Color Channels Research Articles

Post-irradiation darkening model for EBT-3 films characterized using a single lot calibration approach

Objective.In this study, we present a model to correct the progressive post-irradiation darkening of EBT3 films. The model allows for a clinical use of EBT3 using application and calibration films scanned with different post-irradiation times.Approach.The model is a post-irradiation time- and dose-dependent power-law function, projecting the scanned transmittance of application films to the transmittance matching the same post-irradiation time of calibration films. The model was characterized for two EBT3 production lots within the dose range 0.1-12.8 Gy. A first characterization was performed utilizing calibration films scanned repeatedly for 54 d post-irradiation (lot 1), while a fast re-characterization of a second lot used three post-irradiation times (lot 2). For a long-term validation of the model, 16 film strips were irradiated at 2 Gy on different time points starting from the day of film calibration up to 43 d afterwards (lot 1). For the multiple-dose validation of the model, 8 strips were irradiated with dose levels ranging 0-12 Gy deposited 25 d after the calibration (lot 2). As a proof of principle, the model was applied to four clinical patient-specific quality assurance film measurements with prescribed dose/fraction ranging 2.66 Gy-8 Gy.Main results. The post-irradiation transmittance decreased for higher doses up to -2.5% at 12.8 Gy, and 54 d post-irradiation. With a lot-specific model correction, the mean dose accuracy of validation strips that ranged from initial -3.4% (triple-channel) and -9.90% (blue-channel) reduced to within 3.0% (all colour channels) for doses above 1 Gy. The median dose difference with the planned dose improved from -3.5% to -1.1%, and the 3%/2 mm local gamma ranged from (48.5-92.5)% to (81.2-99.2)%.Significance.A film darkening model corrects the transmittance of EBT3 films and increases the flexibility of existing dosimetry protocols. The correction ensures dose accuracies within 3%.

Multi-Color Spaceplates in the Visible.

The ultimate miniaturization of any optical system relies on the reduction or removal of free-space gaps between optical elements. Recently, nonlocal flat optic components named "spaceplates" were introduced to effectively compress space for light propagation. However, space compression over the visible spectrum remains beyond the reach of current spaceplate designs due to their inherently limited operating bandwidth and functional inefficiencies in the visible range. Here, we introduce "multi-color" spaceplates performing achromatic space compression at three distinct color channels across the visible spectrum to markedly miniaturize color imaging systems. In this approach, we first design monochromatic spaceplates with high compression factors and high transmission amplitudes at visible wavelengths based on a scalable structure and dielectric materials widely used in the fabrication of meta-optical components. We then show that the dispersion-engineered combination of monochromatic spaceplates with suitably designed transmission responses forms multicolor spaceplates that function achromatically. The proposed multicolor spaceplates, composed of amorphous titanium dioxide and silicon dioxide layers, efficiently replace free-space volumes with compression ratios as high as 4.6, beyond what would be achievable by a continuously broadband spaceplate made of the same materials. Our strategy for designing monochromatic and multicolor spaceplates, along with the presented theoretical and computational results, show that strong space-compression effects can be achieved in the visible range. Our findings may ultimately enable a new generation of ultrathin optical devices for various applications.

Performance Analysis of a Color-Code-Based Optical Camera Communication System

In this study, we present a visible light communication (VLC) system that analyzes the performance of an optical camera communication (OCC) system, utilizing a mobile phone camera as the receiver and a computer monitor as the transmitter. By creating color channels in the form of a 4 × 4 matrix within a frame, we determine the parameters that affect the successful transmission of data packets. Factors such as the brightness or darkness of the test room, the light color of the lamp in the illuminated environment, the effects of daylight when the monitor is positioned in front of a window, and issues related to dead pixels and light bleed originating from the monitor’s production process have been considered to ensure accurate data transmission. In this context, we utilized the PyCharm, Pydroid, Python, Tkinter, and OpenCV platforms for programming the transmitter and receiver units. Through the application of image processing techniques, we mitigated the effects of daylight on communication performance, thereby proposing a superior system compared to standard VLC systems that incorporate photodiodes. Additionally, considering objectives such as the maximum number of channels and the maximum distance, we regulated the sizes of the channels, the distances between the channels, and the number of channels. The NumPy library, compatible with Python–Tkinter, was employed to determine the color levels and dimensions of the channels. We investigate the effects of RGB and HSV color spaces on the data transmission rate and communication distance. Furthermore, the impact of the distance between color channels on color detection performance is discussed in detail.

A Hybrid Spatial Fuzzy Inference and CNN Approach for Forecasting Changes in Remote Sensing

Satellite cloud images, captured by Earth observation satellites, provide crucial information for weather forecasting, aviation, natural resource management, and disaster response. This study presents a novel method for detecting changes in satellite cloud images, combining convolutional neural networks (CNNs) for feature extraction with a spatial complex fuzzy inference system (CFIS). The CNNs effectively capture color channel features in the image blurring and deblurring processes, while the CFIS models the spatiotemporal relationships in the data. Experiments conducted on a US Navy satellite image dataset demonstrate the effectiveness and stability of our proposed method, outperforming related studies in terms of both root mean square error (RMSE) and R-squared (R2) metrics.

DCPNet: Deformable Control Point Network for image enhancement

In this paper, we present a novel image enhancement network consisting of global and local color enhancement. The proposed network model is constructed using global transformation functions, which are formed by a set of piece-wise quadratic curves and a local color enhancement network based on the encoder–decoder network. To adaptively and dynamically control the ranges of each piece-wise curve, we introduce deformable control points (DCPs), which determine the overall structure of the global transformation functions. The parameters for piece-wise quadratic curve fitting and DCPs are estimated using the proposed DCP network (DCPNet). DCPNet processes a down-sampled image to derive the DCP parameters: The DCP offsets and the curve parameters. Then, we obtain a set of DCPs from the DCP offsets and connect each adjacent DCP pair by using the curve parameter to construct a global transformation function for each color channel. The original input images are then transformed based on the resulting transformation functions to obtain globally enhanced images. Finally, the intermediate image is fed into the local enhancement network, which has a U-Net architecture, to produce the spatially enhanced images. Extensive experimental results demonstrate the superiority of the proposed method over state-of-the-art methods in various image enhancement tasks, such as image retouching, tone-mapping, and underwater image enhancement.

TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment.

The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner. Merging the research of cognitive science and intuition of the human visual system (HVS), in this article, we evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first make space segmentation for the reference and distorted point clouds based on a 3D Voronoi diagram to obtain a series of local patch pairs. Next, inspired by the predictive coding theory, we utilize a space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch with and without the distorted patch, respectively. Assuming that the residual errors follow the multi-variate Gaussian distributions, the self-complexity of the reference and transformational complexity between the reference and distorted samples are computed using covariance matrices. Additionally, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases. The results demonstrate that TCDM achieves state-of-the-art (SOTA) performance, and further analysis confirms its robustness in various scenarios.

Deep-learning based single-shot 3D reconstruction with simulated color-crosstalk and randomized extrinsics

In recent years, many single-frame 3D reconstruction schemes based on fringe projection profilometry (FPP) have been proposed. However, most single-frame reconstruction schemes still face the following three issues: (1) obtaining large datasets is very time-consuming, (2) focusing only on achieving single-frame reconstruction for white objects, and (3) requiring fixing the camera-projector positional relationship, increasing operational difficulty. By building a virtual FPP simulation system, our method can quickly render the required datasets, avoiding cumbersome manual operations. When rendering the datasets, we simulate adverse factors such as color channel crosstalk, system extrinsic parameter variations, and object surface colors to guide the training of the neural network. Ultimately, from a single three-frequency color image, the corresponding three-frequency three-step phase-shift images are predicted, achieving single-frame 3D reconstruction of colored objects and allowing some variation in system extrinsic parameters. Real-world experiments demonstrate that the network trained with the diverse data generated by our virtual system has good accuracy, providing valuable guidance for the practical application of deep learning methods.

New Colorful Image Encryption Method Using Triple Chaotic Maps and Grey Wolf Optimization (GWO)

A novel image encryption algorithm employing triple chaotic maps has been developed to address the shortcomings of existing methods in terms of security and efficiency. The algorithm leverages the interconnectivity of color channels in images, using distinct keys to disrupt pixel correlations within each channel. The three chaotic maps utilized URUK, WAM, and Nahrain to generate two sets of keys. The first set is used to shuffle pixel positions, creating scrambled channels. Subsequently, the second set is applied to diffuse these scrambled channels independently. A gray wolf optimization (GWO) algorithm is then employed to further optimize the shuffling process, minimizing pixel correlations and enhancing security. The triple chaotic maps of varying orders contribute to the unpredictability and robustness of the cipher image. A comprehensive security analysis, including entropy, correlation coefficients, and attack resistance, demonstrates the superior performance of the proposed method compared to existing image encryption algorithms

Estimation of isoniazid by digital image colorimetry: an integration of green chemistry and smartphone technology

BackgroundThis study proposes a simple digital image colorimetric method using a smartphone for the quantitative analysis of isoniazid in pharmaceutical medications. The analytical methodology employed in this study involved the utilization of the reaction between isoniazid and FC reagent under alkaline conditions. The chemical reaction led to the creation of a complex with a blue-gray color, exhibiting a peak absorption at a wavelength of 760 nm. An Android application was employed to perform a smartphone-based determination based on separating the captured color into different color channels such as red, blue, green, etc. The experimental procedure involved the utilization of three smartphones for the determination, which was subsequently followed by a comparative analysis of the results obtained from these devices using spectrophotometric measurements.ResultsThe B channel had the highest level of optimization in terms of analytical parameters. The limits of detection (LOD) were 0.586, 2.478, and 1.396 µg/ml for S1, S2, and S3, respectively, and for the spectrophotometer, it was found to be 0.416 µg/ml. Similarly, LOQs were determined as 1.673, 7.511, 4.232, and 1.302 µg/ml. A %RSD of 1.3 for precision and an LOD of 0.013 g dm−3 were obtained for the spectroscopic method. The % recovery in the accuracy study was found to be 99.69, 101.804, 109.28, and 100.13 for S1, S2, S3, and spectrophotometer, respectively.ConclusionThe colorimetric results from smartphone application are similar to those from spectrophotometric analyses.

New color channel driven physical lighting model for low-light image enhancement

Outdoor imaging systems, affected by low-light conditions, generally produce low-quality images with poor visibility. Low-quality images can directly influence high-level tasks such as surveillance and autonomous navigation systems. Enhancing the images captured under inadequate lighting conditions aims to generate higher visual quality in these images. However, current low-light enhancement methods may result in color unnaturalness, information loss, and strange artifacts. We propose a new color channel-driven physical lighting model (NCC-PLM) to respond to these issues to improve image quality. More concretely, we first apply a gamma correction to the input image according to its darkness degree, which is determined by its average intensity value. Then, we introduce a new color channel prior to estimate the environmental light (EL) and light scattering attenuation rate (LSAR). Finally, the enhanced image is obtained through the estimations and physical lighting model. Experimental results on various datasets demonstrate the proposed method's effectiveness and superiority over the compared methods both visually and qualitatively. Specifically, we enhance the visual quality of low-light images by revealing intricate details and maintaining color consistency, leading to a natural appearance.

MultiMatch: geometry-informed colocalization in multi-color super-resolution microscopy

With recent advances in multi-color super-resolution light microscopy, it is possible to simultaneously visualize multiple subunits within biological structures at nanometer resolution. To optimally evaluate and interpret spatial proximity of stainings on such an image, colocalization analysis tools have to be able to integrate prior knowledge on the local geometry of the recorded biological complex. We present MultiMatch to analyze the abundance and location of chain-like particle arrangements in multi-color microscopy based on multi-marginal optimal unbalanced transport methodology. Our object-based colocalization model statistically addresses the effect of incomplete labeling efficiencies enabling inference on existent, but not fully observable particle chains. We showcase that MultiMatch is able to consistently recover existing chain structures in three-color STED images of DNA origami nanorulers and outperforms geometry-uninformed triplet colocalization methods in this task. MultiMatch generalizes to an arbitrary number of color channels and is provided as a user-friendly Python package comprising colocalization visualizations.

CCM-Net: Color compensation and coordinate attention guided underwater image enhancement with multi-scale feature aggregation

Due to the light scattering and wavelength absorption in water, underwater images exhibit blurred details, low contrast, and color deviation. Existing underwater image enhancement methods are divided into traditional methods and deep learning-based methods. Traditional methods either rely on scene prior and lack robustness, or are not flexible enough resulting in poor enhancement effects. Deep learning methods have achieved good results in the field of underwater image enhancement due to their powerful feature representation ability. However, these methods cannot enhance underwater images with various degradations because they do not consider the inconsistent attenuation of different color channels and spatial regions. In this paper, we propose a novel asymmetric encoder-decoder network for underwater image enhancement, called CCM-Net. Concretely, we first introduce the prior knowledge-based encoder, which includes color compensation (CC) modules and feature extraction modules that consist of depth-wise separable convolution and global-local coordinate attention (GLCA). Then, we design a multi-scale feature aggregation (MFA) module to integrate shallow, middle, and deep features. Finally, we deploy a decoder to reconstruct the underwater images with the extracted features. Extensive experiments on publicly available datasets demonstrate that our CCM-Net effectively improves the visual quality of underwater images and achieves impressive performance.

Attenuated color channel adaptive correction and bilateral weight fusion for underwater image enhancement

Due to the absorption and scattering of light and the influence of suspended particles, underwater images commonly exhibit color distortions, reduced contrast, and diminished details. This paper proposes an attenuated color channel adaptive correction and bilateral weight fusion approach called WLAB to address the aforementioned degradation issues. Specifically, a novel white balance method is first applied to balance the color channel of the input image. Moreover, a local-block-based fast non-local means method is proposed to obtain a denoised version of the color-corrected image. Then, an adaptive stretching method that considers the histogram's local features to get a contrast-enhanced version of the color-corrected image. Finally, a bilateral weight fusion method is proposed to fuse the above two image versions to obtain an output image with complementary advantages. Experimental studies are conducted on three benchmark underwater image datasets and compared with ten state-of-the-art methods. The results show that WLAB has a significant advantage over the comparative methods. Notably, WLAB exhibits a degree of independence from camera settings and enhances the precision of various image processing applications, including key points and saliency detection. Additionally, it demonstrates commendable adaptability in improving low-light and foggy images.

Characterization of a new low-dose and low-energy Gafchromic film LD-V1.

To characterize the dose-response, energy dependence, postexposure changes, orientation dependence, and spatial capabilities of LD-V1, a new low-dose Gafchromic film for low-energy x-ray dosimetry. A single sheet of LD-V1 Gafchromic film was cut into 15 × 20mm2 rectangles with a notch to track orientation. Eight different doses between 5 and 320mGy were delivered by an MXR-160/22 x-ray tube using x-ray beams of 90, 100, and 120kVp filtered with 3mm of Al and 2mm of Ti. The 120kVp films were scanned at 1, 1.5, 2, 3, 12, 24, 48, 72, and 168h postexposure in portrait orientation and additionally scanned in landscape orientation at 24h. The 90 and 100kVp films were scanned at 24h postexposure in portrait orientation. Lastly, a 20 × 200mm2 strip of film was irradiated using a thin-slit imaging collimator and scanned 24h postexposure to test the film performance in an x-ray imaging application. Of the three color channels, the red channel was found to produce a dose-response curve with a large range of net optical density (netOD) values across the considered dose range. A prominent energy dependence was discovered, resulting in dose discrepancies on the scale of 17mGy between 90 and 120kVp for a dose of 80mGy. The measured postexposure changes suggest that the calibration irradiation-to-scan time should be longer than 12h with a±4h scanning time window for dose errors of<0.5%. An average dose difference of 3.4% was found between the two scanning orientations. Lastly, noise of 4% was measured in the thin slit collimator film for a dose of 30mGy. We have characterized the LD-V1 film for low-energy, low-dose x-ray dosimetry. Energy, scan-time, and orientation dependencies should be considered when using this film.

Quaternion-Aware Low-Rank Prior for Blind Color Image Deblurring

Blind image deblurring is a critical and challenging task in the field of imaging science due to its severe ill-posedness. Appropriate prior information and regularizations are normally introduced to alleviate this problem. Inspired by the fact that the matrix representing a natural image is intrinsically low-rank or approximately low-rank, we employ the low-rank matrix approximation (LRMA) approach for tackling blind image deblurring problems with unknown kernels. When applied to color image restoration tasks, making use of the quaternion representation in the hypercomplex domain enables us to better illustrate the inner relationships among color channels and thus more accurately characterize color image structure. Following this idea, we develop a novel model for color image blind deblurring by implementing the quaternion representation to the LRMA method. This proposed model facilitates better results for blur kernel estimation through preserving the sharper color intermediate latent image, which is first implemented for addressing the blind color image deblurring problem. Extensive numerical experiments demonstrate that our proposed quaternion-aware low-rank prior model greatly improves the performance when compared with the conventional low-rank based scheme and outperforms some of the state-of-the-art methods in terms of some criteria and visual quality.

An underwater image enhancement method based on multi-scale layer decomposition and fusion

High-quality underwater images can intuitively reflect the most realistic underwater conditions, guiding for underwater environmental monitoring and resource exploration strongly. But when factors like light absorption affect underwater optical imaging, it has been found that poor visibility and blurred texture details occur in acquired images, posing challenges for the identification and detection of underwater targets. To obtain natural images, an enhancement algorithm is proposed based on multi-scale layer decomposition and fusion. The algorithm employs different strategies to recover image attenuation information from both local and global perspectives, generating two complementary preprocessed fusion inputs. For fusion input 1, operations are conducted in the RGB color space. Initially, the mean proportion of each color channel is used to identify the attenuated color channel. Then, a local compensation strategy is adaptively applied to restore the pixel intensity of the attenuated color channel. Finally, a statistical color correction method is used to eliminate color cast in the image. Fusion input 2 involves two processing stages. In the Lab color space, the algorithm uses the grayscale information to reduce the deviation in the mean values of channels a and b globally. The local mean information of the component L enhances detail textures. In the RGB color space, linear stretching is applied to correct color deviations. To fuse the structural features of two complementary preprocessed inputs and avoid interference between signals from different layers, the color channels of fused input image are first decomposed into muti-scale structural layers based on structural priors. Then, the image enhancement is achieved through layer-by-layer fusion of the corresponding color channels of the two inputs. By testing and analyzing with the, it was found that the proposed method can improve the clarity of attenuated images in various underwater scenarios of UIEBD and RUIE datasets effectively, enhancing image detail and texture richness, increases contrast, and achieving natural and comfortable visual quality. Compared with the quantitative metrics of 14 other algorithms, the proposed algorithm shows an average score improvement of 10.14, 90.48, and 2.06, respectively, in metrics AG (average gradient), EI (edge intensity), and NIQE (natural image quality evaluator). In the RUIE dataset, it shows an average score improvement of 10.21, 94.76, and 1.86, respectively.

Real-time data processing in colorimetry camera-based single-molecule localization microscopy via CPU-GPU-FPGA heterogeneous computation.

Because conventional low-light cameras used in single-molecule localization microscopy (SMLM) do not have the ability to distinguish colors, it is often necessary to employ a dedicated optical system and/or a complicated image analysis procedure to realize multi-color SMLM. Recently, researchers explored the potential of a new kind of low-light camera called colorimetry camera as an alternative detector in multi-color SMLM, and achieved two-color SMLM under a simple optical system, with a comparable cross-talk to the best reported values. However, extracting images from all color channels is a necessary but lengthy process in colorimetry camera-based SMLM (called CC-STORM), because this process requires the sequential traversal of a massive number of pixels. By taking advantage of the parallelism and pipeline characteristics of FPGA, in this paper, we report an updated multi-color SMLM method called HCC-STORM, which integrated the data processing tasks in CC-STORM into a home-built CPU-GPU-FPGA heterogeneous computing platform. We show that, without scarifying the original performance of CC-STORM, the execution speed of HCC-STORM was increased by approximately three times. Actually, in HCC-STORM, the total data processing time for each raw image with 1024 × 1024 pixels was 26.9 ms. This improvement enabled real-time data processing for a field of view of 1024 × 1024 pixels and an exposure time of 30 ms (a typical exposure time in CC-STORM). Furthermore, to reduce the difficulty of deploying algorithms into the heterogeneous computing platform, we also report the necessary interfaces for four commonly used high-level programming languages, including C/C++, Python, Java, and Matlab. This study not only pushes forward the mature of CC-STORM, but also presents a powerful computing platform for tasks with heavy computation load.

Underwater image enhancement using contrast correction

AbstractLight‐induced degeneration of underwater images occurs by physical features of seawater. According to the wavelength of the colour spectrum, light reduces intensity significantly when it moves through water. The greatest wavelength of light that is visible gets absorbed first. Red and blue absorb the most and least, respectively. Because of the reducing consequences of the light spectrum, underwater images having poor contrast can be obtained. As a result, the crucial data contained inside these images will not be effectively retrieved for later analysis. The recent research suggests a novel approach to enhance the contrast while decreasing noise in underwater images. The recommended approach involves image histogram transformation using two significant colour spaces, Red‐Green‐Blue (RGB) and Hue‐Saturation‐Value (HSV). The histogram of the dominant colour channel (blue channel) in the RGB colour model is extended towards the lower level, containing a maximum limitation of 95%, while the inferior red colour channel has been extended towards the upper side, containing a minimum limitation of 5%. During the entire dynamic range, the green colour channel having the dominant and inferior colour channels expands in each direction. The Rayleigh distribution has been utilized for developing various stretching actions within the RGB colour space. The image has been converted to the HSV colour space, having the S and V elements adjusted within 1% of their minimum and maximum values. The suggested approach is examined in both qualitative and quantitative analysis. According to qualitative analysis, the recommended approach substantially boosts image contrast, lowers its blue and green effect, and minimizes over‐enhanced and under‐enhanced sections in the final resultant underwater image. The quantitative examination of 500 large scale underwater images dataset reveals that the suggested technique generates better results. The dataset images are grouped into small fish images, blue coral images, stone wall images, and coral branch images. The quantitative examination of all these four groups have been evaluated and shown. The average mean square error, peak signal to noise ratio, underwater image quality measurement, and underwater colour image quality evaluation values of dataset images are 76.69, 31.25, 3.85, and 0.64, respectively. These values of our proposed work outperform six other previous methods.

Response characterization of radiochromic OC-1 films in photon, electron, and proton beams.

Radiochromic film (RCF) dosimeters with their high spatial resolution and tissue equivalent properties are conveniently used for two-dimensional and small-field dosimetry. OC-1 is a new model of RCF dosimeter that was commercially introduced recently. Due to its novelty there is a need to characterize its response in various radiation beam types. To study the response of OC-1 RCFs to megavoltage clinical x-ray, electron, and proton beams, as well as kilovoltage x-ray beams used in a small animal research irradiator. OC-1 RCFs were cut into ∼4×4 cm2 pieces. RCF samples were irradiated at various dose levels in the range 0.5-120Gy using different modalities; a small animal radiation research platform (SARRP) (220 kVp), a medical linear accelerator (6 MV, 10 MV, 15 MV, 6 MV FFF, 10 MV FFF photon beams, as well as 6 and 20 MeV electron beams), and a gantry-mounted proton therapy synchrocyclotron. In order to study any dependency on the fractionation scheme, same dose was delivered at several fractions to a set of films. Different dose rates in the range 200-600 MU/min were delivered to a set of films to investigate any dose rate dependency. The films were scanned pre-irradiation and at 48h post-irradiation using a flatbed scanner. The net optical density (OD) was measured for red, green, and blue color channel for each film. The orientation dependency was studied by scanning the films at eight different orientations. In order to study the temporal evolution of the response of the films, film samples were irradiated at 10 and 50Gy using 6 MV photon beams and were scanned upon irradiation at certain time intervals up to 3 months. The spectral response of the films were studied over the visible range using a spectrometer. For megavoltage photon, electron, and plateau region of the proton beams, we did not observe a significant dependency on the beam quality, dose rate, and fractionation scheme. At the kV beam, an unusual over-response was observed in the films' net OD. An orientation dependency in the response of the films with a sinusoidal trend was observed. The response of the films increased with time following a double or triple exponential trend. The spectral absorption peaks were blue-shifted with dose. OC-1 RCFs were found to be reliable dosimeters with no significant energy dependency in MV range for photon and electron beams including the FFF beams. They over-respond when irradiated bykV x-ray beams compared to MV x-ray beams. Caution must be exercised to maintain the orientation of the films when scanning. Due to the temporal growth in the net OD of the films, same post-irradiation time interval must be used for scanning the calibration and test films.

A ‘‘Turn-on’’ Al(III)-Mediated Fluorescent Micro-Probe for Quercetin Sensing

Quercetin (Qr) is a common flavonoid widely found in plants with promising pharmacological activities. It is thus worthwhile to design a convenient method for its detection. In this work, a specific Al3+-regulated poly(cyclotriphosphazene-co-curcumin) (PC-Al) micro-probe was designed for real-time detection of Qr, therein significant “turn-on” fluorescence was observed after addition of Qr to PC-Al. Under optimal conditions, the probe has a detection limit of 23.2 nM for Qr sensing with good linearity in the range of 0–5 × 10−5 M. Moreover, real-time applications of the PC-Al probe in red wine, orange juice, and honey samples were validated. Furthermore, assisted by a consumer smartphone sensing platform, the sensor shows an excellent linear response between the red/blue ratio (R/B) of the two color channels in the picture and the concentration of Qr in the range of 1–120 μM, achieving quantitative detection of Qr. The introduction of smartphone system to realize convenient detection of Qr demonstrates good application prospects of this Al3+-mediated fluorescence probe.