Related papers: Deep learning framework for carbon nanotubes: mech…
Nano-diameter tubules made of folded graphitic sheets have drawn the attention of physicists ever since the advent of fullerene research. These are interesting because of their conductivity (depends upon chirality) as well as their…
This research investigates the use of artificial intelligence and machine learning techniques to predict the toxicity of nanoparticles, a pressing concern due to their pervasive use in various industries and the inherent challenges in…
In this dissertation, thermal and electrical properties of aligned multiwall carbon nanotubes (MWNTs) prepared by thermal decomposition of hydrocarbons have been experimentally studied. The thesis consists of six chapters. Ch1 is an…
Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO$_2$ emissions that are associated with the production of concrete. In this paper, a method is presented that…
We present a theoretical study of the intrinsic quality factor of the fundamental flexural vibration in a carbon nanotube and its dependence on temperature, radius, length and tension. In particular, we examine three- and four-phonon decays…
We present a new approach for predictive modeling and its uncertainty quantification for mechanical systems, where coarse-grained models such as constitutive relations are derived directly from observation data. We explore the use of a…
Modeling the ion concentration profile in nanochannel plays an important role in understanding the electrical double layer and electroosmotic flow. Due to the non-negligible surface interaction and the effect of discrete solvent molecules,…
Biphenylene nanotubes (BPNNTs) represent a novel class of carbon-based nanomaterials, constructed by rolling a biphenylene network (BPN) monolayer into a one-dimensional tubular structure. In this study, the thermal transport properties of…
Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant.…
Carbon nanotubes are the focus of considerable research efforts due to their fascinating physical properties. They provide an excellent model system for the study of one dimensional materials and molecular electronics. The chirality of…
The accurate and precise extraction of information from a modern particle physics detector, such as an electromagnetic calorimeter, may be complicated and challenging. In order to overcome the difficulties we propose processing the detector…
The design of periodic nanostructures allows to tailor the transport of photons, phonons, and matter waves for specific applications. Recent years have seen a further expansion of this field by engineering topological properties. However,…
Recent molecular dynamics simulation methods have enabled thermal conductivity of bulk materials to be estimated. In these simulations, periodic boundary conditions are used to extend the system dimensions to the thermodynamic limit. Such a…
Classical molecular dynamics (MD) simulations employing Brenner potential for intra-nanotube interactions and Van der Waals forces for polymer-nanotube interfaces are used to invetigate the thermal expansion and diffusion characteristics of…
We propose an experimentally viable setup for the realization of one-dimensional ultracold atom gases in a nanoscale magnetic waveguide formed by single doubly-clamped suspended carbon nanotubes. We show that all common decoherence and atom…
Turbulence plays a key role in star formation in molecular clouds, affecting star cluster primordial properties. As modelling present-day objects hinges on our understanding of their initial conditions, better constraints on turbulence can…
Although significant efforts have been directed towards a selective single wall carbon nanotube synthesis, the resulting diameter and chirality distributions are still too broad and their control remains a challenge. Progress in this…
Topologically interlocking architectures can generate tough ceramics with attractive thermo-mechanical properties. This concept can make the material design pathway a challenging task, since modeling the whole design space is neither…
Machine learning plays an important role in the operation of current wind energy production systems. One central application is predictive maintenance to increase efficiency and lower electricity costs by reducing downtimes. Integrating…
We present a study of the phononic thermal conductivity of isotopically disordered carbon nanotubes. In particular, the behavior of the thermal conductivity as a function of the system length is investigated, using Green's function…