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ABSTRACT Elucidating the crystalline‐amorphous interface during decrystallization processes in semi‐crystalline polyethylene (PE) is crucial for the advancement of polymer theory and plastic‐to‐plastic recycling technologies. In this study, we carried out an in‐depth investigation of PE thin films undergoing melting or dissolution using a temperature‐controlled liquid flow‐cell experimental setup which provided in situ mid‐infrared (MIR, 4000–700 cm⁻¹) and near‐infrared (NIR, 6000–4000 cm⁻¹) spectra in real time. The spectroscopic results yielded molecular‐level information regarding PE decrystallization and chain disentanglement via fundamental vibrations, combination bands, and overtones which were correlated using hetero‐spectral two‐dimensional correlation spectroscopy (2D‐COS). A quantitative procedure for the calculation of PE degree of crystallinity was developed to track transformations of crystalline domains during melting and dissolution. This semi‐empirical model achieved a strong linear correlation of at least +0.93 in four spectral regions: 750–700 cm⁻¹, 1500–1400 cm⁻¹, 3000–2800 cm⁻¹, and 4400–4200 cm⁻¹. This analysis revealed important spectral trends about the interfacial solvation environment during these processes. Lastly, the time evolution of the unraveling, terminal methyl (CH₃) groups of PE cilia was examined in relation to the decrystallization mechanism of PE. The insights obtained from this study advance the fundamental understanding necessary for developing new depolymerization and dissolution‐precipitation recycling strategies. 

Polypropylene (PP) is widely used and currently very little recycled. A promising method for recycling the PP present in plastic waste involves its selective dissolution and subsequent separation from undissolved compounds. We address here the fundamentals of PP dissolution. Specifically, we present a model that describes the different phenomena involved in the dissolution of semicrystalline PP and validate the model with the experimental results on the decrystallization and dissolution kinetics of PP pellets. The model provides detailed time-resolved and position-resolved information on composition (i.e., crystalline PP, amorphous PP, and solvent) and solvent diffusivity (which depends on composition) across the dissolving polymer particle, in different solvents and temperatures. Such information is unavailable experimentally or difficult to obtain. The key fitted parameters that capture decrystallization and polymer chain disentanglement decrease with increasing temperature following an Arrhenius relationship, with activation energies higher than that for crystallization and comparable to that for melt viscosity. Both decrystallization and dissolution times increase with particle size. For smaller particles, decrystallization and dissolution occur nearly simultaneously, while for larger particles, their interior remains solvent-poor and crystalline for longer times. This work offers insights into the interplay of decrystallization and polymer chain disentanglement during the time-course of PP dissolution. Further, this work facilitates the design and optimization of a dissolution–precipitation recycling process that can unlock value from the million tons of PP annually that is currently being landfilled or incinerated following its use. 

Plastics recycling is an important component of the circular economy. In mechanical recycling, the recovery of high-quality plastics for subsequent reprocessing requires plastic waste to be first sorted by type, color, and size. In chemical recycling, certain types of plastics should be removed first as they negatively affect the process. Such sortation of plastic objects at Materials Recovery Facilities (MRFs) relies increasingly on automated technology. Critical for any sorting is the proper identification of the plastic type. Spectroscopy is used to this end, increasingly augmented by machine learning (ML) and artificial intelligence (AI). Recent developments in the application of ML/AI in plastics recycling are highlighted here, and the state of the art in the identification and sortation of plastic is presented. Commercial equipment for sorting plastic recyclables is identified from a survey of publicly available information. Automated sorting equipment, ML/AI-based sorters, and robotic sorters currently available on the market are evaluated regarding their sensors, capability to sort certain types of plastics, primary application, throughput, and accuracy. This information reflects the rapid progress achieved in sorting plastics. However, the sortation of film, dark plastics, and plastics comprising multiple types of polymers remains challenging. Improvements and/or new solutions in the automated sorting of plastics are forthcoming. 

The growing textile industry is polluting the environment and producing waste at an alarming rate. The wasteful consumption of fast fashion has made the problem worse. The waste management of textiles has been ineffective. Spurred by the urgency of reducing the environmental footprint of textiles, this review examines advances and challenges to separate important textile constituents such as cotton (which is mostly cellulose), polyester (polyethylene terephthalate), and elastane, also known as spandex (polyurethane), from blended textiles. Once separated, the individual fiber types can meet the demand for sustainable strategies in textile recycling. The concepts of mechanical, chemical, and biological recycling of textiles are introduced first. Blended or mixed textiles pose challenges for mechanical recycling which cannot separate fibers from the blend. However, the separation of fiber blends can be achieved by molecular recycling, i.e., selectively dissolving or depolymerizing specific polymers in the blend. Specifically, the separation of cotton and polyester through dissolution, acidic hydrolysis, acid-catalyzed hydrothermal treatment, and enzymatic hydrolysis is discussed here, followed by the separation of elastane from other fibers by selective degradation or dissolution of elastane. The information synthesized and analyzed in this review can assist stakeholders in the textile and waste management sectors in mapping out strategies for achieving sustainable practices and promoting the shift towards a circular economy. 

An accurate molecular identification of plastic waste is important in increasing the efficacy of automatic plastic sorting in recycling. However, identification of real-world plastic waste, according to their resin identification code, remains challenging due to the lack of techniques that can provide high molecular selectivity. In this study, a standoff photothermal spectroscopy technique, utilizing a microcantilever, was used for acquiring mid-infrared spectra of real-world plastic waste, including those with additives, surface contaminants, and mixed plastics. Analysis of the standoff spectral data, using Convolutional Neural Network (CNN), showed 100% accuracy in selectively identifying real-world plastic waste according to their respective resin identification codes. Standoff photothermal spectroscopy, together with CNN analysis, offers a promising approach for the selective characterization of waste plastics in Material Recovery Facilities (MRFs). 

Materials recovery facilities (MRFs) require new automated technologies if growing recycling demands are to be met. Current optical screening devices use visible (VIS) and near-infrared (NIR) wavelengths, frequency ranges that can experience challenges during the characterization of postconsumer plastic waste (PCPW) because of the overly-absorbing spectral bands from dyes and other polymer additives. Technological bottlenecks such as these contribute to 91% of plastic waste never actually being recycled. The mid-infrared (MIR) region has attracted recent attention due to inherent advantages over the VIS and NIR. The fundamental vibrational modes found therein make MIR frequencies promising for high fidelity machine learning (ML) classification. To-date, there are no ML evaluations of extensive MIR spectral datasets reflecting PCPW that would be encountered at MRFs. This study establishes quantifiable metrics, such as model accuracy and prediction time, for classification of a comprehensive MIR database consisting of five PCPW classes that are of economic interest: polyethylene terephthalate (PET #1), high-density polyethylene (HDPE #2), low-density polyethylene (LDPE #4), polypropylene (PP #5), and polystyrene (PS #6). Autoencoders, an unsupervised ML algorithm, were applied to the random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR) models. The RF model achieved accuracies of 100.0% in both the C–H stretching region (2990–2820 cm −1 ) and molecular fingerprint region (1500–650 cm −1 ). The C–H stretching region was found to be free from additives that were responsible for misclassification in other regions, making it a fruitful frequency range for future PCPW sorting technologies. The MIR classification of black plastics and polyethylene PCPW using ML autoencoders was also evaluated for the first time. 

Molecular-level spectroscopy is crucial for sensing and imaging applications, yet detecting and quantifying minuscule quantities of chemicals remains a challenge, especially when they surface-adsorb in low numbers. Here, we introduce a photothermal spectroscopic technique that enables the sensing and quantification of adsorbates with an attogram detection limit. Our approach utilizes the Seebeck effect in a microfabricated nanoscale thermocouple junction, incorporated into the apex of a microcantilever. We observe minimal thermal mass exhibited by the sensor which maintains exceptional thermal insulation. The temperature variation driving the thermoelectric junction arises from the non-radiative decay of molecular adsorbates' vibrational states on the tip. We demonstrate the detection of physisorbed trinitrotoluene (TNT) and dimethyl methylphosphonate (DMMP) molecules, as well as representative polymers, with an estimated mass sensitivity of 10-18 g and a temperature resolution of 40 mK. 

Recycling plastic is an important step towards a circular economy. Attaining high-quality recycled plastics requires the separation of plastic waste by type, color, and size prior to reprocessing. Automated technology is key for sorting plastic objects in medium- to high-volume plants. The current state of the art of commercial equipment for sorting plastic as well as challenges faced by Material Recovery Facilities (MRFs) to sort post-consumer plastics are analyzed here. Equipment for sorting plastic recyclables were identified using publicly available information obtained from manufacturers’ websites, press releases, and journal articles. Currently available automated sorting equipment and artificial intelligence (AI)-based sorters are evaluated regarding their functionality, efficiency, types of plastics they can sort, throughput, and accuracy. The information compiled captures the progress made during the ten years since similar reports were published. A survey of MRFs, reclaimers, and brokers in the United States identified methods of sorting used for plastic, sorting efficiency, and current practices and challenges encountered at MRFs in sorting plastic recyclables. The commercial sorting equipment can address some of the challenges that MRFs face. However, sorting of film, multilayered, blended, or mixed-material plastics is problematic, as the equipment is typically designed to sort single-component materials. Accordingly, improvements and/or new solutions are considered necessary. 

Standoff detection based on optical spectroscopy is an attractive method for identifying materials at a distance with very high molecular selectivity. Standoff spectroscopy can be exploited in demanding practical applications such as sorting plastics for recycling. Here, we demonstrate selective and sensitive standoff detection of polymer films using bi-material cantilever-based photothermal spectroscopy. We demonstrate that the selectivity of the technique is sufficient to discriminate various polymers. We also demonstrate in situ, point detection of thin layers of polymers deposited on bi-material cantilevers using photothermal spectroscopy. Comparison of the standoff spectra with those obtained by point detection, FTIR, and FTIR-ATR show relative broadening of peaks. Exposure of polymers to UV radiation (365 nm) reveal that the spectral peaks do not change with exposure time, but results in peak broadening with an overall increase in the background cantilever response. The sensitivity of the technique can be further improved by optimizing the thermal sensitivity of the bi-material cantilever and by increasing the number of photons impinging on the cantilever.