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The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment searches for evidence of heavy neutrino mass eigenstates in the nuclear electron capture decay of $^7$Be by precisely measuring the recoil energy of the $^7$Li daughter. In Phase-III, the BeEST experiment has been scaled from a single superconducting tunnel junction (STJ) sensor to a 36-pixel array to increase sensitivity and mitigate gamma-induced backgrounds. Phase-III also uses a new continuous data acquisition system that greatly increases the flexibility for signal processing and data cleaning. We have developed procedures for signal processing and spectral fitting that are sufficiently robust to be automated for large data sets. This article presents the optimized procedures before unblinding the majority of the Phase-III data set to search for physics beyond the standard model.

Despite their high relative abundance in our Universe, neutrinos are the least understood fundamental particles of nature. They also provide a unique system to study quantum coherence and the wavelike nature of particles in fundamental systems due to their extremely weak interaction probabilities. In fact, the quantum properties of neutrinos emitted in experimentally relevant sources are virtually unknown and the spatial extent of the neutrino wavepacket is only loosely constrained by reactor neutrino oscillation data with a spread of 13 orders of magnitude. Here, we present the first direct limits of this quantity through a new experimental concept to extract the energy width, $\sigma_{\textrm{N},E}$, of the recoil daughter nucleus emitted in the nuclear electron capture (EC) decay of $^7$Be. The final state in the EC decay process contains a recoiling $^7$Li nucleus and an electron neutrino ($\nu_e$) which are entangled at their creation. The $^7$Li energy spectrum is measured to high precision by directly embedding $^7$Be radioisotopes into a high resolution superconducting tunnel junction that is operated as a cryogenic sensor. The lower limit on the spatial uncertainty of the recoil daughter was found to be $\sigma_{\textrm{N}, x} \geq 6.2$\,pm, which implies the final-state system is localized at a scale more than a thousand times larger than the nucleus itself. From this measurement, the first direct lower limits on the spatial extent of the neutrino wavepacket were extracted using two different theoretical methods. These results have wide-reaching implications in several areas including the nature of spatial localization at sub-atomic scales, interpretation of neutrino physics data, and the potential reach of future large-scale experiments., Comment: 20 pages, 3 figures, v3 corrects and updates one of the wavepacket width calculations

As various property studies continue to emerge on high entropy and entropy-stabilized ceramics, we seek further understanding of property changes across the phase boundary between \enquote{high-entropy} and \enquote{entropy-stabilized}. The thermal and mechanical properties of bulk ceramic entropy stabilized oxide composition Mg$_{0.2}$Co$_{0.2}$Ni$_{0.2}$Cu$_{0.2}$Zn$_{0.2}$O are investigated across this critical transition temperature via the transient plane-source method, temperature-dependent X-ray diffraction, and nano-indentation. Thermal conductivity remains constant within uncertainty across the multi-to-single phase transition at a value of ~2.5 W/mK, while the linear coefficient of thermal expansion increases nearly 24 % from 10.8 to 14.1 x 10$^{-6}$ K$^{-1}$. Mechanical softening is also observed across the transition., Comment: 14 pages, 4 figures, to be published in APL Materials

Next generation telecommunication technologies would benefit from strong piezoelectric and ferroelectric response in materials that are compatible with nitride radio-frequency electronic devices. Ferroelectric oxides with perovskite structure have been used in sensors and actuators for half a century, and halide perovskites transformed photovoltaics research in the past decade, but neither of them is compatible with nitride semiconductors. Nitride perovskites, despite numerous computational predictions, have not been experimentally demonstrated and their properties remain unknown. Here we report the experimental realization of the first nitride perovskite: lanthanum tungsten nitride (LaWN3). Oxygen-free LaWN3 thin films in a polar perovskite structure are confirmed by spectroscopy, scattering, and microscopy techniques. Scanning probe measurements confirm a large piezoelectric response and strongly suggest ferroelectric behavior, making it the first stable nitride ferroelectric compound. These results should lead to integration of LaWN3 with nitride semiconductors for wireless telecommunication applications, while enabling synthesis of many other predicted nitride perovskites.

Using a combination of numerical simulations and atom-probe tomography experiments, we determine the interfacial energy of Cu nanocrystals precipitated within the amorphous matrix of FINEMET (molar composition Fe72.89Si16.21B6.90Nb3Cu1). Specifically, we use the Langer-Schwartz model implemented in the software Thermocalc to carry out parametric simulations of growth and coarsening of Cu clusters for different interface energies. We have carried out atom-probe tomography (APT) experiments to determine the interface energy as the value for which the simulated particle size distribution best matches the experimental data. This combination of APT and precipitation modeling can be applied to other nanocrystals precipitated within amorphous matrices.

In this paper, we demonstrate that atom probe tomography (APT) can be applied to small-molecule organic materials. We show that APT can provide an unprecedented combination of mass resolution of $\lt 1~\mathrm{Da}$, spatial resolution of $\sim 0.3~\mathrm{nm}$ in z and $\sim 1~\mathrm{nm}$ in x-y, and analytic sensitivity of $\sim 50~\mathrm{ppm}$ with no evidence of molecular fragmentation. We discuss two systems that demonstrate the power of APT to uncover structure-property relationships in organic systems that have proven extremely difficult to probe using existing techniques: (1) a previously published model organic photovoltaic system in which we show a chemical reaction occurs at the heterointerface; and (2) a model organic light-emitting diode system in which we show molecular segregation occurs in the emissive layer bulk. These examples illustrate the power of APT to enable new insights into organic molecular materials., Comment: 14 pages, 5 figures, plus SI

We aim to investigate relationships between select processing parameters or inputs (composition, temperature, annealing time) and two structural parameters, specifically, the mean radius and volume fraction of the Fe$_3$Si nanocrystals. To this end, we have deviced a combined CALPHAD and machine learning approach that led to well-calibrated metamodels able to predict structural parameters quickly and accurately for any desired inputs. In order to generate data for the mean radius and volume fraction of Fe$_3$Si nanocrystals, we have used a precipitation model based in the software Thermocalc to perform annealing simulations at a set of temperatures (490-550~\degree C) and for varying Fe and Si concentrations (Fe$_{72.89 +x}$Si$_{16.21-x}$B$_{6.90}$Nb$_{3}$Cu$_{1}$, $-3\leq x \leq 3$ atomic \%). Thereafter, we used the data to develop metamodels for the mean radius and volume fraction via the \emph{k}-Nearest Neighbour algorithm. The metamodels are shown to reproduce closely the trends obtained from the precipitation model over the entire annealing timescale. Our further analysis via parallel coordinate charts shows the effect of composition, temperature, and annealing time, and helps identify combinations thereof that lead to the desired mean radius and volume fraction for the nanocrystalline phase. This approach utilizes experimental (thermodynamic and kinetic) databases from the CALPHAD approach so as to capture the physics of nucleation and growth, while the machine learning algorithm provides the robustness needed to analyze the effects of processing parameters for this complex precipitation problem. This work contributes to understanding the linkages between processing parameters and desired microstructural characteristics (crystal size and volume fraction) responsible for achieving targeted properties, and illustrates ways to reduce the time from alloy discovery to deployment., Comment: 10 pages, 15 figures

Research over the years has shown that the formation of the Fe$_3$Si phase in FINEMET (Fe-Si-Nb-B-Cu) alloys leads to superior soft magnetic properties. In this work, we use a CALPHAD approach to derive Fe-Si phase diagrams to identify the composition-temperature domain where the Fe$_3$Si phase can be stabilized. Thereafter, we have developed a precipitation model capable of simulating the nucleation and growth of Fe$_3$Si nanocrystals via Langer-Schwartz theory. For optimum magnetic properties, prior work suggests that it is desirable to precipitate Fe$_3$Si nanocrystals with 10-15 nm diameter and with the crystalline volume fraction of about 70 \%. Based on our parameterized model, we simulated the nucleation and growth of Fe$_3$Si nanocrystals by isothermal annealing of Fe$_{72.89}$Si$_{16.21}$B$_{6.90}$Nb$_{3}$Cu$_{1}$ (composition in atomic \%). In numerical experiments, the alloys were annealed at a series of temperatures from 490 to 550 \degree C for two hours to study the effect of holding time on mean radius, volume fraction, size distribution, nucleation rate, number density, and driving force for the growth of Fe$_3$Si nanocrystals. With increasing annealing temperature, the mean radius of Fe$_3$Si nanocrystals increases, while the volume fraction decreases. We have also studied the effect of composition variations on the nucleation and growth of Fe$_3$Si nanocrystals. As Fe content decreases, it is possible to achieve the desired mean radius and volume fraction within one hour holding time. The CALPHAD approach presented here can provide efficient exploration of the nanocrystalline morphology for most FINEMET systems, for cases in which the optimization of one or more material properties or process variables are desired., Comment: Submitted to Journal of Alloys and Compounds

Recent advances in theoretical structure prediction methods and high-throughput computational techniques are revolutionizing experimental discovery of the thermodynamically stable inorganic materials. Metastable materials represent a new frontier for studies, since even simple binary non ground state compounds of common elements may be awaiting discovery. However, there are significant research challenges related to non-equilibrium thin film synthesis and crystal structure predictions, such as small strained crystals in the experimental samples and energy minimization based theoretical algorithms. Here we report on experimental synthesis and characterization, as well as theoretical first-principles calculations of a previously unreported mixed-valent binary tin nitride. Thin film experiments indicate that this novel material is N-deficient SnN with tin in the mixed II/IV valence state and a small low-symmetry unit cell. Theoretical calculations suggest that the most likely crystal structure has the space group 2 (SG2) related to the distorted delafossite (SG166), which is nearly 0.1 eV/atom above the ground state SnN polymorph. This observation is rationalized by the structural similarity of the SnN distorted delafossite to the chemically related Sn3N4 spinel compound, which provides a fresh scientific insight into the reasons for growth of polymorphs of the metastable material. In addition to reporting on the discovery of the simple binary SnN compound, this paper illustrates a possible way of combining a wide range of advanced characterization techniques with the first-principle property calculation methods, to elucidate the most likely crystal structure of the previously unreported metastable materials.