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Different infrared (IR) planar geometric calibration targets have been developed over the years that exploit a well-established and flexible optical camera geometric calibration procedure following the pinhole approximation. This geometric calibration is typically neglected in IR cameras, due to the relatively low resolution of thermal images and the complex IR targets needed for the geometric calibration in comparison to the optical targets. In this study, a thorough literature review of numerous IR camera geometric calibration targets, along with their respective outcomes, were summarized and leveraged to deliver a practical checkerboard target for less experienced end users, while offering the lowest reprojection errors. It was concluded that the fabrication of high emissivity contrast and precise square points of intersection within a checkerboard pattern extends the accuracy of capturing these control points in a thermal image for an optimized IR camera geometric calibration. Accordingly, two simple planar checkerboard targets were fabricated using laser engraving and ultraviolet (UV) printing technologies on a polished stainless steel (SS304) plate. The UV-printed checkerboard target on a polished metallic alloy delivered the lowest mean reprojection error (MRE) of 0.057 pixels and the lowest root mean square error (RMSE) of reprojection of 0.063 pixels, with a standard deviation lower than 0.003 pixels. The UV-printed design offers better accuracy than any other checkerboard calibration target, and comparable results to the best prominent circular pattern results reported in the literature. [ABSTRACT FROM AUTHOR]

The role of minerals in the origin of life and prebiotic evolution remains unknown and controversial. Mineral surfaces have the potential to facilitate prebiotic polymerization due to their ability to adsorb and concentrate biomolecules that subsequently can catalyse reactions; however, the precise nature of the interaction between the mineral host and the guest biomolecule still needs to be understood. In this context, we spectroscopically characterized, using infrared, X-ray photoemission spectroscopy (XPS) and X-ray diffraction (XRD) techniques, the interaction between L-proline and montmorillonite, olivine, iron disulphide, and haematite (minerals of prebiotic interest), by evaluating their interaction from a liquid medium. This work provides insight into the chemical processes occurring between proline, the only cyclic amino acid, and this selection of minerals, each of them bearing a particular chemical and crystal structures. Proline was successfully adsorbed on montmorillonite, haematite, olivine, and iron disulphide in anionic and zwitterionic chemical forms, being the predominant form directly related to the mineral structure and composition. Silicates (montmorillonite) dominate adsorption, whereas iron oxides (haematite) show the lowest molecular affinity. This approach will help to understand structure-affinity relationship between the mineral surfaces and proline, one of the nine amino acids generated in the Miller-Urey experiment. [ABSTRACT FROM AUTHOR]

Maize is the second most plentiful cereal grown for human consumption. It constitutes 36% of total grain production worldwide and it is cultivated in about 160 countries on nearly 150 m ha. Maize faces fungal diseases causing extraordinary reduction in the grain yield. Fungi are responsible for many maize foliar diseases. Fungicides show hazardous effects on human health and also soil and water pollution. Near-infrared (NIR) images can disclose damage patterns not visible to the naked eye or depicted in RGB images. Unmanned aerial vehicles (UAV) are an inexpensive way to collect low altitude images. State-of-the-art Convolutional Neural Networks (CNN) have proven excellent results in image classification in computer vision. This study presents a novel Transfer Learning (TL) based CNN technique and states the hypothesis that NIR images acquired by UAVs contribute to a more precise classification of pathogens in maize. GPS coordinates of the infested areas are also provided for precision spraying with fungicide agents for specific targets, representing an economical mean for yield protection and with the least possible hazard to people and to the ecosystem. The proposed model was evaluated on its performance using different metrics achieving an accuracy of 86.7%, precision 98%, sensitivity 86.9% and F1 Score 92%. According to the state-of-the-art literature consulted, this is the first time that a validated deep learning-based approach has been applied in fungal diseases classification using infrared images. [ABSTRACT FROM AUTHOR]

In this study, the effect of drying methods (hot-air and infrared (IR)) and precooking process on spinach-enriched lasagna pasta was investigated. Combination of precooking and IR drying process ended in a lasagna pasta with a shorter optimal cooking time (about 10.60 times) and higher water absorption (about 1.34 times) compared with samples produced with the conventional hot-air drying. Also, precooking and IR drying method had a significant effect on the color of final samples; according to the results of sensory evaluation, application of precooking-IR drying process resulted in a lasagna pasta with better color. X-ray diffractometry (XRD) results showed that the crystal structures of the non-precooked samples were completely different from the precooked ones. These findings were confirmed by scanning electron microscopy (SEM) images. Texture evaluation indicated that the drying methods influenced the hardness of products; in precooking-IR dried lasagna pasta, the hardness was 1.85 times higher than those produced with the conventional hot-air drying. Precooking the samples and drying them with IR improved the retention of phenolic compounds during cooking about 2.75 times higher than conventional samples. From a sensory point of view, the highest score of overall desirability was related to precooked lasagna pasta processed with IR dryer. [ABSTRACT FROM AUTHOR]

Post-Hartree-Fock calculations performed at the MP2/aug-cc-pVDZ level of theory has been used to analyze the formation of intermolecular complexes between B5H11 and W = CO, NCH, NH3, H2O or HOCH3. The interactions on the structure of the arachno-pentaborane(11) are manifested by the terminal and bridge hydrogen atoms, whereby are formed the hydrogen bonds (H···Y with Y = O, C or N) as well as dihydrogen bonds (H···H). In this context, the B5H11 shows a host-guest capability for trapping molecules, of course depending on the strength of each aforementioned interactions. The topological descriptors of the Quantum Theory of Atoms in Molecules (QTAIM) were decisive for unveiling each one of the following structures B5H11···CO, B5H11···NCH, B5H11···NH3, B5H11···H2O and B5H11···HOCH3, and ideally, all hydrogen bonding formed by them. [ABSTRACT FROM AUTHOR]

The drying of sea bass was examined using oven and infrared (IR) at 60–80°C, and microwave (MW) at 140–350W. Effective moisture diffusivities and activation energies were determined, and mathematical models were applied to the data. Moisture content was found as 1.8571 kg water/kg dry matter and reduced to 0.1009–0.0798, 0.0767–0.0520, and 0.0783–0.066 kg water/kg dry matter for oven, IR, and MW, respectively. Drying times were found as 300–180, 315–135, and 18–5 for oven, IR, and MW, respectively. The most compatible models found as Midilli and Kucuk for oven, Aghbashlo et al. for IR, and Alibas for MW. [ABSTRACT FROM AUTHOR]

RAPOC (Rosseland and Planck Opacity Converter) is a Python 3 code that calculates Rosseland and Planck mean opacities (RPMs) from wavelength-dependent opacities for a given temperature, pressure, and wavelength range. In addition to being user-friendly and rapid, RAPOC can interpolate between discrete data points, making it flexible and widely applicable to the astrophysical and Earth-sciences fields, as well as in engineering. RAPOC uses ExoMol, DACE, or any user defined input data, provided that it is in a readable format. In this paper, we present the RAPOC code and compare its calculated Rosseland and Planck mean opacities with other values in the literature. RAPOC is open-source and available on Pypi and GitHub. [ABSTRACT FROM AUTHOR]

Here, a new picrate salt containing a protonated Schiff base (SB) is presented. The new compound presents a high melting point (246-250 °C), compared to base free (99-101 °C), such as expected for a salt. Also, UV-Vis and infrared experiments were carried out to in order to confirm the presence of both components, a protonated SB and the picrate anion. The single-crystal X-ray diffraction reveals the formation of the new compound, containing an ion pair in the asymmetric unit. The intramolecular interactions and, mainly, the intermolecular interactions were explored in detail. The salt presents strong N–H...O hydrogen bonds and π–π stacking interactions stabilize the crystal self-assembly, differing from neutral SB which presents only weak interactions. Finally, we investigate the fluorescence properties and capability coordination with metals by Jobs method of the SB, however, no significant results were observed. [ABSTRACT FROM AUTHOR]

Adrenaline complexes with bivalent ions Mg2+, Ca2+, Sr2+ and Ba2+ in methanol at 60 oC were prepared. The prepared complexes were isolated and characterized using elemental analyses, conductivity measurements, mid infrared, Raman laser, UV-Vis., ¹H-NMR spectra, X-ray powder diffraction, scanning electron microscopy and energy-dispersive X-ray spectroscopy (EDX). Upon the spectroscopic, conductivity and elemental analyses, the stoichiometric reactions indicated 1:2 (M:L) for Ca2+, Sr2+ and Ba2+ complexes [Ca(Adr)2(H2O)2](H2O)4Cl2, [Sr(Adr)2(H2O)2]Cl2 and [Ba(Adr)2(H2O)6]Cl2 while the molar ratio of 1:1 (M:L) for Mg2+ with a formula [Mg2(Adr)2(H2O)7]Cl4. The interpretation of Raman and infrared spectra confirmed the coordination mode, which was through the two-catechol moiety OH phenolic groups. The in vitro antibacterial activity of adrenaline chelates that have been tested against three gram-positive bacteria, two gram-negative bacteria, and two strains of fungus showed that antimicrobial activity of the metal chelates are more effective than the adrenaline free ligand. [ABSTRACT FROM AUTHOR]

Ultra-broadband and high absorption properties are significant for energy harvesting, solar cells, fire detection and imaging. An ultra-broadband and high absorption metamaterial absorber is proposed, which consists of a grid structure and a SiO2 rectangular structure. The simulation results of the metamaterial absorber show that the absorber has an absorption rate of more than 90% with a bandwidth of 3815 nm centered at about 2045 nm. Due to the asymmetry of the absorber structure, it is sensitive to the polarization angle, but still has certain polarization independence and wide-angle absorption characteristics in the whole band (300 ~ 4000 nm). The analysis shows that surface plasmon resonance, local surface plasmon resonance, cavity resonance, and coupling between resonant modes play a dominant role in the broadband and absorption of the absorber. The absorber with broadband and high absorption performance has great potential for solar energy harvesting, thermoelectric, and thermal emitter applications. [ABSTRACT FROM AUTHOR]

Sensors based on quantum dot photodiodes promise quality and accessibility improvement of infrared imaging. We demonstrate miniaturization by sub‐2‐μm pixel pitch arrays. Functionality is confirmed with external quantum efficiencies above 40% at 1450 nm. Monolithic integration enables high throughput and wide deployment of short‐wave infrared (SWIR) imagers in applications that previously could not afford them. [ABSTRACT FROM AUTHOR]

In this paper, a biosensing absorber based on phase transition material is presented. Different phases of the Ge2Sb2Te5 (GST) substrate have been studied for the suggested absorber with controllable characteristics. The structure has been examined to determine the infrared absorption characteristics. The detection of varying volumes of hemoglobin and urine biomolecules is studied. The graphene-GST material is utilized for spectrum tuning. The tuning for two distinct phases of GST material, amorphous GST and crystalline GST is examined. The results for aGST and cGST are reported in the form of absorption. Different amounts of hemoglobin and urine biomolecules are used to tune these two GST stages. Based on the wavelength shifts at these various concentrations, the sensitivity is computed. The highest achievable sensitivity for hemoglobin and urine biomolecules is 1500 nm/RIU and 1667 nm/RIU. The developed model is observed for various geometrical parameters and incidence angles, from which it is determined that the suggested structure is insensitive to wide angles between 0° and 60°. For urine biomolecules, the aGST design is more sensitive than the cGST design, but similar results are achieved for hemoglobin biomolecules. Experiments are conducted with Machine Learning-based regression models to minimize the simulation time and resource requirements of biosensor design. The findings of the trials indicate that a regression model can accurately estimate the absorption values for intermediate wavelengths with an R2 score of 0.9999. [ABSTRACT FROM AUTHOR]

Plastics and related contaminants (including microplastics; MPs and nanoplastics; NPs) have become a serious global safety issue due to their overuse in many products and applications and their inadequate management, leading to possible leakage into the environment and eventually to the food chain and humans. There is a growing literature reporting on the occurrence of plastics, (MPs and NPs) in both marine and terrestrial organisms, with many indications about the harmful impact of these contaminants on plants and animals, as well as potential human health risks. The presence of MPs and NPs in many foods and beverages including seafood (especially finfish, crustaceans, bivalves, and cephalopods), fruits, vegetables, milk, wine and beer, meat, and table salts, has become popular research areas in recent years. Detection, identification, and quantification of MPs and NPs have been widely investigated using a wide range of traditional methods, such as visual and optical methods, scanning electron microscopy, and gas chromatography-mass spectrometry, but these methods are burdened with a number of limitations. In contrast, spectroscopic techniques, especially Fourier-transform infrared spectroscopy and Raman spectroscopy, and other emerging techniques, such as hyperspectral imaging are increasingly being applied due to their potential to enable rapid, non-destructive, and high-throughput analysis. Despite huge research efforts, there is still an overarching need to develop reliable analytical techniques with low cost and high efficiency. Mitigation of plastic pollution requires establishing standard and harmonized methods, adopting holistic approaches, and raising awareness and engaging the public and policymakers. Therefore, this chapter focuses mainly on identification and quantification techniques of MPs and NPs in different food matrices (mostly seafood). [ABSTRACT FROM AUTHOR]

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In recent years, 3D printing glass optics has gained massive attention in industry and academia since glass could be an ideal material to make optical elements, including the lens. However, the limitation of materials and printing methods has prevented 3D printing glass optics progress. Therefore, we have developed a novel printing strategy for germanate glass printing instead of pure silica. Moreover, compared with traditional multi-component quartz glass, germanate glass has unmatched advantages for its mid-infrared (MIR) transparency and outstanding visible light imaging performance. Furthermore, compared with non-oxide glass (fluoride glass and chalcogenide glass), germanate glass has much better mechanical, physical, and chemical properties and a high refractive index. Germanate glass has been widely applied in remote sensing, ranging, environmental detection, and biomedical detection. However, it is difficult to shape, cast, polish, and grind for optical and photonics applications such as imaging optics and laser-collimation optics. These drawbacks have made germanate glass inaccessible to complex optical elements and greatly increased their cost. In this report, we use germanate glass fibers with a diameter of 125 µm based on fiber-fed laser heating technology to fabricate an mm-size optical application. In this paper, we combine the fiber-fed laser heating technology with an optimized temperature control process to manufacture high-precision optical elements. Germanate glass optics can be printed with excellent visible light and IR transparency and a smooth surface with roughness under 4 nm. By optimizing the layer-by-layer 3D printing process and the thermal feedback in the printing process, we avoid cracks and minimize surface deformation. This work shows the possibility of the mm-size glass optical elements 3D printing and widens its application for IR optics. [ABSTRACT FROM AUTHOR]

The current study further examines the use of a Focal-Plane Array (FPA) imaging system for making maturity determinations in small cotton fiber bundles. The pixel array in the system was adjusted to examine ~ 140 points per sample, an improvement over a previous study that examined ~ 36 points per sample. A linear regression comparing FPA and Cottonscope maturity determination for 30 cotton samples produced an R2 value of 0.96, suggesting a strong correlation between both sets of determinations. Additionally, Gaussian fits for maturity histograms included ~ 560 sampling points per sample and mirrored maturity distributions observed with the Cottonscope. Measurements were quick, required a small amount of fiber and were not destructive to the sample. A bias examination of the 140 pixels across four calibration cottons suggests little recurring bias for most of the pixels, an indication that the observed sample variability is inherent to the sample. Taken together, these results support the use of the FPA IR system for the non-destructive examination and imaging of cotton maturity distributions. [ABSTRACT FROM AUTHOR]

Background: Infrared thermography in women undergoing caesarean section has promise to identify a surgical site infection prodrome characterised by changes in cutaneous perfusion with concomitant influences on temperature distribution across the abdomen. This study was designed to compare abdominal and wound regions of interest (ROI) and feature extraction agreement between two independent users after a single training session. Methods: Image analysis performed manually in MATLAB with each reviewer 'blind' to results of the other. Image ROIs were annotated via pixel-level segmentation creating pixel masks at four time-points during the first 30 days after surgery. Results: A total of 366 matched image pairs (732 wound and abdomen labels in total) were obtained. Distribution of mask agreement using Jacquard similarity co-efficient ranged from 0.35 to 1. Good segmentation agreement (coefficient ≥ 0.7) (for mask size and shape) was observed for abdomen, but poor for wound (coefficient < 0.7). From feature extraction, wound cold spots were observed most in those who later developed wound infections. Conclusions: Reviewer performance, with respect to the input (image) data in the first stage of algorithm development, reveals a lack of correspondence (agreement) of the ROI indicating the need for further work to refine the characteristics of output labels (masks) before an unsupervised algorithm works effectively to learn patterns and features of the wound. [ABSTRACT FROM AUTHOR]

Observations from a hyperspectral infrared (IR) sounding interferometer such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) are crucial to numerical weather prediction (NWP). By measuring radiance at the top of the atmosphere using thousands of channels, these observations convey accurate atmospheric information to the initial condition through data assimilation (DA) schemes. The massive data volume has pushed the community to develop novel approaches to reduce the number of assimilated channels while retaining as much information content as possible. Thus, channel-selection schemes have become widely accepted in every NWP center. Two significant limitations of channel-selection schemes are (1) the deficiency in retaining the observational information content and (2) the higher cross-channel correlation in the observational error (R) matrix. This paper introduces a hyperspectral IR observation DA scheme in the principal component (PC) space. Four-month performance comparison case studies using the Weather Research and Forecasting model (WRF) as a forecast module between PC-score assimilation and the selected-channel assimilation experiment show that the PC-score assimilation scheme can reduce the initial condition's root-mean-squared error for temperature and water vapor compared to the channel-selection scheme and thus improve the forecasting of precipitation and high-impact weather. Case studies using the Unified Forecast System Short-Range Weather (UFS-SRW) application as forecast module also indicate that the positive impact can be retained among different NWP models. [ABSTRACT FROM AUTHOR]

Red pepper (Capsicum annuum L. cv. Capia) is an important vegetable obtained from a 1-year cultivated plant belonging to the Solanaceae family. Among the products of red pepper, its paste is very similar to tomato paste in appearance; however, it differs in terms of flavor properties. The wide-ranging quality parameters (dry matter, soluble solids, pH, titration acidity, salt, insoluble ash, total reducing sugars, color values, total carotenoid, antioxidant properties, browning index, and 5-hydroxymethylfurfural) of commercial sweet pepper pastes (n = 52) were determined. To the best of our knowledge, this study is the most comprehensive study on determining the quality parameters of red pepper paste, and its in-depth results will help food authorities issue registrations related to commercial pepper paste in countries such as Türkiye. Additionally, near-infrared (NIR) spectroscopy-based machine-learning algorithms were evaluated for the first time in paste samples as a rapid alternative or complementary to the conventional analytical methods. Regarding the conditional entropy-based models, successful predictions with high accuracy were developed for all the quality parameters of pepper paste. Except for the coefficient of determination value of 0.69 and 0.85 in the prediction of HMF and a/b color values in the developed models, respectively, 0.96 or higher coefficients of determination were obtained for all other parameters. Therefore, NIR spectroscopy, along with the conditional entropy-based predictions, enables simple, fast, multiparametric and accurate quantitative prediction of the numerous quality parameters for commercial sweet pepper pastes and has the potential to be used in routine quality control analyses by pepper paste producers. [ABSTRACT FROM AUTHOR]

Many problems concerning appropriate calibration besides camera placement are focused by various researchers during measurement operations while dealing with thermal imaging camera. For easy processing of video stream, it is greatly necessitated to correct camera on a stand yaw/pitch/roll angles by utilizing various algorithms. The task is regarded as an easy one for hot object besides obviously visible in the infrared. Heat exchange process is greatly necessitated for registering initiation from a cold object. Boundary markers set positioning is accomplished on the supervised object in addition it requires an algorithm for recognition. A fuzzy assessed spatial relations-based approach is exploited previously for visual markers set detection on a rotating steel cylinder. However, that fuzzy assessed spatial relations-based approach not producing enough detection accuracy. To mitigate the above-mentioned issue this work introduces Intelligent Water Drop Optimization based Fuzzy Inference System (IWD-FIS) on the basis of fuzzy-intrinsic shape aspects such as objects, during a source image, and also their reciprocal reference frame. In this work Otsu algorithm is used for background as well as foreground segmentation. And then Features Extraction and Object Labelling are performed. Markers detection is done by using Proposed IWT-FIS based on the extracted features. The rule conclusions, parameter optimization and Membership Function (MF) parameters are concentrated mainly through this IWD-FIS. A state-of-the-art optimization sequence for the different FIS parameters is recommended rather than presenting a new algorithm. [ABSTRACT FROM AUTHOR]

The task of detecting and identifying low contrast objects by thermal imaging optoelectronic systems in a scene with a large dynamic range requires the use of special brightness conversion algorithms. However, the most popular and high-quality methods, such as digital detail enhancement (DDE), introduce large frame delays and require significant hardware resources. This article presents a review of dynamic range enhancement algorithms among which algorithms that do not require a large number of FPGA logic elements and allow for minimal frame delay. Based on them, developed mathematical models of gradational transformation of brightness, which can detect low-contrast details of the image. The results of their implementation on FPGA as a part of a domestic optoelectronic module are given. [ABSTRACT FROM AUTHOR]

Background: Previous studies have confirmed the superiority of using erbium lasers (2940, 2780 nm) over other lasers in the debonding of ceramic brackets due to their safety and effectiveness. The most important factor in the debonding of aesthetic brackets is the transmission of the erbium laser through the aesthetic bracket to the adhesive resin. Objective: To identify the transmission of the 2940 nm wavelength through different types of aesthetic brackets. Materials and methods: A total of 60 aesthetic brackets were divided into six equal groups (10 monocrystalline sapphire brackets—Radiance, AO; 10 monocrystalline sapphire brackets—Absolute, Star Dentech; 10 polycrystalline brackets—20/40, AO; 10 polycrystalline brackets—3M Unitek Gemini Clear Ceramic; 10 silicon brackets—Silkon Plus, AO; 10 composite brackets—Orthoflex, OrthoTech). The aesthetic brackets were mounted in a Fourier transform infrared spectrophotometer (FTIR IRPrestige-21, SHIMADZU) following the typical spectroscopy lab procedure for such samples. The transmission ratio for the 2940 nm wavelength was obtained using IRsolution software. The mean transmission values of the tested groups were compared using a one-way analysis of variance (ANOVA) test followed by a Bonferroni test (post-hoc test). Results: The highest transmission ratio was observed for the Radiance sapphire brackets (64.75%) and the lowest was observed for the 3M polycrystalline brackets (40.48%). The differences among the Aesthetic brackets were significant (p < 0.05). Conclusions: The thick polycrystalline and composite brackets have the lowest transmissibility, whereas the monocrystalline sapphire brackets have the highest transmissibility for the 2940 nm wavelength, meaning that there is a higher possibility of debonding them with a hard tissue laser through thermal ablation. [ABSTRACT FROM AUTHOR]

In this study, the effect of time and heating rate was investigated on stabilization of AA5083 aluminum sheets with a thickness of 2.5 mm using cold rolling and heat treatment. For this purpose, the initial plate with a thickness of 10 mm was subjected to homogenization treatment at 520 °C for 12 h and the microstructure and mechanical properties were studied. Then, the plate was rolled to a thickness of 4 mm, and recrystallization annealing was performed between each stage of rolling at 330 °C for 30 min. Finally, during the H34 thermo-mechanical treatment, 35% reduction was performed in thickness, and stabilization treatments were applied at 220 °C for 15 min, 1, and 4 h in an electrical resistance and an infrared furnace. It was observed that by using appropriate heating rate and stabilizing time, in addition to improving the mechanical properties and achieving fine-grained structure, the corrosion resistance of the alloy is also suitable. The yield and tensile strength were increased in the final sample by 91 and 27%, respectively. Microstructure was studied using optical and scanning electron microscopy. It revealed fine-grained structure after the process. The grain size was reduced by 37 and 27% for the stabilized samples compared to as-received sample in the infrared and resistance furnaces, respectively. Potentiodynamic polarization and electrochemical impedance spectroscopy tests showed that the sample stabilized at higher heating rate had the best corrosion resistance. The corrosion current density was reduced by 92% compared to the base alloy. [ABSTRACT FROM AUTHOR]

In recent years the use of hybrid drying methods has been noticed because of the improvement of the dried products quality. The effect of infrared (IR) power (0, 400, 600, and 800 W) in combination with convective hot air (60 °C, 3 m/s) on the quality properties of black raspberry pulp during foam mat drying was investigated in this study. According to the findings, increasing the IR power, (IR-CHA) had no effect on the moisture content, moisture ratio, or drying rate of the product; however, the effective moisture diffusion coefficient (Deff) was significantly increased. The effect of IR power on the physicochemical properties of black raspberry powder revealed that combining infrared with convective hot air (IR-CHA) improved the powder's flowability and total polyphenol content while decreasing its moisture content (p

As a result of the special advantages of infrared such as ambient does not affect by IR heating, being environmentally friendly, reducing the size of equipment used in food heating and ease of control. Moreover, it is one of the most promising techniques, so the current study aimed to investigate the application of infrared ray in extracting the oil of common carp fish wastes (Cyprinus carpio L.), and the study of the effect of the independent factors, which are: power (130, 190, 250 watts), temperature (50, 60, 70 ºC), the distance between the radiation source and the surface of the waste (1, 15.5, 30 cm) on the freezing point, cloud point and pour of carp waste oil using response surfaces methodology. The results showed that the lowest practical and predicted values for the above traits were -4.6, -3.1, 11.9, 12.2, 6.8, and 7.01, respectively, and that the increase in power and temperature had a negative effect, as it led to an increase in the cloud point. The results illustrated that the interaction between distance, power and temperature led to a decrease in the freezing point. The results also clarified that the increase in power with a decrease in temperature and stability of the distance led to an increase in the pour point. The results indicated that the optimal conditions were the power 315.94W, the temperature 66.14° C, and the distance is 20.02 cm. The results of the statistical showed that the nonlinear multiple regression equation was used to predict the values of the cloud point, while the multiple linear regression equation was used to predict the pour point values. The nonlinear multiple regression equation had a non-significant effect to predict the freezing point values, with an error rate ranging from 0.32% to 30%.

Introduction: The condition of low back pain is a musculoskeletal problem without age limit. Patients with lower back pain tend to take analgesic drugs to reduce pain. Apart from pharmacological consumption, the tendency for low back pain sufferers to seek other alternatives, namely; physiotherapy treatment. One of the physiotherapy treatments for low back pain is giving infrared and William flexion exercise or a combination of both. This study aims to determine the effectiveness of infrared and William flexion in reducing pain and flexibility of the lumbar muscles. Method: The research design is quasi experimental pre-posttest with control group. The study population consisted of 44 patients with low back pain at Physiomar Clinic by dividing 40 samples into intervention groups (William flexion exercise) and control group (Infrared and William Flexion Exercise) 2 times/week for 1 month. The pain level measured by using the Visual Analog Scale (VAS) and flexibility using the Modified Schober Test (MST). The data was analyzed by Wilcoxon and Mann Whitney (α≤0.05). Results: This study in intervention group showed a median pre-post pain score of 6.00-5.00 and a median pre-post flexibility value of 3.00-3.00 (p=0.000); while control group showed a median pre-post pain score of 6.00-2.00 and a median pre-post flexibility value of 2.50-10.00 (p=0.000). The combination of infrared and William flexion exercise is better than William flexion exercise for changes in pain and flexibility in low back pain patients at the physiomar clinic

Understanding the structure-activity relationship of catalysts and the reaction pathway is crucial for designing efficient, selective, and stable electrocatalytic systems. In situ vibrational spectroscopy provides a unique tool for decoding molecular-level factors involved in electrocatalytic reactions. Typically, spectra are recorded when the system reaches steady states under set potentials, known as steady-state measurements, providing static pictures of electrode properties at specific potentials. However, transient information that is crucial for understanding the dynamic of electrocatalytic reactions remains elusive. Thus, time-resolved in situ vibrational spectroscopies are developed. This mini review summarizes time-resolved in situ infrared and Raman techniques and discusses their application in electrocatalytic research. With different time resolutions, these time-resolved techniques can capture unique dynamic processes of electrocatalytic reactions, short-lived intermediates, and the surface structure revolution that would be missed in steady-state measurements alone. Therefore, they are essential for understanding complex reaction mechanisms and can help unravel important molecular-level information hidden in steady states. Additionally, improving spectral time resolution, exploring low/ultralow frequency detection, and developing operando time-resolved devices are proposed as areas for advancing time-resolved techniques and their further applications in electrocatalytic research.

Abstract High spectral resolution infrared sounders are an integral component of the global observing system and are used in a broad range of applications. This is enabled by their high accuracies which are ensured by rigorous calibration/validation activities. One of these activities is the post‐launch intercomparison with other high spectral resolution infrared sounders using simultaneous nadir overpasses (SNOs). This paper introduces a novel application of the previously developed SNO methodology by including time difference histogram symmetrization and a spatial sampling uncertainty. Where possible, radiometric measurement uncertainties are included and propagated through the statistics. Comparisons of Atmospheric Infrared Sounder (AIRS), METOP‐A/B/C Infrared Atmospheric Sounding Interferometer (IASI), and Cross‐track Infrared Sounder (CrIS) from 1 October 2019, to 1 October 2020, are analyzed. Results show AIRS and IASI differences as well as CrIS and AIRS differences are generally less than 0.4 K across the spectrum, and CrIS and IASI differences are generally less than 0.25 K. Comparison of the Suomi National Polar‐Orbiting Partnership and NOAA‐20 CrIS instruments via IASI and AIRS shows differences are generally less than 0.1 K across all bands and that the two CrIS instruments statistically agree within their radiometric uncertainties except for the narrow 2,370 cm−1 region where artifacts due to brightness temperature conversion are prone to occur.

Semiconductors-core optical fibers have gathered attention for light guidance in the infrared spectrum. Cladded with glasses, fibers can be the ideal medium to transfer the favorable bulk properties of semiconductors into the micro/nano scaled one-dimensional form. The integration of these fibers with optical circuits, lasers and photonic crystals offers a wide variety of applications. In this perspective, the role of semiconductors in the future of optical fibers and their integration with photonic crystal structures are analyzed. The past and present efforts of semiconductor-core fibers are briefly reviewed, and the potential future application areas benefited by semiconductors as fiber materials are discussed.

Abstract Condensation and sublimation of ices at the surface of the planet is a key part of both the Martian H2O and CO2 cycles, either from a seasonal or diurnal aspect. While most of the ice is located within the polar caps, surface frost is known to be formed during nighttime down to equatorial latitudes. Here, we use data from the Emirates Mars Infrared Spectrometer onboard the Emirates Mars Mission to monitor the diurnal and seasonal evolution of the ices at the surface of Mars over almost one Martian year. The unique local time coverage provided by the instrument allows us to observe the apparition of equatorial CO2 frost in the second half of the Martian night around the equinoxes, to its sublimation at sunrise.

Abstract Clouds present one of the largest sources of uncertainty in infrared (IR) measurements of the vertical atmospheric state. Hyperspectral instruments onboard Earth orbiting satellites, such as the Atmospheric Infrared Sounder (AIRS) on Aqua (2002–present), measure the top of atmosphere (TOA) radiances emitted by Earth surface and atmosphere. IR measurements are limited in their ability to distinguish different cloud structures. Lack of prior knowledge about clouds at the time of measurement can therefore significantly reduce the quality of retrieved soundings. The Community Long‐term Infrared Microwave Atmospheric Product System (CLIMCAPS) uses the AIRS Science Team (AST) cloud clearing methodology to remove the radiative effect of clouds from TOA IR measurements to improve the quality of its sounding retrievals and enable vertical observations in clear and partly cloudy conditions. In this paper we discuss how AST cloud clearing affects CLIMCAPS soundings and enable a global product for long‐term analysis of the atmospheric state.

The main objective of this article is to provide angle-dependent spectral reflectance measurements of various materials in the near infrared spectrum. In contrast to already existing reflectance libraries, e.g., NASA ECOSTRESS and Aster reflectance libraries, which consider only perpendicular reflectance measurements, the presented dataset includes angular resolution of the material reflectance. To conduct the angle-dependent spectral reflectance material measurements, a new measurement device based on a 945 nm time-of-flight camera is used, which was calibrated using Lambertian targets with defined reflectance values at 10, 50, and 95%. The spectral reflectance material measurements are taken for an angle range of 0° to 80° with 10° incremental steps and stored in table format. The developed dataset is categorized with a novel material classification, divided into four different levels of detail considering material properties and distinguishing predominantly between mutually exclusive material classes (level 1) and material types (level 2). The dataset is published open access on the open repository Zenodo with record number 7467552 and version 1.0.1 [1]. Currently, the dataset contains 283 measurements and is continuously extended in new versions on Zenodo.

The goal of smart cities is to improve efficiencies, enhance sustainability, advance quality of life and reduce energy consumption. One of the key factors to accomplish a smart city involves the use of IoT and information technology infrastructure which can be described as the foundation of a smart city. Its effective implementation will allow the city to meet its wide range of requirements while being able to respond to innovations, such as advanced sensors, analytic tools, measurement, and artificial intelligent based solutions. This paper investigates and compares between the use of low and high resolution infrared sensors as part of the Internet of Things (IoT) to estimate crowds in cities to enhance and optimise the efficiency of the transportation process and other public services for low density scenarios. A case study was conducted in Nottingham city at one of the tram stops. An experimental methodology is used where different number of people are captured and the results are compared using different image processing techniques. The findings show that both technologies are useful in the estimation of crowd density, however, the high resolution camera has been found to be more accurate in estimating the number of people albeit it is more expensive for the integration into infrastructures. The practical implication is that low-cost and low resolution infrared cameras could provide reasonable results. However, for higher accuracy, high resolution infrared cameras will be needed; and they are potentially more expensive. So a compromise might be needed between cost and performance to encourage the installation of more IoT systems using infrared technologies.

In this paper, we address the challenge of detecting small moving targets in dynamic environments characterized by the concurrent movement of both platform and sensor. In such cases, simple image-based frame registration and optical flow analysis cannot be used to detect moving targets. To tackle this, it is necessary to use sensor and platform meta-data in addition to image analysis for temporal and spatial anomaly detection. To this end, we investigate techniques that utilize inertial data to enhance frame-to-frame registration, consistently yielding improved detection outcomes when compared against purely feature-based techniques. For cases where image registration is not possible even with metadata, we propose single-frame spatial anomaly detection and then estimate the range to the target using the platform velocity. The behavior of the estimated range over time helps us to discern targets from clutter. Finally, we show that a KNN classifier can be used to further reduce the false alarm rate without a significant reduction in detection performance. The proposed strategies offer a robust solution for the detection of moving targets in dynamically challenging settings.

The protection of ITER in-vessel components and the plasma-wall interaction studies will be based on a large network of infrared (IR) cameras covering 70% of the tokamak. The surface temperature measurement from IR images remains challenging due to the presence of metallic targets, with changes in surface thermo-radiative properties (emissivity) and the presence of multiple reflections. The paper provides an overview of major progress to improve the interpretation of IR image and to get more reliable surface temperature from IR synthetic diagnostics. The paper presents the latest development of (1) the forward model to include the modelling of the edge localised modes and a new advanced camera that is better adapted to experimental data (2) the inverse model to retrieve the emissivity of the targets and the surface temperature from a neural network trained exclusively from synthetic IR images. Promising results have been obtained both from simulated test images with an estimated emissivity better than 0.05 and a surface temperature better than 10%, and from WEST experimental images of ITER-like wide-angle to filter reflection patterns.

In the past ten years, rates of forest fires around the world have increased significantly. Forest fires greatly affect the ecosystem by damaging vegetation. Forest fires are caused by several causes, including both human and natural causes. Human causes lie in intentional and irregular burning operations. Global warming is a major natural cause of forest fires. The early detection of forest fires reduces the rate of their spread to larger areas by speeding up their extinguishing with the help of equipment and materials for early detection. In this research, an early detection system for forest fires is proposed called Forest Defender Fusion. This system achieved high accuracy and long-term monitoring of the site by using the Intermediate Fusion VGG16 model and Enhanced Consumed Energy-Leach protocol (ECP-LEACH). The Intermediate Fusion VGG16 model receives RGB (red, green, blue) and IR (infrared) images from drones to detect forest fires. The Forest Defender Fusion System provides regulation of energy consumption in drones and achieves high detection accuracy so that forest fires are detected early. The detection model was trained on the FLAME 2 dataset and obtained an accuracy of 99.86%, superior to the rest of the models that track the input of RGB and IR images together. A simulation using the Python language to demonstrate the system in real time was performed.

Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.

Infrared and visible remote sensing image registration is significant for utilizing remote sensing images to obtain scene information. However, it is difficult to establish a large number of correct matches due to the difficulty in obtaining similarity metrics due to the presence of radiation variation between heterogeneous sensors, which is caused by different imaging principles. In addition, the existence of sparse textures in infrared images as well as in some scenes and the small number of relevant trainable datasets also hinder the development of this field. Therefore, we combined data-driven and knowledge-driven methods to propose a Radiation-variation Insensitive, Zero-shot learning-based Registration (RIZER). First, RIZER, as a whole, adopts a detector-free coarse-to-fine registration framework, and the data-driven methods use a Transformer based on zero-shot learning. Next, the knowledge-driven methods are embodied in the coarse-level matches, where we adopt the strategy of seeking reliability by introducing the HNSW algorithm and employing a priori knowledge of local geometric soft constraints. Then, we simulate the matching strategy of the human eye to transform the matching problem into a model-fitting problem and employ a multi-constrained incremental matching approach. Finally, after fine-level coordinate fine tuning, we propose an outlier culling algorithm that only requires very few iterations. Meanwhile, we propose a multi-scene infrared and visible remote sensing image registration dataset. After testing, RIZER achieved a correct matching rate of 99.55% with an RMSE of 1.36 and had an advantage in the number of correct matches, as well as a good generalization ability for other multimodal images, achieving the best results when compared to some traditional and state-of-the-art multimodal registration algorithms.

The noise-equivalent power is used to predict performance of infrared (IR) sensors. In a general IR or remote sensing system, the axes of the incoming chief ray of each pixel in the optical system and the center of that detector pixel are slightly misaligned, due to optical tolerances, jitter, and other environmental conditions, leading to error in the sensor performance prediction and estimates. A new figure-of-merit, the optical centroid efficiency (OCE), considers the energy on the rectangular detector pixel, averaged over all positions on the pixel. We calculate the OCE as a function of energy on detector (EOD) for a popular IR imaging system, a three-mirror reflector.

The management of the increasing volume of municipal solid waste is an essential activity for the health of the environment and of the population. The organic matter of waste deposited in landfills is subject to aerobic decomposition processes, bacterial aerobic decomposition, and chemical reactions that release large amounts of heat, biogas, and leachates at high temperatures. The control of these by-products enables their recovery, utilization, and treatment for energy use, avoiding emissions to the environment. UAVs with low-cost thermal sensors are a tool that enables the representation of temperature distributions for the thermal control of landfills. This study focuses on the development of a methodology for the generation of 3D thermal models through the projection of TIR image information onto a 3D model generated from RGB images and the identification of thermal anomalies by means of photointerpretation and GIS analysis. The novel methodological approach was implemented at the Meruelo landfill for validation. At the facility, a 4D model (X,Y,Z-temperature) and a 13.8 cm/px GSD thermal orthoimage were generated with a thermal accuracy of 1.63 °C, which enabled the identification of at least five areas of high temperatures associated with possible biogas emissions, decomposing organic matter, or underground fires, which were verified by on-site measurements and photointerpretation of the RGB model, in order to take and assess specific corrective measures.

Diabetic foot ulcers represent the most frequently recognized and highest risk factor among patients affected by diabetes mellitus. The associated recurrent rate is high, and amputation of the foot or lower limb is often required due to infection. Analysis of infrared thermograms covering the entire plantar aspect of both feet is considered an emerging area of research focused on identifying at an early stage the underlying conditions that sustain skin and tissue damage prior to the onset of superficial wounds. The identification of foot disorders at an early stage using thermography requires establishing a subset of relevant features to reduce decision variability and data misinterpretation and provide a better overall cost–performance for classification. The lack of standardization among thermograms as well as the unbalanced datasets towards diabetic cases hinder the establishment of this suitable subset of features. To date, most studies published are mainly based on the exploitation of the publicly available INAOE dataset, which is composed of thermogram images of healthy and diabetic subjects. However, a recently released dataset, STANDUP, provided data for extending the current state of the art. In this work, an extended and more generalized dataset was employed. A comparison was performed between the more relevant and robust features, previously extracted from the INAOE dataset, with the features extracted from the extended dataset. These features were obtained through state-of-the-art methodologies, including two classical approaches, lasso and random forest, and two variational deep learning-based methods. The extracted features were used as an input to a support vector machine classifier to distinguish between diabetic and healthy subjects. The performance metrics employed confirmed the effectiveness of both the methodology and the state-of-the-art features subsequently extracted. Most importantly, their performance was also demonstrated when considering the generalization achieved through the integration of input datasets. Notably, features associated with the MCA and LPA angiosomes seemed the most relevant.

The primary physicochemical effect upon exposure to infrared radiation (IR) is the temperature increase of cells. The degradation of proteins via the hydrolysis of peptide bonds is related to cell malfunction. In this work, the degradation of proteins/peptides under the influence of IR radiation is theoretically studied. It is shown that the low value of enthalpy of peptide bond hydrolysis has two consequences: (a) the enthalpy of hydrolysis is sensitive to small variations in the bond strength, and the hydrolysis of weak peptide bonds is exothermic, while the hydrolysis of stronger bonds is endothermic; (b) the increase in temperature (e.g., due to IR exposure) changes the enthalpy of the reaction of some weak peptide bonds from exothermic to endothermic (that is, their hydrolysis will be favored upon further increase in temperature). Simple calculations reveal that the amount of absorbed energy during the overtone and hot band transitions of the H–O–H and C–N stretching vibrations is comparable to the activation energy of the (uncatalyzed) hydrolysis. A critical discussion is provided regarding the influence of different IR wavelengths on peptide bond hydrolysis.

Infrared (IR) spectroscopy has provided considerable insight into the structures, dynamics, and formation mechanisms of amyloid fibrils. IR probes, such as main chain 13C═18O, have been widely employed to obtain site-specific structural information, yet only secondary structures and strand-to-strand arrangements can be probed. Very few nonperturbative IR probes are available to report on the side-chain conformation and environments, which are critical to determining sheet-to-sheet arrangements in steric zippers within amyloids. Polar residues, such as glutamine, contribute significantly to the stability of amyloids and thus are frequently found in core regions of amyloid peptides/proteins. Furthermore, polyglutamine (polyQ) repeats form toxic aggregates in several neurodegenerative diseases. Here we report the synthesis and application of a new nonperturbative IR probe-glutamine side chain 13C═18O. We use side chain 13C═18O labeling and isotope dilution to detect the presence of intermolecularly hydrogen-bonded arrays of glutamine side chains (Gln ladders) in amyloid-forming peptides. Moreover, the line width of the 13C═18O peak is highly sensitive to its local hydration environment. The IR data from side chain labeling allows us to unambiguously determine the sheet-to-sheet arrangement in a short amyloid-forming peptide, GNNQQNY, providing insight that was otherwise inaccessible through main chain labeling. With several different fibril samples, we also show the versatility of this IR probe in studying the structures and aggregation kinetics of amyloids. Finally, we demonstrate the capability of modeling amyloid structures with IR data using the integrative modeling platform (IMP) and the potential of integrating IR with other biophysical methods for more accurate structural modeling. Together, we believe that side chain 13C═18O will complement main chain isotope labeling in future IR studies of amyloids and integrative modeling using IR data will significantly expand the power of IR spectroscopy to elucidate amyloid assemblies.

The physics and chemistry of liquid solutions play a central role in science, and our understanding of life on Earth. Unfortunately, key tools for interrogating aqueous systems, such as infrared and soft X-ray spectroscopy, cannot readily be applied because of strong absorption in water. Here we use gas dynamic forces to generate free-flowing, sub-micron, liquid sheets which are 2 orders of magnitude thinner than anything previously reported. Optical, infrared and X-ray spectroscopies are used to characterize the sheets, which are found to be tunable in thickness from over 1 micron down to less than 20 nanometers, which corresponds to fewer than 100 water molecules thick. At this thickness, aqueous sheets can readily transmit photons across the spectrum, leading to potentially transformative applications in infrared, X-ray, electron spectroscopies and beyond. The ultrathin sheets are stable for days in vacuum, and we demonstrate their use at free-electron laser and synchrotron light sources.

Short-wave infrared (SWIR) emitters are at the center of ground-breaking applications in biomedical imaging, next-generation optoelectronic devices, and optical communications. Colloidal nanocrystals based on indium arsenide are some of the most promising SWIR emitters to date. However, the lack of single-particle spectroscopic methods accessible in the SWIR has prevented advances in both nanocrystal synthesis and fundamental characterization of emitters. Here, we demonstrate an implementation of a solution photon correlation Fourier spectroscopy (s-PCFS) experiment utilizing the SWIR sensitivity and time resolution of superconducting nanowire single-photon detectors to extract single-particle emission linewidths from colloidal indium arsenide/cadmium selenide (InAs/CdSe) core/shell nanocrystals emissive from 1.2 to 1.6 μm. We show that the average single InAs/CdSe nanocrystal fluorescence linewidth is, remarkably, as narrow as 52 meV, similar to what has been observed in some of the most narrowband nanostructured emitters in the visible region. Additionally, the single nanocrystal fluorescence linewidth increases with increasing shell thickness, suggesting exciton-phonon coupling as the dominant emission line-broadening mechanism in this system. The development of the SWIR s-PCFS technique has enabled measurements of spectral linewidths of colloidal SWIR-emissive NCs in solution and provides a platform to study the single NC spectral characteristics of SWIR emitters.

Diagnosis and treatment of fibromyalgia (FM) remains a challenge owing to the lack of reliable biomarkers. Our objective was to develop a rapid biomarker-based method for diagnosing FM by using vibrational spectroscopy to differentiate patients with FM from those with rheumatoid arthritis (RA), osteoarthritis (OA), or systemic lupus erythematosus (SLE) and to identify metabolites associated with these differences. Blood samples were collected from patients with a diagnosis of FM (n = 50), RA (n = 29), OA (n = 19), or SLE (n = 23). Bloodspot samples were prepared, and spectra collected with portable FT-IR and FT-Raman microspectroscopy and subjected to metabolomics analysis by ultra-HPLC (uHPLC), coupled to a photodiode array (PDA) and tandem MS/MS. Unique IR and Raman spectral signatures were identified by pattern recognition analysis and clustered all study participants into classes (FM, RA, and SLE) with no misclassifications (p < 0.05, and interclass distances > 2.5). Furthermore, the spectra correlated (r = 0.95 and 0.83 for IR and Raman, respectively) with FM pain severity measured with fibromyalgia impact questionnaire revised version (FIQR) assessments. Protein backbones and pyridine-carboxylic acids dominated this discrimination and might serve as biomarkers for syndromes such as FM. uHPLC-PDA-MS/MS provided insights into metabolites significantly differing among the disease groups, not only in molecular m/z+ and m/z- values but also in UV-visible chromatograms. We conclude that vibrational spectroscopy may provide a reliable diagnostic test for differentiating FM from other disorders and for establishing serologic biomarkers of FM-associated pain.

Unnatural nucleosides possessing unique spectroscopic properties that mimic natural nucleobases in both size and chemical structure are ideally suited for spectroscopic measurements of DNA/RNA structure and dynamics in a site-specific manner. However, such unnatural nucleosides are scarce, which prompts us to explore the utility of a recently found unnatural nucleoside, 4-cyanoindole-2'-deoxyribonucleoside (4CNI-NS), as a site-specific spectroscopic probe of DNA. A recent study revealed that 4CNI-NS is a universal nucleobase that maintains the high fluorescence quantum yield of 4-cyanoindole and that among the four natural nucleobases, only guanine can significantly quench its fluorescence. Herein, we further show that the C≡N stretching frequency of 4CNI-NS is sensitive to the local environment, making it a useful site-specific infrared probe of oligonucleotides. In addition, we demonstrate that the fluorescence-quencher pair formed by 4CNI-NS and guanine can be used to quantitatively assess the binding affinity of a single-stranded DNA to the protein system of interest via fluorescence spectroscopy, among other applications. We believe that this fluorescence binding assay is especially useful as its potentiality allows high-throughput screening of DNA⁻protein interactions.