Eye Detection Research Articles

Detecting Established Glaucoma Using OCT Alone: Utilizing an OCT Reading Center in a Real-World Clinical Setting.

We evaluated the ability of an optical coherence tomography (OCT)-based reading center for glaucoma (ORG) to detect established glaucoma using OCT alone. This study included eyes from 70 consecutive patients with established glaucoma (i.e. moderate or severe glaucoma according to the International Classification of Diseases [ICD]-10 guidelines) and 20 consecutive healthy subjects, who had no evidence of glaucomatous optic neuropathy (GON) or visual field (VF) loss in either eye. Using a standardized ORG quality assessment, 33 eyes were excluded due to media opacity (12), poor image quality (13), or epiretinal membrane (8). Of the remaining 147 eyes, 86 had established glaucoma and 36 were from healthy controls (total n = 122). Based on the OCT report alone and applying a previously described evaluation method, the presence of GON in each eye was determined by two masked ORG graders. The main outcome measures were sensitivity and specificity for detection of eyes with established glaucoma. Of the 86 eyes with established glaucoma (average mean deviation [MD] = -10.9 ± 7.7dB, range = -0.5 to -31.5dB), only one eye (MD = -0.46) was missed (sensitivity = 98.8%). However, the other eye of this patient was correctly classified as GON. Therefore, at a patient level, sensitivity was 100%. None of the 36 healthy eyes was classified as GON by the ORG (specificity = 100%). An OCT-based reading center is able to identify eyes with established glaucoma using OCT alone with high sensitivity and specificity. Our study validates the use of a systematic OCT-based approach for glaucoma detection in a real-world setting.

Naked eye detection of arsenite, arsenate, and H2S by a Schiff base naphthaldehyde conjugate using a single paper strip, based on a deprotonation mechanism

A ‘three-in-one’ paper strip-based colorimetric chemosensor for rapid, cost-effective, and selective sensing of arsenite, arsenate, and sulfide for on-site and real-time monitoring in environmental samples.

Enhancing facial feature de-identification in multiframe brain images: A generative adversarial network approach.

The collection of head images for public datasets in the field of brain science has grown remarkably in recent years, underscoring the need for robust de-identification methods to adhere with privacy regulations. This paper elucidates a novel deep learning-based approach to deidentifying facial features in brain images using a generative adversarial network to synthesize new facial features and contours. We employed the precision of the three-dimensional U-Net model to detect specific features such as the ears, nose, mouth, and eyes. Results: Our method diverges from prior studies by highlighting partial regions of the head image rather than comprehensive full-head images. We trained and tested our model on a dataset comprising 490 cases from a publicly available head computed tomography image dataset and an additional 70 cases with head MR images. Integrated data proved advantageous, with promising results. The nose, mouth, and eye detection achieved 100% accuracy, while ear detection reached 85.03% in the training dataset. In the testing dataset, ear detection accuracy was 65.98%, and the validation dataset ear detection attained 100%. Analysis of pixel value histograms demonstrated varying degrees of similarity, as measured by the Structural Similarity Index (SSIM), between raw and generated features across different facial features. The proposed methodology, tailored for partial head image processing, is well suited for real-world imaging examination scenarios and holds potential for future clinical applications contributing to the advancement of research in de-identification technologies, thus fortifying privacy safeguards.

Expression of concern: Water-soluble pyridine-based colorimetric chemosensor for naked eye detection of silver ions: design, synthesis, spectral and theoretical investigation

Expression of concern for ‘Water-soluble pyridine-based colorimetric chemosensor for naked eye detection of silver ions: design, synthesis, spectral and theoretical investigation’ by B. Annaraj and M. A. Neelakantan, Anal. Methods, 2014, 6, 9610–9615, https://doi.org/10.1039/C4AY01592D.

Synthesis of fluorescent carbon dots by B/P doping and application for Co2+ and methylene blue detection

During the synthesis of carbon dots, different heteroatom doping can change the fluorescence color and the emission wavelength when the same precursor is applied. In this study, we used o-phenylenediamine and L-lysine as raw materials, boric acid or phosphoric acid as different heteroatom dopants, and water as solvent to synthesize doped carbon dots through a simple microwave-assisted method. Finally, two kinds of doped carbon dots with different fluorescence colors under 365 nm UV light were obtained, of which the B-doped carbon dots (B-CDs) showed orange fluorescence, and the P-doped carbon dots (P-CDs) showed red fluorescence. Both carbon dots had satisfactory fluorescence quantum yields and can be used as fluorescence probes. Therefore, we investigated the detection performance of these two carbon dots when they were used as fluorescent probes. B-CDs can selectively detect Co2+ with a detection limit of 0.1102 μM, and at the same time, it can realize the naked eye detection of Co2+ in a certain concentration range. The P-CDs could sensitively detect methylene blue with a detection limit of 0.048 μM.

A rhodamine-6G based chromo and fluorogenic sensor for the selective turn-ON detection of Fe3+ in aqueous medium with potential applications as INHIBIT logic gate, memory device and live cell imaging

A novel rhodamine-6G based biocompatible chromo- and fluorogenic “OFF-ON” sensor L2, having amide based metal ion receptor, has been synthesized using Michael addition reaction and characterized by IR, NMR and HRMS spectroscopic studies. It exhibits an excellent selectivity and sensitivity towards Fe3+ in aqueous medium over mono- and di-valent and other trivalent metal ions. A 14-fold enhancement of fluorescence intensity was found upon addition of 11.5 equivalent Fe3+ ion into the probe L2 in H2O (pH 7.2, 10 mM HEPES buffer) with colour change from colourless to reddish yellow, providing an opportunity for naked eye detection of Fe3+. The corresponding binding constant Kf was evaluated to be (1.16 ± 0.04) x 104 M−1 with 1:1 stoichiometry. Quantum yields of L2 and [L2 - Fe3+] complex in H2O (pH 7.2, 10 mM HEPES buffer) were found to be 0.013 and 0.523, respectively using Rhodamine-6G as standard. LOD of L2 for Fe3+ detection in aqueous medium determined by 3σ/slope method was found to be 4.184 μM. Cyanide ion scavanges Fe3+ from [L2 - Fe3+] resulting quenching of fluorescence due to re-attainment of the non-fluorescent spirolactam ring form of the probe. As the sensing phenomenon is reversible, it helps to frame INHIBIT logic gate and memory devices.

Label-Free Detection of Unbound Bilirubin and Nitrophenol Explosives in Water by a Mechanosynthesized Dual Functional Zinc Complex: Recognition of Picric Acid in Various Common Organic Media.

High levels of unconjugated bilirubin (UB) in serum lead to asymptomatic and neonatal jaundice and brain dysfunctions. Herein, we have reported the detection of UB at as low as 1 μM in an aqueous alkaline medium using a Zn(II) complex. The specificity of the complex has been validated by the HPLC in the concentration window 6-90 μM, which is rare. The sensory response of the probe at physiological pH against nitro explosives developed it as an instant-acting fluorosensor for picric acid (PA) and 2,4-dinitrophenol (2,4-DNP). Spectroscopic titration provided a binding constant of 4×105 M-1 with PA. The naked eye detection was found to be 15 μM. The solid-state photoluminescent nature of the complex enabled it for PA sensing in the solid phase. Interestingly, the probe remained fluorescent in various volatile and non-volatile organic solvents. As a result, it can also detect PA and 2,4-DNP in a wide range of common organic media. NMR studies revealed the coordination of PA, 2,4-DNP, and UB to the Zn(II) center of the probe, which is responsible for the observed quenching of the probe with the analytes.

Pig eye area temperature extraction algorithm based on registered images

The body temperature of pigs is closely related to pig diseases. People have tried various methods to use infrared thermography technology for extracting pig body temperature. However, infrared images may vary with changes in pig body temperature. When the body temperature of pigs is abnormal, these body temperature extraction methods using only infrared images may not be able to locate the target area, thereby failing to complete the body temperature extraction operation. Taking the eye area as the target area, this study proposes apig eye area temperature extraction algorithmbased on registered images. This algorithm first aligns the infrared image with the visible image using registration techniques, then uses an object detection model to locate the pig's eye area on the visible light image, and finally completes the temperature extraction by combining the infrared temperature matrix. This algorithm uses visible images to locate the pig's eye area, fully utilizing the fact that visible images do not change with temperature, which can effectively avoid the problem that the pig's eye area may not be located by using only infrared images when the pig's body temperature is abnormal. To apply the pig eye detection model to pig farms and facilitate deployment on edge devices, this study conducts lightweight modification to the model by introducing the Ghost module to modify the neck and head structures of YOLOv7-tiny. The improved algorithm effectively reduces the number of parameters and computational cost. Compared with the original YOLOv7-tiny model, themean average precisiondecreased by 0.8%, while the number of parameters andcomputational costdecreased by 42.3% and 31.5%, respectively. The detection speed of the improved algorithm on the Jetson Nano (an edge device) increased by 12.8%. The improved algorithm achieves a balance betweendetection accuracyand detection speed, making it more suitable for deployment on edge devices.

Revisiting Pentacam Parameters in the Diagnosis of Subclinical and Mild Keratoconus Based on Different Grading System Definitions

To retest the performance of Pentacam parameters in the detection of eyes with subclinical keratoconus (KC) and mild KC based on different definitions from the Amsler-Krumeich (AK), Collaborative Longitudinal Evaluation of Keratoconus (CLEK), and ABCD systems. This cross-sectional university-based study comprised 24 eyes with subclinical KC, 144 eyes with mild KC (based on AK in 101 eyes, CLEK in 28 eyes, and ABCD in 15 eyes), and 70 controls. Diagnostic ability of the thinnest point (TP) pachymetry, KISA% index, inferior-superior asymmetry, corneal aberrations, Pentacam indices, front/back elevations, pachymetric progression index, Ambrósio-Relational Thickness (ARTmax), and Belin/Ambrósio Enhanced Ectasia Display scores (Df, Db, Dp, Dt, Da, and D-final) were evaluated. ARTmax (83.3% sensitivity/74.3% specificity) had the highest ability in distinguishing subclinical KC from normal, followed by TP pachymetry, Dt, and Da. D-final showed excellent sensitivity/specificity in mild KC diagnosis based on AK (98%/100%) and CLEK (97.4%/100%) descriptions. In the mild KC-ABCD group, index of vertical asymmetry accurately detected all eyes with mild KC and 97.1% of the controls. This study points out the gray zone in the detection of eyes with subclinical and mild KC due to overlapping terminology and grading criteria. Pentacam parameters seem to have modest capability in subclinical KC detection, indicating the necessity for additional diagnostic modalities. However, eyes with mild KC can be diagnosed with high accuracy using Pentacam parameters, although the strongest parameters may vary according to the definition of “mild KC.” Nevertheless, uniform and definitive criteria for subclinical and clinical KC classification are required for a diagnostic and therapeutic consensus in KC.

Rapid and sensitive detection of SARS-CoV-2 virus in human saliva samples using glycan based nanozyme: a clinical study.

A highly sensitive colorimetric method (glycan-based nano(e)zyme) was developed for sensitive and rapid detection of the SARS-CoV-2 virus based on N-acetyl neuraminic acid (sialic acid)-functionalized gold nanoparticles (SA-Au NZs). A number of techniques were used to characterize the prepared nanomaterials including XRD, FT-IR, UV-vis, DLS, and TEM. DLS analysis indicates an average hydrodynamic size of 34nm, whereas TEM analysis indicates an average particle size of 15.78nm. This observation confirms that water interacts with nanoparticle surfaces, resulting in a large hydrodynamic diameter. The peroxidase-like activity of SA-Au NZs was examined with SARS-CoV-2 and influenza viruses (influenza A (H1N1), influenza A (H3N2), and influenza B). UV-visible spectroscopy was used to monitor and record the results, as well as naked eye detection (photographs). SA-Au NZs exhibit a change in color from light red to purple when SARS-CoV-2 is present, and they exhibit a redshift in their spectrum. N-acetyl neuraminic acid interacts with SARS-CoV-2 spike glycoprotein, confirming its ability to bind glycans. As a result, SA-Au NZs can detect COVID-19 with sensitivity and specificity of over 95% and 98%, respectively. This method was approved by testing saliva samples from 533 suspected individuals at Ghaem Hospital of Mashhad, Mashhad, Iran. Sensitivity and specificity were calculated by comparing the results with the definitive results. The positive results were accompanied by a color change from bright red to purple within five minutes. Statistical analysis was performed based on variables such as age, gender, smoking, diabetes, hypertension, and lung involvement. In clinical trials, it was demonstrated that this method can be used to diagnose SARS-CoV-2 in a variety of places, such as medical centers, hospitals, airports, universities, and schools.

FPGA-Based CNN for Eye Detection in an Iris Recognition at a Distance System

Neural networks are the state-of-the-art solution to image-processing tasks. Some of these neural networks are relatively simple, but the popular convolutional neural networks (CNNs) can consist of hundreds of layers. Unfortunately, the excellent recognition accuracy of CNNs comes at the cost of very high computational complexity, and one of the current challenges is managing the power, delay and physical size limitations of hardware solutions dedicated to accelerating their inference process. In this paper, we describe the embedding of an eye detection system on a Zynq XCZU4EV UltraScale+ multiprocessor system-on-chip (MPSoC). This eye detector is used in the application framework of a remote iris recognition system, which requires high resolution images captured at high speed as input. Given the high rate of eye regions detected per second, it is also important that the detector only provides as output images eyes that are in focus, discarding all those seriously affected by defocus blur. In this proposal, the network will be trained only with correctly focused eye images to assess whether it can differentiate this pattern from that associated with the out-of-focus eye image. Exploiting the neural network’s advantage of being able to work with multi-channel input, the inputs to the CNN will be the grey level image and a high-pass filtered version, typically used to determine whether the iris is in focus or not. The complete system synthetises other cores and implements CNN using the so-called Deep Learning Processor Unit (DPU), the intellectual property (IP) block released by AMD/Xilinx. Compared to previous hardware designs for implementing FPGA-based CNNs, the DPU IP supports extensive deep learning core functions, and developers can leverage DPUs to conveniently accelerate CNN inference. Experimental validation has been successfully addressed in a real-world scenario working with walking subjects, demonstrating that it is possible to detect only eye images that are in focus. This prototype module includes a CMOS digital image sensor that provides 16 Mpixel images, and outputs a stream of detected eyes as 640 × 480 images. The module correctly discards up to 95% of the eyes present in the input images as not being correctly focused.

A colorimetric sensor array based on transparent photographic film (triacetyl cellulose polymer) as a smart label for naked eye detection of chicken meat spoilage

A new transparent colorimetric sensor array is presented for naked eye monitoring of the chicken meat freshness. This sensor array consists of four sensors fabricated by using pH indicators, immobilized on triacetyl cellulose (TAC) membrane. The sensor array represented obvious color change when it was exposed to the volatile organic compounds emitted from the chicken meat after 20 h keeping at room temperature or after four days of storage in the refrigerator. Therefore, it could easily identify spoiled meat from the fresh one assisted by chemometrics data analysis. Also, the color change of sensors due to the spoilage was sharp enough that could be read even by untrained people, providing naked eye monitoring the chicken meat freshness. The TAC polymer represented high physical stability for industrial scale-up. In addition, the immobilized indicators represented good stability against color change in humid condition. This offered the potential for continuous monitoring of the meat spoilage as smart labeling in the markets.

A simple dual responsive chemosensor for selective sensing of Cs+ for environmental monitoring and mimicking molecular logic gates

Detection of toxic metals is of vital importance to safeguard both public health and the ecosystem. Herein, we investigate the newly designed and synthesised isoxazole-based azo dye, (E)-cyclopentyl(5-((5-(4-fluorophenyl) isoxazole-3-yl) diazenyl)-2-hydroxyphenyl) methanone (FPAZ), as a dual chromogenic and fluorogenic sensor. FPAZ demonstrates high selectivity, reusability and ultra-sensitivity towards Cs+ ions manifested through naked eye detection in aqueous medium by employing simple and economic optical spectroscopy techniques. The color change from colourless to dark yellow and enhancement of fluorescence intensity reveal about FPAZ-Cs+ complexation by UV–Vis and fluorescence spectroscopy respectively. The complexation is also supported by DFT calculations. The LOD is estimated to be 0.476 µM, which by far, is the lowest LOD obtained for Cs+ detection. Further, FPAZ is fabricated with various flexible materials (paper, cotton, non-woven fabric) which provide information about on-site Cs+ ion contamination by means of change in relative RGB values using a handy smart-phone camera. Besides this, the logic gate as IMPLICATION and INHIBIT is designed employing Cs+ and Cl- ions as inputs and absorbance maxima as output. Overall, the developed chemosensor is simple, quick, and more promising than previously reported systems, as it does not need any chemical modification, expensive instruments, or expertise.

A cascaded deep learning framework for iris centre localization in facial image

AbstractAccurate iris centre localization is crucial in many computer vision and facial biometric applications such as gaze estimation, human–computer interaction, iris recognition, and liveness detection. However, it is challenging in an uncontrolled environment due to variations like pose, scale, rotation, specular reflection, and image quality. Therefore, a cascaded deep learning framework for iris centre localization in facial images is proposed that is robust to the abovementioned variations. The proposed approach consists of (i) YOLOv3 for eye detection, (ii) UNet for iris segmentation, and (iii) statistical modelling for iris centre localization. The eyes are first detected using the YOLOv3, and subsequently, iris segmentation is performed within the detected eyes using the UNet. Following iris segmentation, statistical modelling is employed to enhance the localization accuracy of the iris centre. Experiments were performed on benchmark databases, resulting in a standardized error measure SED of 3.405 pixels for BioID and 3.259 pixels for GI4E databases. In addition, the robustness of the proposed eye detection model was further evaluated on the Yale B for illumination variations and the CAS‐PEAL for pose variations.

A rhodamine NIR probe for naked eye detection of mercury ions and its application

Fluorescence imaging technology has developed rapidly with its advantages, and near-infrared probes are worthy of attention because of their less background interference, low light damage, and infinite potential. Rhodamine and its derivatives have the unique structure of lactam helices, which is an ideal platform for the construction of on-off fluorescent sensors. In this paper, a novel near-infrared fluorescent probe (RBLS) based on rhodamine derivatives was synthesized for the transient detection of mercury ions. The closed-on structure can realize reversible sensor recovery by adding S2-. The superior imaging capability in living cells and in vivo in zebrafish holds promise for biological applications. In addition, the naked eye test strips prepared with RBLS probes can be used to detect and screen Hg2+ in the environment and show good gradient change performance.

Design and Usability Study of a Point of Care mHealth App for Early Dry Eye Screening and Detection.

Significantly increased eye blink rate and partial blinks have been well documented in patients with dry eye disease (DED), a multifactorial eye disorder with few effective methods for clinical diagnosis. In this study, a point of care mHealth App named "EyeScore" was developed, utilizing blink rate and patterns as early clinical biomarkers for DED. EyeScore utilizes an iPhone for a 1-min in-app recording of eyelid movements. The use of facial landmarks, eye aspect ratio (EAR) and derivatives enabled a comprehensive analysis of video frames for the determination of eye blink rate and partial blink counts. Smartphone videos from ten DED patients and ten non-DED controls were analyzed to optimize EAR-based thresholds, with eye blink and partial blink results in excellent agreement with manual counts. Importantly, a clinically relevant algorithm for the calculation of "eye healthiness score" was created, which took into consideration eye blink rate, partial blink counts as well as other demographic and clinical risk factors for DED. This 10-point score can be conveniently measured anytime with non-invasive manners and successfully led to the identification of three individuals with DED conditions from ten non-DED controls. Thus, EyeScore can be validated as a valuable mHealth App for early DED screening, detection and treatment monitoring.

Predicting clinical outcome of sulfur mustard induced ocular injury using machine learning model

The sight-threatening sulfur mustard (SM) induced ocular injury presents specific symptoms in each clinical stage. The acute injury develops in all exposed eyes and may heal or deteriorate into chronic late pathology. Early detection of eyes at risk of developing late pathology may assist in providing unique monitoring and specific treatments only to relevant cases. In this study, we evaluated a machine-learning (ML) model for predicting the development of SM-induced late pathology based on clinical data of the acute phase in the rabbit model.Clinical data from 166 rabbit eyes exposed to SM vapor was used retrospectively. The data included a comprehensive clinical evaluation of the cornea, eyelids and conjunctiva using a semi-quantitative clinical score. A random forest classifier ML model, was trained to predict the development of corneal neovascularization four weeks post-ocular exposure to SM vapor using clinical scores recorded three weeks earlier.The overall accuracy in predicting the clinical outcome of SM-induced ocular injury was 73%. The accuracy in identifying eyes at risk of developing corneal neovascularization and future healed eyes was 75% and 59%, respectively. The most important parameters for accurate prediction were conjunctival secretion and corneal opacity at 1w and corneal erosions at 72 h post-exposure.Predicting the clinical outcome of SM-induced ocular injury based on the acute injury parameters using ML is demonstrated for the first time. Although the prediction accuracy was limited, probably due to the small dataset, it pointed out towards various parameters during the acute injury that are important for predicting SM-induced late pathology and revealing possible pathological mechanisms.

Rhodamine hydrazide-linked naphthalimide derivative: Selective naked eye detection of Cu2+, S2− and understanding the therapeutic potential of the copper complex as an anti-cervical cancer agent

A naphthalimide-labeled rhodamine hydrazone derivative HL has been synthesized, characterized and examined in metal ion recognition. It shows selective colorimetric detection of Cu2+ over a number of other metal ions with a detection limit of 1.66 × 10−7 M in CH3CN/HEPES buffer (v/v = 2:1, pH = 6.8). The spirolactam ring of rhodamine and the imino-phenol motif of naphthalimide in HL are involved in complexation of Cu2+ as shown by single crystal X-ray. Single crystal of the copper-complex is prepared by utilizing NaSCN and it is characterized as CuL(SCN). The emergence of new absorption at 550 nm in UV–vis and the pink color of the solution reveal the selective interaction toward Cu2+. HL is characterized as a fluorescence resonance energy transfer (FRET) system that remains ‘turned OFF’ while spirolactam ring exists. In the presence of Cu2+, FRET is ‘turned ON’ via the opening of spirolactam ring to give emission at 580 nm which is less intense due to the quenching effect of Cu2+ ion. The complexation is reversible and the ensemble of Cu2+.HL selectively recognizes S2− over a series of different anions involving a color change from pink to colorless via the formation of spirolactam ring. The copper complex CuL(SCN) is further employed to understand its efficacy as a therapeutic agent. The complex is cytotoxic to high-risk HPV positive cervical cancer cell lines like SiHa and HeLa and is efficient in the generation and accumulation of reactive oxygen species (ROS). The complex also initiates nuclear blebbing and shows DNA degradation as understood by DNA laddering assay.

Authenticity detection of eye images using deep learning technology in online exams

The use of online education technologies is one of the most effective ways to achieve the universal goal of quality education throughout life for the sustainable development of UNESCO. During the writing of the article, the features that have appread in the field of application of online exam technologies will be described. The existing forms, methods and tools for conducting the exam in an online format, their potential and risks are analyzed. Attention is drawn to the fact that the methods and tools used for conducting online control have disadvantages, such as significant costs in organizing proctoring and difficulties in assessing one of the student’s key competencies, namely, readiness to use systematized theoretical and practical knowledge to formulate and solve research problems. This research introduces an innovative proctoring system that harnesses deep learning techniques, specifically the Yolo algorithm. The primary objective of this system is to accurately detect the eyes of the exam-taker by utilizing a standard web camera as the sole input source. The obtained results from the deep learning model are subsequently subjected to processing through the Cascaded Haar Classifier to yield a definitive outcome. The proposed approach demonstrates considerable proficiency in discriminating between authentic exam-takers and static images, achieving a notable level of accuracy. Leveraging the capabilities of deep learning in conjunction with the cascaded classifier enables real-time eye detection and localization within images or video frames, thereby establishing the proctoring system as a reliable and effective solution for ensuring academic integrity during online examinations.

Amorphous amEu-NH2BDC and amTb-NH2BDC as ratio fluorescence probes for smartphone-integrated naked eye detection of bacillus anthracis biomarker

The abnormal concentration of anthrax spore biomarker 2,6-pyridinedicarboxylic acid (2,6-DPA) will seriously affect public health. Therefore, a sensitive and rapid assay for 2,6-DPA monitoring is of vital importance. In this work, novel nano-sized amorphous Eu-NH2BDC (amEu-NH2BDC) and amorphous Tb-NH2BDC (amTb-NH2BDC) metal organic frameworks are prepared by adjusting the ratio of metal and ligand, respectively. Both of them exhibit highly sensitive and selective ratiometric fluorescence detection for 2,6-DPA with wider linear range and lower detection limit in aqueous solutions and human serum. Attributed to the coordination effect of 2,6-DPA in triggering the characteristic fluorescence emissions of Eu3+or Tb3+ by replacing coordinated solvent molecules, as evidenced by ultraviolet–visible spectroscopy, the fluorescence lifetimes analysis, thermal gravimetric analysis, Fourier-transform infrared spectroscopy, density functional theory (DFT) simulations and X-ray photoelectron spectroscopy. In addition, the amEu-NH2BDC or amTb-NH2BDC loaded paper-based microsensors are constructed for real-time and sensitive detection of 2,6-DPA and coupled with a smartphone-assisted visual portable device.