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Grain boundaries (GBs) trigger structure-specific chemical segregation of solute atoms. According to the three-dimensional (3D) topology of grains, GBs - although defined as planar defects - cannot be free of curvature. This implies formation of topologically-necessary arrays of secondary GB dislocations. We report here that these pattern-forming secondary GB dislocations can have an additional and, in some cases, even a much stronger effect on GB segregation than defect-free GBs. Using nanoscale correlative tomography combining crystallography and chemical analysis, we quantified the relationship between secondary GB dislocations and their segregation energy spectra for a model Fe-W alloy. This discovery unlocks new design opportunities for advanced materials, leveraging the additional degrees of freedom provided by topologically-necessary secondary GB dislocations to modulate segregation.

Grain boundaries are dominant imperfections in nanocrystalline materials that form a complex 3-dimensional (3D) network. Solute segregation to grain boundaries is strongly coupled to the grain boundary character, which governs the stability and macroscopic properties of nanostructured materials. Here, we develop a 3-dimensional transmission electron microscopy and atom probe tomography correlation framework to retrieve the grain boundary character and composition at the highest spatial resolution and chemical sensitivity by correlating four-dimensional scanning precession electron diffraction tomography (4D-SPED) and atom probe tomography (APT) on the same sample. We obtain the 3D grain boundary habit plane network and explore the preferential segregation of Cu and Si in a nanocrystalline Ni-W alloy. The correlation of structural and compositional information reveals that Cu segregates predominantly along high angle grain boundaries and incoherent twin boundaries, whereas Si segregation to low angle and incommensurate grain boundaries is observed. The novel full 3D correlative approach employed in this work opens up new possibilities to explore the 3D crystallographic and compositional nature of nanomaterials. This lays the foundation for both probing the true 3D structure-chemistry at the sub-nanometer scale and, consequentially, tailoring the macroscopic properties of advanced nanomaterials.

Determining the full five-parameter grain boundary characteristics from experiments is essential for understanding grain boundaries impact on material properties, improving related models, and designing advanced alloys. However, achieving this is generally challenging, in particular at nanoscale, due to their 3D nature. In our study, we successfully determined the grain boundary characteristics of an annealed nickel-tungsten alloy (NiW) nanocrystalline needle-shaped specimen (tip) containing twins using Scanning Precession Electron Diffraction (SPED) Tomography. The presence of annealing twins in this face-centered cubic (fcc) material gives rise to common reflections in the SPED diffraction patterns, which challenges the reconstruction of orientation-specific virtual dark field (VDF) images required for tomographic reconstruction of the 3D grain shapes. To address this, an automated post-processing step identifies and deselects these shared reflections prior to the reconstruction of the VDF images. Combined with appropriate intensity normalization and projection alignment procedures, this approach enables high-fidelity 3D reconstruction of the individual grains contained in the needle-shaped sample volume. To probe the accuracy of the resulting boundary characteristics, the twin boundary surface normal directions were extracted from the 3D voxelated grain boundary map using a 3D Hough transform. For the sub-set of coherent Sigma 3 boundaries, the expected {111} grain boundary plane normals were obtained with an angular error of less than 3{\textdegree} for boundary sizes down to 400 nm${}^2$. This work advances our ability to precisely characterize and understand the complex grain boundaries that govern material properties.

Here, we explore the different characteristics of a possible coupling between tropospheric and ionospheric activities during the impact of tropical cyclones (TC) like Amphan and Nisarga in the Indian subcontinent. We have analyzed the effect of TCs Amphan and Nisarga on the low latitude ionosphere using the measurements from several IGS stations around India and a GPS+ NavIC station in Indore, India. For the first time, this study assesses the impact of tropical cyclones on the equatorial ionosphere using both GPS and NavIC. After the landfall of TC Amphan, the VTEC analysis shows a significant drop from nominal values in both NavIC as well in GPS by $5.1$ TECU and $3.6$ TECU, respectively. In contrast to TC Amphan, Nisarga showed a rise in VTEC which ranged from $0.9$ TECU in GPS to $1.7$ - $5$ TECU in NavIC satellites except for PRN6. The paper examines Outgoing Longwave Radiation as a proxy to the convective activity which may be responsible for the ionospheric variation through the generation of gravity waves. In addition, the horizontal neutral wind observations at the location of TC landfall confirm the presence of ionospheric disturbances. VTEC perturbation analysis using a band-pass filter reveals a variation in differential TEC values between $\pm0.4$ and $\pm0.8$ based on the IGS station measurements. This indicates that the gravity wave is one of the responsible mechanisms for the lower-upper atmospheric coupling during both cyclones.

Intense geomagnetic storms can have a strong impact on the signals (termed ionospheric scintillations) emitted by any global navigation satellite system (GNSS). The paper reports the first studies of scintillations at the Indore region on the NavIC signals due to the impact of the intense geomagnetic storm event reported on September 8, 2017, at 0151 and 1304 UT. The variation of the planetary indices as well as the DST index which dropped to a value of -124 nT on September 8, 2017, indicates the occurrence of an intense geomagnetic storm on September 8, 2017. The observations presented are carried out at Indore, which is located at the equatorial anomaly crest. The S4 index measurements of colocated GNSS receiver showed values of 0.5 or above on the disturbed day between 15 and 18 UT. The analysis presented clearly signifies the degradation of the carrier to noise measurements of the NavIC L5 signal during the same time, which in turn affected the positional accuracy of NavIC, an important consideration for performance., Comment: 6 pages, 5 figures, accepted as a part of the Lecture Notes in Networks and Systems (LNNS) book series, 2021

This article implements a Convolutional Neural Network (CNN)-based deep learning model for solar-wind prediction. Images from the Atmospheric Imaging Assembly (AIA) at 193\.A wavelength are used for training. Solar-wind speed is taken from the Advanced Composition Explorer (ACE) located at the Lagrangian L1 point. The proposed CNN architecture is designed from scratch for training with four years' data. The solar-wind has been ballistically traced back to the Sun assuming a constant speed during propagation, to obtain the corresponding coronal intensity data from AIA images. This forecasting scheme can predict the solar-wind speed well with a RMSE of 76.3 km\s and an overall correlation coefficient of 0.57 for the year 2018, while significantly outperforming benchmark models. The threat score for the model is around 0.46 in identifying the HSEs with zero false alarms., Comment: 21 pages,13 figures. Accepted for publication in Solar Physics. After published, it will be available at https://www.springer.com/journal/11207

This paper emphasizes on NavIC's performance in ionospheric studies over the Indian subcontinent region. The study is performed using data of one year (2017-18) at IIT Indore, a location near the northern crest of Equatorial Ionization Anomaly (EIA). It has been observed that even without the individual error corrections, the results are within $\pm20\%$ of NavIC VTEC estimates observed over the 1\ensuremath{^{\circ}} x 1\ensuremath{^{\circ}} grid of IPP surrounding the GPS VTEC estimates for most of the time. Additionally, ionospheric response during two distinct geomagnetic storms (September 08 and 28, 2017) at the same location and other IGS stations covering the Indian subcontinent using both GPS and NavIC has also been presented. This analysis revealed similar variations in TEC during the geomagnetic storms of September 2017, indicating the suitability of NavIC to study space weather events along with the ionospheric studies over the Indian subcontinent., Comment: 38 Pages, 16 Figures, Accepted for publication in Advances in Space Research