In this contribution we describe the formation of gold nanoparticles (AuNP)and polyaniline (PANI) AuNP-PANI nanocomposite via in situ enzymaticpolymerization. The method consists of electrostatic adsorption of aniliniummonomers on AuNPs citrate stabilized surface of 50 nm diameters, followedby oxidation with horseradish peroxidase (HRP) enzyme and its cofactorH2O2. All reaction steps were monitored by UV-Vis-NIR spectroscopy includ-ing in situ detection of the polymerization process. UV-Vis-NIR, Cyclicvoltammetry (CV) and surface enhanced Raman scattering (SERS) measure-ments supported the formation of a nanoshell of PANI on the AuNP core. Two templates for anilinium assembly were compared revealing a strongdependence of the enzymatic kinetics on the template. The kinetic study hadshown that the rigid template of the AuNP contributes to higher reaction rateon the AuNP compared with the more flexible polyanion template. The mildreaction condition enables an easy and precise method for obtaining PANInano-shell on anionic templates for advanced bioelectronic applications

Due to its superior self-assembly properties and vast functionalization pos-sibilities DNA has long been one of the most promising candidates forfabrication of nanoscale electrical components using molecular buildingblocks. There exist already many demonstrations on optical devices based onorganizing metallic nanoparticles (NP) via DNA self-assembly, but despite thepromises only few DNA based electrical devices or studies have been realizedso far. Here we study the gold NP conjugated and metallized DNA TX-tile-structure, which we recently showed to exhibit the room temperature Coulombblockade, the pre-requisition for a single electron transistor. The properties ofthe obtained Coulomb blockade are further characterized via the differentialconductance measurements at temperatures ranging from 4.2 K to 10.2 K.The results show sharp blockade plateaus with varying threshold voltages,which yields further evidence of a gating effect by background charges. Thisstrongly indicates that the DNA-NP assembly functions as a single electrontransistor. Also, the additional growth of gold NPs and electrodes via chemicalgold deposition process, needed to achieve the Coulomb blockade, is studiedhere in more details, yielding more insight to the process, and thus helping torealize better control of it.

Protein-nanoparticle associations have important applications in nanoscienceand nanotechnology but the recognition mechanisms and the determinantsof specificity are still poorly understood at the microscopic level. Crucialquestions remain open, related to the association mechanisms, control ofbinding events, and preservation of functionality. Gold is a promising materialin nanoparticles for nanobiotechnology applications because of the ease ofits functionalization and its tunable optical properties. We present a conciseoverview of recent computational modeling advances which were pursued inthe quest for a theoretical framework elucidating the association mechanismsand the ability to design and control the recognition events of a specificclass of systems, namely, interfaces between polypeptides/proteins and a goldsurface in the presence of water. We select two different methodologicaladvances, the first related to the effect of surfactants covering the surfaceof nanoparticles and altering their interactions with proteins and the secondrelated to the immobilization of proteins on inorganic surfaces and conservingtheir functionality. Both cases, demonstrate how the understanding of thepolypeptide-surface coupling mechanisms is essential to the control of theprocess and exploitation for biotechnological and nanotechnological purposes.

Self-assembled porous alumina structures (por Al2O3) were prepared by two-step anodization process and characterized by scanning electron microscopy.Filling quasi one-dimensional parallel nanochannels of por Al2O3 host matrixwith iodine guest substance by vapor phase adsorption method resulted inthe formation of I/por Al2O3 nanocomposite. Electrical properties of thesenanocomposite samples were studied by alternating-current measurements ata frequency of 1 kHz. Ellipsometric measurements were carried out in thespectral range 350–1000 nm. Structural transition of iodine species from thechain structures to molecular iodine was found in I/por Al2O3 nanocompositeat ∼ 70 ◦C.

Recent developments in structural DNA nanotechnology have made com-plex and spatially exactly controlled self-assembled DNA nanoarchitectureswidely accessible. The available methods enable large variety of differ-ent possible shapes combined with the possibility of using DNA structuresas templates for high-resolution patterning of nano-objects, thus openingup various opportunities for diverse nanotechnological applications. TheseDNA motifs possess enormous possibilities to be exploited in realization ofmolecular scale sensors and electronic devices, and thus, could enable fur-ther miniaturization of electronics. However, there are arguably two mainissues on making use of DNA-based electronics: (1) incorporation of indi-vidual DNA designs into larger extrinsic systems is rather challenging, and(2) electrical properties of DNA molecules and the utilizable DNA templatesthemselves, are not yet fully understood. This review focuses on the abovementioned issues and also briefly summarizes the potential applications ofDNA-based electronic devices.

First proposed in 1994, molecular combing of DNA is a technique that allowsadsorption and alignment of DNA on the surface with no need for prior modi-fication of the molecule. Since then, many variations of the original methodhave been devised and used in a wide range of applications from genomicstudies to nanoelectronics. While molecular combing has been applied in avariety of DNA-related studies, no comprehensive review has been publishedon different combing methods proposed so far. In this review, the underly-ing mechanisms of molecular combing of DNA are described followed bydiscussion of the main methods in molecular combing as well as its majorapplications in nanotechnology.

Fouling of membrane filters is the key performance limiting factor in mem-brane filtration. Thus fouling and anti-fouling have received much attentionin recent years, covering topics from fouling mechanisms and characteristicsto anti-fouling surface modifications. This paper presents a method to achievecontrolled grafting of an anti-fouling poly(ethylene glycol) (PEG) brush poly-mer layer onto a sintered alumina membrane filter surface without reductionof the filter permeability. The obtained PEG layers were characterized usinga broad range of surface techniques including Fourier transformed infraredspectroscopy, ellipsometry, atomic force microscopy, and contact angle mea-surements. Dead-end filtration experiments with PEG-brush modified filtersshowed improved fouling reversibility for the filtration of a BSA solutionand significantly slowed down the fouling rate during filtration of a lysozymesolution. The cross-flow filtration of model lake-water demonstrated improvedfoulant removal for the PEG modified filters during backflush cleaning and thereby increased the overall throughput during a filtration cycle as comparedto the bare membrane filters

Nitrophenolates (NPs) are molecular anions that can undergo charge-transfer(CT) transitions determined by the degree of electron delocalization betweenthe phenolate oxygen (donor group) and the nitro group (acceptor). Herewe have studied four different NPs: 4’-nitro-[1,1’-biphenyl]-4-olate (1),7-nitro-9H -carbazol-2-olate (NH linker, 2), 7-nitrodibenzo[b,d]furan-3-olate (oxygen linker, 3), and 7-nitrodibenzo[b,d]thiophen-3-olate (sulphurlinker, 4), and recorded their electronic absorption spectra when isolatedin vacuo to determine the effect of locking the biphenyl spacer group betweenthe donor and acceptor on transition energies. Absorption was identified fromion dissociation (action spectroscopy) using a homebuilt setup (sector massspectrometer combined with pulsed laser). We find that the absorption isbroad in the visible region for all four NPs with significant vibronic features.The lowest energy peak is at 601 ± 4 nm, 606 ± 4 nm, 615 ± 4 nm, and620 ± 4 nm, for 3, 4, 2, and 1, respectively. NP 1 is flexible, and its lowestenergy structure is nonplanar while the other three NPs are planar accordingto density functional theory calculations. Hence in the case of 1 the electronic transition has a higher degree of CT than for the other three, accounting for itsabsorption furthest to the red. Our work demonstrates that oxygen and sulphurare best at conveying the electronic coupling between the donor and acceptorsites as 3 and 4 absorb furthest to the blue (i.e., the degree of CT is lowestfor these two NPs). Based on the average spacing between the peaks in thevibrational progressions, coupling occurs to skeleton vibrational modes withfrequencies of 649 ± 69 cm−1 (3), 655 ± 49 cm−1 (4), and 697 ± 52 cm−1 (2).

DNA may be the most versatile molecule discovered to date. Beyond its well-known central role in genetics, DNA has the potential to be a remarkably usefultechnological material. It has been demonstrated as a scaffold for the assemblyof organic and inorganic nanomaterials [1]; a vehicle for drug delivery [2]; amedium for computation [3]; and a possible wire for transporting electricalsignals [4]. A key factor in exploiting DNA in these ways is the ability tointegrate DNA with other materials. In this paper, we review two approachesto forming DNA complexes with functional nanomaterials: (1) linking DNAwith single-wall carbon nanotubes (SWCNTs), which can then be used asnanoscale electrical contacts for probing electron transport in DNA; and (2)directed nanoassembly of Au nanoparticles using DNA/PNA (peptide nucleicacid) hybrid scaffolds.

Charge transport at the molecular scale builds the cornerstone of molecularelectronics (ME), a novel paradigm aiming at the realization of nanoscaleelectronics via tailored molecular functionalities. Biomolecular electronics,lying at the borderline between physics, chemistry and biology, can be con-sidered as a sub-field of ME. In particular, the potential applications of DNAoligomers either as template or as active device element in ME have stronglydrawn the attention of both experimentalist and theoreticians in the past years.While exploiting the self-assembling and self-recognition properties of DNAbased molecular systems is meanwhile a well-established field, the poten-tial of such biomolecules as active devices is much less clear mainly due tothe poorly understood charge conduction mechanisms. One key componentin any theoretical description of charge migration in biomolecular systems,and hence in DNA oligomers, is the inclusion of conformational fluctuationsand their coupling to the transport process. The treatment of such a problemaffords to consider dynamical effects in a non-perturbative way in contrastto, e.g., conventional bulk materials. Here we present an overview of recentwork aiming at combining molecular dynamic simulations and electronicstructure calculations with charge transport in coarse-grained effective model Hamiltonians. This hybrid methodology provides a common theoretical start-ing point to treat charge transfer/transport in strongly structurally fluctuatingmolecular-scale physical systems.

Diphenylalanine is well known to form complex self-assembled structures,including peptide nanowires, with morphologies depending on N- and C-terminal modifications. Here we report that significant morphological vari-ations of self-assembled structures are attainable through pH variation ofunmodified diphenylalanine in trifluoroethanol. The obtained self-assembleddiphenylalanine nanostructures are found to vary drastically with pH, incu-bation time, and diphenylalanine concentration in solution. The observedstructures ranged from structured films at neutral and alkaline conditions tovertically aligned nanowires and sponge-like structures at acidic conditions.These observations are corroborated by the results of electrostatic modelling,indicating the disappearance of the dipole moment at high pH values. Thisalso emphasizes the importance of the dipole moment for the resulting self-assembled structures. Our results suggest that, in comparison to the commonlydescribed procedure of diphenylaniline nanowire growth through anilinevapor treatment, strictly anhydrous conditions are not necessarily required.

Genome editing allows scientists to change an organism’s DNA. One promis-ing genome editing protocol, already validated in living organisms, is basedon clustered regularly interspaced short palindromic repeats (CRISPR)/Casprotein-nucleic acid complexes. When the CRISPR/Cas approach was firstdemonstrated in 2012, its advantages with respect to previously availabletechniques, such as zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs), immediately got attention and the methodhas seen a surge of experimental and computational investigations since then.However, the molecular mechanisms involved in target DNA recognition andcleavage are still not completely resolved and need further attention. The largesize and complex nature of CRISPR/Cas9 complexes has been a challengefor computational studies, but some seed results exist and are illuminatingon the cleavage activity. In this short review, we present recent progressin studying CRISPR/Cas9 systems by molecular dynamics simulations withcoarse-grained and atomistic descriptions, including enhanced sampling.

Upconversion materials, a type of novel functional materials, have manygood physicochemical properties, such as high luminescence stability, highchemical stability, long luminous life, and low potential biotoxicity. How toregulate the luminescence of upconversion materials is a significant chal-lenge. Two-dimensional (2D) Van der Waals materials are a new type ofnanomaterials with a layer structure. Some of them are capable of quenchingthe luminescence of dyes, which could be applied in the modulation of opticalresponse of upconversion materials. Therefore, in this work, we explored thephotoresponse of β-NaYF4:Yb/Er triggered by 2D Van der Waals materialsincluding graphene oxide, MoS2, g-C3N4 and BN. The results obtained inthis work would be beneficial for the further application of luminescencemodulation of UCNPs. Using UCNPs as a potential fingerprint of 2D Vander Waals materials, their optical response might be used to distinguish the2D Van der Waals materials.

We report synthesis of dimers composed of a single short double-stranded(ds)DNA molecule connecting two gold nanoparticles (GNPs). Such struc-tures may be useful for electrical transport measurements through dsDNAmolecules and for other research purposes. When the DNA molecules areshort with respect to the size of the GNP, gel electrophoresis cannot separ-ate GNPs with different numbers of DNA molecules attached to them. Wepresent two methods to separate GNPs connected to single short thiolatedsingle-stranded (ss)DNA. The separation is performed by hybridizing theDNA/GNP conjugates with long, partially complementary, ssDNA or withcomplementary ssDNA connected to GNPs of smaller size. The separatedGNPs with a single short ssDNA were used to form dimers consisting ofGNPs connected by a single short dsDNA molecule.

The interaction of molecules with inorganic substrates is a crucial issue forapplications in molecular electronics. It influences important factors suchas the immobilization efficiency and the charge injection through the inter-face. Moreover, mechanical aspects connected to the unfolding of biologicalmolecules are important.We hereby present recent efforts in our group to tackle these problems,based on density functional theory calculations. In particular, we discussour results on the adsorption of cytosine on Au(111) and on the interactionof guanine, in its natural and size-expanded forms, with small Au clusters.We find that cytosine binds to the Au(111) surface with a mechanism thatinvolves charge sharing, intermediate between chemisorption and physisorp-tion. The investigation of small complexes between guanine and gold clustersreveals the formation of hydrogen bonds: these configurations with unusualbonds are relevant at the corners of nanoparticles, while they can probably beneglected when DNA binds on flat extended metal surfaces.

β2-microglobulin is a paradigmatic amyloidogenic protein responsible fordialysis-related amyloidosis, a disease associated to long-term hemodialyzedpatients and characterized by accumulation of amyloid deposits in the osteoar-ticular tissues. In the early stages of amyloid fibril formation, β2-microglobulinassociates into dimers and higher oligomers, but clarifications are still neededfor the triggering conditions, mechanisms and specificity of dimer forma-tion. To characterize the dimeric association process, the protein-proteininteractions between three different species are investigated: namely, thenative protein and the two amyloidogenic variants ΔN6 and D76N. Thedimerization process is rationalized relying on state of the art computationalmethods. A comparative mechanism for how different mutations in the threevariants can affect protein dimerization and thus fibril formation is proposed. The number of salt bridges involved at the protein-protein interface correlateswith the degree of amyloidogenicity of each individual species. The findingscan offer possible strategies in controlling the dimerization mechanism basedon different β2-microglobulin protein mutations, which have significant rolesin the fibrillogenical process.

This article aims to explore and research GANs as a tool for mobile devices that can generate high-resolution images from low-resolution samples and reduce blurring. In addition, the authors also analyse the specifics of GAN, SRGAN, and ESRGAN loss functions and their features. GANs are widely used for a vast range of applied tasks for image manipulations. They’re able to synthesize, combine, and restore graphical samples of high quality that are almost indistinguishable from real data. The main scope of the research is to study the possibility to use GANs for the said tasks, and their potential implementation in mobile applications.

The paper analyzes from a technical point of view the search engine optimization (SEO) techniques and technologies, which lead to effective results up to date. More specifically, it examines the potential SEO alternatives, with ultimate target to increase the organic visitors of a website and climb it up on top of the ranking in respective search engine’s queries. The main problem every website’s owner has to solve is which of these SEO techniques have to be implemented in order to increase the organic traffic with the minimum budget. This paper aims to present the dominant SEO techniques and evaluate their effectiveness to organic traffic. This paper aims to present the way well-known websites apply SEO nowadays. Taking advantage of this information, Webmasters will know in advance which exactly SEO techniques should apply to their websites and in which specific order to gain higher search results, resulting to higher traffic.

Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.

The paper was conducted to understand the factors affecting the student’s learning location. The official study carried out an online survey through Google forms using a questionnaire with the participation of 125 samples. The Bayesian Model Selection shows that 03 factors are affecting student studying location (SSL), which are Students’ perception (PP), Price perception (PRI), Perception of universities in a big city (UNI). From the results, we have proposed many implications for improving student learning. This study uses the optimal choice of Bayesian Model Selection for the student learning location. Students’ perceptions (PP), price perceptions (PRI), and university perceptions in big cities (UNI) all have a 97.1 percent impact on student studying places (SSL). Model 1 is the best option by BIC, and four variables have a probability of 100%.

In late 2019, we witnessed the apparition of the covid-19 virus. The virus appeared first in Wuhan, and due to people travel was spread worldwide. Exponential spread as well as high mortality rates, the two characteristics of the SARS-CoV-2 virus that pushed the entire world into a global lock-down. Health and economic crisis, along with social distancing have put the globe in a highly challenging situation. Unprecedented pressure on the health care system exposed many loopholes not only in this industry but many other sectors, which resulted in a set of new challenges that researchers and scientists among others must face. In all these circumstances, we could attend, in a surprisingly short amount of time, the creation of multiple vaccine candidates. The vaccines were clinically tested and approved, which brought us to the phase of vaccination. Safety, security, transparency, and traceability are highly required in this context. As a contribution to assure an efficient vaccination campaign, in this paper we suggest a Blockchain-based system to manage the registration, storage, and distribution of the vaccines. This manuscript has been presented as preprint in Blockchain technology for a Safe and Transparent Covid-19 Vaccination: https://arxiv.org/abs/2104.05428

The purpose of this study is to analyse the impact of the risk significance of audit results, the quality of the recommendations given on how easy it is to implement them, and the added benefit to the organization in implementing the recommendations. After a comprehensive literature review, the study provides a statistical analysis through a questionnaire that has been distributed to investigate the effect of Risk Significance, Ease of Implementation, and the Added Value on the implementation of the recommendations within organizations. Regarding the results obtained from the questionnaire, all Cronbach’s Alpha values are within the acceptable level, whereas the first three variables (Implementation of Recommendations, Risk Significance and Ease of Implementation) have a strong positive correlation between each other. There is a weak positive correlation between Added Value of Recommendations with other variables. In the regression analysis was found that all independent variables have a positive effect on the depended variable.

Massive reliance on practical systems has resulted in several security concerns. The ability to identify anomalies is a critical safety feature enabled by anomaly diagnostic techniques. The construction of a data system faces a significant issue in cyber security. Because of the exploitation of valuable data, cybersecurity impacts the privacy of such data. Attack incidents must be examined using an appropriate analytics approach in elevating the safety level. Design of advanced analytical, conceptual model creation gives practical guidance and prioritizes threats/attacks across the network system. There is now substantial effectiveness in attack categorization, and evaluation through Convolution Neural Network (CNN) based classifiers. In light of the drawbacks of previous approaches, this research proposes an approach relying on the Deep Learning (DL) strategies for cyberattacks detection and categorization in the context of cyberspace incidents. Likewise, this article presents an XGBoost Regression Classifier (XRC) using Inception V4 to address those restrictions. XGBoost refers to Extreme Gradient Boosting, a decentralized gradient-boosted decision tree (GBDT) supervised learning framework that is robust and can be used in a decentralized context. XGBoost is a well-known machine learning technique because of its ability to produce outstanding accuracy. The concepts of both XGBoost and Regression classifiers are integrated and represented as a suggested hybridized classifier, which is implemented in Inception V4 to further train and test the model. The proposed XRC categorizes and forecasts several common types of network cyberattacks that includes Distributed Denial of Service (DDoS), Phishing, Cross-site Scripting (CS), Internet of Things (IoT). The sigmoidal function is used as a supportive activator to the hybridized classifier to lower the erroneous ratio and increase the effectiveness. Research shows that training and testing errors were substantially decreased when using XRC. In 9 out of 13 instances, over 97% of threats are detected by the XRC, and over 75% of threats are detected in its most challenging datasets.

In the area where privacy is of greater concern, federated learning,a distributed machine learning strategy for preserving privacy,is widely employed in several privacy concern applications. In the meantime, neural architectures became familiar with deep learning approaches for automatic tuning of the architecture of deep neural networks (DNN). While searching with neural architecture and federated learning has experienced several challenges, optimized neural architecture research in federated learning is extensively on demand. DNN faces numerous issues while training such user privacy and ensuring the integrity of the aggregated results obtained from a server. To provide solutions for the above-mentioned issues, enormous federated learning techniques worked towards preserving privacy and were applied in different situations. Still, it is an open challenge that enables users to verify if the cloud server functions appropriately while ensuring users’ privacy while training. Federated Learning Method is a new way to improve the accuracy and precision, since the previous approach failed to opt the solutions. Here, Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL)is proposed to ensure the confidentiality of users’ local gradients while performing federated learning. The parameters such as classification accuracy, running time, Communication overhead, Computation overhead, and transaction speed are considered. The values obtained for these parameters are compared against three standard methods, namely Biparing Method (BM) Homomorphic Cryptosystem (HC), and Multiple Authorities with Attribute-Based Signature scheme (MA-ABS)against proposed Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL). As a result, the proposed ECC-BFL achieved 95% of classification accuracy, 65 sec of running time, 76% of communication overhead, 63% of computation overhead, and 92% of transaction speed.