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Journal articles
Magazine articles
Open Access
Convolutional neural networks enhance pyrolysis gas chromatography mass spectrometry identification of coated papers, TAPPI Journal August 2024

ABSTRACT: In the evolving paper industry, accurate identification of coated paper components is essential for sustainability and recycling efforts. This study employed pyrolysis-gas chromatography mass spectrometry (Py-GCMS) to examine six types of coated paper. A key finding was the minimal interference of the paper substrate with the pyrolysis products of the coatings, ensuring reliable analysis. A one-dimensional convolutional neural network (1D-CNN) was employed to process the extracted ion chromatograms directly, simplifying the workflow and achieving a predictive accuracy of 95.2% in identifying different coating compositions. Additionally, the study high-lighted the importance of selecting an optimal pyrolysis temperature for effective feature extraction in machine learning models. Specific markers for coated papers, including polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polybutylene succinate (PBS), polylactic acid (PLA), and waterborne polyacrylates (WP), were identified. This research demonstrates a novel approach to coated paper identification by combining Py-GCMS with machine learning, offering a foundation for further studies in product quality and environmental impact.

Journal articles
Open Access
A roadmap for future papermakers: Dr. Peter W. Hart’s TAPPI Journal contributions, TAPPI Journal December 2024

ABSTRACT: Dr. Peter W. Hart’s passing earlier this year, many of his fellow TAPPI Journal Editorial Board members, peers, and colleagues have reflected on the lasting significance of his contributions to TAPPI and the industry at large. In this special December tribute issue, we have compiled some of Peter’s past contributions to TAPPI Journal as selected by those individuals familiar with his research. Many of the papers highlight Peter’s dedication to collaborative work.