Colorimetric Imaging Research Articles

Combustion Temperature Measurement of the Solid Propellant Specimen by Colorimetric Radiation Image

This paper investigates the influencing factors of the colorimetric radiation image temperature measurement method, including the representative wavelength, the calibration method, and the camera exposure time. A relatively high-precision method is next obtained for selecting a representative wavelength and calibration method. Then it is applied to the measurement of the combustion temperature of the solid propellant specimen. Finally, the temperature variation regularity of a different propellant was analyzed in the process of combustion. The results show that selecting the maximum response of each channel of the camera as the representative wavelength and the function construction method is more accurate for calibration and calculation. In the range of the photoelectric response of the camera, the exposure time has little effect on the temperature measurement accuracy. Compared with thermocouple temperature measurement results, the relative deviation of radiation image thermometry is −1.2%, which verifies the accuracy of radiation image thermometry.

A Paper-Based Assay for the Determination of Total Antioxidant Capacity in Human Serum Samples.

Determining the total antioxidant capacity (TAC) of biological samples is a valuable approach to measuring health status under oxidative stress conditions, such as infertility and type 2 diabetes. The Trolox equivalent antioxidant capacity (TEAC) assay is the most common approach to evaluating TAC in biological matrices. This assay is typically performed in clinical settings on a microtiter plate using a plate reader. However, the instrumentation and expertise requirements, and the resulting delay in the reporting of assay outcomes, make solution-based TEAC assays impractical for point-of-care or at-home testing, where individuals may want to monitor their health status during treatment. This work introduces the first microfluidic paper-based analytical device (µPAD) that measures TAC in human serum using TEAC assay chemistry. TAC was determined through a colorimetric image analysis of the degree of decolorization of 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical cations (ABTS●+) by serum antioxidants. The µPAD showed a linear response to Trolox, ranging from 0.44 to 2.4 mM, (r = 0.999). The performance of paper-based TEAC assays was validated through direct comparison to solution-based TEAC assays. There was a 0.04 mM difference in TAC values between the two platforms, well within one standard deviation of a standard solution-based assay conducted on an aliquot of the same serum sample (±0.25 mM). The µPAD had a limit of detection (LOD) of 0.20 mM, well below the TAC of normal human serum. The results suggest that the proposed device can be used for biological TAC determination and expands the field of TAC analysis in point-of-care health monitoring.

Nanozyme-based colorimetric and smartphone imaging advanced sensing platforms for tetracycline detection and removal in food

The presence of antibiotic residues poses a significant threat to food assurance, triggering widespread concerns. Therefore, the prompt and accurate detection and removal of antibiotic residues are essential for ensuring food safety. In this study, an aptmer modified triple-metal nanozyme (apt-TMNzyme) sensor was developed, which achieved a portable, visual, intelligent, and fast determination for tetracycline (TET). The proposed apt-TMNzyme exhibited willow leaf-like morphology, high specific surface area and excellent TET adsorption and removal properties. The experiments showed that the apt-TMNzyme had outstanding peroxidase activity and could catalyze the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) to produce a blue product in the presence of H2O2, which provided a visual response signal to TET. This sensor was capable of quantifying TET within a concentration range of 0.2 nM–70 μM, achieving a detection limit of 7.1 nM under optimal conditions. When tested on real food samples, our sensor produced results that closely paralleled those achieved through high-performance liquid chromatography. To improve accessibility and user-friendliness, we also designed a colorimetric testing paper integrated with a smartphone application for intuitive and intelligent detection of TET, which enables the quantitative determination of TET in the concentration range of 0.003–60 μM, the detection limit was 5.1 μM. This integrated portable sensor not only streamlines the testing process, saving time and costs, but also offers a promising solution for rapid and sensitive detection of antibiotic residues.

Study on the Electrochemiluminescence Emission Mechanism of HOF-14 and Its Multimode Sensing and Imaging Application.

A novel hydrogen-bonded organic framework (HOF-14) has attracted much attention due to its excellent biocompatibility and low toxicity, but its research in the electrochemiluminescence (ECL) field has not been reported. In this work, the annihilation-type and coreactant-type ECL emission mechanisms of HOF-14 were studied systematically for the first time. It was found that the ECL quantum efficiency of HOF-14/TEA coreactant system was the highest, which was 1.82 times that of Ru(bpy)32+/TEA. Further, the ECL emission intensity of HOF-14/TEA system could achieve colorimetric (CL) imaging of mobile phone. We also discovered that HOF-14 had superior photoelectrochemical (PEC) performance. Based on the above research results, a unique HOF-14-based multimode sensing and imaging platform was constructed. The antibiotic Enrofloxacin (ENR) was selected as the detection target, and the Cu-Zn bimetallic single-atom nanozyme (Cu-Zn/SAzyme) with excellent peroxidase (POD)-like activity was used to prepare quenching probes. When the target ENR was present, Cu-Zn/SAzyme quenching probes were introduced to the surface of HOF-14 by the dual-aptamer sandwich method. Cu-Zn/SAzyme could catalyze diaminobenzidine (DAB) to produce brown precipitations to quench the ECL, PEC, and CL signals of HOF-14, realizing multimode detection of ENR. This work not only discovered excellent ECL and PEC property of new HOF-14 material but also systematically studied the ECL emission mechanism of HOF-14, and proposed a novel multimode sensing and imaging platform, which greatly improved the detection accuracy of target and showed great contributions to the field of ECL analysis.

Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides

The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety analysis and environmental pollution traceability. Here, a fluorescent nanozyme Cu-ATP@ [Ru(bpy)3]2+ with laccase-like activity was designed to develop a versatile machine learning-assisted colorimetric and fluorescence dual-modal assay for efficient onsite intelligent decision recognition and quantification of PPs residues. In the presence of alkaline phosphatase (ALP), the laccase-like activity of Cu-ATP@ [Ru(bpy)3]2+ was enhanced to oxidize colorless o-phenylenediamine (OPD) into dark-yellow 2,3-diaminophenazine (DAP) via electron transfer, appearing a new yellow fluorescence at 550 nm. Meanwhile, the red fluorescence of Cu-ATP@ [Ru(bpy)3]2+ at 600 nm was quenched due to the internal filter effect (IFE) of DAP towards Cu-ATP@ [Ru(bpy)3]2+. However, the selective inhibition of PPs toward ALP activity enabled to observe a dual-modal response of PPs concentration-dependent decrease in colorimetric signal and enhancement in the fluorescence intensity ratio of F600 nm/F550 nm. On this basis, both the colorimetric and fluorescence images were captured and processed with a home-made WeChat applet-installed smartphone to extract the corresponding image color information, thus achieving machine learning-assisted onsite real-time and dynamic intelligent decision recognition and quantification of PPs residues in real samples, which shows a promising potential in safeguarding food safety and environmental health.

Programming of a Portable Digital Monitoring System-Integrated DNA Aptamer Reversely Regulated Oxidase-Like Nanozyme for Real-Time Dynamic Analysis of Atmospheric Perfluorooctanoic Acid.

Timely and efficient analysis of the fluorinated per- and polyfluoroalkyl substances (PFAS) in an atmospheric environment is critical to environmental pollution traceability, early warnings, and governance. Here, a portable, reliable, and intelligent digital monitoring device for onsite real-time dynamic analysis of atmospheric perfluorooctanoic acid (PFOA) is proposed. The sensing mechanism is attributed to the oxidase-like activity of PtCoNPs@g-C3N4 that is reversely regulated by the surface modification of a PFOA-recognizable DNA aptamer, engineering a PFOA-activated oxidase-like activity of nanozyme (Apt-PtCoNPs@g-C3N4) to combine the nonfluorescence o-phenylenediamine (OPD) as the dual-modality response system. The present PFOA interacts with its DNA aptamer and dissociates from the surface of Apt-PtCoNPs@g-C3N4, restoring the oxidase-like activity of PtCoNPs@g-C3N4 to oxidize OPD into yellow fluorescence 2,3-diphenylaniline (DAP), thereby observing a PFOA-triggered colorimetric as well as fluorescence dual-modality change. Then, a hydrogel kit-programmed Apt-PtCoNPs@g-C3N4 + OPD system is used as the sensitive element to incorporate into this homemade portable device, automatically gathering and processing the PFOA-triggered hydrogel colorimetric and fluorescence image gray values by our self-weaving software, ultimately realizing the onsite real-time dynamic analysis of atmospheric PFOA surrounding a fluorochemical production plant. This work provides a direction and theoretical foundation for designing portable onsite screening devices that cater to other atmospheric contaminants detection requirements.

Nanozyme-based colorimetric sensor arrays coupling with smartphone for discrimination and “segmentation-extraction-regression” deep learning assisted quantification of flavonoids

Achieving rapid, cost effective, and intelligent identification and quantification of flavonoids is challenging. For fast and uncomplicated flavonoid determination, a sensing platform of smartphone-coupled colorimetric sensor arrays (electronic noses) was developed, relying on the differential competitive inhibition of hesperidin, nobiletin, and tangeretin on the oxidation reactions of nanozymes with a 3,3′,5,5′-tetramethylbenzidine substrate. First, density functional theory calculations predicted the enhanced peroxidase-like activities of CeO2 nanozymes after doping with Mn, Co, and Fe, which was then confirmed by experiments. The self-designed mobile application, Quick Viewer, enabled a rapid evaluation of the red, green, and blue values of colorimetric images using a multi-hole parallel acquisition strategy. The sensor array based on three channels of CeMn, CeFe, and CeCo was able to discriminate between different flavonoids from various categories, concentrations, mixtures, and the various storage durations of flavonoid-rich Citri Reticulatae Pericarpium through a linear discriminant analysis. Furthermore, the integration of a “segmentation−extraction−regression” deep learning algorithm enabled single-hole images to be obtained by segmenting from a 3 × 4 sensing array to augment the featured information of array images. The MobileNetV3-small neural network was trained on 37,488 single-well images and achieved an excellent predictive capability for flavonoid concentrations (R2 = 0.97). Finally, MobileNetV3-small was integrated into a smartphone as an application (Intelligent Analysis Master), to achieve the one-click output of three concentrations. This study developed an innovative approach for the qualitative and simultaneous multi-ingredient quantitative analysis of flavonoids.

Filterless Bandpass Photodetectors Enabled by 2D/3D Perovskite Heterojunctions

AbstractBandpass photodetectors have tremendous applications in colorimetric analysis, light communication, imaging and machine vision systems. Compared to broadband photodetectors combined with external optical filters, solution‐processed filterless bandpass photodetectors enjoy high integration density and low cost, but suffer from broadened response edges and insufficient spectral rejection ratios (SRRs). Here, prototype filterless bandpass photodetectors with near square‐shape photoresponse are developed based on (CmH2m+1NH3)2PbI4/MAPbI3 (m = 4–8) and (CmH2m+1NH3)2PbBr4/MAPbBr3 (m = 2–8) perovskite heterojunctions. The strong and sharp excitonic absorption of 2D perovskites defines the ultranarrow response onset widths of 10 ± 1 nm. Besides, the strong photoluminescence (PL) self‐absorption of 2D perovskites suppresses the PL leakage from the front absorber to the back photoactive region, which is crucial to the achieved record high SRR of >2000. By integrating Br‐based 2D/3D perovskite photodetectors with onset edge wavelength discrimination of ≈20 nm, a multiple‐channel optical communication system with low spectral crosstalk is demonstrated.

Unveiling the Potential: Can Machine Learning Cluster Colorimetric Images of Cold Atmospheric Plasma Treatment?

In this transformative study, machine learning (ML) and t‐distributed stochastic neighbor embedding (t‐SNE) are employed to interpret intricate patterns in colorimetric images of cold atmospheric plasma (CAP)‐treated water. The focus is on CAP's therapeutic potential, particularly its ability to generate reactive oxygen and nitrogen species (RONS) that play a crucial role in antimicrobial activity. RGB, HSV, LAB, YCrCb, and grayscale color spaces are extracted from the colorimetric expression of oxidative stress induced by RONS, and these features are used for unsupervised ML, employing density‐based spatial clustering of applications with noise (DBSCAN). The DBSCAN model's performance is evaluated using homogeneity, completeness, and adjusted rand index with a predictive data distribution graph. The best results are achieved with 3,3′,5,5′‐tetramethylbenzidine–potassium iodide colorimetric assay solution immediately after plasma treatment, with values of 0.894, 0.996, and 0.826. t‐SNE is further conducted for the best‐case scenario to evaluate the clustering efficacy and find the best combination of features to better present the results. Correspondingly, t‐SNE enhances clustering efficacy and adeptly handles challenging points. The approach pioneers dynamic and comprehensive solutions, showcasing ML's precision and t‐SNE's transformative visualization. Through this innovative fusion, complex relationships are unraveled, marking a paradigm shift in biomedical analytical methodologies.

Glutathione‑iron hybrid nanozyme-based colorimetric sensor for specific and stable detection of thiram pesticide on fruit juices

Given the potential dangers of thiram to food safety, constructing a facile sensor is significantly critical. Herein, we presented a colorimetric sensor based on glutathione‑iron hybrid (GSH-Fe) nanozyme for specific and stable detection of thiram. The GSH-Fe nanozyme exhibits good peroxidase-mimicking activity with comparable Michaelis constant (Km = 0.551 mM) to the natural enzyme. Thiram pesticides can specifically limit the catalytic activity of GSH-Fe nanozyme via surface passivation, causing the change of colorimetric signal. It is worth mentioning that the platform was used to prepare a portable hydrogel kit for rapid qualitative monitoring of thiram. Coupling with an image-processing algorithm, the colorimetric image of the hydrogel reactor is converted into the data information for accurate quantification of thiram with a detection limit of 0.3 μg mL−1. The sensing system has good selectivity and high stability, with recovery rates in fruit juice samples ranging from 92.4% to 106.9%.

Self-calibrating colorimetric sensor assisted deep learning strategy for efficient and precise Fe(II) detection

Colorimetry is to determine the component content usually comparing or measuring the “RGB” values, which has the advantages of quick response and visual comparison. However, the widely reported “RGB” models are limited by requiring professional operation such as accurate sampling, insufficient information, cumbersome calculation, etc., which has significantly restricted the promotional application. Inspired by the PAA-PVA gel with excellent absorbing-swelling ability and superior response ability, indefinite volume addition has a significant difference in the swelling phenomenon of PAA-PVA hydrogel, the extent of this enlargement is directly related to the amount of testing sample absorbed, which can be applied to reflect testing sample volume added. In addition, benefited from a comprehensive and precise parameter network, the deep learning (DL) algorithm solves the defects such as ambient light effect and manual extract RGB values of the colorimetric sensing analysis strategy. Considering Fe(II) is an essential element for life science and environmental monitoring, we have developed a self-calibrating colorimetric sensor assisted deep learning strategy for the Fe(II) detection with indefinite volume. It can directly complete the analysis of Fe(II) concentration via captured images, without going through a complicated process similar to the RGB model. Specifically, we integrated the advantages of colorimetric sensing strategy, absorbing-swelling hydrogel, and DL algorithm to develop a phenanthroline (PHEN) indicator doped PAA-PVA colorimetric sensor (PHEN-PAA-PVA). Then, 2,500 colorimetric response images were collected as the training dataset and assessed 2 DL algorithms and 7 machine learning (ML) algorithms. The convolutional neural network (CNN) model exhibited the most efficiency to quantify 1 × 10−5–9 × 10−4 mol/L Fe(II), with a limit of detection (LOD) of 3.3 × 10−6 mol/L. It provides a promising and blooming in-situ detection approach for Fe(II) analysis, contributes to health management and environment monitoring.

Deep learning-assisted self-volume-calibrating colorimetric sensor for chloride ion detection

Chloride ions (Cl−) are widely present in nature, and accurate measurement of Cl− is of great significance for health management and water quality monitoring. Based on the principle of argentometry, where Ag+ react with Cl− in silver measurement to produce insoluble AgCl, we developed a colorimetric sensor composed of AgNO3, 2′,7′-dichlorofluorescein (DCFS) and starch, which is defined as ADS sensor. Cl− react with Ag+ in the ADS sensor to form positively charged AgCl colloids, which can adsorb negatively charged DCFS dyes and cause a color change in DCFS, thus providing sensing information about Cl− concentrations. To eliminate the error caused by the unfixed sample volume, a novel self-volume-calibrating strategy based on the unique shape of the sample holder was proposed. We further utilized deep learning (DL) algorithms to analyze data from 3,900 colorimetric images, enabling rapid determination of 1 × 10−5 to 7 × 10−2 mol/L Cl− with unfixed sample volumes. The Class Activation Mapping (CAM) algorithm was also used to visualize the decision-making mechanism of Convolutional Neural Network (CNN), verifying that the colorimetric regions and volume change regions of the sensor is the primary decision-making information for CNN. Compared with traditional method, this approach has the advantages of low cost, wide detection range, simple operation and rapid response. We envision that this method for Cl− detection can be used as an alternative and promising tool for the detection of a wider variety of analytes.

Integrated Fruit Ripeness Assessment System Based on an Artificial Olfactory Sensor and Deep Learning.

Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography-mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection.

Three-Dimensional-Printed Instrument for Isothermal Nucleic Acid Amplification with Real-Time Colorimetric Imaging.

Isothermal amplification methods have become popular in research due to the simplicity of the technology needed to run the reactions. Specifically, loop-mediated isothermal amplification (LAMP) has been widely used for various applications since first reported in 2000. LAMP reactions are commonly monitored with the use of colorimetry. Although color changes associated with positive amplification are apparent to the naked eye, this detection method is subjective due to inherent differences in visual perception from person to person. The objectivity of the colorimetric detection method may be improved by programmed image capture over time with simultaneous heating. As such, the development of a novel, one-step, automated, and integrated analysis system capable of performing these tasks in parallel is detailed herein. The device is adaptable to multiple colorimetric dyes, cost-effective, 3D-printed for single-temperature convective heating, and features an easy-to-use LabVIEW software program developed for automated image analysis. The device was optimized and subsequently validated using four messenger-RNA targets and mock forensic samples. The performance of our device was determined to be comparable to that of a conventional thermal cycler and smartphone image analysis, respectively. Moreover, the outlined system is capable of objective colorimetric analysis, with exceptional throughput of up to 96 samples at once.

Construction of Multi-Mode Photoelectrochemical Immunoassays for Accurate Detection of Cancer Markers: Assisted with MOF-Confined Plasmonic Nanozyme.

In clinical diagnostics, sensitive and accurate biomarker monitoring is greatly challenged by the limitations of false positive/negative errors in single-modal photoelectrochemical analysis. Herein, we propose a multimode immunoassay by integrating photoelectrochemical, colorimetric, and photothermal imaging analysis into one electrode. The immunosensors could simultaneously achieve three detection modes at one electrode, which provided a new pathway for the accurate detection of the target prostate-specific antigen (PSA) and circumvented false-positive or negative errors during the detection process. To this end, an integrated multifunctional chip (TiO2/ZIF-8/Cu(II)) was first constructed via in situ embedding of Cu(II) in the Metal-organic framework growth process. Then, an alkaline phosphatase-labeled magnetic probe was designed to achieve split-type detection for PSA. In a sodium thiophosphate solution, the in situ generated H2S could react with Cu(II) to form small-size CuS due to the nanoconfinement of ZIF-8 and thus result in the formation of p-n heterojunctions (TiO2/ZIF-8/CuS). The TiO2/ZIF-8/CuS could efficiently improve the light-harvesting ability and facilitate the charge separation efficiency, thus finally resulting in an increased photocurrent in the PEC mode. Furthermore, by constructing the portable colorimetric and photothermal sensors based on the Arduino microcontroller and photothermal imager, the TiO2/ZIF-8/CuS also provided point-of-care and visual detection modes, as the in situ-formed CuS exhibited peroxidase-mimicking activity and outstanding photothermal properties. The work had important prospects for establishing multimode immunoassays for the accurate detection of cancer markers in early disease diagnosis.

Visualizing plant intracellular inorganic orthophosphate distribution.

Intracellular inorganic orthophosphate (Pi) distribution and homeostasis profoundly affect plant growth and development. However, its distribution patterns remain elusive owing to the lack of efficient cellular Pi imaging methods. Here we develop a rapid colorimetric Pi imaging method, inorganic orthophosphate staining assay (IOSA), that can semi-quantitatively image intracellular Pi with high resolution. We used IOSA to reveal the alteration of cellular Pi distribution caused by Pi starvation or mutations that alter Pi homeostasis in two model plants, rice and Arabidopsis, and found that xylem parenchyma cells and basal node sieve tube element cells play a critical role in Pi homeostasis in rice. We also used IOSA to screen for mutants altered in cellular Pi homeostasis. From this, we have identified a novel cellular Pi distribution regulator, HPA1/PHO1;1, specifically expressed in the companion and xylem parenchyma cells regulating phloem Pi translocation from the leaf tip to the leaf base in rice. Taken together, IOSA provides a powerful method for visualizing cellular Pi distribution and facilitates the analysis of Pi signalling and homeostasis from the level of the cell to the whole plant.

Explainable Deep Learning-Assisted Self-Calibrating Colorimetric Patches for In Situ Sweat Analysis.

Sweat has emerged as a compelling analyte for noninvasive biosensing technology because it contains a wealth of important biomarkers in hormones, organic biomacromolecules, and various ionic mixtures. These components offer valuable insights and can reflect an individual's physiological conditions. Here, we introduced an explainable deep learning (DL)-assisted wearable self-calibrating colorimetric biosensing analysis platform to efficiently and precisely detect the biomarker's concentration in sweat. Specifically, we have integrated the advantages of the colorimetric sensing method, adsorbing-swelling hydrogel, and explainable DL algorithms to develop an enzyme/indicator-immobilized colorimetric patch, which has reliable colorimetric sensing ability and excellent adsorbing-swelling function. A total of 5625 colorimetric images were collected as the analysis data set and assessed two DL algorithms and seven machine learning (ML) algorithms. Zn2+, glucose, and Ca2+ in human sweats could be facilely classified and quantified with 100% accuracy via the convolutional neural network (CNN) model, and the testing results of actual sweats via the DL-assisted colorimetric approach are 91.7-97.2% matching with the classical UV-vis spectrum. Class activation mapping (CAM) was utilized to visualize the inner working mechanism of CNN operation, which contributes to verify and explicate the design rationality of the noninvasive biosensing technology. An "end-to-end" model was established to ascertain the black box of the DL algorithm, promoted software design or principium optimization, and contributed facile indicators for health monitoring, disease prevention, and clinical diagnosis.

Dual-mode detection of 2,6-pyridinedicarboxylic acid based on the enhanced peroxidase-like activity and fluorescence property of novel Eu-MOFs.

2,6-Pyridinedicarboxylic acid (DPA) is a significant biomarker of anthrax, which is a deadly infectious disease for human beings. However, the development of a convenient anthrax detection method is still a challenge. Herein, we report a novel europium metal-organic framework (Eu-MOF) with an enhanced peroxidase-like activity and fluorescence property for DPA detection. The Eu-MOF was one-step synthesized using Eu3+ ions and 2-methylimidazole. In the presence of DPA, the intrinsic fluorescence of Eu3+ ions is sensitized, the fluorescence intensity linearly increases with an increase in DPA concentration, and the fluorescence color changes from blue to purple. Simultaneously, the peroxide-like activity of the Eu-MOF is enhanced by DPA, which can promote the oxidation of TMB to oxTMB. The absorbance values increase linearly with DPA concentrations, and the colorimetric images change from colorless to blue. The dual-mode detection of DPA has good sensitivity with a colorimetric detection limit of 0.67 μM and a fluorescent detection limit of 16.67 nM. Moreover, a simple detection method for DPA was developed using a smartphone with the RGB analysis system. A portable kit with standard color cards was developed using paper test strips. The proposed methods have good practicability for DPA detection in real samples. In conclusion, the developed Eu-MOF biosensor offers a valuable and general platform for anthrax diagnosis.

Homogeneous electroanalysis coupled with colorimetry dual-mode sensing of silver ion in water based on target-inhibited peroxidase activity of Pt@ZIF-8 nanozyme

Electrochemical sensing is the commonly used method for the detection of heavy metal silver ion (Ag+), while the conventional electrochemical sensors generally require the existence of electrode modifications and even electrodeposition step coupled with a single-mode readout. Herein, by preparing ZIF-8 organometallic frame load with platinum nanoparticles (Pt@ZIF-8) as nanozyme and o-phenylenediamine (OPD) as colorless substance, taking advantage of the superior peroxidase activity of Pt@ZIF-8 that can catalyze the oxidation of OPD to product electroactive/yellow 2,3-diaminophenazinc and the target-inhibited effect towards nanozyme activity from Ag+, a homogeneous electroanalysis coupled with colorimetry (HEA-CM) dual-mode sensing platform was designed for the first time to detect Ag+. Furthermore, the colorimetric images were converted on a smartphone to digital signals for the quantitative analysis. The designed strategy could offer multiple advantages such as electrodeposition-free, modification-free and dual channel readout. After investigating the sensing mechanism and experimental parameters, the analytical performance of the designed strategy was evaluated, obtaining extremely low detection limit (0.034 nM), reliable recovery rate (99.61 %-101.6 %) and wide linearity. This work offered an effective method for the simple, sensitive, rapid, accurate and multiplexed determination of Ag+, and also opened a new pathway for the detection of other heavy metals based on HEA-CM dual-mode strategy.

Construction of smartphone-adapted signal visualization platform for dual-mode detection of H2S based on integrated metal-organic framework nanoprobes

Hydrogen sulfide (H2S) is a toxic contaminant and has great influence on many physiological processes. Due to various pathophysiological roles and environmental pollution problems, it is necessary to construct and develop simple and portable monitoring sensors for the precise detection of H2S. Herein, we developed a smartphone-adapted dual-mode detection platform by integrating the colorimetric and photothermal imaging analysis into a metal-organic framework-based chip (ZIF-8/Cu). Due to the nanoconfinement effect of ZIF-8, small-sized plasmonic CuS could be in-situ formed during the detection procedure of H2S and endowed the chips with excellent photothermal properties. By constructing a smartphone-adapted photothermal imager, the metal-organic framework-based chip could achieve a portable photothermal imaging analysis of H2S. Moreover, as the formed CuS was a good peroxidase-like nanozyme, the chips could also be used to trigger the enzymic catalytic reaction toward the chromogenic reaction of 3,3′,5,5′-tetramethylbenzidine (TMB)-H2O2, thus providing another colorimetric sensing mode by using a smartphone App. In this smartphone-adapted visualization platform, the portable chemosensors could simultaneously achieve double detection modes at one electrode, which provided a new pathway for the accurate detection of H2S and circumvented the false-positive or negative errors during the detection process. Besides, by using the finite difference time domain (FDTD) simulation method, the in-depth mechanism, including the plasmonic effect and spatial electromagnetic field distribution, was explored to provide a possible reason for the excellent sensing performance of the dual-mode visualization platform. This work provides a new insight into the construction of the accurate, portable and smart sensing platform in the visual screening of H2S.