Reflective Sensor Research Articles

Development of a Crop Spectral Reflectance Sensor

In this study, a low-cost, self-balancing crop spectral reflectance sensor (CSRS) was designed for real-time, nondestructive monitoring of the spectral reflectance and vegetation index of crops such as tomato and rapeseed. The sensor had a field of view of 30°, and a narrow-band filter was used for light splitting. The filter’s full width at half-maximum was 10 nm, and the spectral bands were 710 nm and 870 nm. The sensor was powered by a battery and used WiFi for communication. Its software was based on the Contiki operating system. To make the sensor work in different light intensity conditions, the photoelectric conversion automatic gain circuit had a total of 255 combinations of amplification. The gimbal of the sensor was mainly composed of an inner ring and an outer ring. Under the gravity of the sensor, the central axis of the sensor remained vertical, such that the up-facing and down-facing photosensitive units stayed in the horizontal position. The mechanical components of the sensor were designed symmetrically to facilitate equal mass distribution and to meet the needs of automatic balancing. Based on the optical signal transmission process of the sensor and the dark-current characteristics of the photodetector, a calibration method was theoretically deduced, which improved the accuracy and stability of the sensor under different ambient light intensities. The calibration method is also applicable for the calibration of other crop growth information sensors. Next, the standard reflectance gray scale was taken as the measurement variable to test the accuracy of the sensor, and the results showed that the root mean square error of the reflectance measured by the sensor at 710 nm and 870 nm was 1.10% and 1.27%, respectively; the mean absolute error was 0.95% and 0.89%, respectively; the relative error was below 4% and 3%, respectively; and the coefficient of variation was between 1.0% and 2.5%. The reflectance data measured by the sensor under different ambient light intensities suggested that the absolute error of the sensor was within ±0.5%, and the coefficients of variation at the two spectral bands were 1.04% and 0.39%, respectively. With tomato and rapeseed as the monitoring targets, the proposed CSRS and a commercial spectroradiometer were used to measure at the same time. The results showed that the reflectance measured by the two devices was very close, and there was a linear relationship between the normalized difference vegetation index of the CSRS and that of the commercial spectroradiometer. The coefficient of determination (R2) for tomato and rapeseed were 0.9540 and 0.9110, respectively.

A novel borate-based 45S5 Bioglass®: In vitro assessment in phosphate-buffered saline solution

A novel borate-based 45S5 Bioglass® has been formulated and synthesized using the melt-quenching method. The in vitro assessment was performed to determine the bioactivity of the glasses using phosphate buffer saline (PBS) as an immersion solution. The pH measurement is performed using a pH meter, while ion dissolution is quantified using Inductively coupled plasma atomic emission spectrometer (ICP-OES). The immersion of borate-containing glass exhibits pH closes to human body fluid (≈7.40). The (Ca/P) atomic ratio was calculated and its value increased (1.7–4.0) along with borate contents, indicating improved bioactivity. The alteration of glass structure after the in vitro test is inspected using attenuated total reflectance sensor–Fourier transform infrared spectrophotometer (ATR-FTIR) and Raman spectrometer. The 45S5 Bioglass that comprised 40–80% of borate showed the appearance of HPO42− a vibration band which specifies the growth of hydroxyapatite (HPA) after the in vitro test. Relative to 45S5 Bioglass®, glass labeled 0.4B2O3 displayed the highest potential of HPA growth (248%), while dissolving BO3 and BO4 units at 57.36 and 8.48%, respectively. The new formulation technique offers an alternative path to control the degradation behavior of 45S5 Bioglass® glass by modifying the ratio of borate constituent. The prepared glass may find its potential in soft-bone tissue engineering applications.

On-site colorimetric detection of adulterated gasoline using highly reflective 1D photonic crystal sensors based on photo-crosslinked polymer–titania hybrids

The adulteration and distribution of pure commercial gasoline remain a global problem because of the lack of a simple method for the rapid on-site detection of adulterated gasoline. Therefore, herein, we propose a simple method, based on highly reflective one-dimensional photonic crystals (1D PCs), for the on-site rapid colorimetric detection of adulterated gasoline by the naked eye. A photo-crosslinked copolymer–titania hybrid (Ti70) with a high refractive index and a photo-crosslinked methyl acrylate (MA)-based polymeric material containing 4-benzoylphenyl acrylate (BPA) (P(MA-co-BPA)) with a low refractive index were synthesized, characterized, and used in the fabrication of an 11-layered 1D PC sensor (Ti70/P(MA-co-BPA) PC). Ti70/P(MA-co-BPA) PC exhibited no color change in pure commercial gasoline. However, when Ti70/P(MA-co-BPA) PC was immersed in methanol, xylene, toluene, and benzene, it rapidly turned from bluish-purple to blue, sky-blue, greenish-yellow, and reddish-orange, respectively, and had high reflectivities. Additionally, when immersed in binary (gasoline/toluene and gasoline/methanol) and ternary (gasoline/toluene/methanol) mixtures, Ti70/P(MA-co-BPA) PC demonstrated significant color changes within short periods (~2 min). Ti70/P(MA-co-BPA) PC exhibited excellent recyclability, which was verified via repeated swelling/deswelling tests for 10 cycles. These results indicate the applicability of the proposed novel Ti70/P(MA-co-BPA) PC for the rapid on-site colorimetric detection of adulterated gasoline by the naked-eye. We believe that the findings of this study will provide considerable insight for developing simple and high-performance 1D PC sensors for practical use and will also serve as a basis for exploring their potential applications.

Seeding rate, fertiliser and herbicide effects on canopy growth and productivity of direct-seeded rice (DSR) under different management practices

A low seeding rate with seed drill is a good management practice to promote good canopy growth and increase yield of direct seeding rice (DSR) in lowland rice fields. Many studies did not evaluate seeding rates in an integrated way under different management practices in smallholder farmers’ fields. Estimating canopy cover from Canopeo and normalised vegetation difference index (NDVI) may offer a non-destructive, rapid way to assess crop growth and productivity under different field conditions. On-farm field experiments were conducted comparing the yield and canopy growth in response to seed drilled seeding rates of 20, 40, 60 and 80 kg ha −1 and hand-broadcast seeding rates of 180 kg ha −1 (farmers’ practice) grown under different management conditions in Cambodia. In the first experiment, different seeding rates were compared in two villages, Kork Ton Loab (KTL) and Svay Cheat (SVC), in the 2018 growing season. In the second experiment, seeding rates and fertiliser application rates were compared at Kork Ton Loab location in the 2019 cropping season. The third experiment compared seeding rates and pre-emergence herbicide options at Oroung village in the 2018 and 2020 cropping seasons. In experiment 1, sowing at 20 and 40 kg ha −1 consistently decreased mean canopy cover with Canopeo index at Kork Ton Loab and Svay Cheat locations. In experiment 2, sowing at 80 kg ha −1 significantly increased the mean canopy cover compared with 20 and 40 kg ha −1 seeding rates with Canopeo index at 60 and 75 DAS at the Kork Ton Loab location. The low seeding rates of 60, 80 and 180 kg ha −1 increased in canopy cover at 60 DAS with NDVI compared with the lower seeding rates at Kork Ton Loab and Svay Cheat locations. Seeding rates had significant effects on grain yield and gross margin only for experiment 1 and experiment 3 in the 2020 cropping season. For experiment 1, the seed drill rates of 60 and 80 kg ha −1 did not significantly differ in grain yield or gross margin compared with broadcast of 180 kg ha −1 in both locations. Sowing at 20, 40, 60 and 80 kg ha −1 produced similar yield and gross margin at Kork Ton Loab except for the 20 kg ha −1 seeding rate that significantly reduced yield and gross margin at Svay Cheat. For experiment 3, the seed drill of 80 kg ha −1 and 180 kg ha −1 by broadcast (Farmer’s Practice) significantly increased yield up to 3.5 t ha −1 compared with 40 and 60 seeding rates in the 2020 cropping season. Increasing fertiliser rates (high fertiliser; N-100, P-80, and K-50 kg ha −1 ) by two times increased grain yield by 12.8% compared with the low fertiliser rate of N-50, P-40, and K-30 kg ha −1 . Pre-emergence herbicide application significantly increased mean grain yield by 16% and 15% in the 2018 and 2020 cropping seasons, respectively. Therefore, the seed drill of 80 kg ha −1 , high fertiliser rate and pre-emergence herbicide interventions improved uniformity in canopy growth and increased grain yield of DSR. This information can be incorporated into integrated crop and weed management programs for the productivity of direct-seeded rice in the lowland ecosystem. • Low seeding rates (60–80 kg ha -1 ) with seed drill provide similar yield and gross margin as hand-broadcast of 180 kg ha -1 . • The use of vegetation-derived information from reflectance sensors estimates canopy growth in dynamic field conditions. • Sowing at 60 and 80 kg ha -1 with seed drill reduced undesirable agronomic traits compared with hand-broadcast of 180 kg ha -1 .

Terahertz polarization sensing for protein concentration and a crystallization process on a reflective metasurface.

Terahertz (THz) waves have attracted much attention in the field of biosensing due to advantages including non-destructiveness, being label-free, and high-sensitivity detection. Here we have experimentally demonstrated a THz polarization sensing method based on reflective metasurface sensors for detecting concentrations of protein solutions and their crystallization process. The protein with varying concentrations has been detected by five different polarization parameters, which show different spectral responses and sensing sensitivities. The sensing accuracy can reach the order of ng/mm2. Furthermore, the crystallization process of the protein sample from the dissolved state to the crystalline has been dynamically measured by polarization sensing, of which the highest sensitivity can reach 0.67 °/%. Therefore, this new sensing platform can have broad development prospects in the trace matter detection of the biological sample.

Monitoring Cross-Linking, the Evolution of Refractive Index and the Glass Transition Temperature of an Epoxy Resin Using an Optical Fiber Sensor

Hyphenated analytical techniques enable the simultaneous measurement of relevant processing and materials parameters under identical environmental conditions. In the current study, a power-compensated differential scanning calorimeter (DSC) was custom-modified to enable the integration of an optical fibre sensor to monitor <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> the progression of the cross-linking reactions by inferring the evolution of the refractive index. A cleaved optical fibre was used and it served as a Fresnel reflection sensor (FRS). The DSC was calibrated with and without the integrated FRS and it was demonstrated that it did not influence the performance of the DSC. The FRS was calibrated using reference refractive index oils within the DSC. An epoxy/amine resin system was cross-linked at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$70 ^{o}\text{C}$ </tex-math></inline-formula> and the enthalpy of cross-linking and the evolution of the refractive index were monitored simultaneously using the DSC and FRS respectively. After the cross-linking was completed, the DSC was programmed to perform a ramped heating schedule from ambient temperature to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$150 ^{o}\text{C}$ </tex-math></inline-formula> . The FRS was capable of detecting glass transition temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> ) of the cross-linked resin. An excellent correlation was observed for the T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> obtained by the FRS and DSC. The contribution of factors affecting the resolution of the data from the FRS are discussed.

Research of Magnetic Shielding on the Closing Gap of Optical Fiber Current Sensor

Reflective optical fiber current sensors have been widely used in electric power and metallurgy. However, the closing gaps between the λ / 4 wave plate and the reflector leads to a strong background current when they are used in strong magnetic fields. In this paper, a mathematical model of the background current was analyzed, and the relationship between the external magnetic field and the background current was identified through experiments. Therefore, a method to reduce the background current by magnetic shielding was proposed. A magnetic ring to shield the magnetic field effectively was designed, and the effects of the structure and relative permeability of the magnetic ring on its shielding rate were established by experiments and numerical simulations. The results showed that the shielding rate exceeded 95% when the length, thickness, and inner diameter of the magnetic ring were 50 mm, 2 mm, and 13 mm, respectively. Overall, this work provided a comprehensive framework that is useful for the analysis and optimization of magnetic shielding and improved the measurement accuracy of optical fiber current sensors in strong magnetic fields.

Strain sensor based on FBG in fiber loop ring down system

AbstractIn this paper, a strain sensor with a high resolution based on fiber Bragg grating (FBG) is proposed by combining a fiber loop ring down (FLRD) technology. When a light pulse enters and circulates in the fiber loop, it experiences a little loss each time as it paases through the sensor head. The ring‐dong time of the system would be altered by intensity loss near the sensor head, which is related to the size of the strain. The experimental results show that the FBG strain sensor with a high resolution is achieved by monitoring the ring‐down time, due to the more precision time measurement compared with the wavelength measurement. In the 0–1250 με strain range, the sensitivity ranged from 5.82&amp;nbsp;to 20.82 ns/με, and the theoretical best resolution is obtained as 0.24 με. The system has the advantages of simple structure, low cost, reflective sensor head, and high resolution.

Spectral indices measured with proximal sensing using canopy reflectance sensor, chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen (N) topdressing decisions. Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field. The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor, soil and plant analyzer development (SPAD) chlorophyll meter, and two different types of leaf color charts (LCCs) for five basmati rice genotypes across different growth stages. Regression analysis was performed using normalized difference vegetation index (NDVI) recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University, Ludhiana (India) (PAU-LCC) and the International Rice Research Institute, Philippines (IRRI-LCC). The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination (R2) and minimum normalized root mean square error (NRMSE) at panicle initiation stage and explained 38.3%-76.4% variation in yield using genotype-specific models. Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained 53.4%–57.2% variation in grain yield. Normalizing different spectral indices with cumulative growing degree days (CGDD) decreased the accuracy of yield prediction. Normalization with days after transplanting (DAT), however, did not reduce or improve the predictability of yield. The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses. The NDVI-based genotype-specific models exhibited a robust linear correlation (R2 = 0.77, NRMSE = 7.37%, n = 180) between observed and predicted grain yields only at 35 DAT (i.e., panicle initiation stage), while the SPAD, PAU-LCC, and IRRI-LCC consistently provided reliable predictions (with respective R2 of 0.63, 0.60, and 0.53 and NRMSE of 10%, 10%, and 13.6%) even with genotype invariant models with 900 data points obtained at different growth stages. The study revealed that unnormalized values of spectral indices, namely NDVI, SPAD, PAU-LCC, and IRRI-LCC, can be satisfactorily used for in-season estimation of grain yield for basmati rice. As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors, they can be preferably used by small farmers in developing countries.

Resonance colors in an amorphous-Si thin layer irradiated by an ultraviolet nanosecond laser

The optical resonances of nanoparticles have drawn considerable attention in academia and industry; however, the formation of uniform nanoparticles has been challenging, hindering their application. Herein, we propose that reflective-type color filters can be achieved using the optical resonance effect arising from Si nanoparticles created on an amorphous Si thin film via laser irradiation. Multiple equally spaced irradiations of an amorphous-Si surface by a Gaussian laser beam created Si nanoparticles with sizes of 100–200 nm at laser fluences in the range of 150–200 mJ/cm2. Furthermore, we demonstrated that the resonance colors could be tuned to red, green, and blue by adjusting the laser fluence and scan speed. The optical properties of the nanoparticles were analyzed using dark-field microscopy, scanning electron microscopy, and transmission electron microscopy. According to the measured nanoparticle geometries and spectral data, the Si nanoparticles are spheroidal shapes embedded in the Si layer, and simulations indicate that the resonance colors originate from electric and magnetic dipoles inside the Si nanoparticles. The results could inform the fabrication of color filters in reflective display device or sensor applications through a simple laser scanning process.

Design of Intelligent Automated Quest Control System in the Covid-19 Era

The rapid spread of corona virus disease 2019 (COVID-19), throughout direct of the human-to-human interaction makes the virus massively infect humans in all around the world. Until now, there has not been found the right way of healing it. This study aims to design of the intelligent automated quest control system capable for detecting COVID-19 by the body of temperature. The method approach was taken applied research, beginning with determining of the hardware using the ArduinoTM UNO microcontroller, the MLX90614 infrared thermometer, the TCRT5000 infrared reflective sensor, motor driver L293D, the output was displayed on a Liquid Crystal Display (LCD) screen, interaction control using Roller Limit Switch and instruction using the C programming language with Arduino IDE user interface. The system testing is done by comparing the temperature sensor readings infrared thermometer versus standard thermometer. Based on the results of a limited scale trial of 5 volunteers, an average error of 2.72% was obtained and the system worked well (opening or locking the door) in accordance with the temperature limits that had been set for detecting COVID-19. This research novelty that the simple and inexpensive design of the device system prevented and minimize the spread of COVID-19. The last, limitations of the system not being tested by the experts and large sample.

Evaluation of Spatial Directional Guidance Using Cheek Haptic Stimulation in a Virtual Environment

Spatial cues play an important role in navigating people in both physical and virtual spaces. In spatial navigation, visual information with additional cues, such as haptic cues, enables effective guidance. Most haptic devices are applied to various body parts to make mechanical stimuli, while few devices stimulate a head despite the excellent sensitivity. This article presents Virtual Whiskers, a spatial directional guidance technique by cheek haptic stimulation using tiny robot arms attached to a Head-Mounted Display (HMD). The tip of the robotic arm has photo reflective sensors to detect the distance between the tip and the cheek surface. Using the robot arms, we stimulate a point on the cheek obtained by calculating an intersection between the cheek surface and the target direction. In the directional guidance experiment, we investigated how accurately participants identify the target direction provided by our guidance method. We evaluated an error between the actual target direction and the participant's pointed direction. The experimental result shows that our method achieves the average absolute directional error of 2.54° in the azimuthal plane and 6.54° in the elevation plane. We also conducted a spatial guidance experiment to evaluate task performance in a target search task. We compared the condition of visual information, visual and audio information, and visual information and cheek haptics for task completion time, System Usability Scale (SUS) score, NASA-TLX score. The averages of task completion time were M = 6.39 s, SD = 3.34 s, and M = 5.62 s, SD = 3.12 s, and M = 4.35 s, SD = 2.26 s, in visual-only condition, visual+audio condition, and visual+haptic condition, respectively. In terms of the SUS score, visual condition, visual+audio condition, and visual+haptic condition achieved M = 55.83, SD = 20.40, and M = 47.78, SD = 20.09, and M = 80.42, SD = 10.99, respectively. As for NASA-TLX score, visual condition, visual+audio condition, and visual+haptic condition resulted in M = 75.81, SD = 16.89, and M = 67.57, SD = 14.96, and M = 38.83, SD = 18.52, respectively. Statistical tests revealed significant differences in task completion time, SUS score, and NASA-TLX score between the visual and the visual+haptic condition and the visual+audio and the visual+haptic condition.

Reflective fiber-optic sensor for on-line nondestructive monitoring of Aspergillus on the surface of cultural paper relics.

A reflective fiber-optic sensor was created to realize on-line nondestructive monitoring of the growth process of Aspergillus on the surface of cultural paper relics. The sensor consisted of one tapered input and six output optical fibers. The operating principle of the device was established. The sensitivity of the sensor was checked. Sensors were used to monitor the growth of Aspergillus niger, Aspergillus flavus, and Aspergillus tamarrii on the papers. The morphology of Aspergillus was characterized. The sensor reveals a linear relationship between the output signal of the sensor and the thickness of Aspergillus biofilm with a detection limit of 10 µm.

Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning.

Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Analysis of laboratory samples is the most popular method for determining the quality characteristics of grapes, although it is time-consuming and expensive. In recent years, several machine learning-based methods have been developed to predict crop quality. Although these techniques require the extensive involvement of experts, automated machine learning (AutoML) offers the possibility to improve this task, saving time and resources. In this paper, we propose an innovative approach for robust prediction of grape quality attributes by combining open-source AutoML techniques and Normalized Difference Vegetation Index (NDVI) data for vineyards obtained from four different platforms-two proximal vehicle-mounted canopy reflectance sensors, orthomosaics from UAV images and Sentinel-2 remote sensing imagery-during the 2019 and 2020 growing seasons. We investigated AutoML, extending our earlier work on manually fine-tuned machine learning methods. Results of the two approaches using Ordinary Least Square (OLS), Theil-Sen and Huber regression models and tree-based methods were compared. Support Vector Machines (SVMs) and Automatic Relevance Determination (ARD) were included in the analysis and different combinations of sensors and data collected over two growing seasons were investigated. Results showed promising performance of Unmanned Aerial Vehicle (UAV) and Spectrosense+ GPS data in predicting grape sugars, especially in mid to late season with full canopy growth. Regression models with both manually fine-tuned ML (R² = 0.61) and AutoML (R² = 0.65) provided similar results, with the latter slightly improved for both 2019 and 2020. When combining multiple sensors and growth stages per year, the coefficient of determination R² improved even more averaging 0.66 for the best-fitting regressions. Also, when considering combinations of sensors and growth stages across both cropping seasons, UAV and Spectrosense+ GPS, as well as Véraison and Flowering, each had the highest average R² values. These performances are consistent with previous work on machine learning algorithms that were manually fine-tuned. These results suggest that AutoML has greater long-term performance potential. To increase the efficiency of crop quality prediction, a balance must be struck between manual expert work and AutoML.

Distribution Network Communication Base on Reflective Fiber Optic Current Sensor

Fiber optic current sensor has been brought into the practical application. The main research trend is to use special fiber or improve the system structure, so as to deal with the unstable operation of fiber optic current sensor, thereby improving the measurement accuracy of current. The working principle of the reflective fiber optic current sensor is analyzed, and the Jones matrix is applied to characterize the optical components of the system. With the analysis of the error source of the sensor, a mathematical model of optical path error is constructed, including the fabrication error, temperature error, and strain error. Under these error analyses, the design scheme of twisted fiber optic current sensor is further proposed. In view of the structure of the traditional reflective fiber optic current sensor, twisted optical fiber is used for the 1/4 wave plate and sensing fiber part, and the constant-rate twisted optical fiber is used to replace the ordinary sensing fiber. The error caused by the linear birefringence in the optical fiber is reduced, the temperature sensitivity is lowered, the anti-stress effect is improved, and the adverse interference by temperature and strain on the sensing optical fiber is eliminated. The developed fiber optic current sensor is utilized for long-term pilot application in low-frequency distribution network communication. In the simulation test, the reflective fiber optic current sensor designed with a twisted fiber optic structure has a measurement accuracy of 0.5, which meets the system requirements. As error factors is added and corresponding compensation modules are introduced, the measurement accuracy is increased to 0.2. In the pilot applications of distribution network, it can provide comprehensive and real-time information support, thus laying the foundation for the communication management of the distribution network.

In-line reflected fiber sensor for simultaneous measurement of temperature and liquid level based on tapered few-mode fiber.

An in-line reflective dual-parameters fiber-optic sensor is proposed in this work, whereas it is experimentally verified by measuring both the liquid level and the local temperature distribution simultaneously. The proposed sensor configuration comprises a single-mode fiber (SMF), a tapered few-mode fiber (TFMF), as well as a silver-coated capillary tube. The extracted experimental results indicate that the liquid level only affects the power of the resonant dips, while having little impact on the wavelength. On the other hand, both the wavelength and the power of the resonant dips vary with the temperature change. Therefore, the simultaneous measurement of the liquid level and temperature can be realized according to the different responses of the resonant dips to the liquid level and temperature. The obtained liquid level and temperature sensitivities can reach the values of 0.106 dB/mm and 0.029 dB/°C, 35 pm/°C, respectively. The sensor exhibits the advantages of high stability and low cost, the demodulation relates on only one wavelength which can shorten the scanning wavelength range during measurement. The proposed sensor can be potentially applied where accurate and simultaneous measurements of both temperature and liquid level are required.

Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients

Livestock manure is widely applied onto agriculture soil to fertilize crops and increase soil fertility. However, it is difficult to provide real-time manure nutrient data based on traditional lab analyses during application. Manure sensing using near-infrared (NIR) spectroscopy is an innovative, rapid, and cost-effective technique for inline analysis of animal manure. This study investigated a NIR sensing system with reflectance and transflectance modes to predict N speciation in dairy cow manure using a spiking method. In this study, 20 dairy cow manure samples were collected and spiked to achieve four levels of ammoniacal nitrogen (NH4-N) and organic nitrogen (Org-N) concentrations that resulted in 100 samples in each spiking group. All samples were scanned and analyzed using a NIR system with reflectance and transflectance sensor configurations. NIR calibration models were developed using partial least square regression analysis for NH4-N, Org-N, total solid (TS), ash, and particle size (PS). Coefficient of determination (R2) and root mean square error (RMSE) were selected to evaluate the models. A transflectance probe with a 1 mm path length had the best performance for analyzing manure constituents among three path lengths. Reflectance mode improved the calibration accuracy for NH4-N and Org-N, whereas transflectance mode improved the model predictability for TS, ash, and PS. Reflectance provided good prediction for NH4-N (R2 = 0.83; RMSE = 0.65 mg mL−1) and approximate predictions for Org-N (R2 = 0.66; RMSE = 1.18 mg mL−1). Transflectance was excellent for TS predictions (R2 = 0.97), and provided good quantitative predictions for ash and approximate predictions for PS. The correlations between the accuracy of NH4-N and Org-N calibration models and other manure parameters were not observed indicating the predictions of N contents were not affected by TS, ash, and PS.

Liquid-Metal-Based Nanophotonic Structures for High-Performance SEIRA Sensing.

Surface-enhanced infrared absorption (SEIRA) spectroscopy can provide label-free, nondestructive detection and identification of analytes with high sensitivity and specificity, and therefore has been widely used for various sensing applications. SEIRA sensors usually employ resonant nanophotonic structures, which can substantially enhance the electric field and hence light-matter interactions by orders of magnitude in certain nanoscale hot spots of the devices. However, as ever, smaller hot spots are employed to further enhance the field, the delivery of analytes into such hot spots becomes increasingly challenging. Here, high-performance nanophotonic SEIRA sensors based on nanopatch antennas with a liquid gallium ground plane are demonstrated, which not only lead to ultrahigh field confinement and enhancement, but also allow for convenient and efficient delivery of analytes into nanometric hot spots by employing a simple procedure suitable for point-of-care applications. The sensors exhibit superior sensitivity in the midinfrared spectral region. Around 10% molecular vibrational signals (i.e., the modulation of a sensor's reflection spectrum owing to the molecular vibrational modes of the analytes) near 2900cm-1 are achieved from sensing monolayer 1-octadecanethiol. This cost-effective and reliable method for realizing liquid-metal-based nanophotonic structures provides a new strategy for developing high-performance sensors and other photonics applications in the infrared region.

Microwave Resonance-Based Reflective Mode Displacement Sensor With Wide Dynamic Range

A wide dynamic range reflective mode displacement sensor is designed in this article based on the resonance frequency shift. The linearity of the sensor is enhanced using a tapered metallic patch that forms the capacitance of the sensor, leading to a better dynamic range. The sensor is designed using a coplanar waveguide (CPW) terminated with a series <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RLC$ </tex-math></inline-formula> resonator with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R=50\,\,\Omega $ </tex-math></inline-formula> . A movable dielectric slab is placed above the capacitive patch of the resonator, where displacing the dielectric slab modifies the overall capacitance of the resonator causing a shift in the resonance frequency. The sensing principle is analyzed in detail, using an accurate circuit model and full-wave electromagnetic simulations. The proposed sensing method is capable of detecting the displacement direction. Furthermore, the sensing concept can be extended to a 2-D displacement sensor as well. The sensing approach and the design concept are verified through the fabrication and measurement of a sensor prototype.

Simultaneous Measurement of Seawater Temperature and Pressure With Polydimethylsiloxane Packaged Optical Microfiber Coupler Combined Sagnac Loop

In the field of physical oceanography, the temperature and pressure of seawater are important fundamental parameters. In order to realize the simultaneous measurement of the temperature and pressure in seawater, a reflective optical fiber sensor based on polydimethylsiloxane (PDMS)-sealed optical microfiber coupler combined with a sagnac loop (OMCSL) is proposed. Benefiting from the high thermo-optical coefficient and large elasticity of PDMS, and the large-scale swift field transmission characteristics of optical microfiber coupler (OMC), the sensitivity and structural stability of our sensor is largely improved. The response performance of the sensor is further analyzed by numerical simulation calculations and theoretical modeling. Experimental results show that the temperature and pressure sensitivity of the sensor could reach &#x2212;2.133 nm&#x002F;&#x00B0;C and 3.416 nm&#x002F;Mpa, respectively, about one order higher than that of bare fiber OMCSL. Furthermore, via inversely calculating the cross-sensitivity matrix, the temperature and pressure sensing can be demodulated simultaneously, and the average errors of the preliminary experimental settlements are 1.61&#x0025; and 5.02&#x0025;, respectively. Due to the merits of compact structure, easy fabrication, high sensitivity, fast response speed and high stability, the basic performance of this dual parametric sensor is comparable to the existing electrical temperature-depth (TD) sensor, which is expected to meet the practical application requirements of marine environmental monitoring and ocean dynamics research.