arXiv subjects

Robert J. W. Frost

Publications and source records attributed to Robert J. W. Frost.

3 recordsLinked to original sources

arXiv2026

Questioning van der Waals Epitaxy of Non-Layered Materials on Mica: The Case of ScN

Growing stress-free epitaxial films by van der Waals epitaxy (vdWE) is of interest for realizing flexible optoelectronics and energy devices from freestanding epilayers. However, vdWE of non-layered materials is often presumed or claimed on layered substrates such as mica with inadequate experimental evidence. Here, we demonstrate that the growth of single-domain rocksalt ScN(111) films by sputter deposition on fluorophlogopite mica(001) occurs by conventional epitaxy. X-ray diffraction and electron microscopy reveal the film/substrate epitaxial relationship to be [-101](111)ScN||[010](001)mica. Our results indicating strain buildup seen from the dependence of (111) interplanar spacings, and strain relaxation by dislocation generation, question prior claims of vdWE of non-layered metal nitrides on mica. Our findings show that conventional epitaxy should be the default assumption for non-layered materials unless conditions for vdWE are explicitly established.

cond-mat.mtrl-sci↗

arXiv2026

Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system

Self-driving labs (SDLs), employing automation and machine learning (ML) to accelerate experimental procedures, have enormous potential in the discovery of new materials. However, in thin film science, SDLs are mainly restricted to solution-based synthetic methods which are easier to automate but cannot access the broad chemical space of inorganic materials. This work presents an SDL based on magnetron co-sputtering. We are using combinatorial frameworks, obtaining accurate composition maps on multi-element, compositionally graded thin films. This normally requires time-consuming ex-situ analysis prone to systematic errors. We present a rapid and calibration-free in-situ, ML driven approach to produce composition maps for arbitrary source combinations and sputtering conditions. We develop a method to predict the composition distribution in a multi-element combinatorial thin film, using in-situ measurements from quartz-crystal microbalance sensors placed in a sputter chamber. For a given source, the sensor readings are learned as a function of the sputtering pressure and magnetron power, through active learning using Gaussian processes (GPs). The final GPs are combined with a geometric model of the deposition flux distribution in the chamber, which allows interpolation of the deposition rates from each source, at any position across the sample. We investigate several acquisition functions for the ML procedure. A fully Bayesian GP - BALM (Bayesian active learning MacKay) - achieved the best performance, learning the deposition rates for a single source in 10 experiments. Prediction accuracy for co-sputtering composition distributions was verified experimentally. Our framework dramatically increases throughput by avoiding the need for extensive characterisation or calibration, thus demonstrating the potential of ML-guided SDLs to accelerate materials exploration.

cs.LG↗

arXiv2024

Charge Transport and Defects in Sulfur-Deficient Chalcogenide Perovskite BaZrS$_3$

Exploring the conduction mechanism in the chalcogenide perovskite BaZrS$_3$ is of significant interest due to its potential suitability as a top absorber layer in silicon-based tandem solar cells and other optoelectronic applications. Theoretical and experimental studies anticipate native ambipolar doping in BaZrS$_3$, although experimental validation remains limited. This study reveals a transition from highly insulating behavior to n-type conductivity in BaZrS$_3$ through annealing in an S-poor environment. BaZrS$_3$ thin films are synthesized $\textit{via}$ a two step process: co-sputtering of Ba-Zr followed by sulfurization at 600 $^{\circ}$C, and subsequent annealing in high vacuum. UV-Vis measurement reveal a red-shift in the absorption edge concurrent with sample color darkening after annealing. The increase in defect density with vacuum annealing, coupled with low activation energy and n-type character of defects, strongly suggests that sulfur vacancies (V$_{\mathrm{S}}$) are responsible, in agreement with theoretical predictions. The shift of the Fermi level towards conduction band minimum, quantified by Hard X-ray Photoelectron Spectroscopy (Ga K$α$, 9.25 keV), further corroborates the induced n-type of conductivity in annealed samples. Our findings indicate that vacuum annealing induces V$_{\mathrm{S}}$ defects that dominate the charge transport, thereby making BaZrS$_3$ an n-type semiconductor under S-poor conditions. This study offers crucial insights into understanding the defect properties of BaZrS$_3$, facilitating further improvements for its use in solar cell applications.

cond-mat.mtrl-sci↗