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Focused-ion beam lift-out and annular milling is the most common method used for obtaining site specific specimens for atom probe tomography (APT) experiments and transmission electron microscopy. However, one of the main limitations of this technique comes from the structural damage as well as chemical degradation caused by the beam of high-energy ions. These aspects are especially critical in highly-sensitive specimens. In this regard, ion beam milling under cryogenic conditions has been an established technique for damage mitigation. Here, we implement a cryo-focused ion beam approach to prepare specimens for APT measurements from a quadruple cation perovskite-based solar cell device with 19.7% efficiency. As opposed to room temperature FIB milling we found that cryo-milling considerably improved APT results in terms of yield and composition measurement, i.e. halide loss, both related to less defects within the APT specimen. Based on our approach we discuss the prospects of reliable atom probe measurements of perovskite based solar cell materials. An insight into the field evaporation behavior of the organic-inorganic molecules that compose the perovskite material is also given with the aim of expanding the applicability of APT experiments towards nano-characterization of complex organo-metal materials.

The electrical and optoelectronic properties of materials are determined by the chemical potentials of their constituents. The relative density of point defects is thus controlled, allowing to craft microstructure, trap densities and doping levels. Here, we show that the chemical potentials of chalcogenide materials near the edge of their existence region are not only determined during growth but also at room temperature by post-processing. In particular, we study the generation of anion vacancies, which are critical defects in chalcogenide semiconductors and topological insulators. The example of CuInSe 2 photovoltaic semiconductor reveals that single phase material crosses the phase boundary and forms surface secondary phases upon oxidation, thereby creating anion vacancies. The arising metastable point defect population explains a common root cause of performance losses. This study shows how selective defect annihilation is attained with tailored chemical treatments that mitigate anion vacancy formation and improve the performance of CuInSe 2 solar cells. Anion vacancies are a hurdle for technologies based on chalcogenide semiconductors and topological insulators. Even at room temperature, oxidation and cyanide etching can lead to selenium vacancies in CuInSe 2 photovoltaic material but suitable post deposition treatments can mitigate their effect.

Copper indium gallium diselenide-based technology provides the most efficient solar energy conversion among all thin-film photovoltaic devices. This is possible due to engineered gallium depth gradients and alkali extrinsic doping. Sodium is well known to impede interdiffusion of indium and gallium in polycrystalline Cu(In,Ga)Se 2 films, thus influencing the gallium depth distribution. Here, however, sodium is shown to have the opposite effect in monocrystalline gallium-free CuInSe 2 grown on GaAs substrates. Gallium in-diffusion from the substrates is enhanced when sodium is incorporated into the film, leading to Cu(In,Ga)Se 2 and Cu(In,Ga) 3 Se 5 phase formation. These results show that sodium does not decrease per se indium and gallium interdiffusion. Instead, it is suggested that sodium promotes indium and gallium intragrain diffusion, while it hinders intergrain diffusion by segregating at grain boundaries. The deeper understanding of dopant-mediated atomic diffusion mechanisms should lead to more effective chemical and electrical passivation strategies, and more efficient solar cells. Sodium doping is necessary to achieve high performance in polycrystalline chalcopyrite solar cells, but retards gallium interdiffusion, and thus efficiency optimisation. Here, Colombara et al. show that in contrast to the polycrystalline case, sodium accelerates atomic interdiffusion in monocrystalline samples.

Layered-structure materials are currently relevant given their quasi-2D nature. Knowledge of their physical properties is currently of major interest. Niobium ditelluride possesses a monoclinic layered-structure with a distortion in the tellurium planes. This structural complexity has hindered the determination of its fundamental physical properties. In this work, NbTe 2 crystals were used to elucidate its structural, compositional, electronic and vibrational properties. These findings have been compared with calculations based on density functional theory. The chemical composition and elemental distribution at the nanoscale were obtained through atom probe tomography. Ultraviolet photoelectron spectroscopy allowed the first determination of the work function of NbTe 2 . Its high value, 5.32 eV, and chemical stability allow foreseeing applications such as contact in optoelectronics. Raman spectra were obtained using different excitation laser lines: 488, 633, and 785 nm. The vibrational frequencies were in agreement with those determined through density functional theory. It was possible to detect a theoretically-predicted, low-frequency, low-intensity Raman active mode not previously observed. The dispersion curves and electronic band structure were calculated, along with their corresponding density of states. The electrical properties, as well as a pseudo-gap in the density of states around the Fermi energy are characteristics proper of a semi metal.

Atom probe tomography (APT) is rising in influence across many parts of materials science and engineering thanks to its unique combination of highly sensitive composition measurement and three-dimensional microstructural characterization. In this invited article, we have selected a few recent applications that showcase the unique capacity of APT to measure the local composition at structural defects. Whether we consider dislocations, stacking faults, or grain boundary, the detailed compositional measurements tend to indicate specific partitioning behaviors for the different solutes in both complex engineering and model alloys we investigated.

Although atom probe tomography (APT) reconstructions do not directly influence the local elemental analysis, any structural inferences from APT volumes demand a reliable reconstruction of the point cloud. Accurate estimation of the reconstruction parameters is crucial to obtain reliable spatial scaling. In the current work, a new automated approach of calibrating atom probe reconstructions is developed using only one correlative projection electron microscopy (EM) image. We employed an algorithm that implements a 2D cross-correlation of microstructural features observed in both the APT reconstructions and the corresponding EM image. We apply this protocol to calibrate reconstructions in a Cu(In,Ga)Se2-based semiconductor and in a Co-based superalloy. This work enables us to couple chemical precision to structural information with relative ease.

Layered-structure materials are currently relevant given their quasi-2D nature. Knowledge of their physical properties is currently of major interest. Niobium ditelluride possesses a monoclinic layered-structure with a distortion in the tellurium planes. This structural complexity has hindered the determination of its fundamental physical properties. In this work, NbTe crystals were used to elucidate its structural, compositional, electronic and vibrational properties. These findings have been compared with calculations based on density functional theory. The chemical composition and elemental distribution at the nanoscale were obtained through atom probe tomography. Ultraviolet photoelectron spectroscopy allowed the first determination of the work function of NbTe . Its high value, 5.32 eV, and chemical stability allow foreseeing applications such as contact in optoelectronics. Raman spectra were obtained using different excitation laser lines: 488, 633, and 785 nm. The vibrational frequencies were in agreement with those determined through density functional theory. It was possible to detect a theoretically-predicted, low-frequency, low-intensity Raman active mode not previously observed. The dispersion curves and electronic band structure were calculated, along with their corresponding density of states. The electrical properties, as well as a pseudo-gap in the density of states around the Fermi energy are characteristics proper of a semi metal.

Failure to enter the coronary sinus (CS) with a guiding catheter and entering its tributaries remains challenging in left ventricle (LV) pacing lead implants for cardiac resynchronization therapy (CRT). A dual telescoping catheter system (8F outer/6F inner) is designed to provide the ability to adjust the catheter curve size, shape and/or reach to the patients' anatomy avoiding the need for catheter change. Five different designs for CS cannulation were randomly tested in 64 patients scheduled for CRT device implant. In 33 consecutive patients three adaptable telescoping guiding catheter systems were tested per patient, the adaptable catheters had higher overall cannulation success rates (68, 63 and 62%) compared to the fixed shape catheter (46%) and an greater cannulation success rate when the CS location was not known (70, 53 and 72% vs 33% for the fixed shape). In a second group of 31 CRT patients the two telescoping catheters had similar high levels of success (71-80%), with or without using the inner catheter. The telescopic system is adaptable to a wide range of anatomical variations in patients and can result in a higher CS cannulation success rate due to its adjustability in the RA in search for the CS ostium. On top of this the inner catheter allows for sub-selecting the CS tributaries.

Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based from patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual user's expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry(ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.