Strong Sensitivity Research Articles

A mini review on biosensor advancements-emphasis on quantum dots

The biosensor is an analytical device that targets the macromolecules and small molecules using a biological recognition system and later converts it into a measurable signal using a transducer. These biosensors are broadly used in various domains, enhancing the existing form and making it more specific for several applications. Presently, it is used for quantifying and detecting the biological-based components in the fields of forensics, medicine, food quality, pharmaceutical and environmental tracking, etc. These biosensors are cost-effective and produce safe and assured treatment for patients by examining the contagious disease biomarkers in the early stage without any side effects. This review has discussed the five essential components of biosensors and the upgraded form of these biosensors. Depending on the detection method, biosensors were classified into different types. Amongst various types, electrochemical and optical biosensors have more sensitivity and are widely used, and are briefly discussed here. Nanomaterials play a significant role in modern medical devices due to their excellent selectivity, high surface-to-volume ratio, strong chemical activity, increased specificity, and sensitivity that pave the way for more discoveries. Several nanomaterial-based biosensors are already on the market. This review mainly focuses on the fabrication methods of metal and metal oxide nanoparticles for sensing applications.

Detection of decomposed gases in SF6 surge arrester contaminated conditions in ultra-high voltage transmission lines based on Ta-doped defective h-BN nanosheet: A DFT study

SF6 surge arrester on ultra-high voltage transmission lines plays a critical role in the entire power transmission system. They not only protect electrical equipment from the dangers of high transient over voltages, but also bear the responsibility of maintaining the safety and stability of the transmission lines. Detecting the decomposed gases under polluted conditions inside SF6 surge arresters is of paramount importance. In this paper, based on extensive investigations and the application of density functional theory and 2D materials, we conducted a study on the detection of decomposed gases (HF, H2S, SOF2) in SF6 surge arresters for ultra-high voltage transmission lines under polluted conditions using Ta-doped defective h-BN nanosheets gas sensor. Revealing the adsorption and sensing mechanisms of these target gases on h-BN sensors through band structure, density of states, projected density of states, and differential charge density analysis. The results showed that Ta metal particle modification significantly improves the electronic activity of defective h-BN, substantially reducing the band gap values from 4.560 eV to 0.153 eV and 0.912 eV in the B atom and N atom defective oping systems. Meanwhile, Ta-doped defective h-BN exhibits strong selectivity and sensitivity to three decomposition components, with the adsorption capability ranking as SOF2 > H2S > HF, all of which are physical chemisorption types. Furthermore, in terms of various adsorption parameters, defective h-BN’s adsorption ability far surpasses other materials, offering broad prospects for practical applications. The results of this work contribute to the development and application of Ta-doped defective h-BN sensors for the detection of decomposed gases inside SF6 surge arresters under polluted conditions.

Price Discovery and Market Efficiency in India's Financial Futures Market within the Derivatives Landscape: An Empirical Analysis

This study offers a robust, long-term analysis of price discovery and the persisting lead-lag relationship between India’s equity futures and spot markets. Utilising monthly data from April 2005 to December 2022, it filters transient noise typically associated with high-frequency data. Information Share and Common Factor Weight methodologies within the Vector Error Correction (VEC) framework consistently reveal the dominance of futures markets in the price discovery process. The insights remain consistent across three distinct periods spanning the global financial crisis and the COVID-19 pandemic. The volatility dynamics and asymmetry effects in the Indian equity futures and spot markets using the Vector Error Correction - Exponential General Autoregressive Conditional Heteroskedastic (VEC-EGARCH) approach uncover notable asymmetry effects, signifying a strong market sensitivity to negative news. This highlights the necessity for comprehensive risk management strategies and stringent regulatory supervision, especially in light of the significant growth and systemic risks in the Indian derivatives market.

ETG turbulent transport in the Mega Ampere Spherical Tokamak (MAST) pedestal

The results of a gyrokinetic analysis of turbulent transport driven by the electron temperature gradient (ETG) in the MAST pedestal are presented. Local nonlinear simulations from the gyrokinetic code, GENE, show that heat flux produced by ETG turbulence is 10%–30% of the total applied heating power in the upper pedestal and pedestal top during both the pre-edge-localized mode (ELM) (80%–99% inter-ELM period) and post-ELM (0%–20%) periods. Increasing strongly with the ETG, the ETG transport appears to be stiff. Considering radiation losses, ion thermal transport, and the strong sensitivity of the transport to the ETG, we propose that ETG transport is a plausible mechanism mediating the inter-ELM temperature profile on MAST. Cognizant of the possibility that sharp variations in the pedestal profiles may violate the local approximation, we conducted global nonlinear simulations; the results are in good agreement with local simulations except near the pedestal top, where extended radial structures and high transport levels (far beyond experimental) develop in the local simulations. We quantify and parameterize the discrepancy between local and global simulations by calculating the ratio of the radial correlation length to a length scale representative of the profile curvature. When this ratio is sufficiently small, local and global simulations agree as expected.

Exploring life-history traits of an endangered plant (Vicia biennis L.) to support the conservation of marginal populations

We aimed to investigate the reproduction-related traits of Vicia biennis L., an endangered and poorly known wetland species in its western marginal populations (in Hungary), and discuss the conservational and ecological implications. We measured the mass, viability, and physical dormancy of half-year-old seeds in five in-situ collected seed lots, while potential seed longevity (i.e., seed bank type) was estimated from repeatedly germinating subsamples from four ex-situ collected seed lots for 3–8 years. Plant survival, flowering, and seed production were studied in different light-, irrigation-, and competition conditions in a botanical garden experiment. We found that 1) half-year-old seeds have a high germination capacity (78–100%), 2) and high level of physical dormancy (72–100%) in all examined Hungarian populations, and 3) the seeds can preserve their germination capacity for more than five years, although their viability sharply decreases, probably falling below 10% within ten years, when they are stored at room temperature. The results of the botanical garden experiment suggested that 1) the species is annual, not biennial; 2) it shows strong sensitivity to precipitation and low competitiveness for water; and 3) it can produce hundreds of seeds even in suboptimal (dry or shady) conditions. Although the species was found to be well-adapted to a temporally heterogeneous environment, its future vulnerability can increase depending on the duration of dry seasons. Further investigation of genetic diversity and soil seed bank is needed to estimate the actual vulnerability of the species while strengthening the populations through seed sowing, and additional vegetation control in the habitats is suggested.

Exploring the Surface Oxidation and Environmental Instability of 2H-/1T'-MoTe2 Using Field Emission-Based Scanning Probe Lithography.

An unconventional approach for the resistless nanopatterning 2H- and 1T'-MoTe2 by means of scanning probe lithography is presented. A Fowler-Nordheim tunneling current of low energetic electrons (E = 30-60eV) emitted from the tip of an atomic force microscopy (AFM) cantilever is utilized to induce a nanoscale oxidation on a MoTe2 nanosheet surface under ambient conditions. Due to the water solubility of the generated oxide, a direct pattern transfer into the MoTe2 surface can be achieved by a simple immersion of the sample in deionized water. The tip-grown oxide is characterized using Auger electron and Raman spectroscopy, revealing it consists of amorphous MoO3 /MoOx as well as TeO2 /TeOx . With the presented technology in combination with subsequent AFM imaging it is possible to demonstrate a strong anisotropic sensitivity of 1T'-/(Td )-MoTe2 to aqueous environments. Finally the discussed approach is used to structure a nanoribbon field effect transistor out of a few-layer 2H-MoTe2 nanosheet.

Research on key process parameters and characteristic vibration of steam turbine shafting

Steam turbines are a major source of vibration and noise when submarines are working. How to improve the technological level of steam turbines and reduce the vibration generated by them so that submarines can achieve the purpose of acoustic stealth has always been a hot topic of research. However, blindly improving the overall technological level of steam turbines will not only increase the manufacturing cost but also prolong the manufacturing cycle of submarines. Therefore, this paper makes a reasonable model of each type of process parameter for a certain type of steam turbine design drawings for ships. Subsequently, a dynamic model of the steam turbine shafting based on the transfer matrix was established, and experiments were designed on five groups of design parameters with strong vibration sensitivity of the steam turbine shafting. The vibration response calculation under multiple process parameters of the steam turbine shafting was carried out in combination with the two. The connection between the key process parameters of the steam turbine shafting and the characteristic vibration was established, and the influence mechanism of the key process parameters of the steam turbine shafting on the characteristic vibration was analyzed.

Hydration State and Rheologic Stratification of the Lithospheric Mantle Beneath the North Anatolian Fault, Turkey

AbstractWe present constraints on the hydration state and rheology of the lithospheric mantle beneath the North Anatolian fault zone (NAFZ). Peridotite xenoliths from the Biyikali and Çorlu volcanic centers record deformational microstructures consistent with shearing in a lithosphere‐scale transcurrent fault system. Analysis by Fourier transform infrared spectroscopy indicates that nominally anhydrous phases retain some OH−, but bulk rock concentrations are generally restricted to <50 ppm H2O by weight. From the rock microstructure, we determined differential stress magnitude and active deformation mechanism(s); combined with estimates of hydration state, we constrained the rheology. Recrystallized grain size piezometry shows that the mantle beneath the NAFZ sustained differential stresses of 10–20 MPa, largely independent of depth. The dominant deformation mechanism(s) change with depth; xenoliths extracted from shallower depths record evidence for grain size‐sensitive creep possibly in the presence of melt. At intermediate depths, both dislocation creep and grain size‐sensitive mechanisms were active, and we did not observe evidence for deformation in the presence of melt. The deepest samples were dominated by dislocation creep. The strong temperature sensitivity of creep mechanisms, combined with the low variability in differential stress, contributes to a stratified viscosity profile ranging from 1018 Pa s for the deepest samples to >1022 Pa s at shallower depths (assuming a melt‐free rheology). Although difficult to quantify from the rock record, melt likely reduced the viscosity of the shallow lithospheric mantle. Vertical stratification in viscosity beneath the NAFZ, the result of melt‐present deformation and/or transitions in the dominant deformation mechanism, has important consequences for the seismic cycle of strike‐slip fault systems.

Temperature sensing: A potassium channel as cold sensor in the rain tree Samanea saman

The rain tree Samanea saman folds its leaves upon rainfall. New results now indicate that rain perception is in fact a temperature-sensing process, and that Samanea possess an ion channel with a strong temperature sensitivity that is involved in leaf movement.

Reimagining local worlds: Wen village conservation and regeneration by Amateur Architecture Studio

This article theorises the local world as a conceptual scaffold for future conservation and regeneration. It aims to catalyse a theoretical dialogue across the East and West to understand how ordinary places and lifeworlds are preserved, reproduced, and possibly reimagined. The local worlds discussed herein are conceptualised as the worlds of many, the worlds of relating, the worlds of structuring and the worlds of becoming. These four cardinal points present a source of open-endedness and futurity for contemporary architectural reinterpretation. The current article examines the characterisation of the Wen village conservation and regeneration project, led by the Amateur Architecture Studio in Zhejiang Province of China (2012–2016), as an architectural reimagination of local worlds, with a strong sensitivity to the ordinary places and lifeworlds of rural China and juxtapositions of new local worlds within old worlds. By piecing together the flows and fragments of the architectural process, this article shows how locally situated designs and dynamics have shaped the project in both its formation and afterlife, both ethically and contemporarily. Using close readings of popular and oral accounts from both architect’s and users’ perspectives, this article extends the case study by broadening theoretical conversations about contemporary architecture’s capacity to reimagine local worlds.

Information multiplexing from optical holography to multi-channel metaholography

Abstract Holography offers a vital platform for optical information storage and processing, which has a profound impact on many photonic applications, including 3D displays, LiDAR, optical encryption, and artificial intelligence. In this review, we provide a comprehensive overview of optical holography, moving from volume holography based on optically thick holograms to digital holography using ultrathin metasurface holograms in nanophotonics. We review the use of volume holograms for holographic multiplexing through the linear momentum selectivity and other approaches and highlight the emerging use of digital holograms that can be implemented by ultrathin metasurfaces. We will summarize the fabrication of different holographic recording media and digital holograms based on recent advances in flat meta-optics and nanotechnology. We highlight the rapidly developing field of metasurface holography, presenting the use of multi-functional metasurfaces for multiplexing holography in the use of polarization, wavelength, and incident angle of light. In the scope of holographic applications, we will focus on high bandwidth metasurface holograms that offer the strong sensitivity to the orbital angular momentum of light. At the end, we will provide a short summary of this review article and our perspectives on the future development of the vivid holography field.

Establishment of a Colloidal Gold Immunochromatographic Method for the Detection of Cypermethrin in Tobacco.

In this study, the establishment of a colloidal gold immunochromatographic method for the detection of cypermethrin in tobacco was achieved by using colloidal gold immunochromatography: strong specificity and high sensitivity of cypermethrin semi-antigens and encapsulants were prepared during the study. The best colloidal gold solution was prepared by spectrophotometer and transmission electron microscope screening; the preparation process of gold-labeled antibodies was optimized, and finally the product of colloidal gold rapid detection test strips for cypermethrin was developed. The results of technical parameters and detection indexes showed that the detection limit of cypermethrin in tobacco was 1 mg/kg, and there was no cross-reaction with bifenthrin, cypermethrin, cyfluthrin and phenothrin, and the detection results of 30 tobacco samples were consistent with those of gas chromatography.

The effect of NaCl-induced corrosion on Ti60′s hot salt stress corrosion cracking

The hot salt stress corrosion cracking (HSSCC) behaviour of Ti60 alloy at 600 °C in moist air has been studied, focusing on NaCl-induced corrosion as an embrittlement mechanism. NaCl-induced active corrosion was proven to happen at the crack front. This corrosion process promoted the loss of metal and the ingress of O, leading to quick O embrittlement of the alloy. In the alloy, the β phase can somewhat inhibit HSSCC due to its less sensitivity to corrosion-promoted O ingress. Phase boundaries have strong corrosion sensitivity to NaCl-induced corrosion. Thus, phase boundaries are prone to cracking.

The establishment and application of a dual Nano-PCR detection method for feline calicivirus and feline herpesvirus type I.

Feline calicivirus (FCV) and Feline herpesvirus type I (FHV-I) are the main pathogens causing upper respiratory tract infections in cats, and some wild animals. These two viruses always coinfection and cause serious harm to pet industry and wild animals protection. Established a rapid and accurate differential diagnosis method is crucial for prevention and control of disease, however, the current main detection method for these two viruses, either is low sensitivity (immunochromatographic strip), or is time-consuming and cannot differential diagnosis (conventional single PCR). Nanoparticle-assisted polymerase chain reaction (Nano-PCR) is a recently developed technique for rapid detection method of virus and bacteria. In this study, we described a dual Nano-PCR assay through combining the nanotechnology and PCR technology, which for the clinical simultaneous detection of FCV and FHV-I and differential diagnosis of upper respiratory tract infections in cats or other animals. Under optimized conditions, the optimal annealing temperature for dual Nano-PCR was 51.5°C, and specificity test results showed it had no cross reactivity to related virus, such as feline panleukopenia virus (FPV), feline Infectious peritonitis virus (FIPV) and rabies virus (RABV). Furthermore, the detection limit of dual Nano-PCR for FCV and FHV-I both were 1 × 10-8 ng/μL, convert to number of copies of virus DNA was 6.22 × 103copies/μL (FCV) and 2.81 × 103copies/μL (FHV-I), respectively. The dual Nano-PCR detected result of 52 cat clinical samples, including ocular, nasal and faecal swabs, and (3 FCV-positive samples), was consistent with ordinary PCR and the clinical detection results. The dual Nano-PCR method established in this study with strong specificity and high sensitivity can be used for virus nucleic acid (FCV and FHV-I) detection of clinical samples of feline upper respiratory tract infections feline calicivirus and feline herpesvirus while providing support for the early diagnosis of cats that infected by FCV and FHV-I.

Implementation and empirical evaluation of a quantum machine learning pipeline for local classification.

In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood of the considered element. A well-known locality technique is the k-nearest neighbors (k-NN) algorithm, of which several quantum variants have been proposed; nevertheless, they have not been employed yet as a preliminary step of other QML models. Instead, for the classical counterpart, a performance enhancement with respect to the base models has already been proven. In this paper, we propose and evaluate the idea of exploiting a quantum locality technique to reduce the size and improve the performance of QML models. In detail, we provide (i) an implementation in Python of a QML pipeline for local classification and (ii) its extensive empirical evaluation. Regarding the quantum pipeline, it has been developed using Qiskit, and it consists of a quantum k-NN and a quantum binary classifier, both already available in the literature. The results have shown the quantum pipeline's equivalence (in terms of accuracy) to its classical counterpart in the ideal case, the validity of locality's application to the QML realm, but also the strong sensitivity of the chosen quantum k-NN to probability fluctuations and the better performance of classical baseline methods like the random forest.

Effect of aging temperature on corrosion resistance of 316L stainless steel in simulated proton exchange membrane fuel cell cathode environment

The effects of aging temperature on the corrosion behavior of 316 L stainless steel in a simulated cathodic environment of a proton exchange membrane fuel cell (PEMFC) were investigated by potentiodynamic polarization curves, electrochemical impedance spectroscopy (EIS), double-loop electrochemical potential response (DL-EPR). The results show that the corrosion resistance of 316 L is significantly degraded after aging at 750–900 °C for 1 h in the simulated cathodic environment of PEMFC. A strong sensitivity to intergranular corrosion takes place after aging at 850 °C, and chromium rich M23C6 carbides precipitate on grain boundaries. The precipitation of M23C6 leads to discontinuity of the passivation film near the grain boundaries, resulting in Cr-depleted zones at the grain boundaries, increasing intergranular corrosion, and decreasing corrosion resistance. When the aging temperature exceeds 950 °C, grain boundary precipitation of M23C6 does not occur and the corrosion resistance is improved. At an aging temperature of 1050 °C, a dense Cr2O3 film is formed on the surface of the alloy during the polarization test, giving it the best corrosion resistance in the simulated cathodic environment of PEMFC.

Ceramic hybrid nanofiber-based elastic scaffold pressure sensor with good sensitivity, breathability, and washability

Flexible pressure sensors have recently attracted increasing attention. Piezoresistive sensors are widely used in wearable devices, human health monitoring, electronic skin, human-computer interactions, and other fields owing to their good sensitivity and rapid response. The assembly of a pressure sensor using SnO2–ZnO hybrid nanofibers is proposed in this study to improve its performance. The heterojunction at the interface between ZnO and SnO2 facilitates electron transfer from SnO2 to ZnO by suppressing electron-hole recombination and accelerating electron motion in ZnO. The scaffold sensor, as compared to other reported pressure sensors, exhibits better flexibility, optimized sensitivity, and pressure sensing range, and can detect dynamic pressure over a wide range (0–60 kPa). The sensor exhibits strong sensitivity (11.5 kPa−1), fast response and recovery times of 42 and 28 ms, respectively, and durable cyclic stability (600 cycles). This sensor can quickly detect human motion from subtle deformations, including weak changes in the joints of the fingers, wrists, and elbows, with a bending degree of approximately 20°. Owing to its excellent performance, breathability, and washability, it can be directly attached to skin or clothing for long-term use and has broad application prospects in wearable electronics.

Air pollution trapping in the Dresden Basin from gray-zone scale urban modeling

Abstract. The microscale variability of urban air pollution is essentially driven by the interaction between meteorology and urban topography, which remains challenging to represent spatially accurately and computationally efficiently in urban dispersion models. Natural topography can additionally exert a considerable amplifying effect on urban background pollution, depending on atmospheric stability. This requires an equally important representation in models, as even subtle terrain-height variations can enforce characteristic local flow regimes. In this model study, the effects of urban and natural topography on the local winds and air pollution dispersion in the Dresden Basin in the Eastern German Elbe valley are investigated. A new, efficient urban microscale model is used within a multiscale air quality modeling framework. The simulations that consider real meteorological and emission conditions focus on two periods in late winter and early summer, respectively, as well as on black carbon (BC), a key air pollutant mainly emitted from motorized traffic. As a complement to the commonly used mass concentrations, the particle age content (age concentration) is simulated. This concept, which was originally developed to study hydrological reservoir flows in a Eulerian framework, is adapted here for the first time for atmospheric boundary-layer modeling. The approach is used to identify stagnant or recirculating orographic air flows and resulting air pollution trapping. An empirical orthogonal function (EOF) analysis is applied to the simulation results to attribute the air pollution modes to specific weather patterns and quantify their significance. Air quality monitoring data for the region are used for model evaluation. The model results show a strong sensitivity to atmospheric conditions, but generally confirm increased BC levels in Dresden due to the valley location. The horizontal variability of mass concentrations is dominated by the patterns of traffic emissions, which overlay potential orography-driven pollutant accumulations. Therefore, an assessment of the orographic impact on air pollution is usually inconclusive. However, using the age-concentration metric, which filters out direct emission effects, previously undetected spatial patterns are discovered that are largely modulated by the surface orography. The comparison with a dispersion simulation assuming spatially homogeneous emissions also proves the robustness of the orographic flow information contained in the age-concentration distribution and shows it to be a suitable metric for assessing orographic air pollution trapping. The simulation analysis indicates several air quality hotspots on the southwestern slopes of the Dresden Basin and in the southern side valley, the Döhlen Basin, depending on the prevailing wind direction.

Identification of the allergenic sensitizing potential of bisphenol A substitutes used in the industry.

Bisphenol (BP-)A is a chemical used in Europe to produce polycarbonate plastics and epoxy resin or as colour developer in thermal paper. Due to its toxicity, BPA presence was restricted by European regulations. Therefore, substitute chemicals are replacing BPA. To assess the allergenic sensitizing potential of 27 substitutes to BPA used in the industry. The expression of two costimulatory molecules and six cytokines were analysed by flow cytometry in mouse bone marrow-derived dendritic cells (BMDCs) exposed to the chemicals. All substances except one induced overexpression of at least one receptor and were thus identified as having allergenic sensitizing potential. Based on the BMDC model, they were classified as extreme (1 out of 27), strong (20 out of 27) and moderate (5 out of 27) sensitizers. BPA was classified as a moderate sensitizer and BPF was the only substitute classified as a non-sensitizer. The more potent substitutes induced more than 2-fold secretion of CCL3, CCL4 and/or CCL5 by dendritic cells. Most of the BPA substitutes tested in this study have an allergenic sensitizing potential; 24 of them being more potent than BPA itself. Only BPE, BPF and 2,4-BPS appeared to be weaker sensitizers than BPA.

On taking advantage of opportunistic meta-knowledge to reduce configuration spaces for automated machine learning

The optimisation of a machine learning (ML) solution is a core research problem in the field of automated machine learning (AutoML). This process can require searching through complex configuration spaces of not only ML components and their hyperparameters but also ways of composing them together, i.e. forming ML pipelines. Optimisation efficiency and the model accuracy attainable for a fixed time budget suffer if this pipeline configuration space is excessively large. A key research question is whether it is both possible and practical to preemptively avoid costly evaluations of poorly performing ML pipelines by leveraging their historical performance for various ML tasks, i.e. meta-knowledge. This paper approaches the research question by first formulating the problem of configuration space reduction in the context of AutoML. Given a pool of available ML components, it then investigates whether previous experience can recommend the most promising subset to use as a configuration space when initiating a pipeline composition/optimisation process for a new ML problem, i.e. running AutoML on a new dataset. Specifically, we conduct experiments to explore (1) what size the reduced search space should be and (2) which strategy to use when recommending the most promising subset. The previous experience comes in the form of classifier/regressor accuracy rankings derived from either (1) a substantial but non-exhaustive number of pipeline evaluations made during historical AutoML runs, i.e. ‘opportunistic’ meta-knowledge, or (2) comprehensive cross-validated evaluations of classifiers/regressors with default hyperparameters, i.e. ‘systematic’ meta-knowledge. Overall, numerous experiments with the AutoWeka4MCPS package, including ones leveraging similarities between datasets via the relative landmarking method, suggest that (1) opportunistic/systematic meta-knowledge can improve ML outcomes, typically in line with how relevant that meta-knowledge is, and (2) configuration-space culling is optimal when it is neither too conservative nor too radical. However, the utility and impact of meta-knowledge depend critically on numerous facets of its generation and exploitation, warranting extensive analysis; these are often overlooked/underappreciated within AutoML and meta-learning literature. In particular, we observe strong sensitivity to the ‘challenge’ of a dataset, i.e. whether specificity in choosing a predictor leads to significantly better performance. Ultimately, identifying ‘difficult’ datasets, thus defined, is crucial to both generating informative meta-knowledge bases and understanding optimal search-space reduction strategies.