CN114989819B - Preparation method and detection application of carbon quantum dot for detecting aluminum ions - Google Patents
Preparation method and detection application of carbon quantum dot for detecting aluminum ions Download PDFInfo
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Abstract
The invention discloses a preparation method and detection application of a carbon quantum dot for detecting aluminum ions, which are characterized by comprising the following steps: 2, 5-dihydroxyl ethyl terephthalate and urea are mixed according to the mass ratio of 2:1, placing the mixture into a reaction kettle, heating the mixture at 160-200 ℃ for 6-8 hours, and after the reaction kettle is naturally cooled to the ambient temperature, 7662XgCentrifuging for 5min, taking supernatant, dialyzing for 24-48h at 1000Da to obtain carbon quantum dot dispersion liquid for detecting aluminum ions, wherein the method for detecting aluminum ions comprises the following steps of 3+ The solution is input into an image classifier for classifying concentration by using a smart phone to shoot fluorescent color pictures, and N solutions are outputPThe i value is calculated by a formula to obtain Al in the sample 3+ The concentration has the advantages of high fluorescence intensity, good dispersibility, good light stability, and good light stability to Al 3+ The specificity response is wide, the detection range is high, the sensitivity and the accuracy are high, and the detection time is short.
Description
Technical Field
The invention relates to a method for detecting aluminum ions, in particular to a method for preparing carbon quantum dots for detecting aluminum ions and detection application thereof.
Background
At present, al 3+ The detection method mainly comprises the following steps: atomic Absorption Spectrometry (AAS), inductively coupled plasma emission spectrometry (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), ion chromatography, and the like. These methods have high accuracy, sensitivity and selectivity, but require expensive equipment, complicated experimental procedures, skilled professionals, etc., and are difficult to apply to field detection. The traditional electrochemical method has the advantages of convenient operation and low analysis cost, but the accuracy, sensitivity and selectivity are difficult to meet the requirements. In recent years, a fluorescent sensor is constructed by utilizing a carbon nanomaterial, and has the advantages of high sensitivity, convenient operation, low instrument cost, real-time detection and the like, and good field detection application prospect is shown, so that the development of a fluorescent chemical sensor for an aqueous medium, and the simple and efficient detection, identification and quantification of aluminum ions in biological, chemical and environmental systems by designing and manufacturing a novel aqueous medium fluorescent probe based on a proper sensing mechanism become very important.
Carbon Quantum Dots (CQDs) are novel fluorescent carbon nano materials, have excellent biocompatibility, water solubility, chemical stability, non-toxicity, high conductivity and photoluminescence characteristics, and are very suitable for developing fluorescent sensors. Al has been detected based on CQDs 3+ Such as a fluorescence photometry method, a fluorescence test strip method, an RGB program identification method, etc., but there are generally problems of narrow linear range, poor linearity, weak quantitative capability, etc., because: CQDs have fluorescence, CQDs-Al 3+ The complex also has fluorescence, the excitation spectrum and the fluorescence spectrum of the complex are not identical, so that the total fluorescence intensity is equal to Al 3+ The concentration is not a simple linear relationship. In other words, a certain concentration range is exhibitedBut in a relatively wide concentration range, is a nonlinear relationship under a complex system, which gives a rapid quantitative determination of Al on site 3+ Which presents difficulties. The problem of nonlinear response signal detection generated by the complex system is solved, and deep learning is a good choice.
The deep learning algorithm is a machine learning method based on an artificial neural network. The main advantages of deep learning can be summarized in three ways: first, the multi-layer feature extraction capability benefits from a layer-by-layer depth architecture, which enables it to fully extract the most valuable information related to the target problem. This makes deep learning superior to most conventional algorithms, even to human experts, in terms of image classification, semantic segmentation, etc. Second, the data-driven nature of deep learning just meets the need to process ever-expanding large data. At the same time, the high flexibility of the deep learning architecture enables it to handle different types of data, such as sequential data, images, or data cubes. Third, the deep learning model utilizes inputs and outputs in an end-to-end fashion, thereby avoiding cumbersome step-by-step processes, reducing human interference, and once the model is trained well, these features make deep learning a very powerful tool for biological spectroscopy and biological spectral imaging. The convolutional neural network can conveniently and rapidly capture the color characteristics of the image and rapidly process and display the results through a complex learning process, so that the aim of expanding the visual recognition range is fulfilled. Compared with the traditional analysis method which needs relatively complex instruments, the digital image colorimetric method embedded with the deep learning algorithm can be conveniently carried out, and target ions are quantitatively analyzed in real time, so that a new thought is provided for detecting heavy metal ions. At present, no related research report about a preparation method of carbon quantum dots for detecting aluminum ions and detection application based on a deep learning algorithm is disclosed at home and abroad.
Disclosure of Invention
The invention aims to solve the technical problems of providing a fluorescent light which has high fluorescence intensity, good dispersibility, good light stability and good light stability to Al 3+ Specifically-responded carbon quantum dot for detecting aluminum ionsThe preparation method and the detection application thereof have wide detection range, high sensitivity and accuracy and short detection time.
The technical scheme adopted for solving the technical problems is as follows: the preparation method of the carbon quantum dot for detecting aluminum ions comprises the following steps: 2, 5-dihydroxyl ethyl terephthalate and urea are mixed according to the mass ratio of 2:1 are placed in a reaction kettle after being uniformly mixed, and heated at 160-200 ℃ for 6-8h, and after the reaction kettle is naturally cooled to the ambient temperature, 7662XgCentrifuging for 5min, and taking supernatant to dialyze for 24-48h at 1000Da to obtain the carbon quantum dot dispersion liquid for detecting aluminum ions.
The method for detecting aluminum ions by using the carbon quantum dots prepared by the method comprises the following steps of: sample Al to be measured 3+ Taking fluorescent color photos of the solution under 365nm ultraviolet lamp by using a smart phone, inputting the fluorescent color photos into an image classifier for concentration classification, and outputting N piecesPi value, N to be obtainedPNegative values in i are removed, the remaining Pi sum P is calculated, when Pi/P is smaller than 5%, the corresponding Pi is removed, P is calculated again, and Al in the sample is calculated according to the following formula 3+ The concentration is as follows:wherein C is Al in the sample 3+ Concentration in nM;Ci is any one of the values of N concentrations set by the image classifier, and the unit is nM;Pi is the concentrationCi is a natural number 1-N, and N is 30.
Preferably, the image classifier is a convolutional neural network written by using a Pytorch framework, and the concentration classification process specifically comprises the following steps: converting the photographed fluorescent color picture into tensor by a Pytorch frame, using a convolution kernel of 3×3, using a ReLU activation function, performing normalization and maximum pooling, performing 8-layer composite processing to obtain parameters related to image color characteristics, transferring the obtained parameters related to image color characteristics by using 5-layer full-connection layers, and finally outputting N concentrationsCClassification weight of iPi。
The principle of the invention: the invention utilizes 2, 5-dihydroxyterephthalic acidEthyl esters and urea developed and newly synthesized a yellow fluorescent CQDs that was specific for Al 3+ Has specific response. Because XPS results show that the nitrogen content of CQDs is higher, the nitrogen atom is provided by urea, and the nitrogen atom is connected to the benzene ring during the heating reaction, so that a shorter pi conjugated structure is caused, and the CQDs have stronger pi-pi interaction and charge transfer capability, so that the fluorescence wavelength of the CQDs is red shifted. Meanwhile, during heating, the ester group is cleaved into hydroxyl groups, and the hydroxyl groups cause the fluorescence wavelength of CQDs to be further red shifted, so that the fluorescence color of CQDs is yellow. When Al is 3+ Al, when present 3+ Chelate with carboxyl and amino on the surface of CQDs, inhibit hydrogen bond in molecules, change electron push-pull structure and lead to the formation of CQDs surface defect state. This change in structure causes the color of the fluorescent light to change from yellow to green, in fact, from a transition with a small energy gap to a transition with a large energy gap.
Compared with the prior art, the invention has the advantages that: the invention relates to a preparation method of carbon quantum dots for detecting aluminum ions and detection application thereof, and synthesizes CQDs with higher fluorescence quantum yield and high fluorescence intensity, wherein the carbon nanomaterial used by the fluorescence probe is novel yellow Carbon Quantum Dots (CQDs) synthesized by 2, 5-dihydroxyethyl terephthalate and urea, the absolute quantum yield is 9.88%, and the CQDs have good dispersibility, good light stability and good Al resistance 3+ Specific response, which is represented by the gradual change of fluorescent probe solution from yellow to green, based on which we developed a set of recognition Al using deep learning technique 3+ The accuracy of the image classifier in the test set is 90%, the accuracy of the image classifier in the test set is as high as 95%, and the image classifier in the test set is excellent in data set and non-data set and has good performance on Al 3+ The accuracy of the concentration prediction of the standard sample in the concentration range of 0.3-420 mu M is higher than 90%. The method is simple and efficient, does not need large detection equipment, can give the concentration of the target ions only by photographing and uploading a computer through a mobile phone, is simple and efficient to operate, and can immediately complete the reaction, thereby greatly shortening the detection time, reducing the detection cost, and being high in reliability and based on a detection platform of a deep learning digital image colorimetric methodThe method has the advantages of high sensitivity, simplicity, rapidness, easiness in operation, low experimental cost and the like, and has good application prospect.
Drawings
FIG. 1 is a graph showing fluorescence emission (black line) spectra and optimal excitation wavelengths of CQDs, with corresponding colors under 365nm UV light;
FIG. 2 is a graph of particle size distribution (left) and transmission electron microscopy (right) of CQDs;
FIG. 3 is a graph of XPS results for CQDs;
FIG. 4 shows the Al pairs of fluorescent probes under 365nm ultraviolet lamp 3+ A selective photograph;
FIG. 5 is a flow chart of image classifier model structure and predicted concentration values;
FIG. 6 is a linear fit of the image classifier to the source dataset;
FIG. 7 is a linear fit of the image classifier to a non-source dataset;
FIG. 8 shows the detection of Al by a fluorometer 3+ Is a linear relationship with the result graph of (2);
FIG. 9 shows the RGB program method for Al detection 3+ Results of (3) are shown.
Detailed Description
The invention is described in further detail below with reference to the embodiments of the drawings.
Detailed description of the preferred embodiments
The preparation method of the carbon quantum dot for detecting aluminum ions comprises the following steps: 2, 5-dihydroxyl ethyl terephthalate and urea are mixed according to the mass ratio of 2:1, and heating the mixture at 160-200 ℃ for 6-8h, and after the reaction kettle is naturally cooled to the ambient temperature, 7662XgCentrifuging for 5min, and taking supernatant to dialyze for 24-48h at 1000Da to obtain the carbon quantum dot dispersion liquid for detecting aluminum ions.
As can be seen from fig. 1, the excitation wavelength and fluorescence wavelength of the carbon quantum dots CQDs prepared by the above method are 365nm and 539, 539 nm, respectively.
As can be seen from the particle size distribution diagram transmission electron microscopy of CQDs in FIG. 2, the particle size of CQDs is about 2.0.+ -. 0.5. 0.5 nm, and the fluorescence absolute quantum yield is 9.88%.
The result of XPS of the carbon quantum dots in FIG. 3 shows that C 1s Binding energies 284.8 eV, 288.4 eV and 286.6 eV correspond to c= C, C =o and C-C (as shown in fig. 3A), respectively, N 1s Binding energy 399.6 eV corresponds to amino nitrogen (as shown in FIG. 3B), O 1s Binding energies 531.7 eV, 532.5 eV and 533.4 eV correspond to c= O, C-OH and C-O (as shown in fig. 3C), respectively, with C, O, N having three element contents of 64.6%, 28.9% and 6.5% (as shown in fig. 3D), respectively. The results show that CQDs contain a large amount of amino and carboxyl groups, and can be Al 3+ Providing a chelating site. When Al is 3+ Al, when present 3+ Chelate with carboxyl and amino on the surface of CQDs, inhibit hydrogen bond in molecules, change electron push-pull structure and lead to the formation of CQDs surface defect state. This structural change causes the color of the fluorescence to change from yellow to green (fig. 4), which is essentially a transition from a smaller energy gap to a larger energy gap.
Second embodiment
The method for detecting aluminum ions by using the carbon quantum dots prepared by the method in the embodiment comprises the following steps:
1. preparing Al with different concentrations 3+ Solutions (0 nM, 50 nM, 100 nM, 200 nM, 400 nM, 600 nM, 800 nM, 1 μM, 5 μM, 10 μM, 15 μM, 20 μM, 25 μM, 30 μM, 35 μM, 40 μM, 50 μM, 60 μM, 80 μM, 100 μM, 120 μM, 140 μM, 160 μM, 180 μM, 200 μM, 250 μM, 300 μM, 350 μM, 400 μM, 450 μM, 500 μM) were then taken as source datasets using a smartphone (iPhone XR) under 365nm uv light. All pictures were randomly split into training and test sets, with the test set accounting for 20% or 30% of the total data set. The training set is used to train the neural network algorithm to obtain an efficient image classifier, and the test set is used to evaluate the performance of the image classifier.
2. As shown in fig. 5, a Convolutional Neural Network (CNN) was written using the Pytorch framework as an image classifier to classify the concentration of the image for the source dataset. The method comprises the following steps: converting the fluorescence picture of the source data set obtained in the step 1 into tensor by a Pytorch framework, using a convolution kernel of 3 multiplied by 3, using a ReLU activation function,after normalization and maximum pooling, 8 layers of compound treatment are carried out to obtain parameters related to the color characteristics of the image, the obtained parameters related to the color characteristics of the image are transmitted by 5 layers of full-connection layers, and finally 30 concentrations are outputCClassification weight of iPi, i is a natural number of 1-30;
3. al of different concentrations 3+ The predicted result of the solution is taken as the average value of five results, and the specific calculation process is as follows: in a first step, 30 are obtainedPNegative values in i are eliminated, because negative numbers in the predicted results represent negative correlations, indicating that the fluorescent signal generated by sample concentration C is significantly different from the fluorescent signal generated by the classification concentration Ci (nM); secondly, calculating the remaining Pi sum P, and when the Pi/P is less than 5%, rejecting the corresponding Pi, wherein the similarity between the fluorescence signal generated by the sample concentration C and the fluorescence signal generated by the classification concentration Ci is low; third, P is recalculated, and the sample concentration C (nM) is calculated according to the following formula:wherein C is Al in the sample 3+ Concentration in nM;Ci30 output density values set for the image classifier;Pi is the concentrationCi is a natural number 1-N, and N is 30.
From the performance of the image classifier of FIG. 6 on the source dataset, the linear fit equation isy= 1.00x+1.10 whereinyTo detect the concentration (mu M),xis the true concentration (μm). Slope of 1.00 and linear correlation coefficient of R 2 =0.998, indicating that the image classifier performed excellently on the source dataset.
To more accurately assess the accuracy and universality of the network, we prepared a new set of data (40 nM, 250 nM, 300 nM, 450 nM, 500 nM, 650 nM, 700 nM, 900 nM, 3 μΜ,7 μΜ, 12 μΜ, 17 μΜ, 22 μΜ, 33 μΜ, 45 μΜ, 70 μΜ, 90 μΜ, 110 μΜ, 130 μΜ, 150 μΜ, 170 μΜ, 210 μΜ, 260 μΜ, 320 μΜ, 380 μΜ, 420 μΜ, 460 μΜ, 520 μΜ) in addition to the source data set, directly let the image classifier predict, and average the predicted results over five results. The results are shown in Table 1:
TABLE 1 detection of Al on non-data set 3+ Manifestation of Standard sample [ ]，n= 5)
From table 1 above, it can be seen that the accuracy of the prediction result of the image classification simulator is greater than 90% from 500 nM. The results of linear fitting of the above results are shown in fig. 7, where the linear equation is y=0.98x+0.01, where y is the detected concentration (μm) and x is the true concentration (μm). Slope of 0.98, linear correlation coefficient of R 2 =0.997, indicating that the image classifier performs equally well on non-source data sets, fully illustrating the reliability and universality of the classification simulator.
Detailed description of the preferred embodiments
Five network model structures with representative different structures were selected: the above data sets were subjected to ablation experiments with VGG16 network, densnet 121 network, resnet34 network, 4-layer convolutional CNN0 network, and 8-layer convolutional CNN1 network, and the test results are shown in table 2 below.
TABLE 2 accuracy contrast of image classification models on source datasets
As can be seen from table 2, the accuracy of the CNN1 model on the test set is highest, and the accuracy on the training set also reaches 90%. Although the densnet 121 and CNN0 models were more accurate on the training set, their performance on the test set was general, indicating that some degree of overfitting had occurred for both models. The CNN1 model is therefore chosen as the basic framework of the image classifier.
Detailed description of the preferred embodiments
Comparison of detection methods
Method I, detecting Al by using fluorometer 3+ : fluorescence intensity of CQDsAl 3+ The concentration is closely related. As shown in FIG. 8, following Al 3+ The concentration is increased, the fluorescence intensity of CQDs is also increased continuously, and the CQDs have a certain linear relation within the concentration range of 1-40 mu M, and the linear equation is thaty= 16.70x+ 432.25, the linear correlation coefficient is R 2 =0.983, whereyFor fluorescence intensity (a.u.),xis Al 3+ Concentration (μm).
Method two, RGB program detection Al 3+ : in the same area of different pictures, 10×10 pixels are cut, and the average value of RGB is extracted by using python programming. R, G, B channels and Al respectively 3+ A scatter plot of the concentrations and fitting the results are shown in fig. 9. The results show that it is difficult to establish any one value with Al 3+ Simple quantitative relationship between concentrations. The reason is that: CQDs, CQDs-Al 3+ The fluorescence wavelength of the complex is not the same, so the contribution to R, G, B value is also different, al 3+ The change in concentration hardly produced a regular change in R, G, B value.
As described above, the RGB method cannot quantitatively detect Al at all 3+ Because of the fluorescence wavelength of CQDs and CQDs-Al 3 + The fluorescence wavelengths of the complexes are not exactly the same. Thus, their contributions to R, G and B values are different, resulting in R, G and B values with Al 3+ Irregular changes in concentration. The fluorescence photometry can quantitatively detect Al in the concentration range of 1-40 mu M 3+ ，R 2 =0.983. The detection performance is not good, and due to the complexity of a fluorescence system, the fluorescence intensity is equal to Al 3+ The linear relationship of concentration is poor. Compared with the method, the image classification method based on deep learning can detect Al with the concentration range of 0.3-320 mu M 3+ R2=0.997 shows obvious advantages of wider concentration range, better linearity, higher sensitivity and accuracy, and the like. I.e. the detection is based on the similarity between the signal generated by the actual sample and the classification signal generated by a series of standard solutions. In this case, the quantitative basis is composed of fluorescence intensity and Al 3+ Nonlinear correlation of concentration translates into similarity to Al 3+ Linear correlation of concentration substantially eliminates nonlinear variation of fluorescence intensity versus Al 3+ The effect of the quantitative detection. It is particularly worth mentioning that the classification signal is not continuous, so that it is often difficult to process continuous objects such as concentrations. The data processing mode provided by the invention creatively and perfectly solves the problem.
Detailed description of the preferred embodiments
To verify the effectiveness and practicality of the method for detecting Al3+ in actual samples, ICP-MS and the method are adopted to simultaneously determine the Al with different concentrations in the marked river water 3+ Is a sample of (a).
TABLE 3 use of ICP-MS method and the present method for Al in standard water sample 3+ Comparison of the detection results of (3),n= 5)
The results are shown in Table 3, and the difference in t-test results between the two groups was not significant (P>0.05 The standard adding recovery rate is 94.7% -106.8%, which indicates that the method can be used for Al in actual water sample 3+ Is an accurate detection of (a). In particular, the whole experimental process is extremely simple, and the result can be obtained within 2 minutes, so that the Al in the surface water can be accurately, sensitively and rapidly detected on site 3+ A realistic solution is provided.
The above description is not intended to limit the invention, nor is the invention limited to the examples described above. Variations, modifications, additions, or substitutions will occur to those skilled in the art and are therefore within the spirit and scope of the invention.
Claims (1)
1. The preparation method of the carbon quantum dot for detecting aluminum ions is characterized by comprising the following steps of: 2, 5-dihydroxyl ethyl terephthalate and urea are mixed according to the mass ratio of 2:1, and heating the mixture for 6 to 8 hours at 160 to 200 ℃, naturally cooling the reaction kettle to the ambient temperature, centrifuging the mixture for 5 minutes at 7662 Xg, and taking supernatant to dialyze for 24 to 48 hours at 1000Da to obtain the carbon quantum dot dispersion liquid for detecting aluminum ions.
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