A system and method for high-speed processing of nanoparticles with Raman enhancement activity

Abstract

The present invention relates to a system for high-speed processing of nanoparticles with Raman enhancement activity. The present invention creatively combines the hydrothermal method with the laser processing method, and further uses the laser for secondary processing on the basis of the noble metal nanostructure synthesized by the hydrothermal method; since the laser processing is difficult to control, the present invention detects the light absorption spectrum while processing, thereby ensuring the stability of the synthesis.

Description

Technical Field

[0001] The present invention relates to the technical field of Raman spectroscopy, and in particular to a system and method for high-speed processing of nanoparticles with Raman enhancement activity. Background Art

[0002] Surface-Enhanced Raman Scattering (SERS) uses conventional Raman spectroscopy to measure samples adsorbed on the surface of colloidal metal particles such as silver, gold or copper, or samples adsorbed on the rough surface of these metal sheets. Although the reason is still unclear, it is found that the intensity of the Raman spectrum of the adsorbed sample can be increased by 10^3-10^6 times. It is mainly used for state analysis of adsorbed species, etc.

[0003] At present, the detection of the famous melamine component is largely carried out using the surface Raman enhancement method. However, the current Raman enhancement is highly dependent on the substrate, and the enhancement multiples of different substrates are often very unstable. The most commonly used Raman enhancement substrate is the enhancement substrate of the noble metal structure, which can achieve an effect close to single-molecule detection. The enhancement effect of the noble metal nano-substrate depends largely on its light absorption performance. However, since the size and shape of the noble metal nanostructure synthesized by the hydrothermal method are difficult to accurately control, and the preparation cycle is very long, it is greatly limited in actual use.

[0004] Laser liquid phase ablation is a method for preparing precious metal nanostructures that has emerged in recent years, but it can generally only produce spherical nanostructures, and spherical nanostructures have certain limitations in their use.

[0005] The present invention creatively combines the hydrothermal method and the laser processing method, and further uses laser for secondary processing based on the precious metal nanostructure synthesized by the hydrothermal method; since laser processing is difficult to control, the present invention detects the light absorption spectrum while processing, thereby ensuring the stability of the synthesis. Summary of the invention

[0006] In view of the above content, in order to solve the above problems, a system for high-speed processing of nanoparticles with Raman enhanced activity is provided, including a host computer, a PC, a Raman spectrometer, a Raman spectrum detection table, a laser processing center and a processing center controller;

[0007] The Raman spectrometer is connected to the Raman spectrometer detection platform, the laser processing center is connected to the processing center controller, and the host computer is connected to the Raman spectrometer and the processing center controller;

[0008] The laser processing center uses laser to perform secondary processing on the precious metal nanostructure synthesized by the hydrothermal method, and monitors the absorption spectrum of the precious metal nanostructure in real time during the secondary processing; the Raman spectrometer detects the Raman enhancement multiples of different components of the precious metal nanostructure that has undergone secondary processing on the Raman spectrum detection table; the host computer establishes a spectrum correlation model in the host computer by collecting the Raman spectrum data of different components detected by the Raman spectrometer and the absorption spectrum data of the processing center controller; the host computer establishes a processing parameter correlation model in the host computer by collecting the absorption spectrum data of the processing center controller and the processing parameter data;

[0009] The PC is connected to the host computer. When a specific Raman detection component is detected, the component to be detected is input into the PC and sent to the host computer. The host computer inputs the detection component input by the PC into the spectral correlation model to obtain the required absorption spectrum of the precious metal nanostructure, and then inputs the absorption spectrum data into the processing parameter correlation model to obtain the required processing parameters.

[0010] The laser processing center is provided with a first laser, a first light source, and a detection dish;

[0011] The detection dish is in the shape of a vertical square tube, sealed at the bottom, and made of fused quartz material; the first laser output by the first laser is expanded by a beam expander and then input into a fly-eye lens, the fly-eye lens converts the expanded first laser into a plurality of sub-beams, the plurality of sub-beams pass through a first side surface of the detection dish and then focus on the middle and lower part of the detection dish, and the central axis of the detection dish is located on the plane where the focusing points of the plurality of sub-beams are located;

[0012] The white light emitted by the first light source is shaped and passes through the second side of the detection dish, then passes through the third side and is received by the light receiver; the light received by the light receiver is input into the spectrophotometer; the spectrophotometer sends the detected absorption spectrum to the processing center controller; wherein the second side and the third side are arranged opposite to each other, and the optical paths of the first laser and the white light are vertical and not on the same horizontal plane.

[0013] The Raman detection platform is provided with a base and a detection head, and the detection head is connected to the second laser and the Raman spectrometer; the base has a heating function, so that the Raman enhanced substrate on the base can be heated and dried quickly; the detection head is arranged directly above the base, the detection head emits infrared laser and collects the light reflected by the Raman enhanced substrate, the detection head sends the collected reflected light to the Raman spectrometer, and the Raman spectrometer sends the detected Raman spectrum to the host computer.

[0014] The first laser is a continuous laser or a pulsed laser, and the machining center controller controls the start time and output power of the first laser, or the machining center controller controls the start time, pulse width and pulse energy of the first laser.

[0015] A method for processing nanoparticles using the above system comprises the following steps:

[0016] Step 1: Establishment of processing parameter association model

[0017] Preparing a noble metal nanostructure by a hydrothermal method, wherein the material of the noble metal nanostructure is selected from one of gold, silver and platinum; the shape of the noble metal nanostructure is a nanosphere, a nanorod, a nanopyramid, a nanotriangle sheet, a nanocube or a nanostar;

[0018] The sol of the noble metal nanostructure is added into the detection dish, and the noble metal nanostructure is subjected to secondary laser processing using a first laser with different laser processing parameters, and its absorption spectrum is monitored in real time; the laser processing parameters include output power, pulse width, and pulse energy.

[0019] The collected absorption spectrum is used as an input end, and the nanostructure shape and the nanostructure material are used as an output end to establish a first processing parameter association model, so that after any absorption spectrum is input, a set of corresponding output results of the nanostructure shape and the nanostructure material can be obtained; then, the absorption spectrum, the nanostructure material and the nanostructure shape are used as input ends, and the laser processing parameters and the laser processing time are used as output ends to establish a second processing parameter association model, so that after any absorption spectrum, the nanostructure material and the nanostructure shape are input, a set of corresponding output results of the laser processing parameters and the laser processing time can be obtained;

[0020] The modeling method of the second processing parameter association model and the first processing parameter association model is BP neural network or SVM method;

[0021] Step 2: Establishment of spectral correlation model

[0022] The prepared noble metal nanostructures with different absorption spectra and samples of different detection components are dropped onto the substrate to form a Raman enhanced substrate;

[0023] Different Raman enhanced substrates are placed on the base of the Raman detection table and heated before monitoring the Raman spectrum; and the noble metal nanostructure with the strongest Raman enhanced signal is screened out for the same detection component; then, a spectral correlation model is established with the detection component as input and the absorption spectrum of the noble metal nanostructure with the strongest Raman enhanced signal as output, so that a corresponding absorption spectrum output can be obtained after each detection component is input;

[0024] The modeling method of the spectral correlation model is BP neural network or SVM method;

[0025] Step 3: Rapid processing of nanoparticles

[0026] When it is necessary to detect the component to be tested, the component to be tested is first input into the spectral correlation model to obtain a corresponding absorption spectrum output;

[0027] Then the obtained absorption spectrum is input into the first processing parameter association model to obtain a set of corresponding output results of nanostructure shape and nanostructure material, and then the absorption spectrum, nanostructure material and nanostructure shape are input into the second processing parameter association model to obtain a set of corresponding laser processing parameters and laser processing time output;

[0028] The host computer controls the processing parameter controller to perform rapid processing of the noble metal nanostructure according to the obtained nanostructure material, nanostructure shape, laser processing parameters and laser processing time.

[0029] The beneficial effects of the present invention are:

[0030] First, the present invention uses laser to perform secondary processing on nanostructures, which can overcome the defects of the existing chemical hydrothermal synthesis, such as poor shape control and long synthesis cycle that cannot be monitored. In addition, since the direct preparation of nanostructures by laser can generally only synthesize spherical nanoparticles, the method of the present invention also overcomes the defects of the existing laser liquid phase ablation method, such as single shape of nanostructures and low yield.

[0031] The present invention combines chemical method and laser processing, and uses absorption spectrum for real-time monitoring during processing, which can accurately control the absorption spectrum of the synthesized nanostructure, and the absorption spectrum has a great influence on the Raman enhancement performance. In addition, the present invention uses a neural network method to establish a correlation model during the preparation process, so that the processing parameters, absorption spectrum and Raman enhancement effect are organically correlated. When testing different components, they can be quickly prepared and used, overcoming the shortcomings of the current laser-processed nanostructures that the storage period is not long and they are easy to deteriorate.

[0032] The device of the present invention can also be directly used in laser liquid phase ablation. In addition to the above advantages, it can also overcome the defect of introducing too many interfering components in the existing hydrothermal synthesis method. The prepared nanosol has better purity and better enhancement effect, and is very useful for scientific research. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are included to provide a further understanding of the disclosed subject matter, will be incorporated into this specification and constitute a part of this specification. The accompanying drawings also illustrate the implementation of the disclosed subject matter and, together with the detailed description, are used to explain the implementation principles of the disclosed subject matter. No attempt is made to show more structural details than necessary for a basic understanding of the disclosed subject matter and its various practical modes.

[0034] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0035] Figure 2 A top view of the laser processing center of the present invention;

[0036] Figure 3 It is a side view of the laser processing center of the present invention. DETAILED DESCRIPTION

[0037] The advantages, features and methods of achieving the above objects of the present invention will become clear from the accompanying drawings and the following detailed description.

[0038] Embodiment 1:

[0039] A system for high-speed processing of nanoparticles with Raman enhanced activity, comprising a host computer 1, a PC 2, a Raman spectrometer 3, a Raman spectrum detection station 4, a laser processing center 5 and a processing center controller 6;

[0040] The Raman spectrometer 3 is connected to the Raman spectrometer detection platform 4, the laser processing center 5 is connected to the processing center controller 6, and the host computer 1 is connected to the Raman spectrometer 3 and the processing center controller 6;

[0041] The laser processing center 5 uses laser to perform secondary processing on the precious metal nanostructure synthesized by the hydrothermal method, and monitors the absorption spectrum of the precious metal nanostructure in real time during the secondary processing; the Raman spectrometer 3 detects the Raman enhancement multiples of different components of the precious metal nanostructure that has undergone secondary processing on the Raman spectrum detection platform 4; the host computer 1 establishes a spectrum correlation model in the host computer 1 by collecting the Raman spectrum data of different components detected by the Raman spectrometer 3 and the absorption spectrum data of the processing center controller 6; the host computer 1 establishes a processing parameter correlation model in the host computer 1 by collecting the absorption spectrum data of the processing center controller 6 and the processing parameter data;

[0042] PC 2 is connected to host computer 1. When a specific Raman detection component is detected, the component to be detected is input into PC 2 and sent to host computer 1. Host computer 1 inputs the detection component input by PC 2 into the spectral correlation model to obtain the required absorption spectrum of the precious metal nanostructure, and then inputs the absorption spectrum data into the processing parameter correlation model to obtain the required processing parameters.

[0043] The laser processing center 5 is provided with a first laser 51, a first light source 52, and a detection dish 53;

[0044] The detection dish 53 is in the shape of a vertical square tube, sealed at the bottom, and made of fused quartz material; the first laser output by the first laser 51 is expanded by a beam expander 54 and then input into a fly-eye lens 55, and the fly-eye lens 55 converts the expanded first laser into a plurality of sub-beams, which pass through a first side surface of the detection dish 53 and are focused on the middle and lower part of the detection dish 53, and the central axis of the detection dish 53 is located on the plane where the focusing points of the plurality of sub-beams are located;

[0045] The white light emitted by the first light source 52 is shaped and passes through the second side of the detection dish 53, then passes through the third side and is received by the light receiver 56; the light received by the light receiver 56 is input into the spectrophotometer; the spectrophotometer sends the detected absorption spectrum to the machining center controller 6; wherein the second side and the third side are arranged opposite to each other, and the optical paths of the first laser and the white light are vertical and not on the same horizontal plane.

[0046] The Raman detection platform is provided with a base 41 and a detection head 42, and the detection head 42 is connected to the second laser and the Raman spectrometer 3; the base 41 has a heating function, so that the Raman enhanced substrate on the base 41 can be heated and dried quickly; the detection head 42 is arranged directly above the base 41, and the detection head 42 emits an infrared laser and collects the light reflected by the Raman enhanced substrate. The detection head 42 sends the collected reflected light to the Raman spectrometer 3, and the Raman spectrometer 3 sends the detected Raman spectrum to the host computer 1.

[0047] The first laser 51 is a continuous laser or a pulsed laser, and the machining center controller 6 controls the start time and output power of the first laser 51 , or the machining center controller 6 controls the start time, pulse width, and pulse energy of the first laser 51 .

[0048] Embodiment 2:

[0049] This embodiment is directed to a method for processing nanoparticles using the above system, comprising the following steps:

[0050] Step 1: Establishment of processing parameter association model

[0051] Preparing a noble metal nanostructure by a hydrothermal method, wherein the material of the noble metal nanostructure is selected from one of gold, silver and platinum; the shape of the noble metal nanostructure is a nanosphere, a nanorod, a nanopyramid, a nanotriangle sheet, a nanocube or a nanostar;

[0052] The sol of the noble metal nanostructure is added into the detection dish 53, and the noble metal nanostructure is subjected to secondary laser processing using a first laser with different laser processing parameters, and its absorption spectrum is monitored in real time; the laser processing parameters include output power, pulse width, and pulse energy.

[0053] The collected absorption spectrum is used as an input end, and the nanostructure shape and the nanostructure material are used as an output end to establish a first processing parameter association model, so that after any absorption spectrum is input, a set of corresponding output results of the nanostructure shape and the nanostructure material can be obtained; then, the absorption spectrum, the nanostructure material and the nanostructure shape are used as input ends, and the laser processing parameters and the laser processing time are used as output ends to establish a second processing parameter association model, so that after any absorption spectrum, the nanostructure material and the nanostructure shape are input, a set of corresponding output results of the laser processing parameters and the laser processing time can be obtained;

[0054] The modeling method of the second processing parameter association model and the first processing parameter association model is BP neural network or SVM method;

[0055] Step 2: Establishment of spectral correlation model

[0056] The prepared noble metal nanostructures with different absorption spectra and samples of different detection components are dropped onto the substrate to form a Raman enhanced substrate;

[0057] Placing different Raman enhanced substrates on the base 41 of the Raman detection table, heating them, and then monitoring the Raman spectrum; and selecting the noble metal nanostructure with the strongest Raman enhanced signal for the same detection component; then establishing a spectral correlation model with the detection component as input and the absorption spectrum of the noble metal nanostructure with the strongest Raman enhanced signal as output; so that a corresponding absorption spectrum output can be obtained after each detection component is input;

[0058] The modeling method of the spectral correlation model is BP neural network or SVM method;

[0059] Step 3: Rapid processing of nanoparticles

[0060] When it is necessary to detect the component to be tested, the component to be tested is first input into the spectral correlation model to obtain a corresponding absorption spectrum output;

[0061] Then the obtained absorption spectrum is input into the first processing parameter association model to obtain a set of corresponding output results of nanostructure shape and nanostructure material, and then the absorption spectrum, nanostructure material and nanostructure shape are input into the second processing parameter association model to obtain a set of corresponding laser processing parameters and laser processing time output;

[0062] The host computer 1 controls the processing parameter controller to perform rapid processing of the noble metal nanostructure according to the obtained nanostructure material, nanostructure shape, laser processing parameters, and laser processing time.

[0063] Embodiment 3:

[0064] In addition to the method of Example 2, the present application can also use direct laser liquid phase ablation to prepare nanostructures.

[0065] Step 1: Establishment of processing parameter association model

[0066] Putting a noble metal target into a detection dish and placing it on the focusing plane of the first light beam, wherein the material of the noble metal target is selected from one of gold, silver and platinum;

[0067] The precious metal target is ablated using a first laser with different pulse laser processing parameters, and its absorption spectrum is monitored in real time; the laser processing parameters include output power, pulse width, and pulse energy.

[0068] A first processing parameter association model is established by using the collected absorption spectrum as an input end and the nanostructured material as an output end, so that a set of corresponding output results of the nanostructured material can be obtained after any absorption spectrum is input; then, a second processing parameter association model is established by using the absorption spectrum and the nanostructured material as input ends and using the laser processing parameters and the laser processing time as output ends, so that a set of corresponding laser processing parameters and laser processing time outputs can be obtained after any absorption spectrum and the nanostructured material are input;

[0069] The modeling method of the second processing parameter association model and the first processing parameter association model is BP neural network or SVM method;

[0070] Step 2: Establishment of spectral correlation model

[0071] The prepared noble metal nanostructures with different absorption spectra and samples of different detection components are dropped onto the substrate to form a Raman enhanced substrate;

[0072] Placing different Raman enhanced substrates on the base 41 of the Raman detection table, heating them, and then monitoring the Raman spectrum; and selecting the noble metal nanostructure with the strongest Raman enhanced signal for the same detection component; then establishing a spectral correlation model with the detection component as input and the absorption spectrum of the noble metal nanostructure with the strongest Raman enhanced signal as output; so that a corresponding absorption spectrum output can be obtained after each detection component is input;

[0073] The modeling method of the spectral correlation model is BP neural network or SVM method;

[0074] Step 3: Rapid processing of nanoparticles

[0075] When it is necessary to detect the component to be tested, the component to be tested is first input into the spectral correlation model to obtain a corresponding absorption spectrum output;

[0076] Then the obtained absorption spectrum is input into the first processing parameter association model to obtain a set of corresponding output results of nanostructured materials, and then the absorption spectrum and nanostructured materials are input into the second processing parameter association model to obtain a set of corresponding laser processing parameters and laser processing time outputs;

[0077] The host computer 1 controls the processing parameter controller to perform rapid processing of the noble metal nanostructure according to the obtained nanostructure material, laser processing parameters, and laser processing time.

[0078] The above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A system for high-speed processing of nanoparticles with Raman enhanced activity, comprising a host computer (1), a PC (2), a Raman spectrometer (3), a Raman spectrum detection station (4), a laser processing center (5) and a processing center controller (6); characterized in that: The Raman spectrometer (3) is connected to the Raman spectrometer detection platform (4), the laser processing center (5) is connected to the processing center controller (6), and the host computer (1) is connected to the Raman spectrometer (3) and the processing center controller (6); The laser processing center (5) uses laser to perform secondary processing on the precious metal nanostructure synthesized by the hydrothermal method, and monitors the absorption spectrum of the precious metal nanostructure in real time during the secondary processing; the Raman spectrometer (3) detects the Raman enhancement multiples of different components of the precious metal nanostructure that has undergone secondary processing on the Raman spectrum detection platform (4); the host computer (1) establishes a spectrum correlation model in the host computer (1) by collecting Raman spectrum data of different components detected by the Raman spectrometer (3) and absorption spectrum data of the processing center controller (6); the host computer (1) establishes a processing parameter correlation model in the host computer (1) by collecting absorption spectrum data of the processing center controller (6) and processing parameter data; The PC (2) is connected to the host computer (1). When a specific Raman detection component is detected, the component to be detected is input into the PC (2) and sent to the host computer (1). The host computer (1) inputs the detection component input by the PC (2) into the spectrum correlation model to obtain the required absorption spectrum of the precious metal nanostructure, and then inputs the absorption spectrum data into the processing parameter correlation model to obtain the required processing parameters. The laser processing center (5) is provided with a first laser (51), a first light source (52), and a detection dish (53); The detection dish (53) is in the shape of a vertical square tube, has a sealed bottom, and is made of fused quartz material; the first laser output by the first laser (51) is expanded by a beam expander (54) and then input into a fly-eye lens (55); the fly-eye lens (55) converts the expanded first laser into a plurality of sub-beams; the plurality of sub-beams pass through a first side surface of the detection dish (53) and then focus on a middle and lower portion of the detection dish (53); and the central axis of the detection dish (53) is located on the surface where the focusing points of the plurality of sub-beams are located; The white light emitted by the first light source (52) is shaped and passes through the second side surface of the detection dish (53), then passes through the third side surface and is received by the light receiver (56); the light received by the light receiver (56) is input into the spectrophotometer; the spectrophotometer sends the detected absorption spectrum to the processing center controller (6); wherein the second side surface and the third side surface are arranged opposite to each other, and the optical paths of the first laser and the white light are perpendicular and not on the same horizontal plane; The Raman spectrum detection platform (4) is provided with a base (41) and a detection head (42), wherein the detection head (42) is connected to a second laser and a Raman spectrometer (3); the base (41) has a heating function, so that a Raman enhanced substrate on the base (41) can be heated and dried quickly; the detection head (42) is arranged directly above the base (41), the detection head (42) emits infrared laser and collects light reflected by the Raman enhanced substrate, the detection head (42) sends the collected reflected light to the Raman spectrometer (3), and the Raman spectrometer (3) sends the detected Raman spectrum to a host computer (1).

2. A system for high-speed processing of Raman-enhanced active nanoparticles according to claim 1, characterized in that: The first laser (51) is a continuous laser or a pulsed laser, and the machining center controller (6) controls the start time and output power of the first laser (51), or the machining center controller (6) controls the start time, pulse width, and pulse energy of the first laser (51).

3. A method for nanoparticle processing using the system of claim 1 or 2; characterized in that The steps include: Step 1: Establishment of processing parameter association model Preparing a noble metal nanostructure by a hydrothermal method, wherein the material of the noble metal nanostructure is selected from one of gold, silver and platinum; the shape of the noble metal nanostructure is a nanosphere, a nanorod, a nanopyramid, a nanotriangle sheet, a nanocube or a nanostar; Adding the sol of the noble metal nanostructure into a detection dish (53), performing secondary laser processing on the noble metal nanostructure using a first laser with different laser processing parameters, and monitoring its absorption spectrum in real time; Laser processing parameters include output power, pulse width, and pulse energy; Using the collected absorption spectrum as an input end, and the nanostructure shape and the nanostructure material as an output end to establish a first processing parameter association model, so that after any absorption spectrum is input, a set of corresponding output results of the nanostructure shape and the nanostructure material can be obtained, and then using the absorption spectrum, the nanostructure material and the nanostructure shape as input ends, and using the laser processing parameters and the laser processing time as output ends, a second processing parameter association model is established, so that after any absorption spectrum, the nanostructure material and the nanostructure shape are input, a set of corresponding output results of the laser processing parameters and the laser processing time can be obtained; The modeling method of the second processing parameter association model and the first processing parameter association model is BP neural network or SVM method; Step 2: Establishment of spectral correlation model The prepared noble metal nanostructures with different absorption spectra and samples of different detection components are dropped onto the substrate to form a Raman enhanced substrate; Placing different Raman enhancement substrates on a base (41) of a Raman spectrum detection platform (4) to monitor the Raman spectrum after heating; And for the same detection component, the noble metal nanostructure with the strongest Raman enhancement signal is selected; then, the detection component is used as input, and the absorption spectrum of the noble metal nanostructure with the strongest Raman enhancement signal is used as output to establish a spectral correlation model; so that a corresponding absorption spectrum output can be obtained after each detection component is input; The modeling method of the spectral correlation model is BP neural network or SVM method; Step 3: Rapid processing of nanoparticles When it is necessary to detect the component to be tested, the component to be tested is first input into the spectral correlation model to obtain a corresponding absorption spectrum output; Then the obtained absorption spectrum is input into the first processing parameter association model to obtain a set of corresponding output results of nanostructure shape and nanostructure material, and then the absorption spectrum, nanostructure material and nanostructure shape are input into the second processing parameter association model to obtain a set of corresponding laser processing parameters and laser processing time output; The upper computer (1) controls the processing parameter controller to perform rapid processing of the noble metal nanostructure according to the obtained nanostructure material, nanostructure shape, laser processing parameters, and laser processing time.

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