Photonic Sensor Research Articles

High-Sensitivity and Wide Detection-Range Refractive-Index Sensor Based on Amplitude Change in Slotted Photonic Crystal Nanobeam Cavity

High Q-factor is essential for the realization of high sensitivity photonic crystal-based sensors. The Q-factor is usually optimized for a specific refractive-index (RI) value of the ambient background. However, a small change in the RI reduces the Q value, and therefore limits the performance of the sensor to a narrow RI range. Here, we report a high-sensitivity RI sensor with a wide detection-range based on amplitude change of the fundamental mode in slotted photonic crystal nanobeam cavity. Both wavelength and amplitude sensitivity of 333 nm/RIU and 188/RIU are realized in the RI range from 1.3 to 1.6, respectively. To the best of our knowledge, this represents the widest sensing range ever reported in photonic crystal cavities. Owing to the wide sensing range and the insignificance of the Q value, this approach would find applications in various research areas in integrated lab-on-chip systems for optofluidic- and bio-sensing applications.

Construction of hybridization chain reaction induced optical signal directed change of photonic crystals-DNA hydrogel sensor and its visual determination for aflatoxin B1.

Herein, we have introduced hybridization chain reaction (HCR) into the photonic crystals (PhCs) hydrogel, for the first time, realizing HCR for inducing the change of the optical signal of PhCs hydrogel and using this hydrogel as a sensor for determination of the aflatoxin B1 (AFB1). By using specific sequences as the cross-linker, the extension of the cross-linker by HCR drives the swelling of the hydrogel, and the optical property of 2D PhCs array converts this swelling into a change of the Debye diffraction ring. Moreover, by further selecting the aptamer to construct the cross-linker, the hydrogel is also endowed with a unique capability for AFB1, making the hydrogel a novel sensor based on the signal amplification strategy. The results show that the designed hairpin DNAs can effectively trigger the HCR and cause the swelling of hydrogel, and the hydrogel sensor has a good determination performance and high specific recognition for AFB1.

First-Order Individual Gas Sensors as Next Generation Reliable Analytical Instruments.

It is conventionally expected that the performance of existing gas sensors may degrade in the field compared to laboratory conditions because (i) a sensor may lose its accuracy in the presence of chemical interferences and (ii) variations of ambient conditions over time may induce sensor-response fluctuations (i.e., drift). Breaking this status quo in poor sensor performance requires understanding the origins of design principles of existing sensors and bringing new principles to sensor designs. Existing gas sensors are single-output (e.g., resistance, electrical current, light intensity, etc.) sensors, also known as zero-order sensors (Karl Booksh and Bruce R. Kowalski, Analytical Chemistry, DOI: 10.1021/ac00087a718). Any zero-order sensor is undesirably affected by variable chemical background and sensor drift that cannot be distinguished from the response to an analyte. To address these limitations, we are developing multivariable gas sensors with independent responses, which are first-order analytical instruments. Here, we demonstrate self-correction against drift in two types of first-order gas sensors that operate in different portions of the electromagnetic spectrum. Our radiofrequency sensors utilize dielectric excitation of semiconducting metal oxide materials on the shoulder of their dielectric relaxation peak and achieve self-correction of the baseline drift by operation at several frequencies. Our photonic sensors utilize nanostructured sensing materials inspired by Morpho butterflies and achieve self-correction of the baseline drift by operation at several wavelengths. These principles of self-correction for drift effects in first-order sensors open opportunities for diverse emerging monitoring applications that cannot afford frequent periodic maintenance that is typical of traditional analytical instruments.

A novel graphene-based circular dual-core photonic crystal fiber pressure sensor with high sensitivity

A novel graphene-based circular dual-core photonic crystal fiber pressure sensor with high sensitivity

High Sensitivity and Wide Range Refractive Index Sensor Based on Surface Plasmon Resonance Photonic Crystal Fiber.

In order to detect the refractive index (RI) of high refractive index materials such as trichlorobenzene and aniline in the near-infrared and mid-infrared spectra and expand the detection range of the refractive index, a surface plasmon resonance (SPR) photonic crystal fiber (PCF) sensor based on an elliptical sensing channel is proposed for high refractive index detection. The fiber core and the analyte channel are surrounded by two types of air holes with different sizes. When the surface plasmon resonance effect appears at the interface between the fiber core and the elliptical sensing layer, obvious resonance peaks appear in the near-infrared and mid-infrared bands. The full vector finite element method (FEM) is used to study the sensing characteristics of the sensor and the influence of structural parameters on the resonance peak. The results demonstrate that the sensor achieves detection in the refractive index range of 1.41-1.58, in the wavelength range of 1600-3200 nm. The average wavelength sensitivity is 9217.22 nm/RIU, and the refractive index resolution is 10.85 × 10-6 RIU. The proposed sensor realizes high refractive index detection in the near-infrared and mid-infrared bands, and obtains an ultra-wide detection range and higher sensitivity. The sensor has broad application prospects in chemical detection, biomedical sensing and other fields, and provides a theoretical reference for the design of a photonic crystal fiber surface plasmon resonance sensor.

Design, fabrication, and characterization of a polymer-based waveguide Bragg grating for blood glucose monitoring

A waveguide Bragg grating (WBG) can facilitate an adaptable method for glucose monitoring according to the optical properties of polymer materials. We propose the design and fabrication of a WBG for glucose monitoring. By exploiting glucose oxidase as the upper cladding, polydimethylsiloxane was used as the substrate, and polymethyl methacrylate was used as the core layer. We investigated the effects of the diffraction order, waveguide structure, and grating period on the reflected spectrum of the WBG. Finally, process reproducibility after long-term storage and the capability to eliminate background solution interference (to achieve more specific glucose detection) were evaluated. The experimental results showed that when the glucose concentration was in the range of [0, 3.6] mg/ml, as the glucose concentration increased, the wavelength decreased approximately linearly, with a sensitivity of approximately 242.9 pm/(mg/ml) in the range of 0–2.7 mg/ml, while maintaining good selectivity and stability. The WBG for glucose monitoring has the advantages of a large measurement range and high sensitivity. This approach facilitates the application potential of such polymer material-based WBG photonic sensors in wearable technology and realizes the measurement of human blood glucose.

A comparative analysis of a D-shaped photonic crystal sensor based on graphene plasmonic

In this paper, a D-shaped photonic crystal fiber (PCF) sensor based on surface plasmon resonance has been presented. In the proposed structure, Graphene, Gold, and Silver have been used in different configurations in order to promote the sensitivity of the sensor. According to the numerical simulations performed by the finite element method (FEM) and considering the refractive index of the analyte from 1.36 to 1.40, the maximum wavelength sensitivity of 12600 nm RIU−1 has been obtained, which is related to the structure when the Gold layer sandwiched between two Graphene layers. This is while, the maximum amplitude sensitivity of the proposed sensor is 5708 (RIU−1), which is related to the configuration that Graphene has been placed under the Silver layer. On the other hand, the proposed structure is easy to use because there is no need to fill the fiber holes with the analyte and it can be easily placed on the D-shaped surface of the sensor. The proposed sensor is appropriate to sense the biological variation because of showing strong surface plasmon resonances. The effect of the fabrication tolerance on the proposed PCF is also studied. It is expected that the proposed model will be compatible with the bio-engineering approaches.

Single-step detection of toxic airborne metallic nanoparticles using Goos–Hänchen effect in photonic Bragg grating structures

A real-time photonic crystal sensor is suggested for the detection of airborne heavy metal nanoparticles (HMNPs). The sensor consists of a sandwiched sampling cell between two stacks of alternating TiO2 and Si-Ge layers, forming the core of the device. The sensor’s performance is based on monitoring changes in both the intensity and phase of a probe beam as it propagates through the core. By analyzing the fluctuations in intensity, central frequency, and full width at half maximum (FWHM) of the resonant mode within the transmittance spectrum bandgap, or by monitoring the phase changes at the angle of maximum transmittance that may result in a remarkable Goos–Hänchen (GH) shift in transmittance, the sensor can identify the pollutant nanoparticles. Tuning the thicknesses of the slabs and the number of unit cells in the photonic crystal can dynamically shift the resonant mode and bandgap edges, allowing for easy adjustment of the sensor’s responsivity. Furthermore, the optical response of the sensor can be tuned through external parameters such as the incident angle of the probe light or an externally applied electric field. Additionally, the sensor exhibits sensitivity not only to changes in the extent of the sample but also to the shape of the present HMNPs. These characteristics make the proposed configuration cost-effective, user-friendly, and suitable for HMNPs detection without the need for complex sample preparation, data analyses or additional tools/accessories.

A highly sensitive multi-channel SPR-PCF based biosensor with deep learning prediction approach

A very sensitive multi-channel refractive index-based biosensor with a detection range of 1.26 to 1.36 has been proposed. Titanium oxide (TiO2) was utilized as the dielectric, and gold as the plasmonic material in the sensor. The suggested sensor achieved a maximum sensitivity value of 48,000 nm/RIU and 7220 RIU-1 using the wavelength and amplitude interrogation techniques with a pitch layer variation of 5.55–5.95 μm and a metal layer variation of 30–50 nm. The suggested sensor can detect many compounds with amazing sensitivity, such as glucose, sucrose, ethanol, methanol, human intestinal mucosa, sevoflurane, and bio-chemicals. Additionally, a deep learning model that forecasts six optical properties has been proposed. The hyperparameters of the deep neural network model are tuned extensively to maximize accuracy. Our model had a Mean Squared Error of 0.006 and was significantly faster than conventional techniques. The results show that deep learning can predict optical features in photonic sensors, which can identify specific analytes or biomolecules in a sample without costly and time-consuming simulations. Photonic sensor researchers and engineers may utilize the suggested model on vast datasets and adjust it to new settings. This study may open the door to the use of deep learning-based optimization techniques to enhancebiosensors.

Measurement of high-power transient electromagnetic pulse field with integrated photonic electric-field sensor

To satisfy certain special requirements for high-power transient electromagnetic pulse (EMP) field measurements, such as high electric field intensity, broad bandwidth, fast rise time, and compact size, here is described an integrated electro-optic sensor. The frequency response in the range from 100 kHz to 12 GHz and the time-domain input/output characteristics of a sub-nanosecond EMP field with an intensity from 5 to 100 kV/m were experimentally characterized. The sensor was demonstrated to measure the time-domain waveform of the ultrawideband EMP field signals with a rise time of 0.35 ns and bandwidth of 1 GHz. In addition, the minimal detectable electric field strength was 16 mV/m/Hz1/2 in the frequency domain, and an intense sub-nanosecond electromagnetic pulse with a peak amplitude of 100 kV/m was observed in the time domain. The reported sensor is expected to be broadly used for intense EMP measurements.

Feasibility of Insect Identification Based on Spectral Fringes Produced by Clear Wings

Due to the growing awareness that insects' diversity and populations are in decline, there is an increased need for monitoring insects. Entomological lidars and photonic sensors can monitor and remotely identify flying insects based on their backscattered signal in terms of oscillations-, polarization-, and spectral content. The backscattered light from insects is predominantly oscillatory and derives from the wings. This part of the signal is also more coherent and co-polarized than the light reflected from the insect's abdomen. Clear membranes can display soap-bubble colors due to thin-film interference, a feature that can be associated with the thickness of the wing. A hyperspectral camera can capture these wing interference patterns with hundreds of spectral bands and accurately identify the wing thickness. Here we investigate whether the spectral fringes can provide complementary information to aid remote species identification. We demonstrate that we can extract wing thickness and modulation depth information from spectral fringes of 87 species of common insect pollinators in Skåne, Sweden. The modulation depth of a fringe provides information related to insect wing thickness homogeneity, wrinkledness, or anti-reflectance features. Our results show that examined species display distinct modulation and wing thickness, and therefore such features can be used to improve the specificity of species identification of photonics sensors.

A Deep Learning Regression Model for Photonic Crystal Fiber Sensor with XAI Feature Selection and Analysis.

A Deep Learning Multi-output regression model is employed to correctly model the relationships between optical design parameters of an asymmetric Twin Elliptical Core Photonic Crystal Fiber (TEC-PCF) and its sensing performances. TEC-PCF acts as a biosensor to detect the blood glucose level taking hemoglobin components into account. Since asymmetric TEC-PCF uses a dual elliptical core, four super modes have to be evaluated to analyze the sensing performance in terms of effective index difference, transmission spectrum, coupling length, and sensor sensitivity. The dataset used in this work is of the optical design parameters of the sensor and Finite Element Method (FEM) results with effective indices of four super modes obtained from the COMSOL Multiphysics by varying hemoglobin concentration to 120 g/L, 140 g/L, and 160 g/L. Gretel.ai's free open-source synthetic data library is used to augment the dataset to make the training more efficient. Explainable AI (XAI) feature analysis using Shapley Additive Explanations (SHAP) framework is used for two purposes: feature selection and to know the feature's effect on prediction. The former led to the development of an optimal model with much fewer computational demands and the latter made the model interpretable. The proposed model can predict the accurate super modes when given input specifications with wavelength ranging from 1.27-1.5 μm and for various glucose concentrations under the influence of hemoglobin much faster compared to the other numerical simulations which are computationally expensive. Computation time taken by the proposed Artificial Neural Network (ANN) model, the proposed model with XAI and FEM is also being compared.

Photonic Crystal Fiber Sensors, Literature Review, Challenges, and Some Novel Trends

Photonic Crystal Fiber Sensors, Literature Review, Challenges, and Some Novel Trends

Chameleon-inspired flexible photonic crystal lens-shaped dynamic pressure sensor based on structural color shift

Chameleon-inspired flexible photonic crystal lens-shaped dynamic pressure sensor based on structural color shift

One-dimensional photonic crystal magnetic sensor design based on Bloch surface waves with high sensitivity and high figure of merit (FOM)

A novel Bloch surface wave (BSW)-based one-dimensional photonic crystal magnetic sensor is proposed. Grating is used to realize the coupling of electromagnetic waves and BSW, and magnetic fluid is placed at the bottom of the sensor as the sensing medium. The sensing characteristics of a one-dimensional photonic crystal magnetic sensor were analyzed using the rigorous coupled wave analysis (RCWA) method. Based on the azimuthal interrogation method, the effects of different pole angles, defect layers, and structural parameters on sensitivity and figure of merit (FOM) were investigated. According to the drift of the resonance peak, the azimuthal sensitivity S = 9 °/mT, and the maximum detection sensitivity S = 14 °/mT were obtained (converted to refractive index sensitivity S = 6536 °/RIU and maximum detection sensitivity S = 9333 °/RIU). Excitingly, we found that the introduction of a defective layer can significantly improve the FOM (1925 RIU-1 to 69,444 RIU-1). The results show that the designed magnetic sensor structure has high sensitivity and a high FOM, which is an order of magnitude higher than other magnetic sensors. This work provides a guideline for the future development of one-dimensional photonic crystals in the field of weak magnetic sensing.

Epsilon-near-zero surface waves enhance infrared vibrational spectroscopy

Thin film epsilon-near-zero (ENZ) supports guided modes analogous to surface plasmon modes in the infrared, where surface plasmons are difficult to excite. Leveraging these modes can enhance the signal of surface-enhanced infrared absorption (SIERA) sensors by a factor of three, enabling the detection of minute amounts of materials. However, material losses can significantly impact sensor performance. To address this issue, we establish a condition that limits the impact of losses while still preserving the advantage of ENZ-surface waves. Our findings may pave the way for the identification and exploration of particularly suitable materials for highly efficient photonic SIERA sensors.

Characteristics of MoO2 Deposited C-Shaped Photonic Crystal Fiber Sensor with a Micro-Opening Based on Surface Plasmon Resonance

Characteristics of MoO2 Deposited C-Shaped Photonic Crystal Fiber Sensor with a Micro-Opening Based on Surface Plasmon Resonance

Determining the Nutrient Content of Hydroponically-Cultivated Microgreens with Immersible Silicon Photonic Sensors: A Preliminary Feasibility Study.

Microgreens have gained attention for their exceptional culinary characteristics and high nutritional value. The present study focused on a novel approach for investigating the easy extraction of plant samples and the utilization of immersible silicon photonic sensors to determine, on the spot, the nutrient content of microgreens and their optimum time of harvest. For the first time, it was examined how these novel sensors can capture time-shifting spectra caused by the molecules' dynamic adhesion onto the sensor surface. The experiment involved four types of microgreens (three types of basil and broccoli) grown in a do-it-yourself hydroponic installation. The sensors successfully distinguished between different plant types, showcasing their discriminative capabilities. To determine the optimum harvest time, this study compared the sensor data with results obtained through standard analytical methods. Specifically, the total phenolic content and antioxidant activity of two basil varieties were juxtaposed with the sensor data, and this study concluded that the ideal harvest time for basil microgreens was 14 days after planting. This finding highlights the potential of the immersible silicon photonic sensors for potentially replacing time-consuming analytical techniques. By concentrating on obtaining plant extracts, capturing time-shifting spectra, and assessing sensor reusability, this research paves the way for future advancements in urban farming.

A D-Shaped SPR-Based PCF Sensor with an Extremely High-Amplitude Sensitivity for Measuring the Refractive Index.

In this work, a new D-shaped photonic crystal fibre sensor structure based on surface plasmon resonance (SPR) is purposed for measurement of analyte refractive index (RI). In this design, a silica cylinder is polished into a D-shaped silica material structure with a flattened surface where two Au nanowires are coated. Some air holes are omitted to form the core mode region. With the implementation of gold nanowires and a core region for the exciting SPR effect with variate physical values, analyte RI can be measured with a wavelength coverage from 850 to 1350nm. The numerical simulation shows the maximum wavelength sensitivity of the purposed design achieves 19,600nm/RIU with an RI coverage from 1.37 to 1.42. Moreover, the sensor has a tremendous amplitude sensitivity and the maximum absolute value is about 2300RIU-1. Benefiting from these outstanding performance, the purposed structure can be given priority when it is applied in biomedical detecting and environmental assessment science.

A Concise Review of the Progress in Photonic Sensing Devices

Photonic sensing devices have become increasingly important in various fields such as agriculture, medicine, biochemical sensing, and manufacturing. They are highly sensitive and can classify minor changes in the physical and chemical properties of the ambient medium with high precision. This makes them practical in applications where accurate measurements are critical, such as medical diagnostics and environmental monitoring. In this review paper, recent advances in different types of photonic sensors are discussed, which include photonic crystal-based sensors, surface plasmon resonance-based sensors, optical fiber-based sensors, optical waveguide-based sensors, and wearable sensors. These highly fascinating sensing devices play a crucial role in countless applications and have several advantages over traditional sensors. As technology continues to advance, we can expect photonic sensors to become even more precise, versatile, and reliable.