False Data Injection Detection for Phasor Measurement Units

Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances

A Novel Technique to Detect False Data Injection Attacks on Phasor Measurement Units

The power industry is in the process of grid modernization with the introduction of phasor measurement units (PMUs), advanced metering infrastructure (AMI), and other technologies. Although these technologies enable more reliable and efficient operation, the risk of cyber threats has increased, as evidenced by the recent blackouts in Ukraine and New York. One of these threats is false data injection attacks (FDIAs). Most of the FDIA literature focuses on the vulnerability of DC estimators and AC estimators to such attacks. This paper investigates FDIAs for PMU-based state estimation, where the PMUs are comparable. Several states can be manipulated by compromising one PMU through the channels of that PMU. A Phase Locking Value (PLV) technique was developed to detect FDIAs. The proposed approach is tested on the IEEE 14-bus and the IEEE 30-bus test systems under different scenarios using a Monte Carlo simulation where the PLV demonstrated an efficient performance.Peer reviewe

Efficient nitrite determination by electrochemical approach in liquid phase with ultrasonically prepared gold-nanoparticle-conjugated conducting polymer nanocomposites

An electrochemical nitrite sensor probe is introduced herein using a modified flat glassy carbon electrode (GCE) and SrTiO3 material doped with spherical-shaped gold nanoparticles (Au-NPs) and polypyrrole carbon (PPyC) at a pH of 7.0 in a phosphate buffer solution. The nanocomposites (NCs) containing Au-NPs, PPyC, and SrTiO3 were synthesized by ultrasonication, and their properties were thoroughly characterized through structural, elemental, optical, and morphological analyses with various conventional spectroscopic methods, such as field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, high-resolution transmission electron microscopy, powder X-ray diffraction, X-ray photoelectron spectroscopy, and Brunauer–Emmett–Teller method. The peak currents due to nitrite oxidation were characterized in detail and analyzed using conventional cyclic voltammetry (CV) as well as differential pulse voltammetry (DPV) under ambient conditions. The sensor response increased significantly from 0.15 to 1.5 mM of nitrite ions, and the sensor was fabricated by coating a conducting agent (PEDOT:PSS) on the GCE to obtain the Au-NPs/PPyC/SrTiO3 NCs/PEDOT:PSS/GCE probe. The sensor’s sensitivity was determined as 0.5 μA/μM∙cm2 from the ratio of the slope of the linear detection range by considering the active surface area (0.0316 cm2) of the flat GCE. In addition, the limit of detection was determined as 20.00 ± 1.00 µM, which was found to be satisfactory. The sensor’s stability, pH optimization, and reliability were also evaluated in these analyses. Overall, the sensor results were found to be satisfactory. Real environmental samples were then analyzed to evaluate the sensor’s reliability through DPV, and the results showed that the proposed novel electrochemical sensor holds great promise for mitigating water contamination in the real samples with the lab-made Au-NPs/PPyC/SrTiO3 NC. Thus, this study provides valuable insights for improving sensors for broad environmental monitoring applications using the electrochemical approach

A smoothing spline algorithm to interpolate and predict the eigenvalues of matrices extracted from the sequence of preconditioned banded symmetric Toeplitz matrices

Understanding the eigenvalue distribution of sequence Toeplitz matrices has advanced significantly in recent years. Notable contributors include Bogoya, Grudsky, Böttcher, and Maximenko, who have derived precise asymptotic expansions for these eigenvalues under certain conditions related to the generating function as the matrix size increases. Building on this foundation, the Stefano Serra-Capizzano conjectured that, under certain assumptions about $\Omega$ and $\Phi$, a similar expansion may hold for the eigenvalues of a sequence of preconditioned Toeplitz matrices $T_{n}^{-1}(\Phi) T_n(\Omega)$, given a monotonic ratio $r = \Omega/\Phi$. In contrast to current eigenvalue solvers, this work presents a novel method for efficiently calculating the eigenvalues of a sequence of large preconditioned banded symmetric Toeplitz matrices (PBST). Our algorithm uses a higher-order spline fitting extrapolation technique to gather spectral data from a smaller sequence of PBST matrices in order to forecast the spectrum of bigger matrices

Facile Synthesis of Low-Cost Copper-Silver and Cobalt-Silver Alloy Nanoparticles on Reduced Graphene Oxide as Efficient Electrocatalysts for Oxygen Reduction Reaction in Alkaline Media

Copper-silver and cobalt-silver alloy nanoparticles deposited on reduced graphene oxide (CuAg/rGO and CoAg/rGO) were synthesized and examined as electrocatalysts for oxygen reduction reaction (ORR) and hydrogen peroxide reduction reaction (HPRR) in alkaline media. Characterization of the prepared samples was done by transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction analysis (XRD), and scanning electron microscopy with integrated energy-dispersive X-ray spectroscopy (SEM-EDS). CuAg/rGO and CoAg/rGO nanoparticles diameter ranged from 0.4 to 9.2 nm. The Ag loading was ca. 40 wt.% for both electrocatalysts, with that for Cu and Co being 35 and 17 wt.%, respectively. CoAg/rGO electrocatalyst showed a Tafel slope of 109 mV dec−1, significantly lower than that for CuAg/rGO (184 mV dec−1), suggesting faster ORR kinetics. Additionally, a higher diffusion current density was obtained for CoAg/rGO (−2.63 mA cm−2) than for CuAg/rGO (−1.74 mA cm−2). The average value of the number of electrons transferred during ORR was 2.8 for CuAg/rGO and 3.3 for CoAg/rGO electrocatalyst, further confirming the higher ORR activity of the latter. On the other hand, CuAg/rGO showed higher peak current densities (−3.96 mA cm−2) for HPRR compared to those recorded for CoAg/rGO electrocatalyst (−1.96 mA cm−2)

A novel In2O3-doped ZnO decorated mesoporous carbon nanocomposite as a sensitive and selective dopamine electrochemical sensor

Dopamine (DA), a critical biomolecule involved in neurotransmission, is implicated in a variety of neurological disorders. Therefore, accurate detection of DA is crucial for the swift diagnosis of conditions arising from abnormal DA levels. Consequently, we utilized a novel nanocomposite material comprising In2O3-doped ZnO decorated on mesoporous carbon (In2O3·ZnO@MC) as the active nanomaterial for the fabrication of a glassy carbon electrode (GCE). The structural and morphological properties of In2O3·ZnO@MC were comprehensively analyzed utilizing a variety of characterization techniques to confirm its functionality as the sensing nanomaterial. This innovative sensor demonstrates the ability to detect a wide range of DA concentrations, ranging from 0.5 to 2056 μM, in a neutral phosphate buffer solution, exhibiting a high sensitivity of 0.2153 μAμM−1cm−2 and an acceptable detection limit of 0.024 μM. This sensor enables precise DA level measurements in real samples due to its high sensitivity and selectivity. Moreover, it is a dependable and trustworthy sensor for DA measurement due to its outstanding reproducibility, repeatability, and stability

Electrochemical detection of hydroquinone as an environmental contaminant using Ga2O3 incorporated ZnO nanomaterial

The primary objective of this research endeavor is to develop a highly sensitive and selective electrochemical sensor for the accurate detection of hydroquinone (HQ), a prevalent environmental contaminant. To achieve this, we employed a novel nanocomposite consisting of Ga2O3-doped ZnO (Ga2O3.ZnO) as the active nanomaterial for fabricating a glassy carbon electrode (GCE). The structure and morphology of the Ga2O3.ZnO nanocomposite were rigorously analyzed using a diverse range of techniques to ensure its suitability as the sensing nanomaterial. This innovative sensor exhibits remarkable capabilities, enabling the detection of HQ across a broad concentration range, spanning from 1 to 11070 µM, in a neutral phosphate buffer solution. It boasts an exceptionally high sensitivity of 1.0229 µA µM−1 cm−2 and an impressive detection limit of 0.063 µM. Thanks to its exceptional sensitivity and specificity, this sensor can precisely quantify HQ levels in real-world samples. Moreover, its outstanding reproducibility, repeatability, and stability establish it as a dependable and resilient choice for HQ determination

Facile synthesis of platinum/polypyrrole-carbon black/SnS2 nanocomposite for efficient photocatalytic removal of gemifloxacin under visible light

Development and designing of effective visible light photocatalysts to overcome the drastrous situation of water pollution requires material with excellent charge transferring skills. In this regard, an efficient ternary nanocomposite photocatalyst comprised of SnS2 nanostructure linked with polypyrrole-doped carbon black (PPC) and platinum nanoparticles (Pt NPs) was successfully fabricated. Effective ternary photocatalyst was synthesized by hydrothermal technique followed by ultra-sonication and photo-reduction methodologies. The XRD measurements confirmed the hexagonal phase of SnS2, and the proper formation of nanocomposite. TEM examination revealed Pt NPs of 5–15 nm in size, dense cocoon like layered structure of PPC along with irregular pellets of SnS2. Acquired diffuse reflectance data confirmed the visible light band gap of synthesized nanomaterials. The Pt@PPC/SnS2 photocatalyst showed excellent destructive potential under visible light with 92.40 % removal of antibiotic gemifloxacin (GFX) in 30 minutes, almost 347 % more efficient than bare SnS2, and was found to be ultrafast for the removal of methylene blue (MB) with total elimination of dye just in 10 minutes. The photoluminescence and photocurrent transient analysis revealed enhanced light absorption capability and increased photo-induced carrier transfer with effective separation behavior, together with increased effective surface area of the ternary photocatalyst as evidenced by the BET surface area measurement

A novel Ga2O3-doped ZnO decorated SWCNT nanocomposite based amperometric sensor for efficient detection of dopamine in real samples

Our current research focuses on developing an efficient electrochemical Dopamine sensor for diagnosing neurological disorders related to abnormal Dopamine levels. We utilized a Ga2O3-doped ZnO decorated SWCNT (Ga2O3·ZnO@SWCNT) nanocomposite to modify a glassy carbon electrode. The structure and morphology of the Ga2O3·ZnO@SWCNT nanocomposite was examined utilizing a variety of approaches to ensure of their appropriateness as the sensing nanomaterial. The sensor effectively detects Dopamine within a wide range (1.0–2056 μM) in neutral phosphate buffer solution, exhibiting high sensitivity (0.2536 μAμM−1cm−2) and a practical detection limit (0.052 μM). It accurately quantifies Dopamine in human blood serum and commercial dopamine injection samples, displaying excellent reproducibility, repeatability, and stability. This proposed Dopamine sensor proves to be a reliable tool for Dopamine determination