Towards bipolar tin monoxide: Revealing unexplored dopants

The advancement of transparent electronics, one of the most anticipated technological developments for the future, is currently inhibited by a shortage of high-performance p-type semiconductors. Recent demonstration of tin monoxide as a successful transparent p-type thin-film transistor and the discovery of its potential for ambipolar doping, suggests that tin monoxide—an environmentally friendly earth-abundant material—could offer a solution to this challenge. With the aim of enhancing the electronic properties, an extensive search for useful dopant elements was performed. Substitutional doping with the family of alkali metals was identified as a successful route to increase the concentration of acceptors in SnO and over ten shallow donors, which, to the best of our knowledge, have not been previously contemplated, were discovered. This work presents a detailed analysis of the most promising n-/p-type dopants—offering new insights into the design of an ambipolar SnO. If synthesized successfully, such a doped ambipolar oxide could open new avenues for many transparent technologies

Emergence of superconductivity in doped H2O ice at high pressure

We investigate the possibility of achieving high-temperature superconductivity in hydrides under pressure by inducing metallization of otherwise insulating phases through doping, a path previously used to render standard semiconductors superconducting at ambient pressure. Following this idea, we study H2O, one of the most abundant and well-studied substances, we identify nitrogen as the most likely and promising substitution/dopant. We show that for realistic levels of doping of a few percent, the phase X of ice becomes superconducting with a critical temperature of about 60 K at 150 GPa. In view of the vast number of hydrides that are strongly covalent bonded, but that remain insulating up to rather large pressures, our results open a series of new possibilities in the quest for novel high-temperature superconductors

A fingerprint based metric for measuring similarities of crystalline structures

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell we introduce crystal fingerprints that can be calculated easily and allow to define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method is an useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms and high-throughput screenings

Computational acceleration of prospective dopant discovery in cuprous iodide

The zinc blende (gamma) phase of copper iodide holds the record hole conductivity for intrinsic transparent p-type semiconductors. In this work, we employ a high-throughput approach to systematically explore strategies for enhancing gamma-CuI further by impurity incorporation. Our objectives are not only to find a practical approach to increase the hole conductivity in CuI thin films, but also to explore the possibility for ambivalent doping. In total 64 chemical elements were investigated as possible substitutionals on either the copper or the iodine site. All chalcogen elements were found to display acceptor character when substituting iodine, with sulfur and selenium significantly enhancing carrier concentrations produced by the native V-Cu defects under conditions most favorable for impurity incorporation. Furthermore, eight impurities suitable for n-type doping were discovered. Unfortunately, our work also reveals that donor doping is hindered by compensating native defects, making ambipolar doping unlikely. Finally, we investigated how the presence of impurities influences the optical properties. In the majority of the interesting cases, we found no deep states in the band-gap, showing that CuI remains transparent upon doping

Computational Screening of Useful Hole Electron Dopants in SnO2

Doped tin dioxide (SnO2) is an important semiconductor that is already used in diverse applications. However, to determine the entire potential of this material in more advanced applications of optoelectronics, further improvements in electrical properties are necessary. In this work, we perform an extensive search for useful substitutional dopants of SnO2. We use a well-converged protocol to scan the entire periodic table for dopants, finding excellent agreement between our predictions and those substitutional dopants that have been experimentally examined to date. The results of this large-scale dopant study allow us to better understand the doping trends in this important transparent conductive oxide material

Applying Pattern Recognition as a Robust Approach for Silicone Oil Droplet Identification in Flow-Microscopy Images of Protein Formulations

Discrimination between potentially immunogenic protein aggregates and harmless pharmaceutical components, like silicone oil, is critical for drug development. Flow imaging techniques allow to measure and, in principle, classify subvisible particles in protein therapeutics. However, automated approaches for silicone oil discrimination are still lacking robustness in terms of accuracy and transferability. In this work, we present an image-based filter that can reliably identify silicone oil particles in protein therapeutics across a wide range of parenteral products. A two-step classification approach is designed for automated silicone oil droplet discrimination, based on particle images generated with a flow imaging instrument. Distinct from previously published methods, our novel image-based filter is trained using silicone oil droplet images only and is, thus, independent of the type of protein samples imaged. Benchmarked against alternative approaches, the proposed filter showed best overall performance in categorizing silicone oil and non-oil particles taken from a variety of protein solutions. Excellent accuracy was observed particularly for higher resolution images. The image-based filter can successfully distinguish silicone oil particles with high accuracy in protein solutions not used for creating the filter, showcasing its high transferability and potential for wide applicability in biopharmaceutical studies

New Route for ``Cold-Passivation`` of Defects in Tin-Based Oxides

Transparent conductive oxides (TCOs) are essential in technologies coupling light and electricity. For Sn-based TCOs, oxygen deficiencies and undercoordinated Sn atoms result in an extended density of states below the conduction band edge. Although shallow states provide free carriers necessary for electrical conductivity, deeper states inside the band gap are detrimental to transparency. In zinc tin oxide (ZTO), the overall optoelectronic properties can be improved by defect passivation via annealing at high temperatures. Yet, the high thermal budget associated with such treatment is incompatible with many applications. Here, we demonstrate an alternative, low-temperature passivation method, which relies on cosputtering Sn-based TCOs with silicon dioxide (SiO2). Using amorphous ZTO and amorphous/polycrystalline tin dioxide (SnO2) as representative cases, we demonstrate through optoelectronic characterization and density functional theory simulations that the SiO2 contribution is twofold. First, oxygen from SiO2 passivates the oxygen deficiencies that form deep defects in SnO2 and ZTO. Second, the ionization energy of the remaining deep defect centers is lowered by the presence of silicon atoms. Remarkably, we find that these ionized states do not contribute to sub-gap absorptance. This simple passivation scheme significantly improves the optical properties without affecting the electrical conductivity, hence overcoming the known transparency–conductivity trade-off in Sn-based TCOs

Pressure tuneable visible-­range band gap in the ionic spinel tin nitride

The application of pressure allows for systematic tuning of the charge density of a material "cleanly", i.e. without changes to the chemical composition via dopants, and exploratory high pressure experiments can inform the design of bulk syntheses of materials that benefit from their properties under compression. Here, we report the electronic and structural response of semiconducting tin nitride Sn3N4 under compression ‐ a continuous opening of the optical band gap from 1.3 eV to 3.0 eV over a range of 100 GPa, a 540 nm blueshift spanning the entire visible spectrum. The pressure‐mediated band gap opening is general to this material across numerous high‐density polymorphs, implicating the predominant ionic bonding in the material as the root of its mechanism ‐ fingerprinted by increased charge localisation with reduced volume. The rate of decompression to ambient conditions permits access to recoverable metastable states with varying band gaps energies, opening the possibility of pressure tuneable electronic properties for future applications