Enhancing protein trapping efficiency of graphene oxide-polybutylene succinate nanofiber membrane via molecular imprinting.

Filtration of biological liquids has been widely employed in biological, medical, and environmental investigations due to its convenience; many could be performed without energy and on-site, particularly protein separation. However, most available membranes are universal protein absorption or sub-fractionation due to molecule sizes or properties. SPMA, or syringe-push membrane absorption, is a quick and easy way to prepare biofluids for protein evaluation. The idea of initiating SPMA was to filter proteins from human urine for subsequent proteomic analysis. In our previous study, we developed nanofiber membranes made from polybutylene succinate (PBS) composed of graphene oxide (GO) for SPMA. In this study, we combined molecular imprinting with our developed PBS fiber membranes mixed with graphene oxide to improve protein capture selectivity in a lock-and-key fashion and thereby increase the efficacy of protein capture. As a model, we selected albumin from human serum (ABH), a clinically significant urine biomarker, for proteomic application. The nanofibrous membrane was generated utilizing the electrospinning technique with PBS/GO composite. The PBS/GO solution mixed with ABH was injected from a syringe and transformed into nanofibers by an electric voltage, which led the fibers to a rotating collector spinning for fiber collection. The imprinting process was carried out by removing the albumin protein template from the membrane through immersion of the membrane in a 60% acetonitrile solution for 4 h to generate a molecular imprint on the membrane. Protein trapping ability, high surface area, the potential for producing affinity with proteins, and molecular-level memory were all evaluated using the fabricated membrane morphology, protein binding capacity, and quantitative protein measurement. This study revealed that GO is a controlling factor, increasing electrical conductivity and reducing fiber sizes and membrane pore areas in PBS-GO-composites. On the other hand, the molecular imprinting did not influence membrane shape, nanofiber size, or density. Human albumin imprinted membrane could increase the PBS-GO membrane's ABH binding capacity from 50 to 83%. It can be indicated that applying the imprinting technique in combination with the graphene oxide composite technique resulted in enhanced ABH binding capabilities than using either technique individually in membrane fabrication. The suitable protein elution solution is at 60% acetonitrile with an immersion time of 4 h. Our approach has resulted in the possibility of improving filter membranes for protein enrichment and storage in a variety of biological fluids.

Sensing Mechanisms of Rough Plasmonic Surfaces for Protein Binding of Surface Plasmon Resonance Detection.

Surface plasmon resonance (SPR) has been utilized in various optical applications, including biosensors. The SPR-based sensor is a gold standard for protein kinetic measurement due to its ultrasensitivity on the plasmonic metal surface. However, a slight change in the surface morphology, such as roughness or pattern, can significantly impact its performance. This study proposes a theoretical framework to explain sensing mechanisms and quantify sensing performance parameters of angular surface plasmon resonance detection for binding kinetic sensing at different levels of surface roughness. The theoretical investigation utilized two models, a protein layer coating on a rough plasmonic surface with and without sidewall coatings. The two models enable us to separate and quantify the enhancement factors due to the localized surface plasmon polaritons at sharp edges of the rough surfaces and the increased surface area for protein binding due to roughness. The Gaussian random surface technique was employed to create rough metal surfaces. Reflectance spectra and quantitative performance parameters were simulated and quantified using rigorous coupled-wave analysis and Monte Carlo simulation. These parameters include sensitivity, plasmonic dip position, intensity contrast, full width at half maximum, plasmonic angle, and figure of merit. Roughness can significantly impact the intensity measurement of binding kinetics, positively or negatively, depending on the roughness levels. Due to the increased scattering loss, a tradeoff between sensitivity and increased roughness leads to a widened plasmonic reflectance dip. Some roughness profiles can give a negative and enhanced sensitivity without broadening the SPR spectra. We also discuss how the improved sensitivity of rough surfaces is predominantly due to the localized surface wave, not the increased density of the binding domain.

Generalized figure of merit for plasmonic dip measurement-based surface plasmon resonance sensors.

We propose a theoretical framework to analyze quantitative sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip position, and figure of merits for different surface plasmon operating conditions for a Kretschmann configuration. Several definitions and expressions of the figure of merit have been reported in the literature. Moreover, the optimal operating conditions for each figure of merit are, in fact, different. In addition, there is still no direct figure of merit comparison between different expressions and definitions to identify which definition provides a more accurate performance prediction. Here shot-noise model and Monte Carlo simulation mimicking the noise behavior in SPR experiments have been applied to quantify standard deviation in the SPR plasmonic dip measurements to evaluate the performance responses of the figure of merits. Here, we propose and formulate a generalized figure of merit definition providing a good performance estimation to the detection limit. The measurement parameters employed in the figure of merit formulation are identified by principal component analysis and machine learning. We also show that the proposed figure of merit can provide a good estimation for the surface plasmon resonance performance of plasmonic materials, including gold and aluminum, with no need for a resource-demanding computation.

Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy.

Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. However, phase images recovered from artificial intelligence are questionable in their correctness and reliability. Here, we propose a theoretical framework to analyze and quantify the performance of a deep learning-based phase retrieval algorithm for quantitative phase imaging microscopy by comparing recovered phase images to their theoretical phase profile in terms of their correctness. This study has employed both lossless and lossy samples, including uniform plasmonic gold sensors and dielectric layer samples; the plasmonic samples are lossy, whereas the dielectric layers are lossless. The uniform samples enable us to quantify the theoretical phase since they are established and well understood. In addition, a context aggregation network has been employed to demonstrate the phase image regression. Several imaging planes have been simulated serving as input and the label for network training, including a back focal plane image, an image at the image plane, and images when the microscope sample is axially defocused. The back focal plane image plays an essential role in phase retrieval for the plasmonic samples, whereas the dielectric layer requires both image plane and back focal plane information to retrieve the phase profile correctly. Here, we demonstrate that phase images recovered using deep learning can be robust and reliable depending on the sample and the input to the deep learning.

Cuff-Less Blood Pressure Prediction from ECG and PPG Signals Using Fourier Transformation and Amplitude Randomization Preprocessing for Context Aggregation Network Training.

This research proposes an algorithm to preprocess photoplethysmography (PPG) and electrocardiogram (ECG) signals and apply the processed signals to the context aggregation network-based deep learning to achieve higher accuracy of continuous systolic and diastolic blood pressure monitoring than other reported algorithms. The preprocessing method consists of the following steps: (1) acquiring the PPG and ECG signals for a two second window at a sampling rate of 125 Hz; (2) separating the signals into an array of 250 data points corresponding to a 2 s data window; (3) randomizing the amplitude of the PPG and ECG signals by multiplying the 2 s frames by a random amplitude constant to ensure that the neural network can only learn from the frequency information accommodating the signal fluctuation due to instrument attachment and installation; (4) Fourier transforming the windowed PPG and ECG signals obtaining both amplitude and phase data; (5) normalizing both the amplitude and the phase of PPG and ECG signals using z-score normalization; and (6) training the neural network using four input channels (the amplitude and the phase of PPG and the amplitude and the phase of ECG), and arterial blood pressure signal in time-domain as the label for supervised learning. As a result, the network can achieve a high continuous blood pressure monitoring accuracy, with the systolic blood pressure root mean square error of 7 mmHg and the diastolic root mean square error of 6 mmHg. These values are within the error range reported in the literature. Note that other methods rely only on mathematical models for the systolic and diastolic values, whereas the proposed method can predict the continuous signal without degrading the measurement performance and relying on a mathematical model.

Measurement precision enhancement of surface plasmon resonance based angular scanning detection using deep learning.

Angular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications. However, the measurement accuracy and precision of the surface plasmon resonance measurements rely on an accurate measurement of the plasmonic angle. Several methods have been proposed and reported in the literature to measure the plasmonic angle, including polynomial curve fitting, image processing, and image averaging. For intensity detection, the precision limit of the SPR is around 10-5 RIU to 10-6 RIU. Here, we propose a deep learning-based method to locate the plasmonic angle to enhance plasmonic angle detection without needing sophisticated post-processing, optical instrumentation, and polynomial curve fitting methods. The proposed deep learning has been developed based on a simple convolutional neural network architecture and trained using simulated reflectance spectra with shot noise and speckle noise added to generalize the training dataset. The proposed network has been validated in an experimental setup measuring air and nitrogen gas refractive indices at different concentrations. The measurement precision recovered from the experimental reflectance images is 4.23 × 10-6 RIU for the proposed artificial intelligence-based method compared to 7.03 × 10-6 RIU for the cubic polynomial curve fitting and 5.59 × 10-6 RIU for 2-dimensional contour fitting using Horner's method.

Analysis of the surface plasmon resonance interferometric imaging performance of scanning confocal surface plasmon microscopy.

Here, we apply rigorous coupled-wave theory to analyze the optical phase imaging performance of scanning confocal surface plasmon microscope. The scanning confocal surface plasmon resonance microscope is an embedded interferometric microscope interfering between two integrated optical beams. One beam is provided by the central part around the normal incident angle of the back focal plane, and the other beam is the incident angles beyond the critical angle, exciting the surface plasmon. Furthermore, the two beams can form an interference signal inside a confocal pinhole in the image plane, which provides a well-defined path for the surface plasmon propagation. The scanning confocal surface plasmon resonance microscope operates by scanning the sample along the optical axis z, so-called V(z). The study investigates two imaging modes: non-quantitative imaging and quantitative imaging modes. We also propose a theoretical framework to analyze the scanning confocal surface plasmon resonance microscope compared to non-interferometric surface plasmon microscopes and quantify quantitative performance parameters including spatial resolution and optical contrast for non-quantitative imaging; sensitivity and crosstalk for quantitative imaging. The scanning confocal SPR microscope can provide a higher spatial resolution, better sensitivity, and lower crosstalk measurement. The confocal SPR microscope configuration is a strong candidate for high throughput measurements since it requires a smaller sensing channel than the other SPR microscopes.

Analysis of Effects of Surface Roughness on Sensing Performance of Surface Plasmon Resonance Detection for Refractive Index Sensing Application.

This paper provides a theoretical framework to analyze and quantify roughness effects on sensing performance parameters of surface plasmon resonance measurements. Rigorous coupled-wave analysis and the Monte Carlo method were applied to compute plasmonic reflectance spectra for different surface roughness profiles. The rough surfaces were generated using the low pass frequency filtering method. Different coating and surface treatments and their reported root-mean-square roughness in the literature were extracted and investigated in this study to calculate the refractive index sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip intensity, plasmonic dip position, and figure of merit. Here, we propose a figure-of-merit equation considering optical intensity contrast and signal-to-noise ratio. The proposed figure-of-merit equation could predict a similar refractive index sensing performance compared to experimental results reported in the literature. The surface roughness height strongly affected all the performance parameters, resulting in a degraded figure of merit for surface plasmon resonance measurement.

Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application.

A deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10-7 to 10-8 RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10-6 RIU compared to conventional intensity measurement methods of 1.73 × 10-5 RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation.

Fabry-Perot Resonance in 2D Dielectric Grating for Figure of Merit Enhancement in Refractive Index Sensing.

We have recently reported in our previous work that one-dimensional dielectric grating can provide an open structure for Fabry-Perot mode excitation. The grating gaps allow the sample refractive index to fill up the grating spaces enabling the sample to perturb the Fabry-Perot mode resonant condition. Thus, the grating structure can be utilized as a refractive index sensor and provides convenient sample access from the open end of the grating with an enhanced figure of merit compared to the other thin-film technologies. Here, we demonstrate that 2D grating structures, such as rectangular pillars and circular pillars, can further enhance refractive index sensing performance. The refractive index theory for rectangular pillars and circular pillars are proposed and validated with rigorous coupled wave theory. An effective refractive index theory is proposed to simplify the 2D grating computation and accurately predict the Fabry-Perot mode positions. The 2D gratings have more grating space leading to a higher resonant condition perturbation and sensitivity. They also provide narrower Fabry-Perot mode reflectance dips leading to a 4.5 times figure of merit enhancement than the Fabry-Perot modes excited in the 1D grating. The performance comparison for thin-film technologies for refractive index sensing is also presented and discussed.

Performance Analysis of Non-Interferometry Based Surface Plasmon Resonance Microscopes.

Surface plasmon microscopy has been of interest to the science and engineering community and has been utilized in broad aspects of applications and studies, including biochemical sensing and biomolecular binding kinetics. The benefits of surface plasmon microscopy include label-free detection, high sensitivity, and quantitative measurements. Here, a theoretical framework to analyze and compare several non-interferometric surface plasmon microscopes is proposed. The scope of the study is to (1) identify the strengths and weaknesses in each surface plasmon microscopes reported in the literature; (2) quantify their performance in terms of spatial imaging resolution, imaging contrast, sensitivity, and measurement accuracy for quantitative and non-quantitative imaging modes of the microscopes. Six types of non-interferometric microscopes were included in this study: annulus aperture scanning, half annulus aperture scanning, single-point scanning, double-point scanning, single-point scanning, at 45 degrees azimuthal angle, and double-point scanning at 45 degrees azimuthal angle. For non-quantitative imaging, there is a substantial tradeoff between the image contrast and the spatial resolution. For the quantitative imaging, the half annulus aperture provided the highest sensitivity of 127.058 rad/µm2 RIU-1, followed by the full annulus aperture of 126.318 rad/µm2 RIU-1. There is a clear tradeoff between spatial resolution and sensitivity. The annulus aperture and half annulus aperture had an optimal resolution, sensitivity, and crosstalk compared to the other non-interferometric surface plasmon resonance microscopes. The resolution depends strongly on the propagation length of the surface plasmons rather than the numerical aperture of the objective lens. For imaging and sensing purposes, the recommended microfluidic channel size and protein stamping size for surface plasmon resonance experiments is at least 25 µm for accurate plasmonic measurements.

Analysis of Dielectric Waveguide Grating and Fabry-Perot Modes in Elastic Grating in Optical Detection of Ultrasound.

In our previous work, we have demonstrated that dielectric elastic grating can support Fabry-Perot modes and provide embedded optical interferometry to measure ultrasonic pressure. The Fabry-Perot modes inside the grating provide an enhancement in sensitivity and figure of merit compared to thin film-based Fabry-Perot structures. Here, in this paper, we propose a theoretical framework to explain that the elastic grating also supports dielectric waveguide grating mode, in which optical grating parameters control the excitation of the two modes. The optical properties of the two modes, including coupling conditions and loss mechanisms, are discussed. The proposed grating has the grating period in micron scale, which is shorter than the wavelength of the incident ultrasound leading to an ultrasonic scattering. The gap regions in the grating allow the elastic grating thickness to be compressed by the incident ultrasound and coupled to a surface acoustic wave mode. The thickness compression can be measured using an embedded interferometer through one of the optical guided modes. The dielectric waveguide grating is a narrow bandpass optical filter enabling an ultrasensitive mode to sense changes in optical displacement. This enhancement in mechanical and optical properties gives rise to a broader detectable pressure range and figure of merit in ultrasonic detection; the detectable pressure range and figure of merit can be enhanced by 2.7 times and 23 times, respectively, compared to conventional Fabry-Perot structures.

Analysis of Embedded Optical Interferometry in Transparent Elastic Grating for Optical Detection of Ultrasonic Waves.

In this paper, we propose a theoretical framework to explain how the transparent elastic grating structure can be employed to enhance the mechanical and optical properties for ultrasonic detection. Incident ultrasonic waves can compress the flexible material, where the change in thickness of the elastic film can be measured through an optical interferometer. Herein, the polydimethylsiloxane (PDMS) was employed in the design of a thin film grating pattern. The PDMS grating with the grating period shorter than the ultrasound wavelength allowed the ultrasound to be coupled into surface acoustic wave (SAW) mode. The grating gaps provided spaces for the PDMS grating to be compressed when the ultrasound illuminated on it. This grating pattern can provide an embedded thin film based optical interferometer through Fabry-Perot resonant modes. Several optical thin film-based technologies for ultrasonic detection were compared. The proposed elastic grating gave rise to higher sensitivity to ultrasonic detection than a surface plasmon resonance-based sensor, a uniform PDMS thin film, a PDMS sensor with shearing interference, and a conventional Fabry-Perot-based sensor. The PDMS grating achieved the enhancement of sensitivity up to 1.3 × 10-5 Pa-1 and figure of merit of 1.4 × 10-5 Pa-1 which were higher than those of conventional Fabry-Perot structure by 7 times and 4 times, respectively.

Optical detection of ultrasound by lateral shearing interference of a transparent PDMS thin film.

A lateral shearing interferometric technique combined with an 11.6 µm polydimethylsiloxane (PDMS) transparent thin film is proposed and demonstrated for optical detection of ultrasound. We experimentally report the device change of reflectivity with pressure of 5.1×10-7 Pa-1, 9.5 times more sensitive than the critical-angle-based sensor, 31 times more sensitive than the surface-plasmon-based sensor, and comparable to the Fabry-Perot sensor. The objective-lens-based angle scanning characterization setup inspired from a laser scanning system allows direct comparison between the PDMS sensor and critical-angle-based sensor by adjusting the incident angle with a scanning mirror, thereby eliminating optical and electronics system dependence. The sensing element is easily fabricated through spin coating and the detection element incorporated into an existing optical system with minimum modification.

Quantitative Cross-Platform Performance Comparison between Different Detection Mechanisms in Surface Plasmon Sensors for Voltage Sensing.

Surface plasmon Resonance (SPR) has recently been of interest for label-free voltage sensing. Several SPR structures have been proposed. However, making a quantitative cross-platform comparison for these structures is not straightforward due to (1) different SPR measurement mechanisms; (2) different electrolytic solution and concentration in the measurement; and (3) different levels of external applied potential. Here, we propose a quantitative approach to make a direct quantitative comparison across different SPR structures, different electrolytic solutions and different SPR measurement mechanisms. There are two structures employed as example in this theoretical study including uniform plasmonic gold sensor and bimetallic layered structure consisting of uniform silver layer (Ag) coated by uniform gold layer (Ag). The cross-platform comparison was carried by several performance parameters including sensitivity (S), full width half maximum (FWHM) and figure of merit (FoM). We also discuss how the SPR measurement mechanisms enhance the performance parameters and how the bimetallic layer can be employed to enhance the FoM by a factor of 1.34 to 25 depending on the SPR detection mechanism.

Enhancement of Long-Range Surface Plasmon Excitation, Dynamic Range and Figure of Merit Using a Dielectric Resonant Cavity.

In this paper, we report a theoretical framework on the effect of multiple resonances inside the dielectric cavity of insulator-insulator-metal-insulator (IIMI)-based surface plasmon sensors. It has been very well established that the structure can support both long-range surface plasmon polaritons (LRSPP) and short-range surface plasmon polaritons (SRSPP). We found that the dielectric resonant cavity under certain conditions can be employed as a resonator to enhance the LRSPP properties. These conditions are: (1) the refractive index of the resonant cavity was greater than the refractive index of the sample layer and (2) when light propagated in the resonant cavity and was evanescent in the sample layer. We showed through the analytical calculation using Fresnel equations and rigorous coupled wave theory that the proposed structure with the mentioned conditions can extend the dynamic range of LRSPP excitation and enhance at least five times more plasmon intensity on the surface of the metal compared to the surface plasmon excited by the conventional Kretschmann configuration. It can enhance the dip sensitivity and the dynamic range in refractive index sensing without losing the sharpness of the LRSPP dip. We also showed that the interferometric modes in the cavity can be insensitive to the surface plasmon modes. This allowed a self-referenced surface plasmon resonance structure, in which the interferometric mode measured changes in the sensor structure and the enhanced LRSPP measured changes in the sample channel.

Application of confocal surface wave microscope to self-calibrated attenuation coefficient measurement by Goos-Hänchen phase shift modulation.

In this paper, we present a direct method to measure surface wave attenuation arising from both ohmic and coupling losses using our recently developed phase spatial light modulator (phase-SLM) based confocal surface plasmon microscope. The measurement is carried out in the far-field using a phase-SLM to impose an artificial surface wave phase profile in the back focal plane (BFP) of a microscope objective. In other words, we effectively provide an artificially engineered backward surface wave by modulating the Goos Hänchen (GH) phase shift of the surface wave. Such waves with opposing phase and group velocities are well known in acoustics and electromagnetic metamaterials but usually require structured or layered surfaces, here the effective wave is produced externally in the microscope illumination path. Key features of the technique developed here are that it (i) is self-calibrating and (ii) can distinguish between attenuation arising from ohmic loss (k″ Ω ) and coupling (reradiation) loss (k″ c ). This latter feature has not been achieved with existing methods. In addition to providing a unique measurement the measurement occurs of over a localized region of a few microns. The results were then validated against the surface plasmons (SP) dip measurement in the BFP and a theoretical model based on a simplified Green's function.

Adjustable microscopic measurement of nanogap waveguide and plasmonic structures.

We investigate the performance of surface plasmon and Fabry-Perot modes formed between two closely spaced layers. The motivation for this study is twofold: first, to look for modes that may be excited at lower incident angles compared to the usual Kretschmann configuration with similar or superior refractive index responsivity and, second, to develop a simple and applicable method to study these structures over a wide range of separations without recourse to the construction of ad hoc structures. Using back focal plane observation and appropriate signal processing, we show results for the Otto configuration at visible wavelengths at a range of separations not reported hitherto. Moreover, we investigate a hybrid structure we call the Kretschmann-Otto configuration that gives modes that change continuously from a hybridized surface plasmon mode to a zero-order Fabry-Perot mode. The ability to change the separation to small gap distances enables us to examine the Fabry-Perot modes where we show that it has superior refractive index responsivity, by more than an order of magnitude, compared to the Kretschmann configuration.

Graphene-Based Materials for Biosensors: A Review.

The advantages conferred by the physical, optical and electrochemical properties of graphene-based nanomaterials have contributed to the current variety of ultrasensitive and selective biosensor devices. In this review, we present the points of view on the intrinsic properties of graphene and its surface engineering concerned with the transduction mechanisms in biosensing applications. We explain practical synthesis techniques along with prospective properties of the graphene-based materials, which include the pristine graphene and functionalized graphene (i.e., graphene oxide (GO), reduced graphene oxide (RGO) and graphene quantum dot (GQD). The biosensing mechanisms based on the utilization of the charge interactions with biomolecules and/or nanoparticle interactions and sensing platforms are also discussed, and the importance of surface functionalization in recent up-to-date biosensors for biological and medical applications.

Whispering-Gallery Mode Resonators for Detecting Cancer.

Optical resonators are sensors well known for their high sensitivity and fast response time. These sensors have a wide range of applications, including in the biomedical fields, and cancer detection is one such promising application. Sensor diagnosis currently has many limitations, such as being expensive, highly invasive, and time-consuming. New developments are welcomed to overcome these limitations. Optical resonators have high sensitivity, which enable medical testing to detect disease in the early stage. Herein, we describe the principle of whispering-gallery mode and ring optical resonators. We also add to the knowledge of cancer biomarker diagnosis, where we discuss the application of optical resonators for specific biomarkers. Lastly, we discuss advancements in optical resonators for detecting cancer in terms of their ability to detect small amounts of cancer biomarkers.