspectral imaging, X-ray transmission, RGB vision, tactile sensing, and machine learning over several of these at once. One system combines four modalities on a single robot. No single signal identifies every material, which is why the multi-sensor approaches keep appearing.
The model learns on the operator's own stream. Actively learned segmentation and classification, deep learning trained on ferrous imagery, high-speed vision with AI in the loop. Scrap composition varies by region, supplier and season, so a model trained centrally is worth less than one that adapts to the material arriving at a particular yard.
Electronic waste recovery covers identifying and separating the materials in a discarded device or a mixed scrap stream. It spans sorting by spectroscopy, sorting by vision and machine learning, and the chemistry that recovers a critical metal once separated. Recyclers, smelters and metal traders are the buyers. On Halo the work leans toward sorting by spectroscopy, sorting by vision and learning, and recovering critical materials.
Four examples of e-waste recycling solutions on Halo include:
The most recently updated e-waste recycling solutions on Halo include:
There are 163 organizations with e-waste recycling solutions on Halo, 30 of them universities and research institutions. Among them are Fraunhofer USA, ZORTENET PC, and Lawrence Technical University. Most offer co-development or licensing. Sign up to see every organization working in the area and to send them your specific need.
Browsing e-waste recycling solutions on Halo is free. Sign up to search the full network and save the ones you want. Post your specific need to get exact matches. Organizations reply directly on the platform.
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