arXiv subjects

Daniel Hedman

Publications and source records attributed to Daniel Hedman.

6 recordsLinked to original sources

arXiv2026

Structures of iron and cobalt bimetallic clusters for optimized chemical vapor deposition growth of single-walled carbon nanotubes

We investigate iron-cobalt (Fe-Co) alloys as a representative high-performance catalyst system for SWCNT growth in a systematic manner by combining chemical vapor deposition (CVD) experiments with chirality-resolved spectroscopic analysis, as well as molecular dynamics (MD) simulations based on density functional theory-derived machine learning force fields while varying the Fe-Co ratio. Using zeolite-based SWCNTs prepared by alcohol CVD, absorption and photoluminescence spectroscopy, together with two-dimensional excitation-emission fitting was employed to quantify chirality-specific growth efficiency. Two distinct growth regimes were identified. At a relatively low temperature of 600 C, pure Co exhibits the highest catalytic activity, promoting efficient growth of small-diameter (0.7-0.9 nm) SWCNTs. In contrast, at 850 C, the Fe0.75Co0.25 alloy shows a pronounced enhancement in growth efficiency compared with pure Fe, pure Co, and other Fe-Co compositions, also yielding larger diameter tubes (0.9-1.1nm). Similar growth behavior was observed on SiO2 substrates, enabling detailed transmission electron microscopy analysis of catalyst nanoparticles. Electron microscopy and energy-dispersive X-ray spectroscopy reveal that high SWCNT yields correlate with the formation of small, uniform Fe-Co nanoparticles with Co-enriched surfaces, in excellent agreement with MD simulations. Lastly, MD results are summarized in a composition-diameter phase diagram that rationalizes the experimentally observed growth trends. The exceptional performance of the Fe0.75Co0.25 catalyst at high temperature is attributed to the stabilization of small and uniform catalyst clusters, providing mechanistic insight into the synergistic roles of alloy composition and temperature in SWCNT growth.

cond-mat.mtrl-sci↗

arXiv2025

Vapor-solid-solid growth of single-walled carbon nanotubes

Single-walled carbon nanotubes (SWCNTs) are promising for nanoscale electronics and photonics, but practical deployment requires chirality control. Most catalytic chemical vapor deposition (CCVD) growth of SWCNTs proceeds on liquid metal nanoparticles via a vapor-liquid-solid (VLS) mechanism and yields broad chirality distributions, whereas improved selectivity has been reported for high-melting-point crystalline catalysts, suggesting vapor-solid-solid (VSS) growth. However, the atomistic mechanism and kinetic of VSS SWCNT growth remain unclear. Here it is shown, using machine-learning interatomic potential-driven molecular dynamics on rhenium nanoparticles, that VSS growth is diffusion-limited and governed by facet-dependent surface carbon transport coupled to carbon-driven facet reconfiguration without catalyst melting. Surface diffusion is up to $\sim 50\times$ slower than carbon diffusion in liquid iron, imposing a strict upper bound on sustainable carbon supply and producing a narrow growth window: insufficient transport drives carbon accumulation and multiple nucleation, whereas higher temperatures favor graphitic encapsulation. In contrast to defect-free VLS growth, defects persist, indicating slow defect healing, and equilibrium simulations reveal suppressed edge configurational entropy with stabilization of zigzag-rich, Klein-decorated edges. Together, these results establish facet evolution and surface diffusion as joint regulators of diffusion-limited VSS growth and motivate stringent control of temperature and carbon supply.

cond-mat.mtrl-sci↗

arXiv2025

Analysis of Fe and Co binary catalysts in chemical vapor deposition growth of single-walled carbon nanotubes

Metal catalysts play a pivotal role in the growth of single-walled carbon nanotubes (SWCNTs), with binary metallic catalysts emerging as an efficient SWCNT synthesis strategy. Among these, iron (Fe), cobalt (Co), and their alloys are particularly effective. However, prior studies have predominantly employed Fe--Co alloy catalysts with fixed atomic ratios as well as unchanged chemical vapor deposition (CVD) conditions, leaving the influence of variable Fe--Co compositions and CVD growth parameters on SWCNT synthesis poorly understood. This study focuses on the role of Fe--Co catalyst ratios, with the aim of elucidating the distinct contributions of Fe and Co atoms in the growth of SWCNTs. By systematically exploring a wide range of Fe--Co ratios and growth conditions, we identified Fe$_{0.75}$Co$_{0.25}$ as a highly efficient binary catalyst at 850~$^\circ$C, primarily forming catalyst clusters with diameters of 2.5--6~nm and yielding SWCNTs with diameters ranging from 0.9--1.1~nm. On the other hand, Fe$_{0}$Co$_{1}$ exhibited higher catalytic activity at 600~$^\circ$C, generating smaller catalyst clusters of 1.5--5~nm and producing SWCNTs with reduced diameters of about 0.6--0.9~nm. Transmission electron microscope (TEM) and energy dispersive X-ray spectroscopy (EDS) analyses reveal that high SWCNT yields correlate with the formation of uniformly sized Fe--Co catalyst particles with surface-segregated Co that optimizes carbon solubility. Molecular dynamics (MD) simulations further corroborate these findings, demonstrating that the structure and melting behavior of Fe$_x$Co$_{1-x}$ clusters depend on cluster size and composition.

cond-mat.mtrl-sci↗

arXiv2025

Surface Reconstructions in Thin-Films of Magnetic Topological Insulator MnBi$_2$Te$_4$

Understanding the nature of surface states and their exchange gaps in magnetic topological insulator MnBi$_2$Te$_4$ (MBT) thin films is crucial for achieving robust topological Chern and Axion insulating phases where the Quantum Anomalous Hall Effect and the Topological Magneto-electric Effect can be realized. Here, we focus on the rather unexplored issue of how surface reconstructions, which are likely to occur in experiments, influence these properties. Using first-principles calculations together with molecular dynamics simulations accelerated by machine learning force field, we demonstrate that interstitial-2H and peripheral-2H type atomic reconstructions are responsible for modifying the exchange gap and surface characteristics of MBT thin films, with important implications for the topological indices and the nature of quasi one-dimensional side-wall edge states dominating quantum transport. \textcolor{blue}{Specifically, the calculation of the energy landscape and barriers for the proposed surface reconstructions indicates that the interstitial-2H reconstruction is thermodynamically more stable than the peripheral-2H reconstruction. The latter case is also hypothesized as providing a plausible explanation for the Rashba surface states observed} in Angle-Resolved Photoemission Spectroscopy (ARPES) measurements. Our analysis provides a theoretical framework to elucidate the nature and effect of reconstructions in MBT thin films, with predictions for the experimental realization of different topological phases.

cond-mat.mtrl-sci↗

arXiv2024

A spiking-domain implementation of electronic structure theory

Electronic Structure Theory (EST) describes the behavior of electrons in matter and is used to predict material properties. Conventionally, this involves forming a Hamiltonian and solving the Schr\"odinger equation through discrete computation. Here, a new perspective to EST is provided by treating a perfectly crystalline material as a Linear Translation Invariant (LTI) system. The validity of this LTI-EST formalism is demonstrated by determining band structures for a one-dimensional chain of atoms, including the phenomenon of band structure folding in super cells. The proposed formalism allows for analytical traceability of band structure folding and offers computational advantage by bypassing the O(N) eigenvalue calculations. The spike-based computing nature of the proposed LTI-EST formalism is highlighted; thereby implying potential for material simulations solely in the spiking domain.

eess.SP↗

arXiv2023

Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations

Carbon nanotubes (CNTs) are currently considered a successor to silicon in future nanoelectronic devices. To realize this, controlled growth of defect-free nanotubes is required. Until now, the understanding of atomic-scale CNT growth mechanisms provided by molecular dynamics simulations has been hampered by their short timescales. Here, we develop an efficient and accurate machine learning force field, DeepCNT-22, to simulate the complete growth of defect-free single-walled CNTs (SWCNTs) on iron catalysts at near-microsecond timescales. We provide atomic-level insight into the nucleation and growth processes of SWCNTs, including the evolution of the tube-catalyst interface and the mechanisms underlying defect formation and healing. Our simulations highlight the maximization of SWCNT-edge configurational entropy during growth and how defect-free CNTs can grow ultralong if carbon supply and temperature are carefully controlled.

cond-mat.mtrl-sci↗