Structural and electrochemical modification of graphitic carbons by vapor-phase iodine-incorporation

Pristine and mechanically-milled graphitic carbons were chemically modified by vapor-phase iodine-incorporation. The effectiveness of iodine uptake during vapor iodation was gauged for pristine and mechanically-milled graphite. The doping of electronegative iodine, which is capable of triggering charge transfer reaction with carbon, was found to develop structural disordering, carbon-polyiodide covalent compounds (C-I₃, C-I₅), enhanced mesoporosity and reduced BET surface area in graphitic carbons. These intrinsic changes in iodine-modified graphite led to improved non-faradaic capacitance and development of faradaic pseudocapacitive reaction at ∼3.2 V versus Li. As a result, iodation develops manifold (∼100%) increment in gravimetric and volumetric capacity of precursor graphite, when tested versus Li. The effect of iodine-incorporation on physical and electrochemical properties of graphite is reported in detail.     

Fast periocular authentication in handheld devices with reduced phase intensive local pattern

To ensure highest security in handheld devices, biometric authentication has emerged as a reliable methodology. Deployment of mobile biometric authentication struggles due to computational complexity. For a fast response from a mobile biometric authentication method, it is desired that the feature extraction and matching should take least time. In this article, the periocular region captured through frontal camera of a mobile device is considered under investigation for its suitability to produce a reduced feature that takes least time for feature extraction and matching. A recently developed feature Phase Intensive Local Pattern (PILP) is subjected to reduction giving birth to a feature termed as Reduced PILP (R-PILP), which yields a matching time speed-up of 1.56 times while the vector is 20% reduced without much loss in authentication accuracy. The same is supported by experiment on four publicly available databases. The performance is also compared with one global feature: Phase Intensive Global Pattern, and three local features: Scale Invariant Feature Transform, Speeded-up Robust Features, and PILP. The amount of reduction can be varied with the requirement of the system. The amount of reduction and the performance of the system bears a trade-off. Proposed R-PILP attempts to make periocular suitable for mobile devices.     

Sulfate Chemistry for High-Voltage Insertion Materials: Synthetic,Structural and Electrochemical Insights

Rechargeable batteries based on Li and Na ions have been growing leaps and bounds since their inception in the 1970s. They enjoy significant attention from both the fundamental science point of view and practical applications ranging from portable electronics to hybrid vehicles and grid storage. The steady demand for building better batteries calls for discovery, optimisation and implementation of novel positive insertion (cathode) materials. In this quest, chemists have tried to unravel many future cathode materials by taking into consideration their eco-friendly synthesis, material/process economy, high energy density, safety, easy handling and sustainability. Interestingly, sulfate-based cathodes offer a good combination of sustainable syntheses and high energy density owing to their high-voltage operation, stemming from electronegative SO₄²⁻ units. This review delivers a sneak peak at the recent advances in the discovery and development of sulfate-containing cathode materials by focusing on their synthesis, crystal structure and electrochemical performance. Several family of cathodes are independently discussed. They are 1) fluorosulfates [AMSO₄F], 2) bihydrated fluorosulfates [AMSO₄F ⋅ 2H₂O], 3) hydroxysulfate [AMSO₄OH], 4) bisulfates [A₂M(SO₄)₂], 5) hydrated bisulfates [A₂M(SO₄)₂ ⋅ nH₂O], 6) oxysulfates [Fe₂(SO₄)₂O] and 7) polysulfates [A₂M₂(SO₄)₃]. A comparative study of these sulfate-based cathodes has been provided to offer an outlook on the future development of high-voltage polyanionic cathode materials for next-generation batteries.     

Structure, surface morphology and electrochemical properties of brominated activated carbons

A study of the incorporation of bromine in activated carbons showed that highly reactive bromine facilitated a charge transfer reaction with the host carbon leading to lateral structural disordering, formation of polybromide compounds, reduction of BET surface area by preferential micropore filling and a net increase (over 100%) in the gravimetric and volumetric electrochemical capacity of activated carbons. The effect of chemical bromination was investigated focusing on various physical and electrochemical properties. Bromination is compared and contrasted with iodation to generalize halidation as a possible route to improve activated carbons for electrochemical energy storage.     

Role of Fuel on Cation Disorder in Magnesium Aluminate (MgAl2O4) Spinel Prepared by Combustion Synthesis

Magnesium aluminate spinel (MgAl₂O₄) forms an interesting system having tetrahedral and octahedral voids filled with near similar sized divalent Mg²⁺ and trivalent Al³⁺ cations. Structural disorder (e.g., Mg–Al antisite defects) can be tuned by synthetic conditions. This study reports the evolution of Mg/Al disorder in MgAl₂O₄ prepared by combustion synthesis using different types of fuels. The effect of nature of fuel and the final calcination temperature (600°C–900°C for 9 h) on degree of cation ordering has been investigated combining powder X‐ray (XRD) and neutron (NPD) diffraction. The results indicate very high degree of inversion in the samples crystallized at low annealing temperature, which on further annealing reduces toward the thermodynamically stable values. Raman spectroscopy, probing MgO₄, and AlO₄ tetrahedral bonds, confirmed the results at a local level.     

A 3.90 V iron-based fluorosulphate material for lithium-ion batteries crystallizing in the triplite structure

Li-ion batteries have empowered consumer electronics and are now seen as the best choice to propel forward the development of eco-friendly (hybrid) electric vehicles. To enhance the energy density, an intensive search has been made for new polyanionic compounds that have a higher potential for the Fe2?/Fe3? redox couple. Herein we push this potential to 3.90 V in a new polyanionic material that crystallizes in the triplite structure by substituting as little as 5 atomic per cent of Mn for Fe in Li(Fe(1-δ)Mn(δ))SO?F. Not only is this the highest voltage reported so far for the Fe2?/Fe3? redox couple, exceeding that of LiFePO? by 450 mV, but this new triplite phase is capable of reversibly releasing and reinserting 0.7-0.8 Li ions with a volume change of 0.6% (compared with 7 and 10% for LiFePO? and LiFeSO?F respectively), to give a capacity of ~125 mA h g?1.     

Network reliability optimization problem of interconnection network under node-edge failure model

The network reliability optimization problem for an interconnection network is to maximize the network reliability subjected to some constraints such as the total cost of the network. Even though, the problem is NP-Hard, many researchers have solved this problem in different ways but with a common assumption that nodes are perfect. But, this assumption is quite unrealistic in nature. In this paper, a new method based on artificial neural network is proposed to solve the network reliability optimization problem considering both the nodes and links of the interconnection networks to be imperfect. The problem is mapped onto an artificial neural network by constructing an energy function whose minimization process drives the neural network into one of its stable states. This stable state corresponds to a solution for the network reliability problem. Some existing methods are studied and compared with proposed method in evaluating the network reliability of some fully connected networks. The comparison reports the proposed method to be better than its counterparts in maximizing the network reliability. The proposed method is used to maximize the reliability of few fully connected networks subjected to some predefined total cost, where the node as well as the links of the networks may fail. Further, the behaviors of the cost as well as the time on the network reliability are discussed.     

A Modification to Kostiakov and Modified Kostiakov Infiltration Models

Incorporating the time variant function of the otherwise used as steady infiltration rate, this paper presents revised versions of the popular and frequently used Kostiakov and modified Kostiakov infiltration models. The proposed, along with the existing, versions are tested on 40 datasets of infiltration observed on five different soils in India and USA The former indicated an improved performance over their respective existing versions. Further generalization of the improved modified Kostiakov model incorporating time to ponding showed an enhanced performance on all the soils.     

A Unique Discrete Wavelet & Deterministic Walk-Based Glaucoma Classification Approach Using Image-Specific Enhanced Retinal Images

Glaucoma is a group of ocular atrophy diseases that cause progressive vision loss by affecting the optic nerve. Because of its asymptomatic nature, glaucoma has become the leading cause of human blindness worldwide. In this paper, a novel computer-aided diagnosis (CAD) approach for glaucomatous retinal image classification has been introduced. It extracts graph-based texture features from structurally improved fundus images using discrete wavelet-transformation (DWT) and deterministic tree-walk (DTW) procedures. Retinal images are considered from both public repositories and eye hospitals. Images are enhanced with image-specific luminance and gradient transitions for both contrast and texture improvement. The enhanced images are mapped into undirected graphs using DTW trajectories formed by the image’s wavelet coefficients. Graph-based features are extracted from these graphs to capture image texture patterns. Machine learning (ML) classifiers use these features to label retinal images. This approach has attained an accuracy range of 93.5% to 100%, 82.1% to 99.3%, 95.4% to 100%, 83.3% to 96.6%, 77.7% to 88.8%, and 91.4% to 100% on the ACRIMA, ORIGA, RIM-ONE, Drishti, HRF, and HOSPITAL datasets, respectively. The major strength of this approach is texture pattern identification using various topological graphs. It has achieved optimal performance with SVM and RF classifiers using biorthogonal DWT combinations on both public and patients’ fundus datasets. The classification performance of the DWT-DTW approach is on par with the contemporary state-of-the-art methods, which can be helpful for ophthalmologists in glaucoma screening.