Abstract

In this work, we explore the use of Radio frequency (RF) based sensing in uniquely identifying nanoparticles and biosamples by utilizing multi-parameter characterization. Most conventional RF sensors suffer from an inability to perform proper identification due to the dependence of the methodology on a single parameter variation. This paper combats this drawback by proposing a technique that extracts multiple parameters from the frequency response of a narrow band, IDC based RF sensor for performing the analysis. Furthermore, to illustrate the method, the identification is performed for different samples, including Biotin, BSA-conjugated Biotin, Gold Nanorods and Silica-coated Goldnanospheres. The results indicate that multi-parameter characterization yields unique spectral signatures for all samples, and thus identification can efficiently be performed.

Abstract

The insulin hormone is stored in the hexameric form and dissociates to the dimeric form and finally to the active monomeric form. When insulin secretion is impaired, thereby affecting various metabolic processes, as a final response, insulin analogs are subcutaneously injected before meals to facilitate glucose metabolism. Depending on the molecular details, analogs are rapid or slow acting based on the dissociation rate of the dimer to monomer. Insulin aspart is a recombinant human insulin analog, acting faster than regular human insulin. Despite its practical and elementary importance, the process of insulin aspart dimer dissociation is relatively unknown. Here, we combined molecular dynamics simulations and umbrella sampling to characterize the energetic and structural features of dissociation of the insulin aspart dimer. Like previous studies on human insulin (another well studied analog), insulin aspart can also display a wide spectrum of pathways for dimer dissociation from dissociation happening without a major change in the monomer structure to dissociation that is coupled with unfolding of a protein. Additionally, water plays a vital role in the dissociation of the insulin aspart by stabilizing the monomers in the dissociated state. Our study shows the molecular details, such as the variation in the structure and orientation and conformational changes along the minimum energy pathways in the process of dissociation of the insulin aspart dimer.

Abstract

In this study, we characterize biosamples using a microstrip patch based Radio Frequency (RF) sensor by integrating both resonant and non-resonant parameters. In particular, the resonance frequency, the resonance amplitude, −10 dB bandwidth, range of operation, and return loss at center frequency were employed to perform the analysis. The idea was to utilize multiple parameters to build a comprehensive understanding of different bio-samples. In order to demonstrate the working of the methodology, two sets of analytes were studied-(i) aqueous solutions of biomolecules and (ii) body fluids. For both sets of analytes, the method demonstrated significant efficiency in performing qualitative characterization.

Abstract

We present our work in using enhanced sampling method of metadynamics in combination with machine learning techniques to speed-up the exploration of all relevantphase space of biomolecular systems that keeps the two related tasks separate: (a) collective variable should cluster configurations into correct states, that is, classify states accurately and (b) collective variable should improve the local sampling in the ‘current free energy basin’, that is, shape the collective variable in line with the data collected (regression). We demonstrate the method on the alanine tetrapeptide system in explicit solvent. We use Elliptic Envelope (EE) classifier to identify outliers from a particular class, and each class will use a different fully-connected autoencoder (ANC) whose bottleneck layer (latent variable) will be used as collective variable in Well-Tempred Metadynamics simulation to generate more data. Comparison against benchmark indicate that the proposed scheme can traverse all relevant phase space

Abstract

In this paper, we develop a Kinetic Monte Carlo (KMC) based model to simulate the atomistic growth behavior of metallic nanoparticle in the solution of its ions and understand the growth pattern. KMC is used as it can model the growth of nanoparticle to the timescale comparable with that actual experiments. Energy minimas where metal atom can adsorb or surface diffuse have been found using Shrake–Rupley algorithm and conjugate gradient energy minimization algorithm. The rate of adsorption, desorption, and surface diffusion was artificially accelerated, and decelerated to observe different shapes. We demonstrate the model by a case study on growth of gold nanoparticles and find that shapes like the truncated octahedron, cuboctahedron, truncated cube, cube, rhombic dodecahedron, and sphere are seen to form by the model during the growth of NP. This KMC model provides a simplistic understanding of the mechanism and progression of shapes that may be seen during the growth of nanoparticle; these in turn may provide clues to synthesize NP of specific shapes.

Abstract

In this paper, the role of the shape of gold nanoparticles in improving the efficiency of sensing of biomolecules using Radio Frequency (RF) has been studied. The analysis has been performed with the help of an Interdigitated Capacitor (IDC) based structure using the CAD tool, High Frequency Structure Simulator (HFSS). The frequency response and E-Field distribution of various shapes of gold nanoparticles have been analysed. The results indicate that the detection efficiency of RF sensing of biomolecules improves significantly in the presence of gold nanoparticles. The shape, as well as the position of biomolecules around the gold nanoparticles, affects the efficiency of detection. These results open up new avenues for developing highly efficient nanoparticle-Radiofrequency-based sensors.

Abstract

Radio Frequency (RF) based sensors have shown great potential for the qualitative and quantitative analysis of different chemical and biological entities. In this work, the feasibility of performing RF based sensing by using gold particles has been explored with the help of the computational tool, HFSS (High Frequency Structure Simulator). The analysis is performed for an Interdigitated Capacitor based sensor. Various physical parameters of cylindrical gold particles such as radius and thickness have been varied, and the electrical parameters of the sensor systems have been studied. In particular, the reflection coefficients have been determined for various cases. In addition to the physical parameters, different configurations of the gold particles such as their linear and non-linear arrangements have been analyzed. The analyses offer promising results for the development of efficient sensing platforms.

Abstract

The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a -NetFF machine learning model where, the neural network was trained to predict the di↵erence in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.

Abstract

This paper utilizes RF Sensing to develop a facile methodology for the determination of Critical Micelle Concentration (CMC) of surfactants. The study is undertaken with the help of an Interdigitated Capacitor based RF Sensor. Due to change in permittivity, the sensor yields distinct resonant frequencies when introduced to solutions of varying concentration of Sodium Dodecyl Sulphate as the electrolyte. As the surfactant approaches micellization, the resonant frequency shift drastically alters, and this sudden transition is used to determine the value of CMC. The proposed technique estimates the CMC of SDS to be 6.5 x10 -3 M The introduced method is effective and less cumbersome as compared to traditional techniques.

Abstract

In this paper, RF resonant sensor is designed for measurement of complex permittivity of unknown sample. It takes advantage of peano curve fractal geometry (i.e. higher capacitance in minimum area). It operates in the ISM (industrial, scientific and medical) frequency band of 4-5 GHz. An empirical model of sensor is developed for the accurate calculation of complex permittivity of an unknown sample in terms of shifts in the resonant frequencies and reflection coefficients (S11) under loaded condition. Due to homogeneous E-field at fractal capacitor, placement position of sample will not affect the sensitivity. Significant frequency shifts as high as 60 M Hz, 80 M Hz, 116 M Hz and 134 M Hz are observed for relative permittivity of 5.4, 6.5, 8.9 and 10.6 respectively.