Sensing and Bio-Sensing Research最新文献

The maintenance of good milk quality standards is still a challenge for dairy farmers that requires a rapid control system that is compatible with both the environment and production cost. A patented Hazard Analysis and Critical Control Points-like remote diagnostic (sensor driven) system named BEST was implemented to enable both quality monitoring and traceability in the dairy chain. BEST was daily tested in a dairy farm to identify new reliable indicators of anomalies (safety and quality) in milk production based on a Machine-Learning approach. The database obtained in four months of sensoristic analysis was subjected to a statistical study with AI algorithm to identify outliers. BEST proved ability to spot cows with specific characteristics in the whole herd's database. In particular, AI highlighted the sole cow from a different breed, the only cow that recently gave birth and the only cow in the herd that received treatment with the drug Micospectone® (Lincomycin + Spectinomycin).

An infrared (IR) tunable perfect meta-absorber (TPM) is presented, which is configured of metal-insulator-metal (MIM) structure. The unit cell of the TPM device is composed of two opposite T-shaped and two rod-shaped Au resonators on the reflective mirror layer. The optimized feature size of TPM can achieve perfect absorption and the resonant wavelength can be tuned from blue-shift first and then red-shift by increasing the gap size between two T-shaped resonators continuously. The tuning process of red shift is linear. By changing the distance between two rod-shaped resonators, the TPM device exhibits red shift linearly. Moreover, TPM device shows polarization-dependent characteristic that the main absorption resonance can be attenuated from 100% to 0% by changing polarization angle from 0° to 90° of incident IR light. The resonance of TPM device has an excellent linear relationship with the ambient refractive index, and the sensitivity and maximum figure of merit (FOM) are calculated as 1249 nm/RIU and 21 RIU⁻¹, respectively. These results prove that the design of the proposed TPM device is very suitable for the use of IR sensing, polarization switching, and refractive index sensing applications.

The current study outlines four extremely sensitive SPR-based cancer sensors that have the highest sensitivity to date for the quick and accurate diagnosis of six major cancer cells, including adrenal gland, breast (t1/t2), cervical, blood, and skin cancer, where the first two are blamed as the most fatal and infected ones, respectively. Four nitrides—AlN, GaN, InN and Si₃N₄—are employed in four distinct sensor configurations to identify the aforementioned six cancer cell types. The sensor with AlN is found the most suitable for skin/breast (type-1) cancer detection with sensitivity (S) and quality factor (QF) of 370/385 Deg/RIU and 92/113 RIU⁻¹, the GaN based structure for adrenal gland/blood cancer detection with S and QF of 400/400 Deg/RIU and 108/102 RIU⁻¹, the InN-based structure for breast cancer (type-2) detection with S and QF of 414 Deg/RIU and 108 RIU⁻¹, and finally the Si₃N₄-based structure for cervical cancer detection with S and QF of 341 Deg/RIU and 92 RIU⁻¹, respectively. Furthermore, AlN and GaN based sensors can sense all the six types of cancer cells with a minimum sensitivity of around 230 Deg/RIU, an accepted number as per some recently reported results. Finite element method-based simulator COMSOL is used to study and optimize the structures considering an operating wavelength of 633 nm, anticipating for a low-cost sensor prototype. The highest reported sensitivity in this study is 414 Deg/RIU with QF of 108 RIU⁻¹ for the Au-Ag-InN configuration for the breast cancer (type-2) detection.

The present work demonstrates the applications of a nanocomposite of the cellulose polymer and ZnO nanoparticles with 1:1 weight ratio, prepared by a green assisted precipitation method for a high-performance resistive type humidity sensor. The morphology and nanostructure of prepared cellulose/ZnO composite were characterization by X-ray diffraction, Fourier transform infrared spectroscopy, field emission scanning electron microscopy and transmission electron microscopy are confirmed the decoration of ZnO nanoparticles on cellulose polymer matrix surface. The humidity sensing martials was coated onto the interdigital electrodes. The sensitivity, response/recover and stability studies performance of fabricated humidity sensors have been monitored in 5 to 98% relative humidity (%RH) at room temperature (37 °C). The response and recovery times of the fabricated sensors are observed as ≈ 26 s and ≈ 53 s respectively, and the sensitivity factor (S_f) is 2656 ± 103 Ω. Possible mechanisms of the humidity sensor based on water-induced conductivity increase are discussed. Also, the energy band of cellulose/ZnO nanocomposite was simulated by density functional theory (DFT) studies. The cellulose/ZnO nanocomposite humidity sensor has great potential for practical field applications.

In this paper, an optical chemical sensor is proposed to detect some toxic gases such as Methane (CH₄), Nitrogen (N₂), Nitrogen dioxide (NO2), and Carbon monoxide (CO). This type of chemical sensor consists of graphene ribbons and Kapton materials as sensing elements. Also, exploits electromagnetic properties such as absorption in terms of signal transducing. These kinds of small-scale, flexible architectures and advanced detection techniques are in demand to identify toxic gases as well. To develop the proposed chemical sensor, this study describes the structure in the aspect of an equivalent circuit model (ECM) mathematically, while the full-wave simulation (FEM) is performed as the reference. Acceptable agreement between the ECM and FEM simulations is shown while an interesting tuning capability against external stimulation is obtained. It should be noted that the ECM approach is performed in just a few seconds while the FEM simulation takes more than 3 h to produce results. In addition, the maximum error is around the second absorption peak and is less than 4%.The main contribution of this work is introducing a simple structure to distinguish several toxic gases in the sub-THz gap (0.1–2 THz). Additionally, ample simulations are performed to verify the sensor's reliability. According to the simulation results, the proposed meta-structure can appropriately show different peak frequencies and even different numbers of absorption peaks against different concentrations of toxic gases. Additionally, due to the ultra-thin nature of the graphene and the flexibility of the Kapton, the proposed sensor can be wearable while it is considered non-invasive testing.

For the first time, a novel electrochemical biosensor was fabricated based on modification of a rotating glassy carbon electrode with multi-walled carbon nanotubes-ionic liquid, and molecularly imprinted polymers (MIPs) in which renin (Rn), and aliskiren (AK) were used as templates. By immersion the biosensor in Rn, AK, and their binary system (AK-Rn) solutions, the species (Rn, AK, and AK-Rn) were entrapped within the pathways of the MIPs. These processes and investigation of the inhibition of the Rn by AK helped us to obtain higher electrochemical signals for a good monitorization of the system. The effects of experimental parameters on response of the biosensor to AK were optimized by a small central composite design to obtain the highest response. Hydrodynamic cyclic voltametric (HCV), hydrodynamic differential pulse voltammetric (HDPV), and hydrodynamic linear sweep voltammetric (HLSV) data obtained and recorded in order to be analyzed by classical methods and multivariate curve resolution-alternating least squares (MCR-ALS) as an advanced chemometric method. The results of molecular dockings, classical and chemometric analyses confirmed that the Rn was strongly inhibited by the AK which was good evidence to develop a novel biosensing system for determination of Rn. A novel biosensor was developed for determination of the Rn which had an acceptable performance in determination of Rn in the range of 0–9 fM. This approach opened a new way for investigation of enzymes' inhibition, and developing a new generation of electrochemical biosensors for medical and biomedical applications.

This research work introduces a Surface Plasmon Resonance (SPR) based Photonic Crystal Fiber (PCF) sensor using gold nanowires as plasmonic material designed for the detection of various cancer cells, boasting remarkable sensitivity and ease of fabrication. The sensor's structure was devised and analyzed using the Finite Element Method (FEM) of COMSOL v5.5, with a focus on exploring the impact of varying geometric parameters on its overall performance. The simulation utilized extremely fine mesh elements to ensure the utmost accuracy. Excitation between the core and plasmonic modes is achieved using Gold (Au) nanowires. The determination of the sensor's wavelength sensitivity involves assessing the resonance wavelength shift between samples of normal and cancerous cells. Simultaneously, the measurement of amplitude sensitivity is accomplished through a comparison of the amplitudes associated with their respective confinement losses. Various parameters of the PCF were varied during the experimentation, leading to the achievement of exceptionally high Amplitude Sensitivity (AS) of $-273.16$ RIU⁻¹, $-286.58$ RIU⁻¹, $-455.59$ RIU⁻¹, $-698.76$ RIU⁻¹, $-1172.72$ RIU⁻¹ and $-1971.30$ RIU⁻¹ for Skin Cancer, Cervical Cancer, Blood Cancer, Adrenal Gland Cancer, Breast Type-1 Cancer and Breast Type-2 Cancer respectively. Additionally, the Wavelength Sensitivity (WS) values were found to be $6500$ nm/RIU, $14583.33$ nm/RIU, $16428.57$ nm/RIU, $25714.28$ nm/RIU, $32857.14$ nm/RIU, and $35714.28$ nm/RIU for the same cancer types, respectively. The achieved resolutions for wavelength sensitivity are $1.54\times {10}^{-5}$ RIU, $6.86\times {10}^{-6}$ RIU, $6.09\times {10}^{-6}$ RIU, $3.89\times {10}^{-6}$ RIU, $3.04\times {10}^{-6}$ RIU and $2.80\times {10}^{-6}$ RIU, while the resolutions for amplitude sensitivity are $7.32\times {10}^{-5}$ RIU, $8.37$