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Updated: Aug 14, 2026

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris
Published on: July 28, 2018
Low-Cost Qualitative Screening of Melting Ranges in Recycled Fishing Net Plastics: Comparison of Portable Handheld
Sylvia Waschke1, Julia Tetzner1, Thomas Potempa1
1Institute of Recycling (IFR), Ostfalia University of Applied Sciences, 38440 Wolfsburg, Germany.
Abstract:
Reliable polymer identification is a prerequisite for high-quality recycling of post-consumer fishing nets and ropes. Differential Scanning Calorimetry (DSC) provides precise thermal characterization but requires stationary laboratory infrastructure and is not applicable at coastal collection points or small-scale recycling operations. This study describes and validates Melting Box 1.0 (MB 1.0), a low-cost, field-deployable device for qualitative screening of softening and melting ranges in small polymer samples. MB 1.0 repurposes a commercial vaporizer's existing temperature-controlled heating and display functionality into a purpose-built screening tool, rather than introducing a new analytical measurement principle. Representative materials from Vietnamese fishing net recycling projects-polyethylene (PE), polypropylene (PP), polyamide (PA), and PE/PP blends-were assessed with a standardized three-tier visual scale (0 = no change; * = softening; x = melting) across five replicate measurements per sample. Agreement with the Kofler hot bench is good (mean bias for Tmelt,start: +2.9 °C; SD = 9.1 °C; n = 7), and a systematic offset of +20.6 °C (range +14.1 to +27.6 °C) was observed for PE relative to DSC first-heating peak-end temperatures. The correct material ranking (PE < PP < PA) is preserved, and all four polymer groups are reliably distinguished. At an estimated cost of 150-200 EUR-one to three orders of magnitude below the Kofler hot bench, portable NIR, or DSC-MB 1.0 is intended as a field-deployable screening complement to, not a replacement for, laboratory-grade analytics in resource-constrained recycling contexts.
