Rational Design of Peptides Derived from Odorant-Binding Proteins for SARS-CoV-2-Related Volatile Organic Compounds Recognition

Peptides are promising molecular-binding elements and have attracted great interest in novel biosensor development. In this study, a series of peptides derived from odorant-binding proteins (OBPs) were rationally designed for recognition of SARS-CoV-2-related volatile organic compounds (VOCs). Ethanol, nonanal, benzaldehyde, acetic acid, and acetone were selected as representative VOCs in the exhaled breath during the COVID-19 infection. Computational docking and prediction tools were utilized for OBPs peptide characterization and analysis. Multiple parameters, including the docking model, binding affinity, sequence specification, and structural folding, were investigated. The results demonstrated a rational, rapid, and efficient approach for designing breath-borne VOC-recognition peptides, which could further improve the biosensor performance for pioneering COVID-19 screening and many other applications

Design and validation of microfluidic parameters of a microfluidic chip using fluid dynamics

The internal fluidic parameters of microfluidic channels must be analyzed to solve fundamental microfluidic problems, including microscale transport problems involving thermal analysis, chemical reactivity, velocity, pressure drop, etc., for developing good-quality chemical and biological products. Therefore, the characterization and optimization of the interaction of chemical and biological solutions through microfluidic channels are vital for fluid flow design and engineering for quality assurance in microfluidic platforms. As the internal structures and kinetics of microfluidic channels are becoming increasingly complex, experiments involving optimal fluidic and transport designs are challenging to perform with high accuracy. However, highly integrated simulation tools can guide researchers without specialized computational fluid backgrounds to design numerical prototypes of highly integrated devices. In this study, a microfluidic chip with two inlet wells and one outlet well was fabricated from polydimethylsiloxane following which simulations were performed using an ANSYS Fluent tool influenced by computational fluid dynamics at a nearly identical scale. The pressure drop and velocity profiles of the interaction of two pH buffer solutions (pH 4 and 10) through the designed microfluidic chip were qualitatively estimated from experimental data analysis and validated with the simulation results obtained from the CFD-influenced ANSYS Fluent tool

Optimization of Microchannels and Application of Basic Activation Functions of Deep Neural Network for Accuracy Analysis of Microfluidic Parameter Data

The fabrication of microflow channels with high accuracy in terms of the optimization of the proposed designs, minimization of surface roughness, and flow control of microfluidic parameters is challenging when evaluating the performance of microfluidic systems. The use of conventional input devices, such as peristaltic pumps and digital pressure pumps, to evaluate the flow control of such parameters cannot confirm a wide range of data analysis with higher accuracy because of their operational drawbacks. In this study, we optimized the circular and rectangular-shaped microflow channels of a 100 mu m microfluidic chip using a three-dimensional simulation tool, and analyzed concentration profiles of different regions of the microflow channels. Then, we applied a deep learning (DL) algorithm for the dense layers of the rectified linear unit (ReLU), Leaky ReLU, and Swish activation functions to train and test 1600 experimental and interpolation of data samples which obtained from the microfluidic chip. Moreover, using the same DL algorithm, we configured three models for each of these three functions by changing the internal middle layers of these models. As a result, we obtained a total of 9 average accuracy values of ReLU, Leaky ReLU, and Swish functions for a defined threshold value of 6 x 10(-5) using the trial-and-error method. We applied single-to-five-fold cross-validation technique of deep neural network to avoid overfitting and reduce noises from data-set to evaluate better average accuracy of data of microfluidic parameters

Ultrathin Platinum Film Hydrogen Sensors with a Twin-T Type Notch Filter Circuit

In recent years, hydrogen energy has garnered attention as a potential solution for mitigating greenhouse gas emissions. However, concerns regarding the inherent risk of hydrogen gas leakage and potential explosions have necessitated the development of advanced sensors. Within our research group, we have innovated an ultrathin platinum (Pt) film hydrogen sensor that gauges resistance changes in Pt thin films when exposed to hydrogen gas. Notably, the sensitivity of each sensor is contingent upon the thickness of the Pt film. To address the challenge of detecting hydrogen using multiple sensors, we integrated the ultrathin Pt film as a resistance element within a twin-T type notch filter. This filter exhibits a distinctive reduction in output signals at a specific frequency. The frequency properties of the notch filter dynamically alter with changes in the resistance of the Pt film induced by hydrogen exposure. Consequently, the ultrathin Pt film hydrogen sensor monitors output signal variations around the notch frequency, responding to shifts in frequency properties. This innovative approach enables the electrical control of sensor sensitivity by adjusting the operating frequency in proximity to the notch frequency. Additionally, the simultaneous detection of hydrogen by multiple sensors was successfully achieved by interconnecting sensors with distinct notch frequencies in series

Estimation of core size distribution of magnetic nanoparticles using high-Tc SQUID magnetometer and particle swarm optimizer-based inversion technique

In this work, the core size estimation technique of magnetic nanoparticles (MNPs) using the static magnetization curve obtained from a high-Tc SQUID magnetometer and a metaheuristic inversion technique based on the Particle Swarm Optimizer (PSO) algorithm is presented. The high-Tc SQUID magnetometer is constructed from a high-Tc SQUID sensor coupled by a flux transformer to sense the modulated magnetization signal from a sample. The magnetization signal is modulated by the lateral vibration of the sample on top of a planar differential detection coil of the flux transformer. A pair of primary and excitation coils are utilized to apply an excitation field parallel to the sensitive axis of the detection coil. Using the high-Tc SQUID magnetometer, the magnetization curve of a commercial MNP sample (Resovist) was measured in a logarithmic scale of the excitation field. The PSO inverse technique is then applied to the magnetization curve to construct the magnetic moment distribution. A multimodal normalized log-normal distribution was used in the minimization of the objective function of the PSO inversion technique, and a modification of the PSO search region is proposed to improve the exploration and exploitation of the PSO particles. As a result, a good agreement on the Resovist magnetic core size was obtained between the proposed technique and the non-negative least square (NNLS) inversion technique. The estimated core sizes of 8.0484 nm and 20.3018 nm agreed well with the values reported in the literature using the commercial low-Tc SQUID magnetometer with the SVD and NNLS inversion techniques. Compared to the NNLS inversion technique, the PSO inversion technique had merits in exploring an optimal core size distribution freely without being regularized by a parameter and facilitating an easy peak position determination owing to the smoothness of the constructed distribution. The combination of the high-Tc SQUID magnetometer and the PSO-based reconstruction technique offers a powerful approach for characterizing the MNP core size distribution, and further improvements can be expected from the recent state-of-the-art optimization algorithm to optimize further the computation time and the best objective function value

A sensitive ac magnetometer using a resonant excitation coil for characterization of magnetic fluid in nonlinear magnetization region

In order to tailor magnetic nanoparticles (MNPs) for intended applications, it is important to unravel their dynamics with respect to excitation magnetic field. In this work, we report on the development of a sensitive AC magnetometer using a resonant excitation coil for this purpose. The excitation coil fabricated from a Litz wire is connected to a capacitor network to effectively reduce the impedance of the circuit. The high efficiency showed by the excitation coil enables investigation of MNP’s dynamics in the nonlinear magnetization region. We demonstrate the sensitivity of the developed system by measuring the harmonics of a multicore iron oxide nanoparticle solution down to 300 ng/ml of iron concentration. We experimentally show that the first harmonic component is not completely ‘transparent’ to the diamagnetic background of the carrier liquid compared to the higher harmonics. We also demonstrate the complex magnetization measurement of the iron oxide nanoparticles in solution and dry states from 3 Hz to 18 kHz. A highly sensitive exploration of MNPs’ dynamics can be expected using the developed AC magnetometer

A sensitive magnetometer utilizing high-Tc SQUID for magnetic property characterization

Magnetometer is one of the indispensable instruments utilized for the magnetic property characterization of materials, where it evaluates the magnetic response originated from the materials. The non-invasive magnetic technique has promoted magnetometer to be utilized in new applications such as in biomedical applications. In this work, we report the development of a magnetometer utilizing a high critical temperature superconducting quantum interference device (high-Tc SQUID) and a flux transformer composed of an induction coil. The high-Tc SQUID is used in order to realize high sensitivity, compact, and low-running cost magnetometer for biomedical applications such as characterization of magnetic nanoparticles. A first-order planar gradiometer with a compensation coil was used as the detection coil to achieve high sensitivity and cancellation factor. We fabricate an electromagnet with primary and small secondary excitation coils to enable a wide range of the excitation magnetic field with a high resolution. To reduce the magnetic field’s drift, we apply a digital feedback program to control the electrical current of the electromagnet. The performance of the developed system is demonstrated by measuring the magnetization curve and AC responses of an iron oxide composite sample. The sensitivity showed by the developed magnetometer reveals its potential for a highly sensitive magnetic property characterization

Magnetic characterization change by solvents of magnetic nanoparticles in liquid-phase magnetic immunoassay

Liquid-phase magnetic immunoassay (MIA) using magnetic nano-particles (MNPs) has been studied as a more rapid method compared to optical methods for inspecting proteins and viruses. MIA can estimate the number of conjugated antibodies without being washed differently from conventional optical immunoassay. However, in the case of the liquid phase, it is considered that the magnetic properties of MNPs are affected by physical properties such as viscosity and impurity substances such as biological substances contained in the blood. In this study, the effect of sodium chloride (NaCl) in buffer and serum solution was evaluated to reveal the effect of serum because the sodium (Na+) and chloride (Cl-) ions in the serum dominate ion balance of blood. The measurement results of AC magnetic susceptibility and a dynamic light scattering (DLS) showed that the aggregation of MNPs was largely affected by the concentration of NaCl. This effect of the NaCl could be explained by shielding of the surface charge of MNPs by ions in the solution. Although the concentrations of NaCl in the buffer and serum solution were almost same, we found that MNPs were aggregated more in their size for those in the serum solution because of other impurities, such as proteins. These results suggest evaluation of effects of the contaminants in serum and optimization of polymer coatings of MNPs could be important factors to realize measurements of magnetic immunoassay with high accuracy. (C) 2019 Author(s)