Interrogating a Mixed Actinide Basket Using High-Resolution γ-Ray Spectrometry: A Nuclear Forensic Perspective on Possible Smuggling Scenarios.

The total fissile content in seized nuclear materials is of immense importance and needs to be estimated with reasonable accuracy as a part of nuclear forensics for early decision-making in legal proceedings. High-resolution γ-ray spectrometry (HRGRS), because of its nondestructive nature, is a powerful tool for the assay of such samples to reach a quick "on-site" decision on the severity, intended use, and associated radiological threat. If the seized package contains fissile isotopes of more than one actinide in a multicompartmental heterogeneous mixture, analogous to the most likely scenario of a "smuggled mixed actinide basket", its "on-site" quantification can be extremely challenging. This makes up an increasing share of the absolute HRGRS in nuclear forensics and demands for fundamentally new approaches. In the present work, the challenges associated with varying attenuation experienced by γ-rays of different actinides at different subcontainments of the heterogeneous sample matrix have been addressed by an iterative efficiency transfer approach from "point" to "extended" source for individual actinides and demonstrated for the assay of four mock-up samples and a legacy packet, mimicking seized packages containing nuclear materials. An absolute isotopic inventory of the fissile and other radioisotopes has been obtained within <10% along with the assay of total U and Pu within <3% of the expected values with measurement uncertainty <10% for the majority. The present approach has a good potential for "on-site" nuclear forensics in nuclear smuggling scenarios and also can be adapted easily for a wide variety of other applications.

In View of "On-Site" Nuclear Forensics and Assay of Fissile Materials in Sealed Packages by High-Resolution γ-Ray Spectrometry.

Several incidences of nuclear smuggling during the past few decades have raised the demand for the development of a strong "on-site" nuclear forensic infrastructure. High-resolution γ-ray spectrometry (HRGRS) plays an important role in nuclear forensics. However, the existing methodologies, developed primarily for nuclear fuel cycle applications, are relative and rely on the availability of a standard, limiting their use for the absolute assay of special nuclear materials in nonstandard geometry samples with an unknown matrix, which is vital to make a quick "on-site" decision on the severity, potential radiological threat, and intended use of an interdicted package. In this work, a methodology has been developed using HRGRS for quantifying fissile (235U, 239Pu) and other radioisotopes, which is applicable to sealed packages without requiring the knowledge of the sample geometry and the matrices. By combining experiments and Monte Carlo simulations, an iterative methodology has been proposed for "point" to "extended" source absolute efficiency transformation and demonstrated further for the absolute isotopic assay of uranium and plutonium standards, mock-up nuclear forensic samples, and an unknown nuclear material mixture with a nonstandard geometry, compound matrices, and a wide variation in the elemental and isotopic compositions with a view to imitate an "on-site" experience. The present methodology requires an assay time of only a few minutes to an hour and thus promises "on-site" nuclear forensic analysis of suspected flagged packages at borders and ports using high-resolution γ-ray spectrometry. Furthermore, the present methodology is versatile and can also be adopted for wider applications, beyond nuclear forensics.

Probing Kinetics and Mechanism of Formation of Mixed Metallic Nanoparticles in a Polymer Membrane by Galvanic Replacement between Two Immiscible Metals: Case Study of Nickel/Silver Nanoparticle Synthesis.

Galvanic replacement between metals has received notable research interest for the synthesis of heterometallic nanostructures. The growth pattern of the nanostructures depends on several factors such as extent of lattice mismatch, adhesive interaction between the metals, cohesive forces of the individual metals, etc. Due to the difficulties in probing ultrafast kinetics of the galvanic replacement reaction and particle growth in solution, real-time mechanistic investigations are often limited. As a result, the growth mechanism of one metal on the surface of another metal at the nanoscale is poorly understood so far. In the present work, we could successfully probe the galvanic replacement of silver ions with nickel nanoparticles, stabilized in a polymer membrane, using two complementary methods, namely, small-angle X-ray scattering (SAXS) and radiolabeling, and the results are supported by density functional theory (DFT) computations. The silver-nickel system has been chosen for the present investigation because of the high degree of bulk immiscibility caused by the large lattice mismatch (15.9%) and the weak adhesive interaction, which makes it a perfect model system for immiscible metal pairs. Membrane, as a host medium, plays a crucial role in retarding the kinetics of atomic and particle rearrangements (nucleation and growth) due to slower mobility of the atoms (monomers) and particles within the polymer network. This allowed us to examine the real-time concentration of silver monomers during galvanic replacement of silver ions with nickel nanoparticles and evolution of Ni/Ag nanoparticles. From combined experiment and DFT computations, it has been demonstrated, for the first time to the best of our knowledge, that the majority of silver atoms, which are produced on the nickel nanoparticle surface by galvanic reactions, do not form traditional core-shell nanostructures with nickel and undergo a self-governing sequential nucleation and growth of silver nanoparticles via formation of intermediate prenucleation silver clusters, leading to the formation of mixed metallic nanoparticles in the membrane. The surface of NiNPs has a heterogeneous effect on the silver nucleation pathway, which is evident from the reduced critical free energy barrier of nucleation (ΔGcrit). The present work establishes an original mechanistic pathway based on a sequential nucleation model for formation of mixed metallic nanoparticles by the galvanic replacement route, which opens up future possibilities for size-controlled synthesis in mixed systems.

Aerial Bots in the Supply Chain: A New Ally to Combat COVID-19.

The rapid global spread of COVID-19 has caused disruptions in various supply chains and people's lives. At the same time, it has paved the way for drone technology (Aerial bots). With the countries gone into lockdown for an unspecified time, it is self-evident that people will run out of food, medicine, and other essentials because of the middleman's unavailability to move products from supply to demand point. Lack of medical infrastructure and distant testing laboratories is another challenge faced by the countries, which result in a delayed testing report leading to delay in medical treatment-such critical problems arising in the fight against COVID-19 highlight the need for improving the efficiency of supply chains. Recently used for commercial purposes, drone technology has already proved its utility in inventory and logistics management. Therefore, we argue that drones could be a viable option to improve the efficiency and effectiveness of the supply chains working for humanitarian aid to combat COVID-19. Specifically, the focus is on food, administrative, and healthcare supply chains that are the core to combat the pandemic. Moreover, in this article, we highlight various present and future application areas for drone technology, which could pave the way for future research and industry applications.

Evaluating the mechanism of nucleation and growth of silver nanoparticles in a polymer membrane under continuous precursor supply: tuning of multiple to single nucleation pathway.

Size controlled synthesis of nanoparticles in a structured media, such as a membrane, has not yet been achieved successfully in comparison to that in solution due to the lack of mechanistic investigations on the nucleation and growth of nanoparticles in these media. Slower diffusion of precursor and monomer species inside these structured media complicates the nanoparticle formation mechanism. We herein report a novel experimental approach to reveal the mechanism of nucleation and growth during the synthesis of silver nanoparticles in a Nafion-117 membrane using radiolabeling and small angle X-ray scattering (SAXS). The study has been conducted under the conditions of continuous supply of precursor (silver citrate). Repetitive "LaMer type" nucleations have been found to occur in the membrane leading to the formation of polydispersed spherical nanoparticles as evident from time resolved small angle X-ray scattering. These repetitive nucleations have been shown to be responsible for continuous birth of new seeds, which grow to larger particles, mainly by random coagulation introducing non-uniformity in the growth profile of nanoparticles. The additional nucleation events have been successfully ceased by careful tuning of reaction temperature and precursor concentration, thereby eliminating the nanoparticle growth by random coagulation. This has led to the formation of silver nanoparticles with improved morphology and size distributions, which has been manifested in remarkable improvement in the optical quality of the silver nanoparticles. The present study is the first of its kind showing the crucial role of the membrane host in retarding the reaction kinetics which allowed successful probing of temporal variation of monomer concentration during nucleation and growth using a radiotracer. This was hitherto difficult to probe in solution due to its ultrafast kinetics. Additionally, using the experimental monomer concentrations during nucleation, the free energy of activation (ΔGcrit) and the critical radius (rcrit) for nucleation have been estimated and found to be 73 kJ mol-1 and 6.6 Å, respectively. The present work validates the well known theoretical model by La Mer for the synthesis of nanoparticles in a membrane under continuous precursor supply.

Redox decomposition of silver citrate complex in nanoscale confinement: an unusual mechanism of formation and growth of silver nanoparticles.

We demonstrate for the first time the intrinsic role of nanoconfinement in facilitating the chemical reduction of metal ion precursors with a suitable reductant for the synthesis of metal nanoparticles, when the identical reaction does not occur in bulk solution. Taking the case of citrate reduction of silver ions under the unusual condition of [citrate]/[Ag(+)] ≫ 1, it has been observed that the silver citrate complex, stable in bulk solution, decomposes readily in confined nanodomains of charged and neutral matrices (ion-exchange film and porous polystyrene beads), leading to the formation of silver nanoparticles. The evolution of growth of silver nanoparticles in the ion-exchange films has been studied using a combination of (110m)Ag radiotracer, small-angle X-ray scattering (SAXS) experiments, and transmission electron microscopy (TEM). It has been observed that the nanoconfined redox decomposition of silver citrate complex is responsible for the formation of Ag seeds, which thereafter catalyze oxidation of citrate and act as electron sink for subsequent reduction of silver ions. Because of these parallel processes, the particle sizes are in the bimodal distribution at some stages of the reaction. A continuous seeding with parallel growth mechanism has been revealed. Based on the SAXS data and radiotracer kinetics, the growth mechanism has been elucidated as a combination of continuous autoreduction of silver ions on the nanoparticle surfaces and a sudden coalescence of nanoparticles at a critical number density. However, for a fixed period of reduction, the size, size distribution, and number density of thus-formed Ag nanoparticles have been found to be dependent on physical architecture and chemical composition of the matrix.

Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques.

The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks.

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein-protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

UV-Visible photo-reactivity of permanently polarized inorganic nanotubes coupled to gold nanoparticles.

Hybrid aluminosilicate nanotubes (Imo-CH3) have the ability to trap small organic molecules inside their hydrophobic internal cavity while being dispersed in water owing to their hydrophilic external surface. They also display a curvature-induced polarization of their wall, which favors reduction outside the nanotubes and oxidation inside. Here, we coupled bare plasmonic gold nanoparticles (GNPs) with Imo-CH3 and analyzed for the first time the redox reactivity of these hybrid nano-reactors upon UV illumination. We show that the coupling between GNPs and Imo-CH3 significantly enhances the nanotube photocatalytic activity, with a large part of water reduction occurring directly on the gold surface. The coupling mechanism strongly influences the initial H2 production rate, which can go from ×10 to more than ×90 as compared to bare Imo-CH3 depending on the synthesis route of the GNPs. The present results show that this hybrid photocatalytic nano-reactor benefits from a synergy of polarization and confinement effects that facilitate efficient H2 production.

Discovery of network motifs based on induced subgraphs using a dynamic expansion tree.

Biological networks are powerful representations of topological features in biological systems. Finding network motifs in biological networks is a computationally hard problem due to their huge size and abrupt increase of search space with the increase of motif size. Motivated by the computational challenges of network motif discovery and considering the importance of this topic, an efficient and scalable network motif discovery algorithm based on induced subgraphs in a dynamic expansion tree is proposed. This algorithm uses a pruning strategy to overcome the space limitation of the static expansion tree. The proposed algorithm can identify large network motifs up to size 15 by significantly reducing the computationally expensive subgraph isomorphism checks. Further, the present work avoids the unnecessary growth of patterns that do not have any statistical significance. The runtime performance of the proposed algorithm outperforms most of the existing algorithms for large network motifs.

Application of gamma-ray spectrometry, neutron multiplicity counting and calorimetry for non-destructive assay of U-Pu mixed samples.

This paper presents a standardless non-destructive method for simultaneous assay of uranium and plutonium in mixed samples relevant to nuclear safeguards, forensics and fuel cycle. The method is based on an in-situ absolute efficiency calibration of a γ-ray detector using plutonium γ-rays that can subsequently be used for quantification of uranium in the sample. The method was tested by assaying U-Pu samples with known amounts of U and Pu with varying mass, geometry, composition, reactor type, age and fissile isotope enrichment.

Experimental determination of the curvature-induced intra-wall polarization of inorganic nanotubes.

Inspired by a natural nano-mineral known as imogolite, aluminosilicate inorganic nanotubes are appealing systems for photocatalysis. Here, we studied two types of synthetic imogolites: one is completely hydrophilic (IMO-OH), while the other has a hydrophilic exterior and a hydrophobic interior (IMO-CH3), enabling the encapsulation of organic molecules. We combined UV-Vis diffuse reflectance spectroscopy of imogolite powders and X-ray photoelectron spectroscopy of deposited imogolite films and isolated nanotubes agglomerates to obtain not only the band structure, but also the quantitative intra-wall polarization of both synthetic imogolites for the first time. The potential difference across the imogolite wall was determined to be 0.7 V for IMO-OH and around 0.2 V for IMO-CH3. The high curvature of the nanotubes, together with the thinness of their wall, favors efficient spontaneous charge separation and electron exchange reactions on both the internal and external nanotube surfaces. In addition, the positions of their valence and conduction band edges make them interesting candidates for co-catalysts or doped catalysts for water splitting, among other possible photocatalytic reactions relevant to energy and the environment.

Protein complex prediction in interaction network based on network motif.

The enormous size of Protein-Protein Interaction (PPI) networks demands efficient computational methods to extract biologically significant protein complexes. A wide variety of algorithms have been proposed to predict protein complexes from PPI networks. However, it is still a challenging task to detect protein complexes with high accuracy and manageable sensitivity. In this manuscript, a novel complex prediction algorithm based on Network Motif (CPNM) is proposed. This algorithm addresses the role of proteins in the embeddings of network motif. These roles are used to define feature vectors and feature weights of proteins. Based on these features, a neighborhood search technique predict the protein complexes that consider both the inherent organization of proteins as well as the dense regions in PPI networks. The performance of the proposed algorithm is evaluated using various evaluation metrics like Precision, Recall, F-measure, Sensitivity, PPV, and Accuracy. The research finding indicates that the proposed algorithm outperforms most of the competing algorithms like MCODE, DPClus, RNSC, COACH, ClusterONE, CMC and PROCODE over the PPI network of Saccharomyces cerevisiae and Homo sapiens.

Review of tools and algorithms for network motif discovery in biological networks.

Network motifs are recurrent and over-represented patterns having biological relevance. This is one of the important local properties of biological networks. Network motif discovery finds important applications in many areas such as functional analysis of biological components, the validity of network composition, classification of networks, disease discovery, identification of unique subunits etc. The discovery of network motifs is a computationally challenging task due to the large size of real networks, and the exponential increase of search space with respect to network size and motif size. This problem also includes the subgraph isomorphism check, which is Nondeterministic Polynomial (NP)-complete. Several tools and algorithms have been designed in the last few years to address this problem with encouraging results. These tools and algorithms can be classified into various categories based on exact census, mapping, pattern growth, and so on. In this study, critical aspects of network motif discovery, design principles of background algorithms, and their functionality have been reviewed with their strengths and limitations. The performances of state-of-art algorithms are discussed in terms of runtime efficiency, scalability, and space requirement. The future scope, research direction, and challenges of the existing algorithms are presented at the end of the study.

TriRNSC: triclustering of gene expression microarray data using restricted neighbourhood search.

Computational analysis of microarray data is crucial for understanding the gene behaviours and deriving meaningful results. Clustering and biclustering of gene expression microarray data in the unsupervised domain are extremely important as their outcomes directly dominate healthcare research in many aspects. However, these approaches fail when the time factor is added as the third dimension to the microarray datasets. This three-dimensional data set can be analysed using triclustering that discovers similar gene sets that pursue identical behaviour under a subset of conditions at a specific time point. A novel triclustering algorithm (TriRNSC) is proposed in this manuscript to discover meaningful triclusters in gene expression profiles. TriRNSC is based on restricted neighbourhood search clustering (RNSC), a popular graph-based clustering approach considering the genes, the experimental conditions and the time points at an instance. The performance of the proposed algorithm is evaluated in terms of volume and some performance measures. Gene Ontology and KEGG pathway analysis are used to validate the TriRNSC results biologically. The efficiency of TriRNSC indicates its capability and reliability and also demonstrates its usability over other state-of-art schemes. The proposed framework initiates the application of the RNSC algorithm in the triclustering of gene expression profiles.

Segmented gamma-ray assay of large volume radioactive waste drums containing plutonium lumps.

Segmented gamma-ray scanning (SGS) is a traditional practice, globally, for the non-destructive assay of special nuclear materials (SNMs) in large volume radioactive waste drums. The conventional SGS is a relative two pass method and requires a standard drum of identical geometry. The present work is focused on identifying the limitations of traditional segmented gamma scanning methodology for the assay of waste drums containing plutonium lumps. It has been observed that, for drums containing Pu lumps, the conventional SGS methodology severely underestimates the assay results (~ 2-6 times depending on the gamma-ray energy) due to attenuation under-correction. An alternate single pass absolute efficiency approach following the principle of infinite energy extrapolation of apparent mass has been proposed for the assay of waste drums containing Pu lumps in various random and biased spatial distributions and has been found to agree within 1-10% with the actual value with a maximum uncertainty of 8%. The method has been further validated at higher collimator widths and it has been demonstrated that an increase in collimator width from 5.1 to 10.3 cm increases the throughput of the present system without much of losing the accuracy.

Application of dynamic expansion tree for finding large network motifs in biological networks.

Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, and protein function annotation. However, identification of large network motifs is a challenging task as it involves the graph isomorphism problem. Although this problem has been studied extensively in the literature using different computational approaches, still there is a lot of scope for improvement. Motivated by the challenges involved in this field, an efficient and scalable network motif finding algorithm using a dynamic expansion tree is proposed. The novelty of the proposed algorithm is that it avoids computationally expensive graph isomorphism tests and overcomes the space limitation of the static expansion tree (SET) which makes it enable to find large motifs. In this algorithm, the embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of a parent node, either by adding a vertex or by adding an edge. This process does not involve any graph isomorphism check. The time complexity of vertex addition and edge addition are O(n) and O(1), respectively. The growth of a dynamic expansion tree (DET) depends on the availability of patterns in the target network. Pruning of branches in the DET significantly reduces the space requirement of the SET. The proposed algorithm has been tested on a protein-protein interaction network obtained from the MINT database. The proposed algorithm is able to identify large network motifs faster than most of the existing motif finding algorithms.

Disjoint motif discovery in biological network using pattern join method.

The biological network plays a key role in protein function annotation, protein superfamily classification, disease diagnosis, etc. These networks exhibit global properties like small-world property, power-law degree distribution, hierarchical modularity, robustness, etc. Along with these, the biological network also possesses some local properties like clustering and network motif. Network motifs are recurrent and statistically over-represented subgraphs in a target network. Operation of a biological network is controlled by these motifs, and they are responsible for many biological applications. Discovery of network motifs is a computationally hard problem and involves a subgraph isomorphism check which is NP-complete. In recent years, researchers have developed various tools and algorithms to detect network motifs efficiently. However, it is still a challenging task to discover the network motif within a practical time bound for the large motif. In this study, an efficient pattern-join based algorithm is proposed to discover network motif in biological networks. The performance of the proposed algorithm is evaluated on the transcription regulatory network of Escherichia coli and the protein interaction network of Saccharomyces cerevisiae. The running time of the proposed algorithm outperforms most of the existing algorithms to discover large motifs.

Full energy peak efficiency calibration for the assay of large volume radioactive waste drums in a segmented gamma scanner.

Large volume radioactive waste drums with low/intermediate level of alpha activity, generated in radiochemical laboratories, are in general screened for special nuclear materials (SNM) in a segmented gamma scanner (SGS) before disposal. The assay methodology traditionally requires a standard drum of identical geometry and thereby making the procedure relying on the availability of a true standard, which is often difficult to organize. Here, we report a non-conventional absolute segmented gamma scanning (ASGS) methodology for the assay of 200 L waste drums, avoiding the use of a standard drum. The present analysis employ the full energy peak (FEP) efficiency, ingeniously determined using a standard 152Eu point source. From combined experiment and Monte Carlo simulation, it has been established that, the FEP efficiencies of the detector for a 200 L cylindrical sample can be well reproduced using a point source. While verifying the applicability of the point source FEP efficiencies for the assay of plutonium in 200 L drums, an energy dependent bias has been seen, which confirms the presence of lump attenuation in addition to the general matrix attenuation. An infinite energy extrapolation of apparent mass approach has been adopted for the assay of large volume waste drums which takes care of the gamma-ray attenuation from all sources that is otherwise difficult to correct for in a sample drum of unknown history.

Using the 90-105 keV gamma-rays for isotopic composition determination of plutonium in dilute solutions.

Using the 90-105 keV gamma-rays for determining Pu isotopic composition is studied for dilute Pu solutions (0.0001-0.05 µg/mm3) as well as Pu-U mixed solutions. It is shown that for concentrations higher than 0.001 µg/mm3 Pu, results match well with those of mass spectrometric results. However, in mixed solutions, beyond 0.005 mg/mm3 U concentration, the errors on isotopic compositions of Pu increased as U content increased.