Towards recycling of challenging waste fractions: Identifying flame retardants in plastics with optical spectroscopic techniques

Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial-Spectral Network

The emerging field of nanoscale infrared (nano-IR) offers label-free molecular contrast, yet its imaging speed is limited by point-by-point traverse acquisition of a three-dimensional (3D) data cube. Here, we develop a spatial-spectral network (SS-Net), a miniaturized deep-learning model, together with compressive sampling to accelerate the nano-IR imaging. The compressive sampling is performed in both the spatial and spectral domains to accelerate the imaging process. The SS-Net is trained to learn the mapping from small nano-IR image patches to the corresponding spectra. With this elaborated mapping strategy, the training can be finished quickly within several minutes using the subsampled data, eliminating the need for a large-labeled dataset of common deep learning methods. We also designed an efficient loss function, which incorporates the image and spectral similarity to enhance the training. We first validate the SS-Net on an open stimulated Raman-scattering dataset; the results exhibit the potential of 10-fold imaging speed improvement with state-of-the-art performance. We then demonstrate the versatility of this approach on atomic force microscopy infrared (AFM-IR) microscopy with 7-fold imaging speed improvement, even on nanoscale Fourier transform infrared (nano-FTIR) microscopy with up to 261.6 folds faster imaging speed. We further showcase the generalization of this method on AFM-force volume-based multiparametric nanoimaging. This method establishes a paradigm for rapid nano-IR imaging, opening new possibilities for cutting-edge research in materials, photonics, and beyond.

Ultrahigh Heat/Fire-Resistant, Mechanically Robust, and Closed-Loop Chemical Recyclable Polycarbonate Enabled by Facile Bond Dissociation Energy Modulation

Plastics serve as an essential foundation in contemporary society. Nevertheless, meeting the rigorous performance demands in advanced applications and addressing their end-of-life disposal are two critical challenges that persist. Here, an innovative and facile method is introduced for the design and scalable production of polycarbonate, a key engineering plastic, simultaneously achieving high performance and closed-loop chemical recyclability. The bisphenol framework of polycarbonate is strategically adjusted from the low-bond-dissociation-energy bisphenol A to high-bond-dissociation-energy 4,4'-dihydroxydiphenyl, in combination with the incorporation of polysiloxane segments. As expected, the enhanced bond dissociation energy endows the polycarbonate with an extremely high glow-wire flammability index surpassing 1025 °C, a 0.8 mm UL-94 V-0 rating, a high LOI value of 39.2%, and more than 50% reduction of heat and smoke release. Furthermore, the π-π stacking interactions within biphenyl structures resulted in a significant enhancement of mechanical strength by as more as 37.7%, and also played a positive role in achieving a lower dielectric constant. Significantly, the copolymer exhibited outstanding closed-loop chemical recyclability, allowing for facile depolymerization into bisphenol monomers and the repolymerized copolymer retains its high heat and fire resistance. This work provides a novel insight in the design of high-performance and closed-loop chemical recyclable polymeric materials.

Quantification of aluminium trihydrate flame retardant in polyolefins via in-line hyperspectral imaging and machine learning for safe sorting

The extensive use of aluminium trihydrate (ATH) flame retardant in plastics poses challenges and hazards in plastic waste recycling, thus it is crucial to accurately identify ATH. This study demonstrates the effectiveness of an industrial in-line shortwave infrared (SWIR) hyperspectral imaging system and principal component analysis (PCA) for detecting and quantifying ATH in low-density polyethylene (LDPE) and polypropylene (PP). The samples were characterized by elemental analysis, ATR-FTIR, DSC, and TGA. A quantitative estimation model was developed by analysing spectra with varying ATH concentrations. PCA and SWIR band area ratio were fitted to estimate the ATH concentration. The PCA model outperformed the SWIR band area ratio model and achieved good predictions between measured and predicted ATH concentrations ranging from 22.9 to 1.6 wt% (R2LDPE = 0.95) in LDPE and from 24.0 to 2.5 wt% in PP (R2PP = 0.94). The obtained in-line control tool is relevant to the recycling industries, enabling real-time assessment of additives.

In-line quantitative estimation of ammonium polyphosphate flame retardant in polyolefins via industrial hyperspectral imaging system and machine learning

Due to developments in European legislation, several halogenated flame retardants are banned due to their toxicity, and the use of phosphor-based flame retardants in plastics is increasing. A revision of ammonium polyphosphate (APP) flame retardant revealed that it is an eye irritant and toxic, thus posing a health issue. Hence APP identification is needed for enabling safe recycling of plastic waste streams. Herein an industrial in-line method for quantitative estimation of APP in low density polyethylene (LDPE) and polypropylene (PP) is demonstrated, by using an industrial hyperspectral imaging system (955 to 1700 nm) and principal component analysis (PCA). Spectra of plastic samples with varying concentrations of APP were applied to build and calibrate a quantitative determination method. PCA and band area ratios (of selected bands) were made and fitted with continuous functions for concentration determination. The plastic samples were characterised by elemental analysis, attenuated total reflection, differential scanning calorimetry, and thermogravimetric analysis. The PCA model outperforms the band area ratio model and predicts APP concentrations between 24.3 and 1.5 wt% in LDPE (R2 = 0.98) and 20.0 and 1.7 wt% in PP (R2 = 0.97). Unknown samples with APP ranging from 23.7 to 2.7 wt% in LDPE and from 18.6 to 2.3 wt% in PP were predicted and correlated to the actual concentrations. The proposed approach is valuable for the plastic recyclers and waste management industries where inline concentration determination of flame retardants is key.