Growing of 2D/3D graphene structures on natural substrates from aromatic plastic wastes by scalable thermal-based upcycling process with a comparative CO2 footprint analysis

Recycling aromatic plastics, such as polystyrene (PS) and polyethylene terephthalate (PET), is complex and strongly sensitive to the process design and conditions due to the presence of polycyclic aromatic hydrocarbons and interunit C–O and/or C–C linkages. Herein, the upcycling process becomes crucial to attain high-value-added products from aromatic plastics. With this study, graphene growth was achieved on the natural substrates of talc and organically modified montmorillonite (OMMT), from waste PS and PET sources via a sustainable, affordable, and environment-friendly upcycling technique. This promising method promotes the formation of 2D and 3D graphene structures from waste PS and PET. This provides dimension-controlled graphene growth by tailoring the substrate type and size, surface composition of the substrate, and the degree of aromaticity in the polymer. In addition, polymer processing techniques of twin-screw extrusion and thermokinetic mixing, were investigated in the development of graphene-grown hybrid additives to tailor the degree of crystallinity. Regarding structural characterization results, a high shear rate mixer led to the change in crystalline planes of talc, whereas conventional twin screw extrusion preserved the structure of talc indicating that high shear rate triggered the exfoliation of talc. Furthermore, a systematic life cycle assessment was conducted to evaluate the CO2 footprint of upcycled graphenes grown on talc and OMMT compared to graphene produced from graphite. Upcycled graphene structures obtained by direct carbonization and even catalyst impregnated natural substrate-based graphene growth process with PS or PET source have comparably lower CO2 emission than graphene received from graphite. Therefore, these newly developed and upcycled hybrid additives opens a way for the conversion of aromatic complex plastic wastes into value-added nanomaterials being candidate as reinforcing agent by adopting a circular economy model

Selective thermal recycling for graphene growth on natural substrates: a comparative study on life cycle assessment, energy consumption, and mechanical performance in recompounding

Manufacturing of carbon-based materials from waste thermoplastics is a keystone to reduce adverse environmental impacts. There are numerous attempts for sustainable graphene manufacturing from various waste sources by thermal treatment but there is no clear distinction on the effective conversion process by addressing reliable CO2 footprints. This study provides a comprehensive benchmarking study on the conversion of waste polypropylene plastics coming from yogurt containers into graphene on the substrate of talc by applying two upcycling techniques of catalytic carbonization (CC) and flash pyrolysis (FP) by comparing energy and speed of the processes and a dimensional stability and physical characteristics of the produced graphene substances by adopting a comparative life cycle assessment. FP led to the sphericalization of graphenes due to fast dehydration, cross-linking, and carbonization of aromatic structures. On the other hand, gradual heating in CC caused the formation of tubular-like graphene structures. In addition, FP became advantageous by resulting in 52% of CO2 emission compared with CC process. On the other hand, graphenes separated from talcs exhibited a remarkable 70% reduction in global warming potential compared with conventional graphene production from graphite. In order to complete the value chain and circularity, the mechanical performance of two different hybrid additives produced by selective thermal recycling in recompounding with copolymer polypropylene was examined, and additives from CC enhanced the flexural and tensile properties two times better than the one from FP. With this study, it becomes possible to compare analysis of graphene growth on natural substrates by exploring life cycle assessment, energy consumption, and mechanical performance with selective thermal recycling and recompounding

Multi-objective evolutionary product bundling: a case study

[EN] Product bundling is a strategy conducted by marketing decisionmakers to combine items or services for targeted sales in today¿s competitive business environment. Targeted sales can be in various forms, like increasing the likelihood of a purchase, promoting some products among a specific customer segment, or improving user experience. In this study, we propose an evolutionary product bundle generation strategy that is based on the NSGA-II algorithm. The proposed approach is designed as a multi-objective optimization procedure where the objectives are designed in terms of desired bundle feature distributions. The designed genetic algorithm is flexible and allows decision-makers to specify objectives such as price, season, item similarity and association with bundle size constraints. In the experiments,we showthat the evolutionary approach enables us to generate Pareto solutions compared to the initial population.Tunali, O.; Tugrul Bayrak, A.; Sanchez-Anguix, V.; Aydogan, R. (2021). Multi-objective evolutionary product bundling: a case study. ACM. 1622-1629. https://doi.org/10.1145/3449726.34632191622162

Classification of waste plastics for dimension-controlled graphene growth on natural mineral substrates in terms of polymer processing and thermal techniques

Plastic materials have become inevitable daily-life products in modern life due to their low cost, high strength, and suitability for many applications. However, wide utilization of them in many areas causes significant high-volume plastic wastes that threaten the ecosystem, soil, water, and human health. Indeed, recycling is used to decrease the plastic waste amount; however, recycled plastics do not have the same performance as virgin plastic composites. Converting the plastic wastes into high-value-added carbon materials by upcycling provides several benefits because it is an effective method in terms of cost and sustainability. A wide range of waste plastics such as polyethylene, polypropylene, polystyrene, polyethylene terephthalate, polyamide, textile products, organic wastes, and metal wastes are used for upcycling. Moreover, there are few attempts at the growth of carbon-based structures on mineral substrates. Related to recent studies, this chapter focuses on the classification of polyolefin and aromatic polymer-based plastic wastes for the growth of upcycled graphene on natural mineral substrates. In this regard, the impact of both polymer processing techniques and heat-treatment on the chemical, structural, and morphological features of the resultant hybrid additives were comprehended in detail. In addition, an understanding of the influence of polymer backbone on the upcycled graphene growth was provided. Beyond, controlling the dimension of the upcycled graphene growth on natural mineral substrates related to the used mineral size was comprehensively discussed. Consequently, in this chapter, the effect of polymer processing techniques, heat treatment, and mineral size are investigated to choose a selective method for the conversion of plastic wastes into 2D and 3D graphene structures by a circular economy targeted upcycling process in terms of the plastic waste types and their chain length and aromaticity degree

A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts

Objectives: To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke’s cleft cysts (RCCs). Materials and Methods: The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images. Radiomics features were extracted from T2-weighted, T1-weighted, and T1-contrast-enhanced images. Machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), and Light Gradient Boosting (LGB), were then trained and validated using semantic features only and a combination of semantic and radiomic features. Statistical analyses were carried out to compare the performance of these various models. Results: Machine learning models that combined semantic and radiomic features achieved higher levels of accuracy than models with semantic features only. Models with combined semantic and T2-weighted radiomics features achieved the highest test accuracies (93.8%, 92.3%, and 90.8% for LR, SVM, and LGB, respectively). The SVM model combined semantic features with T2-weighted radiomics features had statistically significantly better performance than semantic features only (p = 0.019). Conclusion: Our study demonstrates the significant potential of machine learning for differentiating CPA from RCCs