Synthesis, Sorting, and Applications of Single-Chirality Single-Walled Carbon Nanotubes

Compensation of capacitive currents in high-throughput dielectrophoretic separators

Separation and classification are important operations in particle technology, but they are still limited in terms of suspended particles in the micrometer and nanometer size-range. Electrical fields can be beneficial for sorting such particles according to material properties. A mechanism based on strong and inhomogeneous fields is dielectrophoresis (DEP). It can be used to separate microparticles according to their material properties, such as conductivity and permittivity, by selectively trapping one particle type while the other can pass the separator. Conventional DEP-separators show either a limitation in throughput or frequency bandwidth. A low throughput limits the economical feasibility in many cases. A lower frequency bandwidth limits the variety of materials that can be sorted by DEP. To separate semiconducting particles from a mixture containing particles with higher conductivity according to their material, high frequencies are required. Possible applications are the separation of semiconducting and metallic carbon nanotubes or the separation of carbon-coated lithium iron phosphate particles from graphite in the recycling process of spent lithium-ion batteries. In this publication, we aim to display how to tune the electrical impedance of a high-throughput DEP separator based on custom-designed printed circuit boards to increase its frequency bandwidth. By adding inductors to the electrical circuit, we were able to increase the frequency bandwidth from 500 kHz to over 11 MHz. The experiments in this study act as proof-of-principle. Furthermore, a non-deterministic way to increase the impedance of the setup is shown, yielding a maximum frequency of 39.16 MHz.

Monitoring Enzyme Activity Using Near-Infrared Fluorescent Single-Walled Carbon Nanotubes

Enzymes serve as pivotal biological catalysts that accelerate essential chemical reactions, thereby influencing a variety of physiological processes. Consequently, the monitoring of enzyme activity and inhibition not only yields crucial insights into health and disease conditions but also forms the basis of research in drug discovery, toxicology, and the understanding of disease mechanisms. In this context, near-infrared (NIR) fluorescent single-walled carbon nanotubes (SWCNTs) have emerged as effective tools for tracking enzyme activity and inhibition through diverse strategies. This perspective explores the physicochemical attributes of SWCNTs that render them well-suited for such monitoring. Additionally, we delve into the various strategies developed so far for successfully monitoring enzyme activity and inhibition, emphasizing the distinctive features of each principle. Furthermore, we contrast the benefits of SWCNT-based NIR probes with conventional gold standards in monitoring enzyme activity. Lastly, we highlight the current challenges faced in this field and suggest potential solutions to propel it forward. This perspective aims to contribute to the ongoing progress in biodiagnostics and seeks to engage the wider community in developing and applying enzymatic assays using SWCNTs.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Functionalized Fullerenes and Their Applications in Electrochemistry, Solar Cells, and Nanoelectronics

Carbon-based nanomaterials have rapidly advanced over the last few decades. Fullerenes, carbon nanotubes, graphene and its derivatives, graphene oxide, nanodiamonds, and carbon-based quantum dots have been developed and intensively studied. Among them, fullerenes have attracted increasing research attention due to their unique chemical and physical properties, which have great potential in a wide range of applications. In this article, we offer a comprehensive review of recent progress in the synthesis and the chemical and physical properties of fullerenes and related composites. The review begins with the introduction of various methods for the synthesis of functionalized fullerenes. A discussion then follows on their chemical and physical properties. Thereafter, various intriguing applications, such as using carbon nanotubes as nanoreactors for fullerene chemical reactions, are highlighted. Finally, this review concludes with a summary of future research, major challenges to be met, and possible solutions.

Metal and Metal Halogenide-Filled Single-Walled Carbon Nanotubes: Kinetics, Electronic Properties, Engineering the Fermi Level

Here, we present a review of the major achievements in kinetics, electronic properties, and engineering in the Fermi level of single-walled carbon nanotubes (SWCNTs). Firstly, the kinetics of metal-filled SWCNTs were revealed with precision over several minutes. Secondly, the growth rates of nanotubes were calculated. Thirdly, the activation energies of nanotubes were measured. Fourthly, the methods of the quantitative analysis of the doping level were developed. Indeed, only qualitative analysis has been previously performed. The quantitative analysis allowed us to obtain quantitative data on charge transfer. Fifthly, the correlation between the physical properties, chemical properties, electronic properties of SWCNTs was elucidated.