Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We ...simulate plausible powder X-ray powder diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.
A solid-state combinatorial chemistry approach, which used the A–Ge–O (A = Li, K, Rb) system doped with a small amount of Mn4+ as an activator, was adopted in a search for novel red-emitting ...phosphors. The A site may have been composed of either a single alkali metal ion or of a combination of them. This approach led to the discovery of a novel phosphor in the above system with the chemical formula Li3RbGe8O18:Mn4+. The crystal structure of this novel phosphor was solved via direct methods, and subsequent Rietveld refinement revealed a trigonal structure in the P3̅1m space group. The discovered phosphor is believed to be novel in the sense that neither the crystal structure nor the chemical formula matches any of the prototype structures available in the crystallographic information database (ICDD or ICSD). The measured photoluminescence intensity that peaked at a wavelength of 667 nm was found to be much higher than the best intensity obtained among all the existing A2Ge4O9 (A = Li, K, Rb) compounds in the alkali-germanate system. An ab initio calculation based on density function theory (DFT) was conducted to verify the crystal structure model and compare the calculated value of the optical band gap with the experimental results. The optical band gap obtained from diffuse reflectance measurement (5.26 eV) and DFT calculation (4.64 eV) results were in very good agreement. The emission wavelength of this phosphor that exists in the deep red region of the electromagnetic spectrum may be very useful for increasing the color gamut of LED-based display devices such as ultrahigh-definition television (UHDTV) as per the ITU-R BT.2020-2 recommendations and also for down-converter phosphors that are used in solar-cell applications.
A New Robust Reference Image Hashing System Singh, Satendra Pal; Bhatnagar, Gaurav; Singh, Amit Kumar
IEEE transactions on dependable and secure computing,
07/2022, Volume:
19, Issue:
4
Journal Article
The authentication and content protection of multimedia data is a challenging task in the present scenario. One solution is to generate the perceptual hash which essentially authenticates the ...multimedia data and can also be dealt with image database search problems. In this paper, a novel system for generating an image hash is presented. The proposed system utilizes the global and local features in the hash generation process. The local features are obtained from non-linear scale-space based KAZE features. These KAZE features have the ability to capture the most stable point under several content preserving distortions. In contrast, first the input image is converted to a normalized image, it is transformed to the log-polar coordinate system, and then a reference image using the local contrast in the wavelet domain is obtained to extrcat the global features. An intermediate hash sequence is achieved by extracting the significant information from the reference image using the singular value decomposition. The final hash sequence combines both the vectors followed by the randomization process. Extensive experiments are conducted to demonstrate the feasibility and the robustness of the proposed hashing system against a wide range of intentional/unintentional distortions. Further, the comparative analysis with some state-of-the-art techniques validates the better discrimination of the proposed work.
Candidates for high‐energy cathodes in potassium‐ion batteries (KIBs) are selected by fully screening the inorganic compound structure database. The compounds that satisfy the specific conditions for ...plausible KIB cathodes are further subjected to theoretical and electrochemical verification, and KVP2O7 is finally pinpointed. KVP2O7 can reversibly desert/insert ≈60% of K+ (60 mA h g−1) during either chemical or electrochemical oxidation/reduction. KVP2O7 shows an average discharge potential of ≈4.2 V versus K/K+, which corresponds to an energy density of 253 W h kg−1 at 0.25 C. This high energy density characteristic of KVP2O7 is maintained both during fast charge/discharge (C/D) and prolonged redox cycles. The C/D of KVP2O7 is also accompanied by a phase transition between a monoclinic KVP2O7 (P21/c) and a triclinic K1−xVP2O7(P1¯). The structure interpretation of a new K1−xVP2O7 phase indicates that K+‐extraction induces a conformational change of two tetrahedral PO4 units in pyrophosphates. The P1¯ phase of K1−xVP2O7 (x ≈0.6) remains stable during the C/D process, although it returns to the inborn P21/c phase after thermal treatment. It is believed that the data‐mining protocol designed for this study will provide a new strategy for materials discovery and that the pinpointed KVP2O7 can be utilized as a reliable KIB cathode.
The data‐mining of the inorganic registry and subsequent theoretical/experimental verification, is introduced to pinpoint KVP2O7 as a promising cathode material in potassium ion batteries. KVP2O7 delivers a high‐energy density of 253 W h kg−1, with excellent rate‐capability and stability. During charging, KVP2O7 of P21/c is transformed to K1−xVP2O7 (x ≈0.6) of P‐1, exhibiting good reversibility between the two phases during subsequent redox cycles.
P′3-type Na0.52CrO2 is proposed as a viable cathode material for potassium-ion batteries (KIBs). The in-situ-generated title compound during the first charge of O3-NaCrO2 in K+-containing ...electrolytes can reversibly accommodate 0.35 K+-ions with no interference with Na+. In addition to the sequential interlayer slippage that occurs with Na+-insertion, K+-insertion into Na0.52CrO2 induces a sudden phase separation, which ultimately results in a biphasic structure when fully discharged (K+-free O3-NaCrO2 and K+-rich P3-K0.6Na0.17CrO2). A reversible transition between monophasic (Na0.52CrO2) and biphasic states during repeated K+-insertion/deinsertion is also maintained, which contributes to superior electrochemical properties of the title compound when used as a KIB cathode. Na0.52CrO2 delivers a specific capacity of 88 mA h g–1 with an average discharge potential of 2.95 V versus K/K+. This high level of energy density (260 W h kg–1 at 0.05C) is not substantially decreased at fast C-rates (195 W h kg–1 at 5C). When cycled at 2C, the first reversible capacity of 77 mA h g–1 gradually decreases to 52 mA h g–1 during initial 20 cycles, but no further capacity fading is observed for subsequent cycles (51 mA h g–1 after 200 cycles). Density-functional-theory computation reveals that the rearrangement of Na+ is an energetically favored process rather than a homogeneous distribution of Na+/K+.
A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal ...system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.
This paper presents a new blind image watermarking scheme using binary decimal sequence (
d
-sequence) and lifting wavelet transform (LWT) for copyright protection. The core idea is to produce a
d
- ...sequence based on random number generator (RNG) algorithm and secret keys. A reference set is then generated using the
d
-sequence for embedding purpose. For embedding, the host image is decomposed into different frequency bands using the LWT and watermark bits are then embedded in selected band, considering the reference set. The extensive experimental results, comparative and security analysis demonstrate the better robustness of the proposed scheme against different kind of attacks.
KCrS2 is presented as a stable and high‐rate layered material that can be used as a cathode in potassium‐ion batteries. As far as it is known, KCrS2 is the only layered material with stoichiometric ...amounts of K+, which enables coupling with a graphite anode for full‐cell construction. Cr(III)/Cr(IV) redox in KCrS2 is also unique, because LiCrS2 and NaCrS2 are known to experience S2−/S2
2− redox. O3‐KCrS2 is first charged to P3‐K0.39CrS2 and subsequently discharged to O′3‐K0.8CrS2, delivering an initial discharge capacity of 71 mAh g−1. The following charge/discharge (C/D) shows excellent reversibility between O′3‐K0.8CrS2 and P3‐K0.39CrS2, retaining ≈90% of the initial capacity during 1000 continuous cycles. The rate performance is also noteworthy. A C/D rate increase of 100‐fold (0.05 to 5 C) reduces the reversible capacity only by 39% (71 to 43 mAh g−1). The excellent cyclic stability and high rate performance are ascribed to the soft sulfide framework, which can effectively buffer the stress caused by K+ deinsertion/insertion. During the transformation between P3‐K0.39CrS2 and O′3‐K0.8CrS2, the material resides mostly in the P3 phase, which minimizes the abrupt dimension change and allows facile K+ diffusion through spacious prismatic sites. Structural analysis and density functional theory calculations firmly support this reasoning.
KCrS2 is proposed as a new cathode material with considerable stability and high rate performance in potassium‐ion batteries. When fully charged to 3.0 V versus K/K+, O3‐KCrS2 can be reversibly charged/discharged between P3‐K0.39CrS2 and O′3‐K0.80CrS2, while maintaining its P3‐type within an almost entire range of potential. This feature provides KCrS2 with excellent cyclic stability during 1000 charge/discharge cycles and facile K+ diffusion.
Herein, data‐driven symmetry identification, property prediction, and low‐dimensional embedding from powder X‐Ray diffraction (XRD) patterns of inorganic crystal structure database (ICSD) and ...materials project (MP) entries are reported. For this purpose, a fully convolutional neural network (FCN), transformer encoder (T‐encoder), and variational autoencoder (VAE) are used. The results are compared to those obtained from a well‐established crystal graph convolutional neural network (CGCNN). A task‐specified small dataset that focuses on a narrow material system, knowledge (rule)‐based descriptor extraction, and significant data dimension reduction are not the main focus of this study. Conventional powder XRD patterns, which are most widely used in materials research, can be used as a significantly informative material descriptor for deep learning. Both the FCN and T‐encoder outperform the CGCNN for symmetry classification. For property prediction, the performance of the FCN concatenated with multilayer perceptron reaches the performance level of CGCNN. Machine‐learning‐driven material property prediction from the powder XRD pattern deserves appreciation because no such attempts have been made despite common XRD‐driven symmetry (and lattice size) prediction and phase identification. The ICSD and MP data are embedded in the 2D (or 3D) latent space through the VAE, and well‐separated clustering according to the symmetry and property is observed.
A conventional powder X‐Ray diffraction (XRD) pattern is used for machine‐learning‐driven symmetry identification and property prediction. The inorganic crystal structure database (ICSD) and materials project (MP) entries are embedded in the low‐dimensional latent space (2D or 3D), and a clear clustering based on the symmetry and property is observed. Herein, a fully convolutional neural network, transformer encoder, crystal graph convolutional neural network, and a varialtional autoencoder are utilized.
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