No abstract

Carbon dots have been considered as a solution to the challenges that semiconductor quantum dots have encountered because they are more biocompatible and can be synthesized from abundant and nontoxic materials such as biomass. This review will highlight the advantages of these biomass-based carbon dots in terms of synthesis, properties, and applications in the biomedical field. Furthermore, future applications especially in the biomedical field of biomass-based carbon dots as well as the challenges of semiconductor quantum dots such as biocompatibility, photobleaching, environmental challenges, toxicity, and poor solubility will be discussed in detail. Biomass-derived quantum dots, a subsection of carbon dots that are the most desirable for future research, will be focused upon including from synthesis to applications. Finally, the future development of biomass derived quantum dots in the biomedical field will be discussed and evaluated to unlock the potential for their applications.

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

Energy crisis, environmental deterioration and increasing customer needs have compelled scientists to search facile, low cast, and green routes for the production of novel advanced materials from renewable resources. Among various materials explored, carbon based nanomaterials, especially graphene and graphene quantum dots (GQDs), have attracted extensive attention recently owning to their intriguing properties, such as high conductivity, extensive surface area, good biocompatibility, low toxicity, and long life. This review focuses on the conversion of biomass-waste into GQDs through a number of facile, low cost and scalable routes. Factors affecting the physical and chemical properties of GQDs for numerous promising applications have also been discussed. GQDs have shown promising applications in the field of catalysis, carbon fixation, fuel cells, bio-imaging, drug delivery, and gas sensors. Interestingly, the recent and novel applications of GQDs in the conversion and storage of energy has been discussed here. Finally, the remaining challenges, future perspectives and possible research directions in the field are presented. 1

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence Newcastle University ePrints -eprint.ncl.ac.uk Diaz-Silvarrey LS, Phan AN. Kinetic studies of municipal plastic waste.