Electrostatic composite multi-channel fluorescence array sensor, preparation method and use thereof

Abstract

The present invention discloses an electrostatic complex multi-channel fluorescence array sensor, a preparation method and use thereof, wherein the fluorescence array sensor contains at least two different fluorescence sensing units PPE-AIE, and the fluorescence sensing unit PPE-AIE is composed of positively charged aggregation-induced emission materials AIEgens with different side chain modifications and negatively charged water-soluble fluorescent conjugated polymer PPE through electrostatic interaction. The present invention also discloses a preparation method for the above-mentioned electrostatic complex fluorescence array sensor. The present invention also discloses the use of the above-mentioned electrostatic complex fluorescence array sensor in bacterial detection. The electrostatic complex multi-channel fluorescence array sensor disclosed in the present invention can realize two signals in a single hole, can distinguish and detect twenty types of bacteria at the same time, has high accuracy and sensitivity, takes less than one minute to detect, has low cost, high repeatability, and does not require professional technicians.

Description

Technical Field

[0001] The present invention relates to a fluorescence detection sensor and its application, and in particular to an electrostatic composite multi-channel fluorescence array sensor, a preparation method thereof and its application. Background Art

[0002] Bacterial infections have caused significant harm in medicine, public health, food safety, and other fields, and have garnered widespread attention. Bacteria are complex organisms, and the number of people sickened and even killed by bacterial infections remains high each year. Therefore, there is an urgent need to develop rapid and effective methods for identifying different pathogens to guide clinical testing, monitor infection trends, improve the accuracy of early diagnosis, and achieve "early detection, early treatment."

[0003] To date, numerous methods for bacterial detection have been established, including polymerase chain reaction (PCR), gene microarrays, plate and culture assays, target-specific immunoassays, and identification technologies (mass spectrometry, Raman spectroscopy, and diagnostic magnetic resonance imaging). These methods all offer high sensitivity, but their high cost, time complexity, and the need for specialized personnel limit their widespread application in the detection and identification of various bacteria.

[0004] Fluorescent probes offer advantages such as ease of operation, high sensitivity, and rapid response, making them a promising approach for pathogen detection. Several sensors based on fluorescence reactions have been developed for pathogen identification. However, conventional fluorescent molecules are often subject to the aggregation-induced quenching (ACQ) effect, whereby their emission is typically weakened or even quenched at higher concentrations or in aggregated states. This limits their concentration to very low levels, resulting in reduced detection sensitivity. Furthermore, the ACQ effect of conventional fluorophores typically operates in a fluorescence "off" mode. This results in the fluorophore's emission being often affected by external factors, further impacting the sensitivity and accuracy of identification. In stark contrast to conventional ACQ fluorophores, aggregation-induced emission luminogens (AIEgens) emit no or weakly when dissolved, but emit higher levels when aggregated. This feature enables AIEgens to operate in an "on" mode. These advantages of AIEgens well meet the requirements of ideal fluorescent sensors, which will greatly improve the sensitivity and reliability of detection.

[0005] Fluorescence array sensors can be used as an ideal tool to identify and detect various analytes. Fluorescence array-based sensing technology can be used to simultaneously identify various analytes based on the differential interactions between the analytes and the sensing elements. This method is based on simulating the olfactory or taste system of mammals for the identification of detectable substances. It is simpler to operate and has a shorter detection time than traditional methods. Compared with specific detection based on the "lock-key" binding mode, there is no need for specific binding between the sensor unit and the analyte, avoiding the complex design of the sensor. Multiple sensors are used to recognize the patterns of different analytes to generate multiple sets of detection signals for distinguishing the analytes. The electrostatic complexes - aggregation-induced luminescence agents and conjugated polymers used bind to bacteria through different binding modes (electrostatic interaction, hydrophobic interaction and specific binding, etc.) to generate unique fluorescent signals, thereby realizing the detection of bacteria.

[0006] Array sensors reported in the literature often require multiple sensing elements, are complex to operate, have high production costs, require specialized personnel, and suffer from narrow detection ranges, low sensitivity, and poor reproducibility. Therefore, there remains a need in the art to develop fluorescent array sensors for bacterial detection that are simple to operate, highly sensitive, and have a wide detection range. Summary of the Invention

[0007] Objectives of the invention: The objective of the present invention is to provide an electrostatic complex multi-channel fluorescence array sensor that is simple to operate, highly sensitive, and has a wide detection range. Another objective of the present invention is to provide applications of the aforementioned fluorescence array sensor.

[0008] Technical solution: The electrostatic complex multi-channel fluorescence array sensor of the present invention contains at least two different fluorescence sensing units PPE-AIE, which are composed of positively charged aggregation-induced emission materials AIEgens with different side chain modifications and negatively charged water-soluble fluorescent conjugated polymer PPE through electrostatic interaction.

[0009] The general structural formula of the aggregation-induced emission material AIEgens is shown in Formula I:

[0010]

[0011] wherein R1 and R2 are independently selected from halogen elements or methoxy groups;

[0012] R3 is selected from linear alkyl, substituted or unsubstituted aromatic ring, heterocycle, hydroxyl, carboxylic acid, dimer and other bacterial recognition groups;

[0013] X - is the counter anion;

[0014] The general structural formula of the water-soluble fluorescent conjugated polymer PPE is shown in Formula II:

[0015]

[0016] wherein R4 and R5 are independently selected from hydrogen, substituted or unsubstituted C1-10 straight or branched alkyl, C1-10 alkoxy, aromatic ring, sulfonic acid and carboxyl;

[0017] n is an integer from 8 to 200.

[0018] Furthermore, in the general structural formula of the water-soluble fluorescent conjugated polymer PPE,

[0019] R4 and R5 are independently selected from

[0020]

[0021] m is an integer from 1 to 10.

[0022] Furthermore, the structural formula of the water-soluble fluorescent conjugated polymer PPE is:

[0023]

[0024] Here, n is an integer from 8 to 200.

[0025] Furthermore, in the general structural formula of the aggregation-induced emission material AIEgens, X - Selected from iodide and bromide.

[0026] Furthermore, the aggregation-induced emission material AIEgens is selected from the following structures:

[0027]

[0028]

[0029]

[0030] The present invention also provides a method for preparing the electrostatic complex multi-channel fluorescence array sensor, comprising the following steps:

[0031] (1) Using p-bromophenylacetonitrile and 4-pyridylboronic acid or their derivatives as raw materials, they are coupled via Suzuki reaction, then reacted with p-dimethylaminobenzaldehyde to form an intermediate skeleton, and finally reacted with brominated or iodinated compounds to form pyridinium salts, which are AIEgens;

[0032] (2) diluting the water-soluble fluorescent conjugated polymer PPE with PBS buffer solution, diluting AIEgens with methanol solution, and mixing the diluted PPE solution and AIEgens solution to obtain the fluorescent sensing unit PPE-AIE;

[0033] (3) Repeat steps (1) to (2) to prepare at least two different fluorescent sensing units PPE-AIE to form a fluorescent array sensor.

[0034] The present invention also provides use of the electrostatic complex fluorescence array sensor in bacteria detection.

[0035] Furthermore, the detection object may be food or urine.

[0036] Furthermore, the bacteria include Stenotrophomonas maltophilia (S. maltophilia), Klebsiella pneumoniae (K. pneumoniae), Proteus mirabilis (P. mirabilis), Morganella morganii (M. morganii), Enterobacter hormaechei (E. hormaechei), Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), E. cloacae (E. cloacae), Pseudomonas aeruginosa (P. aeruginosa), Bacillus cereus (B. cereus), One or more combinations of Staphylococcus aureus (S. lugdunensis), Staphylococcus capitis (S. capitis), Enterococcus faecalis (E. faecalis), Enterobacter aerogenes (E. aerogenes), Acinetobacter baumannii (A. baumannii), Burkholderia aerogenes (B. aerogenes), Citrobacter korseri (C. koseri), Citrobacter freundii (C. freundii), Shewanella putrefaciens (S. putrefaciens) and Salmonella typhi (S. typhi).

[0037] The present invention also provides a method for detecting bacteria using the above-mentioned electrostatic complex fluorescence array sensor, wherein different sensing units of the electrostatic complex fluorescence array sensor are mixed with bacteria respectively, the fluorescence intensity of each sensing unit is measured, the fluorescence data is processed, and the data matrix is classified to achieve the distinction and detection of multiple bacteria.

[0038] Furthermore, the fluorescence data is processed using various algorithms, such as linear discriminant analysis, to classify the data matrix and generate a two-dimensional array fingerprint of the bacterial plasm, enabling visual identification. The resulting two-dimensional array fingerprint is used as a model to detect unknown samples, and the accuracy of the predictions for the unknown samples is calculated, enabling the differentiation and detection of bacteria.

[0039] Furthermore, the method specifically includes the following steps:

[0040] (1) Diluting the PPE solution with a 1-20 mM buffer solution to a final concentration of 1 μM to 250 nM, and diluting the AIEgens solution with a methanol solution to a final concentration of 10 μM to 2.5 μM; mixing the PPE solution and the AIEgens solution as a sensing unit solution, and preparing at least two sensing unit solutions; taking the sensing unit solution and the test solution, mixing and shaking them, and detecting the fluorescence intensity data, and repeating the above detection steps for different sensing units;

[0041] (2) using statistical analysis software to process and analyze the fluorescence data, converting the fluorescence response pattern into a canonical pattern using linear discriminant analysis, classifying the data matrix, and obtaining a two-dimensional array fingerprint of the bacteria contained in the test solution to achieve visual identification;

[0042] (3) The obtained two-dimensional array fingerprint is used as a model to detect unknown samples, and the accuracy of the unknown sample prediction is calculated to achieve the differentiation and detection of bacteria.

[0043] Beneficial effects: Compared with the existing technology, the present invention has the following outstanding significant advantages: the electrostatic complex multi-channel fluorescence array sensor of the present invention utilizes electrostatic action to compound AIEgens and PPE to achieve single-hole two signals, can simultaneously distinguish and detect twenty types of bacteria, has high accuracy and sensitivity, takes less than one minute to detect, has low cost, high repeatability, and does not require professional technicians. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 are the UV absorption spectra and fluorescence emission spectra of the sensing elements AIEgens and PPE selected in Example 7;

[0045] Figure 2 is the quenching curve of the sensing element AIEgens selected in Example 7 to PPE;

[0046] Figure 3 is the fluorescence response signal of the 20 bacteria species to the 6 sensing units in Example 8;

[0047] Figure 4 This is a visualization diagram (LDA) of the rapid identification of bacteria by the six sensing units in Example 8;

[0048] Figure 5 are the fluorescence response signals of the 6 sensor units to the 10 kinds of milk with different freshness in Example 9;

[0049] Figure 6 This is a visualization diagram (LDA) of the rapid identification of milk of different freshness by the six sensor units in Example 9;

[0050] Figure 7 are the fluorescence response signals of the six sensor units to the seven types of urine containing different types and concentrations of bacteria in Example 10;

[0051] Figure 8 This is a visualization diagram (LDA) of the rapid identification of urine containing different types and concentrations of bacteria by the six sensor units in Example 10. DETAILED DESCRIPTION

[0052] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0053] Example 1 Synthesis of Aggregation-Induced Emission Material AIE 1

[0054] The synthetic route is:

[0055]

[0056] The following steps are involved:

[0057] 4-pyridylboronic acid (5.00g, 40.68mmol), p-bromophenylacetonitrile (7.97g, 40.68mmol), potassium carbonate (16.87g, 122.03mmol) and Pd(PPh3)4(470.06mg, 0.41mmol) are added in round-bottom flask.Under argon protection, add 200mL redissolved THF and 40mL distilled water to dissolve, mixture is stirred and heated to reflux 12h, is spin-dried for organic solvent, adds 100mL water in mixture, is extracted three times with dichloromethane, collects organic phase and uses anhydrous sodium sulfate drying.After solvent evaporation, crude product is by using petroleum ether / ethyl acetate (v / v=4:1) as eluent silica gel column chromatography purification, obtains white solid product, i.e. compound 1 (1.88g), yield: 59.4%. 1 H NMR (300MHz, DMSO-d6) δ 8.65 (d, J = 6.2 Hz, 2H), 7.86 (d, J = 8.3 Hz, 2H), 7.73 (d, J = 6.2 Hz, 2H), 7.51 (d, J = 8.5 Hz, 2H), 4.13 (s, 2H).

[0058] Compound 1 (1.00g, 5.15mmol) and p-dimethylaminobenzaldehyde (0.77g, 5.15mmol) are placed in a round-bottomed flask, dissolved in 30mL 95% ethanol. Sodium hydroxide (205.92mg, 5.15mmol) is dissolved in 30mL 95% ethanol, then slowly added to the mixture. After stirring at room temperature for 2 hours, filter and obtain filter cake, use cold ethanol washing filter cake, and dry under reduced pressure and obtain light yellow product, i.e. compound 2 (1.53g), yield: 91.07%. 1 H NMR (300MHz, DMSO-d6) δ8.66(d,J=6.1Hz,2H),7.95(s,2H),7.92(s,2H),7.85(s,1H),7.82(s,2H),7.78(d,J=6.3Hz,2H),6.84(d,J=8.7 Hz,2H),3.05(s,6H).

[0059] Compound 2 (0.20 g, 0.61 mmol) and iodomethane (0.087 g, 0.61 mmol) were mixed and dissolved in 15 mL of tetrahydrofuran. The mixture was heated under reflux for 24 h. After the solution was cooled to room temperature, it was filtered to obtain a dark red filter cake, which was washed three times with tetrahydrofuran to obtain a dark red product AIE 1 (201 mg) with a yield of 77.8%. 1 H NMR (300 MHz, DMSO-d6) δ9.01 (d, J = 6.3 Hz, 2H), 8.56 (d, J = 6.5 Hz, 2H), 8.21 (d, J = 8.3 Hz, 2H), 8.07 (s, 1H), 7.95 (dd, J = 8.6, 5.5 Hz, 4H), 6.85 (d, J = 8.8 Hz, 2H), 4.33 (s, 3H), 3.06 (s, 6H).

[0060] Example 2 Synthesis of Aggregation-Induced Emission Material AIE 2

[0061] The synthetic route is:

[0062]

[0063] The synthesis method was similar to that of Example 1. A deep red solid AIE 2 (45 mg) was obtained with a yield of 31.7%. 1H NMR (400 MHz, DMSO-d6) δ9.12 (d, J = 6.3 Hz, 2H), 8.58 (d, J = 6.5 Hz, 2H), 8.22 (d, J = 8.4 Hz, 2H), 8.07 (s, 1H), 7.95 (dd, J = 8.8, 7.0 Hz, 4H), 6.86 (d, J = 8.9 Hz, 2H), 4.60 (t, J = 7.2 Hz, 2H), 3.06 (s, 6H), 1.94 (p, J = 7.4 Hz, 2H), 1.33 (p, J = 7.4 Hz, 2H), 0.94 (t, J = 7.3 Hz, 3H).

[0064] Example 3 Synthesis of Aggregation-Induced Emission Material AIE 3

[0065] The synthetic route is:

[0066]

[0067] The synthesis method was similar to that of Example 1. A deep red solid AIE 3 (58 mg) was obtained with a yield of 40.6%. 1 H NMR (300 MHz, Methanol-d4) δ8.97(d,J=6.5 Hz,2H),8.46(d,J=6.6 Hz,2H),8.12(d,J=8.5Hz,2H),7.96(t,J=8.2 Hz,4H),7.85(s,1H),6.84(d,J=8.9 Hz, 2H), 4.75 (t, J = 7.0 Hz, 2H), 3.69 (t, J = 5.7 Hz, 2H), 3.12 (s, 6H), 2.27 (q, J = 6.4 Hz, 2H).

[0068] Example 4 Synthesis of Aggregation-Induced Emission Material AIE 6

[0069] The synthetic route is:

[0070]

[0071] The synthesis method was similar to that of Example 1. A deep red solid AIE 6 (105 mg) was obtained with a yield of 68.8%. 1H NMR (300MHz, DMSO-d6) δ9.24(d,J=6.5Hz,2H),8.60(d,J=6.5Hz,2H),8.20(d,J=8.4Hz,2H),8.08(s,1H),7.95(dd ,J=8.7,5.5Hz,4H),7.58(d,J=5.4Hz,2H),7.47(d,J=7.0Hz,3H),6.86(d,J=8.9Hz,2H),5.85(s,2H),3.06(s,6H).

[0072] Example 5 Synthesis of Aggregation-Induced Emission Material AIE 9

[0073] The synthetic route is:

[0074]

[0075] The synthesis method was similar to that of Example 1. A deep red solid AIE 9 (70 mg) was obtained with a yield of 86.4%. 1 H NMR (300MHz, DMSO-d6) δ9.22(d,J=6.7Hz,2H),8.59(d,J=6.7Hz,2H),8.20(d,J=9.4Hz,4H),8.08(s,1H),7.95(dd ,J=8.8,5.2Hz,4H),7.86(d,J=7.8Hz,2H),7.51(d,J=7.8Hz,2H),6.86(d,J=9.1Hz,2H),5.84(s,2H),3.06(s,6H).

[0076] Example 6 Synthesis of Aggregation-Induced Emission Material AIE 22

[0077] The synthetic route is:

[0078]

[0079] The synthesis method was similar to that of Example 1. A deep red solid AIE 22 (102 mg) was obtained with a yield of 75.2%. 1 H NMR(300MHz,DMSO-d6)δ8.89(d,J=6.7Hz,4H),8.40(d,J=6.8Hz,4H),7.95–7.87(m,8H),7.77 (s, 2H), 7.69 (d, J = 8.5Hz, 4H), 6.64 (d, J = 9.0Hz, 4H), 4.75 (s, 4H), 3.96 (s, 4H), 3.03 (s, 12H).

[0080] Example 7 Construction of Fluorescence Array Sensor

[0081] The aggregation-induced emission materials AIEgens (AIE 1, AIE 2, AIE 3, AIE 6, AIE 9 and AIE 22) synthesized in Examples 1 to 6 were dissolved in methanol solution to a final concentration of 10 -3 mol / L AIEgens stock solution was diluted with methanol to a final concentration of 2.5 μM, yielding six AIEgens dilutions. Water-soluble conjugated polymer PPE (n=15) was dissolved in PBS and diluted to a final concentration of 250 nM. The six AIEgens dilutions were mixed with the water-soluble conjugated polymer PPE dilution at a 1:1 ratio and shaken to obtain six complex systems: PPE-AIE 1, PPE-AIE 2, PPE-AIE 3, PPE-AIE 6, PPE-AIE 9, and PPE-AIE 22. These complex systems, representing six fluorescent sensing units, were set aside for future use.

[0082] The structural formula of the water-soluble conjugated polymer PPE used is

[0083]

[0084] The UV absorption spectra and fluorescence emission spectra of the tested sensing elements AIEgens (AIE 1, AIE 2, AIE 3, AIE 6, AIE 9 and AIE 22) and PPE ( Figure 1 ); and the quenching curves of PPE by AIEgens in six fluorescent sensing units PPE-AIE 1, PPE-AIE 2, PPE-AIE 3, PPE-AIE 6, PPE-AIE 9, and PPE-AIE 22 ( Figure 2 ). The results show that the selected AIE molecules all have a certain quenching effect on PPE.

[0085] Example 8 Detection of Different Bacteria by Fluorescence Array Sensor

[0086] Method for distinguishing different types of bacteria: 100 μL of the six sensor units (PPE-AIE 1, PPE-AIE 2, PPE-AIE 3, PPE-AIE 6, PPE-AIE 9, PPE-AIE 22) prepared in Example 7 were respectively mixed with different types of bacterial solutions (final concentration OD 600=0.005) 100 μL was mixed and shaken for 10 seconds. The bacterial liquid species included Stenotrophomonas maltophilia (S. maltophilia), Klebsiella pneumoniae (K. pneumoniae), Proteus mirabilis (P. mirabilis), Morganella morganii (M. morganii), Enterobacter hormaechei (E. hormaechei), Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), E. cloacae (E. cloacae), Pseudomonas aeruginosa (P. aeruginosa), Bacillus cereus (B. cereus), Staphylococcus lugdunensis (S. lugdunensis) unensis), Staphylococcus capitis (S.capitis), Enterococcus faecalis (E.faecalis), Enterobacter aerogenes (E.aerogenes), Acinetobacter baumannii (A.baumannii), Burkholderia aerogenes (B.aerogenes), Citrobacter korseri (C.koseri), Citrobacter freundii (C.freundii), Shewanella putrefaciens (S.putrefaciens) and Salmonella typhi (all of the above bacteria were obtained from the Department of Laboratory of Zhongda Hospital Affiliated to Southeast University and Nanjing Hospital of Traditional Chinese Medicine). The excitation wavelength is 360 nm, and the fluorescence intensity of the complex binding to the bacteria is measured at an emission wavelength of 440 nm to obtain the first group of signals (PPE signals, namely CH1, 3, 5, 7, 9, 11); the excitation wavelength is 450 nm, and the fluorescence intensity of the complex binding to the bacteria is measured at an emission wavelength of 740 nm to obtain the second group of signals (AIE signals, namely CH2, 4, 6, 8, 10, 12). The relative fluorescence intensity change is the detection signal (I-I0) / I0. Each type of bacteria is detected once using 6 sensor units, and two groups of signals (PPE signals and AIE signals) are obtained each time. Each detection has 12 channels (CH1 to 12), and (6 sensor units × 20 types of bacteria × 12 channels) = 1440 fluorescence signals can be obtained. These 1440 fluorescence signals are then used to form a data matrix (Table 1). The 6 sensor units have different fluorescence responses to different bacteria ( Figure 3 ).

[0087] Table 1. Data matrix of fluorescence response of fluorescence array sensor to different bacteria

[0088]

[0089]

[0090] Note: C1 to C12 represent channels 1 to 12; the detection order of each bacterial sensor unit is PPE-AIE 1, PPE-AIE 2, PPE-AIE 3, PPE-AIE 6, PPE-AIE 9, and PPE-AIE 22.

[0091] The fluorescence data were processed and analyzed using statistical analysis software. The fluorescence response patterns were converted to canonical patterns using linear discriminant analysis (LDA). The Mahalanobis distance from each individual pattern to the centroid of each group in the multidimensional space was calculated, and all species were assigned based on the shortest Mahalanobis distance. The LDA graph shows that even if the concentration of bacteria is low and the species are large, different bacteria can still be distinguished ( Figure 4 , Table 2), and using LDA data as a model, the Jackknifed Classification Matrix showed that the accuracy of bacterial differentiation reached 99% (Table 3).

[0092] Table 2 LDA results of fluorescence array sensor fluorescence response data matrix for different bacteria

[0093]

[0094]

[0095] Note: F1~F12 represent Factor 1~Factor 12.

[0096] Table 3Jackknifed Classification Matrix

[0097]

[0098] The array sensor's ability to discriminate unknown samples: 20 bacterial species were randomly tested in a blind test, for a total of 80 unknown samples. Testing was performed according to the above steps, and changes in relative fluorescence intensity were recorded (Table 4). Linear discriminant analysis was then used to verify the model's ability to detect unknown samples. The model's ability to discriminate bacterial species was compared with the above model. Of the 80 unknown samples tested, 75 were correctly detected, for an accuracy rate of 94% (Table 5).

[0099] Table 4 Array sensor fluorescence response data matrix for unknown samples

[0100]

[0101]

[0102] Note: C1 to C12 represent channels 1 to 12; the data for each sample is the average of 6 fluorescence sensing units.

[0103] Table 5 LDA results of the fluorescence array sensor fluorescence response data matrix of unknown samples

[0104]

[0105]

[0106] Note: F1~F12 represent Factor 1~Factor 12.

[0107] Example 9: Detection of milk freshness using a fluorescence array sensor

[0108] Method for identifying milk freshness: 100 μL of each of the six sensor units prepared in Example 7 was pipetted, and 100 μL of milk of different freshness (pure milk purchased from a supermarket) was added. (The milk was exposed to room temperature for 0 h, 4 h, 8 h, 12 h, 16 h, 20 h, 1 day, 36 h, 2 days, and 3 days, and the milk mother liquor was taken for measurement) and shaken for 10 seconds. The excitation wavelength was 360 nm, and the fluorescence intensity of the complex binding to the bacteria was measured at an emission wavelength of 440 nm to obtain a first group of signals (PPE signals, i.e., CH1, 3, 5, 7, 9, 11); the excitation wavelength was 450 nm, and the fluorescence intensity of the complex binding to the bacteria was measured at an emission wavelength of 740 nm to obtain a second group of signals (AIE signals, i.e., CH2, 4, 6, 8, 10, 12). The relative fluorescence intensity change was used as the detection signal (I-I0) / I0. Each type of milk freshness was tested once using 6 sensor units, and two sets of signals (PPE signal and AIE signal) were obtained each time. Each test had 12 channels (CH1-12), and 6 sensor units × 10 groups of milk samples with different freshness × 12 channels = 720 fluorescence signals were obtained. These 720 fluorescence signals were then used to form a data matrix (Table 6). The 6 sensor units have different fluorescence responses to milk of different freshness ( Figure 5 ).

[0109] Table 6 Data matrix of fluorescence response of array sensor to milk of different freshness (milk concentration is mother liquor)

[0110]

[0111]

[0112] Note: C1 to C12 represent channels 1 to 12; the detection order of the sensor units for each fresh milk sample is PPE-AIE 1, PPE-AIE 2, PPE-AIE 3, PPE-AIE 6, PPE-AIE 9, and PPE-AIE 22.

[0113] Statistical analysis software was used to process and analyze the fluorescence data. The fluorescence response patterns were converted to canonical patterns using linear discriminant analysis (LDA). The Mahalanobis distance from each individual pattern to the centroid of each group in the multidimensional space was calculated, and all categories were assigned based on the shortest Mahalanobis distance (Table 7). The LDA graph shows that even if the time interval of milk exposed to air is short and there are many types, milk of different freshness can still be distinguished ( Figure 6 ), and using LDA data as a model, the Jackknifed Classification Matrix showed that the accuracy of bacterial differentiation reached 98% (Table 8).

[0114] Table 7 LDA results of the fluorescence response data matrix of the array sensor to milk of different freshness

[0115]

[0116]

[0117] Note: F1 to F9 represent Factor 1 to Factor 9.

[0118] Table 8 Jackknifed Classification Matrix

[0119]

[0120] The array sensor's ability to discriminate between unknown samples: 10 milk samples of varying freshness were randomly tested in a blind test, yielding a total of 40 unknown samples. Testing was performed according to the aforementioned procedures, and changes in relative fluorescence intensity were recorded (Table 9). Linear discriminant analysis was then used to validate the model's ability to discriminate between unknown samples. The model was then compared with the aforementioned model to discriminate between milk samples of varying freshness. Of the 40 unknown samples tested, 36 were correctly detected, for an accuracy rate of 90% (Table 10).

[0121] Table 9 Array sensor fluorescence response data matrix for unknown samples

[0122]

[0123]

[0124] Note: C1 to C12 represent channels 1 to 12; the data for each sample is the average of 6 fluorescence sensing units.

[0125] Table 10 Detection data of unknown samples by array sensor

[0126]

[0127]

[0128] Note: F1 to F9 represent Factor 1 to Factor 9.

[0129] Example 10: Differentiation of Bacteria of Different Concentrations and Types in Urine by a Fluorescence Array Sensor

[0130] Method for distinguishing different concentrations and types of bacteria in urine: 100 μL of each of the six sensor units prepared in Example 7 was pipetted, and 100 μL of artificial urine containing different bacteria (purchased from: Yuanye Biology, item number: R23032-500ml) was added (a total of seven groups, containing E. coli + E. faecails (OD 600 =0.001), E.coli+E.faecails(OD 600 =0.01), E. coli (OD 600 =0.001), E. coli (OD 600 =0.01), E. faecails (OD 600 =0.001), E. faecails (OD 600 = 0.01) and urine without bacteria) and oscillated for 10 seconds. The excitation wavelength was 360 nm, and the fluorescence intensity of the complex bound to the bacteria was measured at an emission wavelength of 440 nm to obtain the first set of signals (PPE signals, i.e., CH1, 3, 5, 7, 9, 11). The excitation wavelength was 450 nm, and the fluorescence intensity of the complex bound to the bacteria was measured at an emission wavelength of 740 nm to obtain the second set of signals (AIE signals, i.e., CH2, 4, 6, 8, 10, 12). The relative fluorescence intensity change was used as the detection signal (I-I0) / I0. Each urine sample was tested six times with each electrostatic composite sensor, and two sets of signals (PPE signal and AIE signal) were obtained each time. Each set of signals was detected using 12 channels (CH1-12). Six sensor units × 7 sets of artificial urine samples containing different bacteria × 12 channels = 504 fluorescence signals were obtained. These 504 fluorescence signals were then used to form a data matrix (Table 11). The six sensor units had different fluorescence responses to different concentrations and types of bacteria ( Figure 7 ).

[0131] Table 11 Array sensor fluorescence response data matrix for different bacteria in urine

[0132]

[0133]

[0134] Note: C1 to C12 represent channels 1 to 12; the detection order of the sensor units for each artificial urine sample containing different bacteria is PPE-AIE 1, PPE-AIE 2, PPE-AIE 3, PPE-AIE 6, PPE-AIE 9, and PPE-AIE 22.

[0135] Statistical analysis software was used to process and analyze the fluorescence data. The fluorescence response pattern was converted into a canonical pattern using linear discriminant analysis (LDA). The Mahalanobis distance from each individual pattern to the centroid of each group in the multidimensional space was calculated, and all species were assigned based on the shortest Mahalanobis distance (Table 12). The LDA graph shows that even if the bacterial species and concentrations are different, bacteria of different concentrations and species can ultimately be distinguished ( Figure 8 ), and using LDA data as a model, the Jackknifed Classification Matrix showed that the accuracy of bacterial differentiation reached 100% (Table 13).

[0136] Table 12 LDA results of the array sensor fluorescence response data matrix for different bacteria in urine

[0137]

[0138] Note: F1 to F6 represent Factor 1 to Factor 6.

[0139] Table 13Jackknifed Classification Matrix

[0140]

[0141] The array sensor's ability to discriminate between unknown samples: Seven urine samples containing varying bacterial concentrations were blindly tested in random order, for a total of 28 unknown samples. Testing was performed according to the above steps, and changes in relative fluorescence intensity were recorded (Table 14). Linear discriminant analysis was then used to validate the model's ability to discriminate between unknown samples. The model was then compared with the above model to discriminate between different concentrations and types of bacteria in urine. Of the 28 unknown samples tested, 27 were correctly detected, for an accuracy rate of 96% (Table 15).

[0142] Table 14 Array sensor fluorescence response data matrix for unknown samples

[0143]

[0144] Note: C1 to C12 represent channels 1 to 12; the data for each sample is the average of 6 fluorescence sensing units.

[0145] Table 15 Identification results of unknown samples by array sensors

[0146]

[0147]

[0148] Note: F1 to F6 represent Factor 1 to Factor 6.

[0149] In the present invention, 4-pyridylboronic acid, p-bromophenylacetonitrile, and p-dimethylaminobenzaldehyde are used as raw materials. Through a two-step reaction, pyridinium salt AIEgens are generated with various brominated / iodinated compounds. After the negatively charged conjugated polymer PPE binds to the positively charged aggregation-induced luminescence (AIEgens), the fluorescence of the PPE is quenched to a certain extent, facilitating signal amplification in sensor applications. The results of the above examples also demonstrate that the fluorescent array sensor of the present invention is simple to prepare, low-cost, highly sensitive, and has a short detection time. It can accurately distinguish a variety of bacteria, with an accuracy rate of nearly 100% for unknown samples, good reproducibility, and strong anti-interference ability, making it suitable for the detection of actual unknown samples. Furthermore, the sensor array does not require specialized technical personnel to operate.

Claims

1. An electrostatic composite multi-channel fluorescence array sensor, characterized in that: The fluorescent array sensor contains at least two different fluorescent sensing units PPE-AIE, and the fluorescent sensing unit PPE-AIE is composed of positively charged aggregation-induced emission materials AIEgens with different side chain modifications and negatively charged water-soluble fluorescent conjugated polymer PPE through electrostatic interaction. The structure of the aggregation-induced emission material AIEgens is selected from: 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 ； The general structural formula of the water-soluble fluorescent conjugated polymer PPE is shown in Formula II: ； II Wherein, R4 and R5 are independently selected from ； m is an integer from 1 to 10; n is an integer ranging from 8 to 200.

2. The electrostatic complex multi-channel fluorescence array sensor according to claim 1, characterized in that: The structural formula of the water-soluble fluorescent conjugated polymer PPE is: ； Wherein, n is an integer ranging from 8 to 200.

3. A method for preparing the electrostatic complex multi-channel fluorescence array sensor according to claim 1, characterized in that: The following steps are involved: (1) Using p-bromophenylacetonitrile and 4-pyridylboronic acid or their derivatives as raw materials, they are coupled via Suzuki reaction, then reacted with p-dimethylaminobenzaldehyde to form an intermediate skeleton, and finally reacted with brominated or iodinated compounds to form pyridinium salts, which are AIEgens. (2) Diluting the water-soluble fluorescent conjugated polymer PPE with PBS buffer solution, diluting AIEgens with methanol solution, and mixing the diluted PPE solution and AIEgens solution evenly to obtain the fluorescent sensing unit PPE-AIE; (3) Repeat steps (1) to (2) to prepare at least two different fluorescent sensing units PPE-AIE to form a fluorescent array sensor.

4. Use of the electrostatic complex fluorescence array sensor according to any one of claims 1 to 2 in bacterial detection for non-diagnostic and non-therapeutic purposes, characterized in that: The bacteria are selected from Stenotrophomonas maltophilia ( S. maltophilia ), Klebsiella pneumoniae ( K. pneumoniae ), Proteus mirabilis ( P. mirabilis )、Morganella morganii( M. morganii ), Enterobacter hallii ( E. hormaechei ), Staphylococcus aureus ( S. aureus ), Escherichia coli ( E. coli ), Escherichia coli ( E. cloacae ), Pseudomonas aeruginosa ( P. aeruginosa ), Bacillus cereus ( B. cereus ), Staphylococcus lugdunensis ( S. lugdunensis ), Staphylococcus capitis ( S. capitis ), Enterococcus faecalis ( E. faecalis ), Enterobacter aerogenes ( E. aerogenes ), Acinetobacter baumannii ( A. baumannii ) Burkholderia cepacia ( B. aerogenes ), Citrobacter krusei ( C. koseri ), Citrobacter freundii ( C.freundii ), Shewanella putrefaciens ( S. putrefaciens ) and Salmonella ( S. typhi ) in one or more combinations.

5. A method for detecting bacteria for non-diagnostic and non-therapeutic purposes using the electrostatic complex fluorescence array sensor according to any one of claims 1 to 2, characterized in that: Different sensing units of the electrostatic composite fluorescence array sensor are used to interact with bacteria, measure the fluorescence intensity of each sensing unit, process the fluorescence data, and classify the data matrix to achieve the distinction and detection of multiple bacteria. The bacteria are selected from Stenotrophomonas maltophilia ( S. maltophilia ), Klebsiella pneumoniae ( K. pneumoniae ), Proteus mirabilis ( P. mirabilis )、Morganella morganii( M. morganii ), Enterobacter hallii ( E. hormaechei ), Staphylococcus aureus ( S. aureus ), Escherichia coli ( E. coli ), Escherichia coli ( E. cloacae ), Pseudomonas aeruginosa ( P. aeruginosa ), Bacillus cereus ( B. cereus ), Staphylococcus lugdunensis ( S. lugdunensis ), Staphylococcus capitis ( S. capitis ), Enterococcus faecalis ( E. faecalis ), Enterobacter aerogenes ( E. aerogenes ), Acinetobacter baumannii ( A. baumannii ) Burkholderia cepacia ( B. aerogenes ), Citrobacter krusei ( C. koseri ), Citrobacter freundii ( C.freundii ), Shewanella putrefaciens ( S. putrefaciens ) and Salmonella ( S. typhi ) in one or more combinations.

6. The method according to claim 5, characterized in that The specific steps include: (1) Dilute the PPE solution with 1 ~ 20 mM buffer saline solution to a final concentration of 1 μM ~ 250 nM, and dilute the AIEgens solution with methanol solution to a final concentration of 10 μM ~ 2.5 μM; mix the PPE solution and AIEgens solution as the sensing unit solution, and prepare at least two sensing unit solutions; take the sensing unit solution and the test solution, mix and shake them, and then detect the fluorescence intensity data. Repeat the above detection steps for different sensing units; (2) Use statistical analysis software to process and analyze the fluorescence data, use linear discriminant analysis to convert the fluorescence response pattern into a canonical pattern, classify the data matrix, and obtain a two-dimensional array fingerprint of the bacteria contained in the test solution to achieve visual identification; (3) The obtained two-dimensional array distinguishing fingerprint map is used as a model to detect unknown samples, and the accuracy of the unknown sample prediction is calculated to achieve the differentiation and detection of bacteria.

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