High Detection Range Research Articles

Numerical analysis and detection of cancer cells using a dual-core gold coated plasmonic biosensor

In this research, a simple dual-core photonic crystal fiber (PCF) that leverages surface plasmon resonance (SPR) to operate as a bio-sensor in the visible to near-infrared range is proposed. An external analyte channel is constructed by putting a gold plasmonic metal layer over the fiber sensor. A numerical investigation employing the wavelength interrogation approach reveals that the maximal wavelength and amplitude sensitivity are 22000 nm/RIU and 1561.4 RIU-1, respectively. The high wavelength resolution for this suggested sensor is 4.54 × 10−6 RIU, and that suggests a high detection range. The proposed sensor is designed for the detection of any molecules from the RI range 1.33 to 1.42, and it can detect six different types of cancerous cells (the refractive index of different cancerous cells varied from 1.380 to 1.401) in this range. According to the obtained numerical results, MCF-7 cells exhibit the highest wavelength sensitivity of 6428.6 nm/RIU and amplitude sensitivity of 766.361 RIU-1. Furthermore, the sensor will be able to detect different viruses, proteins, DNA/RNA strands, and other things in this sensing range. This sensor offered the potential to detect biochemical solutions and biological samples due to its high sensitivity and simple shape.

A skin-inspired biomimetic strategy to fabricate cellulose enhanced antibacterial hydrogels as strain sensors

With the development of wearable devices, the fabrication of strong, tough, antibacterial, and conductive hydrogels for sensor applications is necessary but remains challenging. Here, a skin-inspired biomimetic strategy integrated with in-situ reduction has been proposed. The self-assembly of cellulose to generate a cellulose skeleton was essential to realize the biomimetic structural design. Furthermore, in-situ generation of silver nanoparticles on the skeleton was easily achieved by a heating process. This process not only offered the excellent antibacterial property to hydrogels, but also improved the mechanical properties of hydrogels due to the elimination of negative effect of silver nanoparticles aggregation. The highest tensile strength and toughness could reach 2.0 MPa and 11.95 MJ/m3, respectively. Moreover, a high detection range (up to 1300%) and sensitivity (gauge factor = 4.4) was observed as the strain sensors. This study provides a new horizon to fabricate strong, tough and functional hydrogels for various applications in the future.

Correction Model for Metal Oxide Sensor Drift Caused by Ambient Temperature and Humidity.

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.

Spider-Web and Ant-Tentacle Doubly Bio-Inspired Multifunctional Self-Powered Electronic Skin with Hierarchical Nanostructure.

For the practical applications of wearable electronic skin (e‐skin), the multifunctional, self‐powered, biodegradable, biocompatible, and breathable materials are needed to be assessed and tailored simultaneously. Integration of these features in flexible e‐skin is highly desirable; however, it is challenging to construct an e‐skin to meet the requirements of practical applications. Herein, a bio‐inspired multifunctional e‐skin with a multilayer nanostructure based on spider web and ant tentacle is constructed, which can collect biological energy through a triboelectric nanogenerator for the simultaneous detection of pressure, humidity, and temperature. Owing to the poly(vinyl alcohol)/poly(vinylidene fluoride) nanofibers spider web structure, internal bead‐chain structure, and the collagen aggregate nanofibers based positive friction material, e‐skin exhibits the highest pressure sensitivity (0.48 V kPa−1) and high detection range (0–135 kPa). Synchronously, the nanofibers imitating the antennae of ants provide e‐skin with short response and recovery time (16 and 25 s, respectively) to a wide humidity range (25–85% RH). The e‐skin is demonstrated to exhibit temperature coefficient of resistance (TCR = 0.0075 °C−1) in a range of the surrounding temperature (27–55 °C). Moreover, the natural collagen aggregate and the all‐nanofibers structure ensure the biodegradability, biocompatibility, and breathability of the e‐skin, showing great promise for practicability.

Semantic Segmentation of Radar Detections using Convolutions on Point Clouds

For autonomous driving, radar sensors provide superior reliability regardless of weather conditions as well as a significantly high detection range. State-of-the-art algorithms for environment perception based on radar scans build up on deep neural network architectures that can be costly in terms of memory and computation. By processing radar scans as point clouds, however, an increase in efficiency can be achieved in this respect. While Convolutional Neural Networks show superior performance on pattern recognition of regular data formats like images, the concept of convolutions is not yet fully established in the domain of radar detections represented as point clouds. The main challenge in convolving point clouds lies in their irregular and unordered data format and the associated permutation variance. Therefore, we apply a deep-learning based method introduced by Point CNN that weights and permutes grouped radar detections allowing the resulting permutation invariant cluster to be convolved. In addition, we further adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Finally, we show that our network outperforms state-of-the-art approaches that are based on Point Net++ on the task of semantic segmentation of radar point clouds.

DFT analysis of nitrogen and Boron doped Graphene sheet as lead detector

In the last few decades, the heavy metal contamination in water has affected the human health in a big way and hence an area of great scientific concern. Among these heavy metals, lead (Pb) is highly toxic and leads to the adverse effect on human body, causing severe health problems like anemia, hypertension, renal impairment, immunotoxicity, toxicity to the reproductive organs, cancer etc. here, the Boron and Nitrogen functionalized graphene sheets (B-Graphene and N-Graphene), have been analysed for its suitability to detect the lead, by computing the band structure, density of state, charge transfer and Current-Voltage characteristics, using the Density Functional Theory based ab-initio approach. The computed results demonstrate that the N-doped Graphene is relatively better sensor for lead in aqueous environment, with a sensitivity of ~ 67% at lower bias voltage, high detection range and short recovery time. However, the Pb sensitivity of the N-Graphene sheet at low bias is very high 329% in absence of water environment.

Numerical development of high performance quasi D-shape PCF-SPR biosensor: An external sensing approach employing gold

In this paper, a proper concept of a kind of photonic crystal fiber known as D-shaped PCF sensor based on SPR or Surface Plasmon Resonance phenomenon has been discussed. The analysis has been performed numerically using FEM or Finite Element Method and PML (Perfectly Matched Layer) boundary conditions. Modal analysis has been performed keeping the smallest mesh as possible. The main mechanism of the sensor is the interaction between the core guided light beam and the metal layer surface that generates free electrons. The proposed sensor shows a refractive index detection range of 1.42–1.46 RIU, with a metal thickness variation of 35 nm-45 nm and a pitch variation of 1.8 μm–2.0 μm. The sensor obtained the highest sensitivity of 15000 nm/RIU with a maximum 6.67 × 10-6 RIU resolution, using the wavelength interrogation method. Average spectral sensitivity is 8000 nm/RIU. The amplitude interrogation method found a maximum sensitivity of 230 RIU−1 with a resolution of 4.348 × 10-5 RIU. The sensor also shows high linearity fitting and Figure of Merit (FoM). For ease of design, high detection range, high wavelength and amplitude sensitivity, less fabrication complexity, high linearity and stability, the sensor can be a great competitor of the PCF sensors previously proposed.

A review on the sensing performances for three different ternary hybrid (Pd/RGO/TiO2-NTs, Pd/RGO/MnO2-NFs and Pd/RGO/WO3-NFs) gas sensor device structures

Present paper aims to study the comparative analysis of the sensing performance between three fabricated metal oxide based ternary hybrid device structures [Pd/RGO/TiO2-nanotubes (NTs), Pd/RGO/MnO2 nanoflowers (NFs) and Pd/RGO/WO3-nanoflowers (NFs)]. In each of the ternary structure, the oxide nanostructure play the role of basic sensing matrix where due to extremely high surface to volume ratio of the corresponding nanostructure, the availability of adsorption sites increases, which leads to increased sensitivity or response magnitude (%RM) of the sensor. On the contrary, RGO acts on a high mobility (~ 1600 cm2/V-s) distributed connector among the neighbouring nanostructure leading towards improved response or recovery kinetics. Finally, Pd nanoparticles due to their catalytic activity (which reduces activation energy requirement for target species dissociation), bring forth as the substantial reduction in operating temperature. By employing those ternary hybrid structure sensing layers, room temperature (27 °C) alcohol sensing with a very high detection range (1–700 ppm) was achieved with appreciably fast response time (~ 12–20 s) and recovery time (~ 23–30 s) without compromising the response magnitude (80–98% at 700 ppm). Finally, a comparative analysis of the sensing performances for the above-mentioned three ternary hybrid gas sensors was illustrated.

Understanding the Improved Vapor Sensor Device Performance of Dual Surface Engineered WO3 Nanospheres Using Semi-Quantitative Energy Band Model

Fast responding room temperature ethanol sensor device, having high detection range, was developed employing dual surface engineered (using palladium (Pd) and Reduced Graphene oxide (RGO)) WO3 nanospheres as the sensing layer. Three different types of vapor sensor devices, viz. i) Pd/WO3 (Sensor 1), ii) RGO/WO3 (Sensor 2) and iii) Pd/RGO/WO3 (Sensor 3) were fabricated to study the influence of each of the elements and to account for its exact contribution in improving the gas sensor device performance towards alcohol vapors. Dual surface engineered device (Sensor 3) was found to offer remarkably superior sensing performance in terms of response magnitude, response time and recovery time with a very high detection range (1-700 ppm) at room temperature (~ 27 °C), compared to any of its single surface modified device counterpart (Sensor 1 or Sensor 2). A comprehensive discussion elucidating the individual role of RGO and Pd on WO3 has also been presented employing a semi-quantitative energy band model in correlation with the corresponding electrical equivalent circuit elements.

Green Synthesis of Reduced Graphene Oxide Decorated with Few-Layered MoS2-Nanoroses and Au/Pd/Ag Trimetallic Nanoparticles for Ultrasensitive Label-Free Immunosensing Platforms

The conventional synthetic methods for graphene oxide are based on the reaction of graphite with mixed strong oxidants, which involves the risk of explosion and toxic gases. Herein, we report a facile, safe, and green method to synthesize green graphene oxide (denoted as gGO) using sodium periodate as an effective oxidizing agent feature with lower Raman intensity (ID/IG = 0.65) and higher C/O atomic ratio ( ∼ 5.15). We also discuss the mechanism underlying the direct growth of MoS2 nanoroses (MoS2-NRs) on the in situ synthesized green reduced graphene oxide (grGO), in which the MoS2-NRs are assembled from few-layered MoS2 nanosheets. Due to the highly exposed active edge sites of MoS2-NRs and good electrical coupling with the underlying grGO, the resulting grGO/MoS2-NRs nanocomposite exhibits excellent electrocatalytic activity. Furthermore, the addition of L-proline assisted Au/Pd/Ag trimetallic nanoparticles to the nanocomposite provide a suitable label-free platform for direct antibody immobilization and increase the effective surface area. The as-prepared grGO/MoS2-NRs/AuPdAg nanocomposite was employed as a label-free immunosensing platform for the ultrasensitive detection of ochratoxin A, a food mycotoxin. The proposed immunosensor exhibits an excellent high detection range from 10 fg mL−1 to 150 ng mL−1 and limit of detection (LOD) of 5 fg mL−1 (S/N = 3).

Performance of colorimetric methods for the analysis of low levels of manganese in water

The present paper describes a laboratory investigation conducted with the main aim to compare two colorimetric methods (1-(2-pyridylazo)-2-naphthol (PAN) from HACH® and porphyrin ligand (T-(4CP)P)) with ICP analyses for the evaluation of low concentrations of manganese in water. The colorimetric porphyrin method was found to provide reliable results compared to those obtained by inductively coupled plasma - optical emission spectroscopy (ICP-OES) even at low Mn2+ concentrations (<10 µg L−1). The presence of MnO2 particles results in an overestimation of the dissolved Mn2+ if unfiltered samples are analyzed by ICP and PAN methods. The presence of particles does not significantly impact sample analysis by porphyrin (T-(4CP)P) colorimetry. Although ICP-OES is still the analytical approach of reference, the porphyrin colorimetric method displays a high detection range and precision and could be a suitable on-site alternative to the current widespread use of the PAN method. In all cases, for both colorimetric methods, the use of Rochelle salt is recommended to alleviate calcium cation interference.

Osteoarthritic sclerotic subchondral osteoblasts secreted elevated concentration of Fibulin-3 fragments in vitro

Osteoarthritic sclerotic subchondral osteoblasts secreted elevated concentration of Fibulin-3 fragments in vitro