Multiplexed structures out of dual optically coupled 4D microtoroids fabricated with two-photon polymerization and their biosensing-relevant application are discussed. We analyze the sensing performance for detection of small pH value alteration around the neutral solution for independent and optically coupled resonators that are characterized by appearance of the additional signal for back-radiated mode. We demonstrate detectability of pH value changes at the level of 0.01 by using the signal back-radiated mode in the coupled regime together with the material-enhanced spectral shift.
AI-based solution for sensing of dynamical processes with optical microresonators to enable automatic and self-adjusting predictions of the probed parameters is proposed. The sensing chip is characterized by dense packaging of more than one thousand individual spherical microcavities with strong spectral variations grouped on up to 10 sensing areas of different functionalities. Spectral diversity of microresonators supplemented with different deep learning models have been utilized to ensure detection of small entities of biochemical nature in the complex aqueous environment.
A deep-learning powered mechanism for detection of complex biochemical compounds with the WGM sensor which comprises various amount of sensing channels and different selectivity with hundreds of individual microcavities in each has been proposed. We demonstrate the possibility for accurate prediction of the biomarker’s concentrations in the complex aqueous solutions by training on non-linear highly specific time series signals of the intensities radiated by the microcavities. The impact of the deep-learning engine architecture and type of the sensor sample on the sensing performance has been studied.
This paper discusses an application of machine-learning solution for processing of the dynamical sensing responses collected with a multiplexed microresonator detector. Performance of a long short-term memory network (LSTM) out of bidirectional and dropout layers is analyzed on example of the experimental data collected for a temporal gradient of the local refractive index. We experimentally demonstrate the possibility for analyte parameters prediction with accuracy of >99% based on a set of complex non-linear highly specific time sequences of the intensities radiated by the microcavities which is obtained within a timescale 4 times shorter than required to reach the steady state. Optimization possibilities in terms of the number of microresonator signals to consider for the LSTM network training along with the complexity of its architecture are analyzed.
This paper discusses an intelligent sensing solution based on the phenomenon of the whispering gallery modes in the optical microcavities realized within an affordable instrument configuration featuring simultaneous excitation of multiple microresonators by a single frequency laser along with parallel collection of their signal. Supplemented with a machine learning engine for complex signal interpretation the sensor demonstrates the accuracy of 10-6 for refractive index prediction and more than 98% for protein concentration classification.
A design and application of distributed microresonator-based systems for biochemical sensing are discussed. Two different configurations of the distributed sensors based on integration of the spherical glass microresonators and fabrication of the toroidal microresonators via two-photon polymerization have been demonstrated. The performance of both configurations for biochemical sensing has been analysed.
A model that describes polymerization unit formation in multilayer absorbing medium for direct laser writing has been proposed. Properties of separate layers including surface roughness, illumination geometry, pulsed laser source and photosensitive material are parameters of the discussed model. A set of simulations has been carried out where the influence of the refractive indexes relation, layer thickness, roughness of the particular layers with respect to the structuring depth on the structure-model match and reproducibility has been analysed and discussed.
In this paper we discuss a near real time digital holographic imaging algorithm achieving ∼4 fps operation speed on a common central processing unit. The hologram recording is performed in the off-axis geometry in the transmission mode. The algorithm follows a standard angular spectrum method routine and utilizes experimental calibration of the optical instrument for aberration correction. The main limiting factor is related to the size of the initial hologram and its Fourier transform (∼57% of the total execution duration). The performance of the approach is tested on different transparent and semi-transparent samples for reconstruction of sample topography and object in-depth allocation.
An approach for localization of the randomly distributed spherical whispering gallery mode resonators on the multiplexed chip has been proposed. The method consists of several steps: chip image enhancement, sensing unit edge detection and light out-coupling area/sensing unit matching. The proposed approach has been successfully tested for detection of the bovine serum albumin protein solution.
An approach for the automated whispering gallery mode (WGM) signal decomposition and its parameter estimation is discussed. The algorithm is based on the peak picking and can be applied for the preprocessing of the raw signal acquired from the multiplied WGM-based biosensing chips. Quantitative estimations representing physically meaningful parameters of the external disturbing factors on the WGM spectral shape are the output values. Derived parameters can be directly applied to the further deep qualitative and quantitative interpretations of the sensed disturbing factors. The algorithm is tested on both simulated and experimental data taken from the bovine serum albumin biosensing task. The proposed solution is expected to be a useful contribution to the preprocessing phase of the complete data analysis engine and is expected to push the WGM technology toward the real-live sensing nanobiophotonics.
New approach to perform a real-time biochemical component detection based on simultaneous analysis of spectral changes of whispering gallery modes (WGM) and fluorescence markers used for biochemical components tagging. Microcavity array sensor was chosen as detection unit. Experimental data on detection of bovine serum albumin protein solution using both techniques simultaneously is represented.
New approach to increase density of sensing units for higher precision as well as the selectivity of biological components under investigation in microcavity evanescent wave optical sensor systems is proposed. Long-term functionalization
results of array sensor cells by different agents are represented.
Microcavity array sensor has been developed for biomedical objects identification. Experimental data on detection and identification of variety of biochemical agents, such as proteins, microelements, antibiotic of different generation etc. in both single and multi-component solutions analyzed on the light scattering of whispering gallery mode optical resonance are represented.
Experimental data on detection and identification of variety of biochemical agents, such as proteins, microelements,
antibiotic of different generation etc. in both single and multi component solutions under varied in wide range
concentration analyzed on the light scattering parameters of whispering gallery mode optical resonance based sensor are
represented. Multiplexing on parameters and components has been realized using developed fluidic sensor cell with
fixed in adhesive layer dielectric microspheres and data processing. Biochemical component identification has been
performed by developed network analysis techniques. Developed approach is demonstrated to be applicable both for
single agent and for multi component biochemical analysis.
Novel technique based on optical resonance on microring structures, plasmon resonance and identification tools has been
developed. To improve a sensitivity of microring structures microspheres fixed by adhesive had been treated previously
by gold nanoparticle solution. Another technique used thin film gold layers deposited on the substrate below adhesive.
Both biomolecule and nanoparticle injections caused considerable changes of optical resonance spectra. Plasmonic gold
layers under optimized thickness also improve parameters of optical resonance spectra. Biochemical component
identification has been also performed by developed network analysis techniques both for single and for multi
component solution. So advantages of plasmon enhancing optical microcavity resonance with multiparameter
identification tools is used for development of a new platform for ultra sensitive label-free biomedical sensor.
Experimental data on detection and identification of variety of biochemical agents, such as proteins (albumin, interferon, C reactive protein), microelements (Na+, Ca+), antibiotic of different generations, in both single and multi component solutions under varied in wide range concentration are represented. Analysis has been performed on the light scattering parameters of whispering gallery mode (WGM) optical resonance based sensor with dielectric microspheres from glass and PMMA as sensitive elements fixed by spin - coating techniques in adhesive layer on the surface of substrate or directly on the coupling element. Sensitive layer was integrated into developed fluidic cell with a digital syringe. Light from tuneable laser strict focusing on and scattered by the single microsphere was detected by a CMOS camera. The image was filtered for noise reduction and integrated on two coordinates for evaluation of integrated energy of a measured signal. As the entrance data following signal parameters were used: relative (to a free spectral range) spectral shift of frequency of WGM optical resonance in microsphere and relative efficiency of WGM excitation obtained within a free spectral range which depended on both type and concentration of investigated agents. Multiplexing on parameters and components has been realized using spatial and spectral parameters of scattered by microsphere light with developed data processing. Biochemical component classification and identification of agents under investigation has been performed by network analysis techniques based on probabilistic network and multilayer perceptron. Developed approach is demonstrated to be applicable both for single agent and for multi component biochemical analysis.
New opportunity to improve a sensetivity of a label-free biomolecule detection in sensing systems based on microcavity evanescent wave optical sensors has been recently found and is being under intensive development. Novel technique based on combination of optical resonance on microring structures with plasmon resonance. Recently developed tools based on neural network data processing can realize real-time identification of biological agents. So combining advantages of plasmon enhancing optical microcavity resonance with identification tools can give a new platform for ulta sensitive label-free biomedical sensor. Our developed technique used standard glass and polymer microspheres as sensetive elements. They are fixed in the solution flow by adhesive layer on the surface being in the field of evanescence wave. Sensitive layer have been treated by gold nanoparticel (GN) solution. Another technique used thin film gold layers deposited on the substrate below adhesive. The light from a tuneable diode laser is coupled into the microsphere through a prism and was sharply focussed on the single microsphere. Images were recorded by CMOS camera. Normalized by free spectral range resonance shift of whispering gallery mode (WGM) and a relative efficiency of their excitation were used as input data for biomolecule classification. Both biomolecules and NP injection was obtained caused WGM spectra modification. But after NP treatment spectral shift and intensity of WGM resonances in biomolecule solutions increased. WGM resonances in microspheres fixed on substrate with gold layer with optimized layer thickness in biomolecule solutions also had higher intensity and spectra modification then without gold layer.
Experimental data on optical resonance spectra of whispering gallery modes of dielectric microspheres in antibiotic
solutions under varied in wide range concentration are represented. Optical resonance was demonstrated could be
detected at a laser power of less than 1 microwatt. Several antibiotics of different generations: Amoxicillin,
Azithromycin, Cephazolin, Chloramphenicol, Levofloxacin, Lincomicin Benzylpenicillin, Riphampicon both in deionized
water and physiological solution had been used for measurements. Both spectral shift and the structure of
resonance spectra were of specific interest in this investigation. Drag identification has been performed by developed
multilayer perceptron network. The network topology was designed included: a number of the hidden layers of
multilayered perceptron, a number of neurons in each of layers, a method of training of a neural network, activation
functions of layers, type and size of a deviation of the received values from required values. For a network training the
method of the back propagation error in various modifications has been used. Input vectors correspond to 6 classes of
biological substances under investigation. The result of classification was considered as positive when each of the region,
representing a certain substance in a space: relative spectral shift of an optical resonance maxima - relative efficiency of
excitation of WGM, was singly connected.
It was demonstrated that the approach described in the paper can be a promising platform for the development of
sensitive, lab-on-chip type sensors that can be used as an express diagnostic tools for different drugs and instrumentation
for proteomics, genomics, drug discovery, and membrane studies.
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