Search

The three-dimensional (3D) printing of functional composite materials has gained tremendous interest in recent years. Nevertheless, research on 3D printing of luminescent composite materials is very limited, and the mechanical properties of such 3D-printed composites are poor. Herein, we report the preparation and characterization of a poly(lactic acid) (PLA) composite that, when 3D printed, exhibits enhanced toughness and high solid-state fluorescence quantum yield. Incorporation of only 1 wt% pyrene butyric acid modified cellulose nanofibers (PBA-m-CNF) and 10 wt% thermoplastic polyurethane (TPU) into PLA led to 223% increase in toughness and 21% increase in tensile modulus of PLA. Scanning electron microscopy (SEM) and X-ray microcomputed tomography ([micro]-CT) analysis of the fractured cross-sections of 3D printed composites revealed a ductile failure mode. The PLA/PBA-m-CNFl/TPUlO 3D printed composite also exhibited a high solid-state fluorescence quantum yield of 38.35%. To the best of our knowledge, this is the first report to show both enhanced mechanical properties and high solid-state fluorescence emission for 3D printable PLA. Such functional PLA composites could have potential applications in the fabrication of complex-shaped sensors, optical light pipes, etc. KEYWORDS 3D printing, composite, fluorescence, nanocellulose, poly(lactic acid), toughness, 1 | INTRODUCTION Additive manufacturing (AM) has garnered tremendous interest in various applications, including electronics, aerospace, biomedical, construction, etc [1,2] There are various AM techniques, and ISO/ASTM 52900 standard classifies [...]

Thermoplastic polyimides (TPIs) are promising lightweight materials for replacing metal components in aerospace, rocketry, and automotive industries. Key TPI attributes include low density, thermal stability, mechanical strength, inherent flame retardancy, and intrinsic fluorescence under UV light. The application of advanced manufacturing techniques, especially 3D printing, could significantly broaden the use of TPIs; however, challenges in melt-processing this class of polymer represent a barrier. This study explored the processability, 3D-printing and hence mechanical, and fluorescence properties of TPI coupons, demonstrating their suitability for advanced 3D-printing applications. Moreover, the study successfully 3D-printed a functional impeller for an overhead stirrer, effectively replacing its metallic counterpart. Defects were shown to be readily detectable under UV light. A thorough analysis of TPI processing examining its rheological, morphological, and thermal properties is presented. Extruded TPI filaments were 3D-printed into test coupons with different infill geometries to examine the effect of tool path on mechanical performance. The fluorescence properties of the 3D-printed TPI coupons were evaluated to highlight their potential to produce intricately shaped thermally stable, fluorescence-based sensors. [ABSTRACT FROM AUTHOR]

Aim/Background: This research aims to develop an automated contextual classifier for scholarly papers by utilizing established algorithms and understanding the information retention of different parts of a scholarly article, such as the Abstract, Article Title, and Keywords. It also seeks to recommend a contextual classifier-based recommender system to help academics identify credible sources. Scholarly articles from various study fields often use similar terms in their titles and keywords. However, finding a publication venue can be challenging for researchers at the beginning of a scientific inquiry. Thus, it is crucial to classify information based on its context, especially when abstracts, keywords, and titles receive equal attention. Materials and Methods: An ensembled model was developed and trained using 114K instances from 38 classes of the Web of Science (WoS) dataset and 40 classes of the Dimensions dataset. The ensemble approach incorporated both machine learning and deep learning algorithms to build a diverse classifier. The model was evaluated by testing it with an 80:20 train-test split to assess performance. The classifier was further integrated into a recommender system designed to suggest probable publication sources based on given article information. Results: The ensemble classification approach demonstrated superior performance with faster inference and efficient training time. The balanced training model, tested on 114K instances, effectively categorized scholarly articles into one of 40 categories. The recommender system was capable of recommending up to 10 probable publication sources based on the article's Title, Keywords, and Abstract. Models utilizing abstractions yielded the best results and provided a better understanding of the context in every iteration of the experiment. Conclusion: This study successfully developed an ensemble-based contextual classifier for academic papers, which can also function as a recommender system. The system aids researchers in choosing the most appropriate sources to publish by categorizing articles into 40 categories and suggesting credible publication venues. This approach simplifies the decision-making process for academics, enabling them to identify relevant publications and suitable sources for their work more efficiently. [ABSTRACT FROM AUTHOR]