CN111189815B - Sewage tracing method - Google Patents
Sewage tracing method Download PDFInfo
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- CN111189815B CN111189815B CN202010024827.1A CN202010024827A CN111189815B CN 111189815 B CN111189815 B CN 111189815B CN 202010024827 A CN202010024827 A CN 202010024827A CN 111189815 B CN111189815 B CN 111189815B
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- 238000000034 method Methods 0.000 title claims abstract description 43
- 239000010865 sewage Substances 0.000 title claims abstract description 42
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- 238000004416 surface enhanced Raman spectroscopy Methods 0.000 claims abstract description 56
- 238000001237 Raman spectrum Methods 0.000 claims abstract description 31
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- 239000003344 environmental pollutant Substances 0.000 claims abstract description 15
- 231100000719 pollutant Toxicity 0.000 claims abstract description 15
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- 239000002105 nanoparticle Substances 0.000 claims description 31
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- 229910052737 gold Inorganic materials 0.000 claims description 14
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- GWOLZNVIRIHJHB-UHFFFAOYSA-N 11-mercaptoundecanoic acid Chemical compound OC(=O)CCCCCCCCCCS GWOLZNVIRIHJHB-UHFFFAOYSA-N 0.000 claims description 7
- 239000003607 modifier Substances 0.000 claims description 7
- 238000002360 preparation method Methods 0.000 claims description 6
- SQGYOTSLMSWVJD-UHFFFAOYSA-N silver(1+) nitrate Chemical compound [Ag+].[O-]N(=O)=O SQGYOTSLMSWVJD-UHFFFAOYSA-N 0.000 claims description 6
- NLJMYIDDQXHKNR-UHFFFAOYSA-K sodium citrate Chemical compound O.O.[Na+].[Na+].[Na+].[O-]C(=O)CC(O)(CC([O-])=O)C([O-])=O NLJMYIDDQXHKNR-UHFFFAOYSA-K 0.000 claims description 6
- 239000001509 sodium citrate Substances 0.000 claims description 6
- 238000011144 upstream manufacturing Methods 0.000 claims description 6
- LMJXSOYPAOSIPZ-UHFFFAOYSA-N 4-sulfanylbenzoic acid Chemical compound OC(=O)C1=CC=C(S)C=C1 LMJXSOYPAOSIPZ-UHFFFAOYSA-N 0.000 claims description 5
- 239000002082 metal nanoparticle Substances 0.000 claims description 5
- 229910000510 noble metal Inorganic materials 0.000 claims description 5
- PLIKAWJENQZMHA-UHFFFAOYSA-N 4-aminophenol Chemical compound NC1=CC=C(O)C=C1 PLIKAWJENQZMHA-UHFFFAOYSA-N 0.000 claims description 4
- FOIXSVOLVBLSDH-UHFFFAOYSA-N Silver ion Chemical compound [Ag+] FOIXSVOLVBLSDH-UHFFFAOYSA-N 0.000 claims description 4
- 239000002253 acid Substances 0.000 claims description 4
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- 238000009833 condensation Methods 0.000 claims description 3
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- 229910001961 silver nitrate Inorganic materials 0.000 claims description 3
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- VRVRGVPWCUEOGV-UHFFFAOYSA-N 2-aminothiophenol Chemical compound NC1=CC=CC=C1S VRVRGVPWCUEOGV-UHFFFAOYSA-N 0.000 claims description 2
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- KWGRBVOPPLSCSI-WPRPVWTQSA-N (-)-ephedrine Chemical compound CN[C@@H](C)[C@H](O)C1=CC=CC=C1 KWGRBVOPPLSCSI-WPRPVWTQSA-N 0.000 description 8
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- 238000000479 surface-enhanced Raman spectrum Methods 0.000 description 3
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- ADRCREMWPUFGDU-UHFFFAOYSA-N 4-(1,3-benzothiazol-2-yl)-n,n-diethylaniline Chemical compound C1=CC(N(CC)CC)=CC=C1C1=NC2=CC=CC=C2S1 ADRCREMWPUFGDU-UHFFFAOYSA-N 0.000 description 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
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Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
- G01N21/658—Raman scattering enhancement Raman, e.g. surface plasmons
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/129—Using chemometrical methods
Abstract
The invention discloses a sewage tracing method, which comprises the following steps: (1) acquiring a signal enhancement substrate; (2) modifying the signal enhancement substrate by adopting at least two modifying agents and correspondingly obtaining at least two modified substrates; (3) acquiring at least two Raman spectrums of each sample under at least two modified substrates by adopting surface enhanced Raman scattering, wherein the at least two Raman spectrums of each sample form an SERS water quality fingerprint of the sample; (4) analyzing the confidence interval distribution diagram of all samples under the same modified substrate by adopting PCA; (5) and selecting the drain water sample which is coincided with the confidence interval of the polluted water sample in each confidence interval distribution map, analyzing the SERS water quality fingerprint of the drain water sample, and judging that the source of the drain water sample with the characteristic peak of the target pollutant is a pollution source. According to the invention, PCA is further introduced on the basis of introducing multiple substrates to carry out SERS spectral analysis and improving the tracing accuracy, so that the subsequent analysis amount is effectively reduced, and the efficiency is remarkably improved.
Description
Technical Field
The invention relates to the technical field of sewage tracing, in particular to a sewage tracing method.
Background
The illegal exceeding-standard stealing discharge of sewage and wastewater and sudden water pollution accidents can cause serious pollution and damage to rivers and other water systems and ecological environments, so that the rapid identification of a pollution source through a water pollution traceability technology has a particularly important significance for emergency disposal of sudden water pollution events. The traditional water pollution tracing technology is mainly used for collecting polluted downstream water samples and water samples of upstream pollution-related enterprise sewage outlets after accidents occur, and the polluted sources are inspected and traced through comparison of fluorescence spectrum analysis. However, if there are more upstream enterprises involved in pollution, the workload for tracing the pollution source is also large, and the pollution source investigation and tracing work is difficult to be completed in time; and the fluorescence spectrum is limited to the substances which can only detect the fluorescence, and if pollutants with poor water solubility or sewage which can cause fluorescence quenching exist, the accuracy of the analysis result of the fluorescence spectrum is poor.
Disclosure of Invention
The invention mainly aims to provide a sewage tracing method to solve the technical problems of poor accuracy and poor efficiency of a water pollution tracing technology in the prior art.
In order to achieve the above purpose, the first sewage tracing method provided by the present invention has the following technical scheme:
the sewage tracing method comprises the following steps:
(1) acquiring a signal enhancement substrate;
(2) modifying the signal enhancement substrate by adopting at least two modifying agents and correspondingly obtaining at least two modified substrates;
(3) acquiring at least two Raman spectrums of each sample under at least two modified substrates by adopting surface enhanced Raman scattering, wherein the at least two Raman spectrums of each sample form an SERS water quality fingerprint of the sample; the samples comprise a polluted water sample from a polluted water body and a drain outlet water sample positioned at a drain outlet at the upstream of the polluted water body;
(4) firstly, determining a characteristic peak of a target pollutant through a characteristic peak of an SERS water quality fingerprint of a polluted water sample; and then, analyzing the SERS water quality fingerprint of the drain water sample, finding out the drain water sample with the target pollutant characteristic peak, and judging that the source of the drain water sample with the target pollutant characteristic peak is a pollution source.
The Surface Enhanced Raman Scattering (SERS) has high analysis speed (ranging from several seconds to several minutes), and the analysis sensitivity can reach a single molecule level. The half-peak width of the SERS spectrum is only about 1nm, and qualitative and quantitative analysis can be simultaneously carried out on various substances without mutual interference. The advantages enable the SERS spectra to correspond to water quality one by one, and realize the SERS water quality fingerprint expression of water quality characteristics, thereby tracing the source with high accuracy. Compared with the three-dimensional fluorescence spectrum, the SERS spectrum has the advantages of wider application range, higher sensitivity and stronger specificity, and can provide single molecule information. The method further realizes the multi-dimensional information representation of the same water body sample by performing different surface modifications on the substrate and capturing substances in the water body specifically, thereby obtaining the SERS water quality fingerprint with stronger specificity and higher accuracy and further improving the accuracy of tracing.
Further, each modified substrate-treated sample is subjected to a surface enhanced raman scattering test under excitation light of at least two wavelengths, and at least four raman spectra of each sample constitute the SERS water quality fingerprint of the sample. Therefore, more substances can be captured, and SERS water quality fingerprints with stronger specificity and higher accuracy are obtained.
In order to achieve the above purpose, the first sewage tracing method provided by the present invention has the following technical scheme:
the sewage tracing method comprises the following steps:
(1) acquiring a signal enhancement substrate;
(2) modifying the signal enhancement substrate by adopting at least two modifying agents and correspondingly obtaining at least two modified substrates;
(3) acquiring at least two Raman spectrums of each sample under at least two modified substrates by adopting surface enhanced Raman scattering, wherein the at least two Raman spectrums of each sample form an SERS water quality fingerprint of the sample; the samples comprise a polluted water sample from a polluted water body and a drain outlet water sample positioned at a drain outlet at the upstream of the polluted water body;
(4) analyzing Raman spectra of all samples under the same modified substrate by adopting a principal component analysis method, and correspondingly obtaining at least two confidence interval distribution maps of all samples under the corresponding modified substrates;
(5) firstly, determining a characteristic peak of a target pollutant through a characteristic peak of an SERS water quality fingerprint of a polluted water sample; and then selecting the drain water sample which is coincided with the confidence interval of the polluted water sample in each confidence interval distribution map, analyzing the SERS water quality fingerprint of the drain water sample, finding out the drain water sample with the characteristic peak of the target pollutant, and judging that the source of the drain water sample with the characteristic peak of the target pollutant is a pollution source.
On the basis of introducing multiple substrates to perform SERS spectral Analysis to improve the tracing accuracy, Principal Component Analysis (PCA) is further introduced, so that the subsequent Analysis amount is effectively reduced, and the efficiency is remarkably improved.
Further, each modified substrate-treated sample is subjected to a surface enhanced raman scattering test under excitation light of at least two wavelengths, at least four raman spectra of each sample form a SERS water quality fingerprint of the sample, and at least four confidence interval distribution maps are correspondingly obtained.
Therefore, more substances can be captured, the SERS water quality fingerprint with stronger specificity and higher accuracy is obtained, and the judgment accuracy is improved.
As a further improvement of any one of the above-mentioned sewage tracing methods, the at least two modifiers are selected from at least two of MUA (11-mercaptoundecanoic acid), MPY (4-mercaptopyridine), MBA (4-mercaptobenzoic acid), 4-ATP (p-aminophenol) and 2-ABT (2-aminothiophenol).
As a further improvement of any one of the sewage tracing methods, the signal enhancement substrate is a noble metal nanoparticle.
As a further improvement of any one of the above sewage tracing methods, the noble metal nanoparticles are silver nanoparticles or gold nanoparticles.
As a further improvement of any one of the above sewage tracing methods, when silver nanoparticles are adopted, the modified substrate preparation process is as follows:
(1) heating the silver nitrate solution to boiling under stirring, adding a sodium citrate solution, keeping the solution in a boiling state for a period of time under a condensation reflux condition, and cooling to room temperature under stirring to obtain a silver nanoparticle solution;
(2) and mixing the silver nanoparticle solution with a modifier solution to obtain the modified substrate.
As a further improvement of any one of the above sewage tracing methods, when gold nanoparticles are adopted, the modified substrate preparation process is as follows:
(1) heating the chloroauric acid solution to boiling under stirring, adding the sodium citrate solution, then continuously heating for a period of time, and then cooling to room temperature under stirring to obtain a gold nanoparticle solution;
(2) and mixing the gold nanoparticle solution with a modifier solution to obtain the modified substrate.
By directly using the dispersion liquid after the noble metal nanoparticles are generated, the process can be remarkably saved.
The invention is further described with reference to the following figures and detailed description. Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to assist in understanding the invention, and are included to explain the invention and their equivalents and not limit it unduly. In the drawings:
fig. 1 shows SERS water quality fingerprints of a sample 1 under three modified substrates in a sewage tracing method according to embodiment a of the present invention.
Fig. 2 shows SERS water quality fingerprints of the sample 2 under three modified substrates in the sewage tracing method according to embodiment a of the present invention.
Fig. 3 shows SERS water quality fingerprints of the sample 3 under three modified substrates in the sewage tracing method according to embodiment a of the present invention.
Fig. 4 shows SERS water quality fingerprints of the sample 4 under three modified substrates in the sewage tracing method according to embodiment a of the present invention.
Fig. 5 shows SERS water quality fingerprints of the sample 4 under three modified substrates and two wavelengths of excitation light in the sewage tracing method according to embodiment B of the present invention.
FIG. 6 is a confidence interval distribution diagram of MUA-AgNPs at 633nm of all samples in the wastewater tracing method of example C of the present invention.
FIG. 7 is a confidence interval distribution diagram of MPY-AgNPs at 633nm of all samples in the sewage tracing method of embodiment C of the present invention.
FIG. 8 is a confidence interval distribution diagram of MUA-AgNPs at 532nm for all samples in the wastewater tracing method of example D of the present invention.
FIG. 9 is a graph showing confidence interval distribution of MUA-AgNPs at 785nm for all samples in the tracing method of wastewater according to example D of the present invention.
Detailed Description
The invention will be described more fully hereinafter with reference to the accompanying drawings. Those skilled in the art will be able to implement the invention based on these teachings. Before the present invention is described in detail with reference to the accompanying drawings, it is to be noted that:
the technical solutions and features provided in the present invention in the respective sections including the following description may be combined with each other without conflict.
Moreover, the embodiments of the present invention described in the following description are generally only some embodiments of the present invention, and not all embodiments. Therefore, all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without any creative effort shall fall within the protection scope of the present invention.
With respect to terms and units in the present invention. The terms "comprising," "having," and any variations thereof in the description and claims of this invention and the related sections are intended to cover non-exclusive inclusions.
Example A
The sewage tracing method of the embodiment adopts three modified substrates to trace the source of the sewage, and specifically comprises the following steps:
first, a silver particle nanoparticle solution was prepared: heating 500 ml of silver nitrate solution with the concentration of 5 mmol/L to boiling while stirring, and quickly adding 10 ml of sodium citrate solution with the mass fraction of 10%; then keeping the solution in a boiling state for 1 hour under the condition of condensation reflux; then, the heating is stopped, and the solution is cooled to room temperature under the condition that the stirring speed is 800 revolutions per minute, so that the silver nanoparticle solution with the silver nanoparticle concentration of 5 millimoles per liter is obtained.
Then, three modified substrates were prepared, each as follows:
A. mixing 100 ml of silver nanoparticle solution with 2 ml of MUA solution with the concentration of 10 mmol/L, and stirring for 4 hours to obtain MUA modified silver nanoparticle solution, which is referred to as MUA-AgNPs for short;
B. mixing 100 ml of silver nanoparticle solution with 2 ml of MPY solution with the concentration of 10 mmol/L, and stirring for 4 hours to obtain MPY modified silver nanoparticle solution, which is abbreviated as MPY-AgNPs;
C. mixing 100 ml of silver nanoparticle solution with 2 ml of MBA solution with the concentration of 10 mmol/L and stirring for 4 hours to obtain MBA modified silver nanoparticle solution, which is abbreviated as MBA-AgNPs;
and then, SERS water quality fingerprints of the four samples are tested, wherein the four samples are named as a sample 1, a sample 2, a sample 3 and a sample 4 respectively, the sample 4 is a polluted water sample, and the samples 1 to 3 are sewage outlet water samples. The test process is as follows:
correspondingly placing the three modified substrates into three samples 1 with the same volume, and performing surface enhanced Raman scattering test under the wavelength of excitation light of 633nm to obtain SERS water quality fingerprints of the samples 1 under the three modified substrates as shown in FIG. 1;
correspondingly placing the three modified substrates into three samples 2 with the same volume, and performing surface enhanced Raman scattering test under the wavelength of excitation light of 633nm to obtain SERS water quality fingerprints of the samples 2 under the three modified substrates as shown in FIG. 2;
correspondingly placing the three modified substrates into three samples 3 with the same volume, and performing surface enhanced Raman scattering test under the wavelength of excitation light of 633nm to obtain SERS water quality fingerprints of the samples 3 under the three modified substrates as shown in FIG. 3;
correspondingly placing the three modified substrates into three samples 4 with the same volume, and performing surface enhanced Raman scattering test under the wavelength of excitation light of 633nm to obtain SERS water quality fingerprints of the samples 4 under the three modified substrates as shown in FIG. 4;
in FIGS. 1 to 4, the line denoted by "MUA" represents the Raman spectrum of the substrate modified with MUA-AgNPs, and the rest is analogized.
As can be seen from FIG. 4, ephedrine is at 831cm-1And 1018cm-1The characteristic peak of the Raman shift is detected under three modified substrates, but the contrast shows that the capture of the MuA-AgNPs corresponding to ephedrine is optimal, so that the specificity of SERS water quality fingerprints can be obviously improved by adopting a plurality of modified substrates.
Comparing fig. 1-4, it can be seen that, under the detection condition of 633nm excitation light wavelength, sample a1, sample a2, sample A3, and sample a4 all have their own characteristic raman spectra, and SERS water quality fingerprints corresponding to silver nanoparticle substrates modified by different modifiers have a large difference, and can realize characteristic expression of three dimensions.
If the target contaminant is ephedrine, then the presence of 831cm was further analyzed in FIGS. 1-3-1And 1018cm-1If SERS water quality fingerprints with characteristic peaks of ephedrine exist in the graphs 1-3, the water outlet of a sewage outlet corresponding to the SERS water quality fingerprints is a pollution source. Of course, the target pollutant may be provided in plural, and the pollution source may be plural. The analysis of the peak position of the characteristic peak in the raman spectrum is a conventional technical means for raman data processing, and is not described herein again.
Example B
The sewage tracing method of the embodiment adopts three modified substrates and two excitation light wavelengths to trace the source of the sewage, and specifically comprises the following steps:
first, a gold nanoparticle solution was prepared: weighing 100 ml of chloroauric acid solution with the mass fraction of 0.01 percent in a conical flask, and heating the chloroauric acid solution to boil under the condition that the stirring speed is 1000 revolutions per minute; then, 2 ml of 1 percent sodium citrate solution is rapidly added, and the mixture is continuously stirred and kept in a boiling state for 15 minutes; then stopping heating and continuously stirring until the temperature is cooled to normal temperature, thus obtaining the gold nanoparticle solution with the concentration of gold nanoparticles of 0.24 millimole/liter.
Then, three modified substrates were prepared, each as follows:
A. mixing 100 ml of gold nanoparticle solution with 2 ml of MUA solution with the concentration of 10 mmol/L, and stirring for 4 hours to obtain MUA modified gold nanoparticle solution, namely MUA-AuNPs for short;
B. mixing 100 ml of gold nanoparticle solution with 2 ml of MPY solution with the concentration of 10 mmol/L, and stirring for 4 hours to obtain MPY modified gold nanoparticle solution, which is referred to as MPY-AuNPs for short;
C. mixing 100 ml of gold nanoparticle solution with 2 ml of MBA solution with the concentration of 10 mmol/L and stirring for 4 hours to obtain MBA modified gold nanoparticle solution, namely MBA-AuNPs for short;
then, SERS water quality fingerprints of the samples 1-4 are tested in the same manner.
FIG. 5 shows SERS water quality fingerprints of sample 4 at three modified substrates and two excitation light wavelengths (532nm and 633 nm); comparing fig. 4 and fig. 5, it can be seen that when the raman test is further performed by using the excitation light with multiple wavelengths on the basis of multiple modified substrates, the specificity of the SERS water fingerprint of the sample is stronger, the tracing accuracy can be significantly improved, but the workload is correspondingly improved.
In FIG. 5, the line denoted by "MUA, 532 nm" represents the Raman spectrum of the MUA-AgNPs-treated sample 4 measured under excitation light of 532nm, and the rest of the lines are analogized.
Example C
Compared with the embodiment a, the sewage tracing method of the embodiment has the following differences: and PCA is introduced to realize rapid and efficient identification. The method comprises the following specific steps:
first, 10 samples of the treated MUA-AgNPs and MPY-AgNPs of example A were tested for three Raman spectra at 633nm, wherein sample C10 was a contaminated water sample and samples C1-C9 were drain samples.
Analyzing the Raman spectra of 10 samples at MUA-AgNPs and 633nm by PCA to correspondingly obtain a confidence interval distribution diagram as shown in FIG. 6;
analyzing the Raman spectra of 10 samples at MPY-AgNPs and 633nm by PCA to correspondingly obtain a confidence interval distribution diagram as shown in FIG. 7;
as can be seen from FIGS. 6 to 7, samples that are difficult to distinguish under certain conditions for modifying the substrate can be distinguished by replacing them with a modifier, thereby achieving specific recognition. For example, in FIG. 6, i.e., under MUA-AgNPs treatment, the PCA confidence intervals of sample C5 and sample C10 overlapped and were indistinguishable, while the confidence intervals under MPY-AgNPs treatment did not overlap at all, indicating that sample C5 and sample C10 still have significant differences.
When further determining the source of contamination, samples whose confidence intervals in FIGS. 6-7 all coincide with the confidence interval of sample C10 were selected and further analyzed for characteristic peaks, in the same manner as in example A or example B. It can be seen that if PCA is not used, the characteristic peaks of the raman spectra of all 10 samples need to be analyzed, which is very laborious, but when PCA is introduced, only the raman spectra of the samples whose confidence intervals coincide with that of sample C10, i.e. only the raman spectra of sample C3, sample C4, sample C7 and sample C9 need to be analyzed, which is significantly less, and the samples selected under the optimized statistics of PCA can guarantee higher accuracy.
Example D
Compared with the embodiment C, the sewage tracing method of the embodiment has the following differences: confidence intervals are further obtained under multiple excitation light wavelengths. The method comprises the following specific steps:
the 10 samples were further tested for six raman spectra after MUA-AgNPs treatment in example a and tested at 532nm and 785nm, respectively;
analyzing the Raman spectra of 10 samples at MUA-AgNPs and 532nm by PCA to correspondingly obtain a confidence interval distribution diagram as shown in FIG. 8;
analyzing the Raman spectra of 10 samples at the MUA-AgNPs and the wavelength of 785nm by PCA to correspondingly obtain a confidence interval distribution diagram as shown in FIG. 9;
comparing fig. 6 and fig. 8-9, it can be seen that when a modified substrate is used, a sample which is difficult to distinguish under excitation light of a certain wavelength can be distinguished by changing the excitation light of a certain wavelength, thereby realizing specific recognition; for example, in fig. 6, at 633nm, sample C3, sample C4, and sample C5 all overlapped with the PCA confidence interval for sample C10; in FIG. 8, sample C4 and sample C5 were differentiated from sample C10 at 532nm, but sample C3 still showed PCA confidence interval overlap with sample C10; however, in FIG. 9, the confidence intervals for sample C3 and sample C10 do not overlap at all at 785 nm. Regardless of the excitation light wavelength, the PCA confidence intervals of sample 7 and sample 9 overlap with that of sample C10, and at this time, sample 7 and sample 9 are selected for further raman peak comparison, which reduces the workload by half compared with the case where the number of excitation light wavelengths is not increased.
As can be seen from the foregoing, compared with embodiment a, although the sewage tracing method of embodiment B can improve the tracing accuracy, the analysis workload is significantly increased accordingly. However, as can be seen from examples C and D, the workload can be significantly reduced when PCA is introduced; on the basis of the embodiment C, the embodiment D can further obtain more characteristic raman spectra under the multi-wavelength excitation light, thereby improving the statistical range of the PCA and maintaining the ultra-high accuracy while reducing the workload. Therefore, the PCA, the multiple modified substrates and the multiple-wavelength laser method are combined, so that the ultrahigh accuracy can be ensured, and the tracing result can be quickly obtained.
In each of fig. 1-5, raman spectra (indicated as "blank" in the figures) of the samples without the addition of the modified substrate are introduced, with the aim of demonstrating that the modified substrate can capture more species and thus show more characteristic peaks.
In the preparation of the modified substrate, the preparation process of the water-insoluble or slightly water-soluble modifying agent is different from that of other water-soluble modifying agents, taking MPY as an example, the preparation process specifically comprises the following steps: firstly, preparing an MPY solution with the concentration of 1 mol/L by adopting absolute ethyl alcohol, and then diluting the MPY solution with water to finally obtain the MPY solution with the concentration of 10 mmol/L.
The rest of the solution is aqueous solution.
The contents of the present invention have been explained above. Those skilled in the art will be able to implement the invention based on these teachings. All other embodiments, which can be derived by a person skilled in the art from the above description without inventive step, shall fall within the scope of protection of the present invention.
Claims (9)
1. The sewage tracing method comprises the following steps:
(1) acquiring a signal enhancement substrate;
(2) modifying the signal enhancement substrate by adopting at least two modifying agents and correspondingly obtaining at least two modified substrates;
(3) acquiring at least two Raman spectrums of each sample under each modified substrate by adopting surface enhanced Raman scattering, wherein all the Raman spectrums of each sample under the at least two modified substrates form the SERS water quality fingerprint of the sample; the samples comprise a polluted water sample from a polluted water body and a drain outlet water sample positioned at a drain outlet at the upstream of the polluted water body;
(4) firstly, determining a characteristic peak of a target pollutant through a characteristic peak of an SERS water quality fingerprint of a polluted water sample; and then, analyzing the SERS water quality fingerprint of the drain water sample, finding out the drain water sample with the target pollutant characteristic peak, and judging that the source of the drain water sample with the target pollutant characteristic peak is a pollution source.
2. The sewage tracing method of claim 1, wherein: and performing surface enhanced Raman scattering test on each modified substrate-treated sample under at least two wavelengths of exciting light, wherein at least four Raman spectrums of each sample form the SERS water quality fingerprint of the sample.
3. The sewage tracing method comprises the following steps:
(1) acquiring a signal enhancement substrate;
(2) modifying the signal enhancement substrate by adopting at least two modifying agents and correspondingly obtaining at least two modified substrates;
(3) acquiring at least two Raman spectrums of each sample under each modified substrate by adopting surface enhanced Raman scattering, wherein all the Raman spectrums of each sample under the at least two modified substrates form the SERS water quality fingerprint of the sample; the samples comprise a polluted water sample from a polluted water body and a drain outlet water sample positioned at a drain outlet at the upstream of the polluted water body;
(4) analyzing Raman spectra of all samples under the same modified substrate by adopting a principal component analysis method, and correspondingly obtaining at least two confidence interval distribution maps of all samples under the corresponding modified substrates;
(5) firstly, determining a characteristic peak of a target pollutant through a characteristic peak of an SERS water quality fingerprint of a polluted water sample; and then selecting the drain water sample which is coincided with the confidence interval of the polluted water sample in each confidence interval distribution map, analyzing the SERS water quality fingerprint of the drain water sample, finding out the drain water sample with the characteristic peak of the target pollutant, and judging that the source of the drain water sample with the characteristic peak of the target pollutant is a pollution source.
4. The sewage tracing method of claim 3, wherein: and performing surface enhanced Raman scattering test on each sample treated by the modified substrate under the excitation light with at least two wavelengths, wherein at least four Raman spectrums of each sample form the SERS water quality fingerprint of the sample, and correspondingly obtaining at least four confidence interval distribution maps.
5. The sewage tracing method of any one of claims 1-4, wherein: the at least two modifying agents are selected from at least two of 11-mercaptoundecanoic acid, 4-mercaptopyridine, 4-mercaptobenzoic acid, p-aminophenol and 2-aminothiophenol.
6. The sewage tracing method of any one of claims 1-4, wherein: the signal enhancement substrate is a noble metal nanoparticle.
7. The sewage tracing method of claim 6, wherein: the noble metal nanoparticles are silver nanoparticles or gold nanoparticles.
8. The sewage tracing method of claim 7, wherein: when silver nanoparticles are used, the modified substrate is prepared as follows:
(1) heating the silver nitrate solution to boiling under stirring, adding a sodium citrate solution, keeping the solution in a boiling state for a period of time under a condensation reflux condition, and cooling to room temperature under stirring to obtain a silver nanoparticle solution;
(2) and mixing the silver nanoparticle solution with a modifier solution to obtain the modified substrate.
9. The sewage tracing method of claim 7, wherein: when gold nanoparticles are used, the modified substrate preparation process is as follows:
(1) heating the chloroauric acid solution to boiling under stirring, adding the sodium citrate solution, then continuously heating for a period of time, and then cooling to room temperature under stirring to obtain a gold nanoparticle solution;
(2) and mixing the gold nanoparticle solution with a modifier solution to obtain the modified substrate.
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