Tuning branching in ceria nanocrystals

Branched nanocrystals (NCs) enable high atomic surface exposure within a crystalline network that provides avenues for charge transport. This combination of properties makes branched NCs particularly suitable for a range of applications where both interaction with the media and charge transport are involved. Herein we report on the colloidal synthesis of branched ceria NCs by means of a ligand-mediated overgrowth mechanism. In particular, the differential coverage of oleic acid as an X-type ligand at ceria facets with different atomic density, atomic coordination deficiency, and oxygen vacancy density resulted in a preferential growth in the [111] direction and thus in the formation of ceria octapods. Alcohols, through an esterification alcoholysis reaction, promoted faster growth rates that translated into nanostructures with higher geometrical complexity, increasing the branch aspect ratio and triggering the formation of side branches. On the other hand, the presence of water resulted in a significant reduction of the growth rate, decreasing the reaction yield and eliminating side branching, which we associate to a blocking of the surface reaction sites or a displacement of the alcoholysis reaction. Overall, adjusting the amounts of each chemical, well-defined branched ceria NCs with tuned number, thickness, and length of branches and with overall size ranging from 5 to 45 nm could be produced. We further demonstrate that such branched ceria NCs are able to provide higher surface areas and related oxygen storage capacities (OSC) than quasi-spherical NCs

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

Impact of catalyst layer morphology on the operation of high temperature PEM fuel cells

Electrochemical impedance spectroscopy (EIS) is a well-established method to analyze a polymer electrolyte membrane fuel cell (PEMFC). However, without further data processing, the impedance spectrum yields only qualitative insight into the mechanism and individual contribution of transport, kinetics, and ohmic losses to the overall fuel cell limitations. The distribution of relaxation times (DRT) method allows quantifying each of these polarization losses and evaluates their contribution to a given electrocatalyst\u27s depreciated performances. We coupled this method with a detailed morphology study to investigate the impact of the 3D-structure on the processes occurring inside a high-temperature polymer electrolyte membrane fuel cell (HT-PEMFC). We tested a platinum catalyst (Pt/C), a platinum-cobalt alloy catalyst (Pt$_{3}$Co/C), and a platinum group metal-free iron-nitrogen-carbon (Fe–N–C) catalyst. We found that the hampered mass transport in the latter is mainly responsible for its low performance in the MEA (along with its decreased intrinsic performances for the ORR reaction). The better performance of the alloy catalyst can be explained by both improved mass transport and a lower ORR resistance. Furthermore, single-cell tests show that the catalyst layer morphology influences the distribution of phosphoric acid during conditioning

Textural features for fingerprint liveness detection

The main topic ofmy research during these three years concerned biometrics and in particular the Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints. Fingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and Samsung Galaxy models with an embedded fingerprint reader as an alternative to passwords. Several videos posted on YouTube show how to violate these devices by using fake fingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a threat to the existing fingerprint recognition systems. Despite the fact that many algorithms have been proposed so far, none of them showed the ability to clearly discriminate between real and fake fingertips. In my work, after a study of the state-of-the-art I paid a special attention on the so called textural algorithms. I first used the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the LPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms in the FLD field. In the last two years I worked especially on what we called the “user specific” problem. In the extracted features we noticed the presence of characteristic related not only to the liveness but also to the different users. We have been able to improve the obtained results identifying and removing, at least partially, this user specific characteristic. Since 2009 the Department of Electrical and Electronic Engineering of the University of Cagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity have organized the Fingerprint Liveness Detection Competition (LivDet). I have been involved in the organization of both second and third editions of the Fingerprint Liveness Detection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition of live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets

An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network

The low-cost, user-friendly, and convenient nature of Automatic Fingerprint Recognition Systems (AFRS) makes them suitable for a wide range of applications. This spreading use of AFRS also makes them vulnerable to various security threats. Presentation Attack (PA) or spoofing is one of the threats which is caused by presenting a spoof of a genuine fingerprint to the sensor of AFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasure intended to protect AFRS against fake or spoof fingerprints created using various fabrication materials. In this paper, we have proposed a Convolutional Neural Network (CNN) based technique that uses a Generative Adversarial Network (GAN) to augment the dataset with spoof samples generated from the proposed Open Patch Generator (OPG). This OPG is capable of generating realistic fingerprint samples which have no resemblance to the existing spoof fingerprint samples generated with other materials. The augmented dataset is fed to the DenseNet classifier which helps in increasing the performance of the Presentation Attack Detection (PAD) module for the various real-world attacks possible with unknown spoof materials. Experimental evaluations of the proposed approach are carried out on the Liveness Detection (LivDet) 2015, 2017, and 2019 competition databases. An overall accuracy of 96.20\%, 94.97\%, and 92.90\% has been achieved on the LivDet 2015, 2017, and 2019 databases, respectively under the LivDet protocol scenarios. The performance of the proposed PAD model is also validated in the cross-material and cross-sensor attack paradigm which further exhibits its capability to be used under real-world attack scenarios

Novel chemistry and applications of polythiazyl

Diverse investigations into the synthesis, formation, reactivity and stability of disulfur dinitride, S2N2, enabled through the fabrication of custom-made apparatus and tailored reaction conditions, are reported. The polymerisation process of the former to the (super)conductive polythiazyl material has also been explored by means of both single crystal X-ray diffraction and in situ reaction chemistry...

Novel active sweat pores based liveness detection techniques for fingerprint biometrics

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Liveness detection in automatic fingerprint identification systems (AFIS) is an issue which still prevents its use in many unsupervised security applications. In the last decade, various hardware and software solutions for the detection of liveness from fingerprints have been proposed by academic research groups. However, the proposed methods have not yet been practically implemented with existing AFIS. A large amount of research is needed before commercial AFIS can be implemented. In this research, novel active pore based liveness detection methods were proposed for AFIS. These novel methods are based on the detection of active pores on fingertip ridges, and the measurement of ionic activity in the sweat fluid that appears at the openings of active pores. The literature is critically reviewed in terms of liveness detection issues. Existing fingerprint technology, and hardware and software solutions proposed for liveness detection are also examined. A comparative study has been completed on the commercially and specifically collected fingerprint databases, and it was concluded that images in these datasets do not contained any visible evidence of liveness. They were used to test various algorithms developed for liveness detection; however, to implement proper liveness detection in fingerprint systems a new database with fine details of fingertips is needed. Therefore a new high resolution Brunel Fingerprint Biometric Database (B-FBDB) was captured and collected for this novel liveness detection research. The first proposed novel liveness detection method is a High Pass Correlation Filtering Algorithm (HCFA). This image processing algorithm has been developed in Matlab and tested on B-FBDB dataset images. The results of the HCFA algorithm have proved the idea behind the research, as they successfully demonstrated the clear possibility of liveness detection by active pore detection from high resolution images. The second novel liveness detection method is based on the experimental evidence. This method explains liveness detection by measuring the ionic activities above the sample of ionic sweat fluid. A Micro Needle Electrode (MNE) based setup was used in this experiment to measure the ionic activities. In results, 5.9 pC to 6.5 pC charges were detected with ten NME positions (50μm to 360 μm) above the surface of ionic sweat fluid. These measurements are also a proof of liveness from active fingertip pores, and this technique can be used in the future to implement liveness detection solutions. The interaction of NME and ionic fluid was modelled in COMSOL multiphysics, and the effect of electric field variations on NME was recorded at 5μm -360μm positions above the ionic fluid.This study is funded by the University of Sindh, Jamshoro, Pakistan and the Higher Education Commission of Pakistan