27 results about "Raman imaging" patented technology

The invention relates to the field of biological cells, and particularly discloses a method for quantitatively analyzing metabolites in biological cells based on a Raman spectrum technology, and the method comprises the following steps: S1, collecting a Raman spectrum data set; s2, metabolite with uniform distribution and metabolite with stable content in the cells to be compared are selected as internal standards; s3, extracting the Raman characteristic peak area of the target object, and performing Raman distribution imaging in the cells according to the Raman characteristic peak area of the target object to obtain Raman imaging pictures of a plurality of substances; s4, performing Raman imaging normalization on the target object; s5, converting the normalized Raman peak intensity of the target object into a thermal image, and presenting Raman signals with different intensities in different colors to obtain an intracellular distribution diagram of each component of the intracellular metabolite; and S6, spatial distribution and quantitative analysis of intracellular metabolites. The method disclosed by the invention has the advantages of in-situ, nondestructive, high speed, quick effect, capability of simultaneously and quantitatively analyzing various metabolites on the subcellular scale and the like.

The invention discloses a two-stage Raman hyperspectral imaging method, which relates to the technical field of spectral signal analysis and comprises the following steps: S0, preprocessing Raman hyperspectral data to remove abnormal values; the method comprises the following steps: S1, taking a wave number where a target characteristic peak is located as a target wave number, and based on a neighborhood of the target wave number in Raman hyperspectral data, performing spectrum denoising on the Raman hyperspectral data by using an adaptive low-rank matrix approximation algorithm to obtain the Raman hyperspectral data after spectrum denoising; and S2, using an improved BM3D method based on a rotating block to carry out image denoising on a Raman image after Raman imaging is carried out on a target wave number in the Raman hyperspectral data after spectrum denoising. According to the method, the neighborhood of the target wave number is used as the target area, the adaptive low-rank matrix approximation algorithm is used for spectrum denoising, then the improved BM3D method based on the rotating block is used for image denoising, Raman hyperspectral fast and efficient imaging is achieved, and the method has the advantages of being good in denoising effect, high in speed and light in weight.

The invention relates to a hybrid nanocarrier system comprising engineered metal nanoparticles (MeNP), a flavonoid (Fl), a Raman-active molecule (RAM), poly(allylamine hydrochloride) (PAH), genetic material (GM), and poly(styrene sulfonate) (PSS), designed for use in fields such as gene therapy, cancer treatment, chemotherapy, treatment of genetic diseases, innovative vaccine technologies, the production of innovative antibiotics for antibacterial and antimicrobial therapies, drug delivery, the dietary supplement market, Raman imaging systems, biotechnological applications, and the agrochemical industry.

This invention belongs to the field of imaging analysis technology and discloses a rapid detection method for illegal food additives based on Raman imaging. The method includes: at the start of the detection task, performing sparse Raman imaging scanning on the boundary of the target region of the sample to be tested to obtain a target pixel spectrum set; constructing a reference spectrum set, and generating a detection wavenumber interval by analyzing the stability changes of the wavenumber highlight ratio in the reference spectrum set and the spatial thermal variation characteristics of different wavenumbers in the sample to be tested; obtaining an evidence heatmap by analyzing the similarity of the detection intensity of candidate gratings in the reference spectrum set and the target pixel spectrum set within the detection wavenumber interval; continuously updating the target pixel spectrum set by analyzing the correlation between the change attributes of evidence gain and the growth of connected regions in the evidence heatmap; and outputting suspicious regions by analyzing the convergence change attributes of the evidence coverage heat increment and the stability characteristics of the region to be matched. This invention generates spatial evidence and location results for suspicious regions more quickly.

Electrode for lithium secondary battery and lithium secondary battery comprising the same. This disclosure relates to an electrode for a lithium secondary battery that not only effectively suppresses heat generation or ignition and thus has additional enhanced stability, but also enables the arrangement of batteries that exhibit excellent charge / discharge characteristics, and a lithium secondary battery comprising the same.The electrode for a secondary lithium battery comprises a metal current collector; and an active electrode material and a conductive material, and also comprises an active material layer formed on the metal current collector, wherein the active material layer further comprises a conductive polymer that exhibits a peak in the 1350 to 1600 cm⁻¹ band during Raman spectrum analysis, and exhibits PTC (positive temperature coefficient) characteristics, and wherein, when a cross-section of the active material layer is subjected to Raman imaging analysis, the conductive polymer is distributed in an amount of 90% by weight or more within a region that reaches 10% of the thickness of the active material layer from the surface of the metal current collector.

This invention discloses a hardware-software co-processing method for real-time spectral signal processing in Raman microscopy, belonging to the field of spectral signal processing technology. The method includes: establishing strict temporal constraints between physical displacement and cloud inference, and initiating asynchronous communication using the physical gaps in stage movement; simultaneously, deploying a deep learning model (F2P) pre-trained with a self-supervised masking strategy in the cloud. This pre-trained model utilizes the structural correlation between the Raman broadband background and narrowband characteristic peaks to directly perform real-time forward inference on the input single-frame noisy spectrum, achieving millisecond-level high-fidelity reconstruction of a single frame without any complex parameter tuning or the need for a clean control spectrum. This invention completely eliminates the physical lag in multi-frame processing and the general technical bias of supervised learning algorithms, achieving true "synchronization between acquisition and display," and significantly improving the real-time processing performance and cross-device automated deployment capabilities of in-situ Raman imaging.

The invention provides a flexible surface enhanced Raman (SERS) material one-step in-situ synthesis method, which comprises the following steps of: preparing a porous Ag NPs (at) PDMS (polydimethylsiloxane) substrate by taking sodium citrate as a pore-forming agent and a reducing agent; the porous structure of the substrate provides dense attachment sites for silver nanoparticles (Ag NPs), a nanoscale adsorption channel network is formed, and micro-plastic pollutants such as polyethylene, polypropylene and polystyrene can be rapidly enriched in a complex water medium. The substrate can simultaneously distinguish, recognize and detect various micro-plastics, multi-component synchronous determination is realized, and the spatial distribution of each component can be intuitively presented by combining a Raman imaging technology. Meanwhile, the substrate can be recycled for multiple times, and the method has remarkable application value in the field of environmental micro-plastic pollution multi-component monitoring.

The application belongs to the technical field of biological probes, and relates to a PD-L1 guided specific targeted CTC detection combined with tumor boundary distinguishing biological probe and a preparation method thereof. The PD-L1 guided specific targeted CTC detection combined with tumor boundary distinguishing biological probe comprises a core, a covalent organic framework and a PD-L1 antibody, the core comprises a superparamagnetic nanoparticle, the covalent organic framework is coated on the surface of the core, and the PD-L1 antibody is combined on the surface of the covalent organic framework. The PD-L1 guided specific targeted CTC detection combined with tumor boundary distinguishing biological probe prepared by the application can be transported into the body across the membrane, smoothly enter a PD-L1 positive malignant tumor lesion, and perform Raman imaging to microscopically distinguish the tumor boundary.

The invention discloses a free silicon dioxide content determination and analysis method which specifically comprises the following steps: A1, quickly screening a dust sample to be detected by adopting laser-induced breakdown spectroscopy to obtain the characteristic spectral intensity of a silicon element in the sample, and preliminarily judging the existence probability of free silicon dioxide; a2, performing synchrotron radiation X-ray diffraction analysis on the sample subjected to laser-induced breakdown spectroscopy screening, and realizing second-level silicon element pre-judgment through laser-induced breakdown spectroscopy rapid screening by utilizing the characteristics of high flux and high resolution of a synchrotron radiation light source, so as to avoid deep analysis of an invalid sample and improve the detection accuracy; synchrotron radiation X-ray diffraction high-throughput scanning is combined with Raman imaging automatic identification, and microwave-ultrasonic cooperative sample preparation shortens the pretreatment time of a traditional pyrophosphoric acid method by 80% or more, the time consumed by the whole process is shortened to 30 minutes or less from several hours of the traditional method, 90% of manual operation links are reduced, and the method is suitable for high-throughput analysis of large-batch industrial dust samples.

The present invention provides a chlorinated polyvinyl chloride resin that provides a molded article having excellent heat cycle characteristics and excellent weather resistance, as well as a resin composition for molding and a molded article each including the chlorinated polyvinyl chloride resin. Provided is a chlorinated polyvinyl chloride resin having an average of a ratio (A / B) of a peak intensity A observed in a range of 300 to 340 cm−1 to a peak intensity B observed in a range of 1,450 to 1,550 cm−1 of 3.5 to 40.0 in Raman imaging measurement by Raman spectroscopy.

The invention relates to the technical field of spectral analysis, in particular to a large-view-field snapshot type Raman imaging system, in a microscopic imaging light path, a first light source irradiates a to-be-detected sample, and a microscopic imaging assembly receives a light beam generated by the to-be-detected sample and performs microscopic imaging on the to-be-detected sample; a first micro-lens array in the Raman excitation light path corrects a light beam emitted by a second light source into a surface-excited point light source array, and the point light source array irradiates a to-be-detected sample, so that the to-be-detected sample is excited by the point light source array to form Raman scattering light; raman scattering light in the imaging spectrum light path enters the spectrograph through the second micro-lens array, the second micro-lens array cuts the image surface of the microscopic imaging light path and divides the image surface into a plurality of units, and the units are rearranged in the two-dimensional space dimension. By adopting the microlens array, Raman spectrum imaging is realized while high-resolution microscopic imaging is performed, and the problems of small imaging area and low analysis speed of Raman spectrum imaging detection are solved.

This invention relates to the field of photoelectric detection technology and discloses a perovskite charge transport element, which consists of a conductive substrate, an electron transport layer, and a perovskite photosensitive layer. The perovskite charge transport element can be used to fabricate perovskite solar cells. This invention also discloses a method for detecting the effectiveness of charge transport at the electron transport layer / perovskite photosensitive layer interface in a perovskite solar cell. By utilizing the signal intensity of probe molecules in surface Raman imaging, the effectiveness of charge transport at the electron transport layer / perovskite photosensitive layer interface in a perovskite solar cell can be rapidly detected using surface-enhanced Raman spectroscopy. This method can complete the detection without assembling a noble metal back electrode, is simple to operate, low in cost, and exhibits high sensitivity and accuracy.

This invention discloses a Raman method for characterizing the domain structure of molecular ferroelectric materials. The method includes the following steps: Step 1: Determine the crystal growth direction and crystal planes of the material; Step 2: Acquire variable-temperature Raman spectra; Step 3: Analyze the variable-temperature Raman spectra to determine the Raman vibrational modes closely related to polarization; Step 4: Calculate the angle between the characteristic Raman vibrational modes and the crystal growth direction; Step 5: Construct a polarization Raman testing system and acquire Raman spectra at different polarization angles; Step 6: Process the angle-dependent polarization Raman spectra; Step 7: Plot the "angle-peak intensity (peak width or peak position)" diagram of the characteristic Raman peaks; Step 8: Fit the data from Step 7 to determine the angle between the characteristic Raman vibrational modes and the crystal growth direction; Step 9: Perform polarization Raman imaging at the angle where the peak intensity (peak width or peak position) reaches its extreme value. This invention effectively fills the gap in non-contact, non-destructive characterization methods for the domain structure of molecular ferroelectric materials.

The invention discloses a near-infrared light triggered nano-drug controlled release integrated system for precise tumor treatment, and relates to the technical field of tumor treatment, and the key points of the technical scheme are as follows: the system comprises a tumor positioning module for precisely positioning a tumor part and obtaining tumor information; the near-infrared light trigger module emits near-infrared light to trigger the nano-drug carrier module to release drugs; the drug release monitoring module monitors the drug release amount and the release rate in real time; and the central control module sends a control signal according to the tumor information and the monitoring data, and adjusts the wavelength, the intensity and the irradiation time of the near-infrared light so as to realize accurate control of drug release. The tumor three-dimensional information is extracted through an image algorithm to realize accurate positioning of the tumor, near-infrared light parameters are dynamically adjusted in combination with drug release monitoring data, accurate regulation and control of drug release are completed, meanwhile, distribution and release of drugs in vivo are tracked through fluorescence or Raman imaging, and the accuracy of drug release is improved. The problems that traditional treatment medicine release is invisible, and the effect is difficult to evaluate are solved.

The invention discloses a Raman spectrum imaging method for a semi-solid preparation, and relates to the technical field of Raman imaging.The method comprises the steps that an original sample (such as cream and gel plaster) of the semi-solid preparation is taken, the original form of the original sample is kept, grinding, dissolving or chemical modification is not conducted, the sample is placed on an inert slide glass of a two-dimensional translation table, and the sample is placed on a Raman spectrometer; the advantages of no mark, no sample preparation, high specificity and the like of the Raman spectrum technology are utilized, microscopic imaging and chemometrics analysis are combined, crystal form identification, component distribution visualization and quantitative analysis of the active pharmaceutical ingredient (API) in the semi-solid preparation are achieved, and the method can be applied to quality control, process optimization and consistency evaluation of the semi-solid preparations such as cream and gel, and has a wide application prospect. The method overcomes the defects that a traditional XRD method needs sample preparation and cannot realize online monitoring, and has the advantages of high resolution, real-time performance and full quantitative analysis capability.

In the Raman imaging of the silicon nitride sintered body in the embodiment through a measurement section on a specific cross section, (1) the region of silicon nitride grains with smaller area in the region of silicon nitride grains oriented in a direction close to the perpendicular direction of the specific cross section and the region of silicon nitride grains oriented in a direction close to the parallel direction of the specific cross section is set as the first region, (2) the region of silicon nitride grains with larger area is set as the second region, (3) the region of rare earth element-aluminum-oxygen grain boundary phase in the measurement section is set as the third region, (4) the region of the measurement section containing at least one of titanium, hafnium, tungsten, molybdenum and silicon carbide is set as the fourth region and the mapping is performed, the area ratios of the first region, the second region, the third region and the fourth region are 0.5% or more and 8% or less, 70% or more and 92% or less, 2% or more and 16% or less, and 0.5% or more and 8% or less, respectively.

The application relates to a Raman denoising method, which comprises the following steps: performing encoding processing on an initial feature map of a Raman spectrum or a Raman image to obtain an encoded feature map; wherein the encoding processing comprises the following steps: performing feature extraction processing on the initial feature map to obtain an encoded separated feature map, and performing feature fusion processing on the encoded separated feature map to obtain the encoded feature map; performing dimension integration and feature integration processing on the encoded feature map to obtain a bearing feature map; performing decoding processing on the bearing feature map to obtain a decoded feature map of the corresponding Raman spectrum or Raman image after filtering out noise; wherein the decoding processing comprises the following steps: performing expansion processing on the encoded feature map to obtain a sampling feature map, splicing the sampling feature map and the encoded separated feature map to obtain a decoded splicing feature map, and performing feature extraction processing on the decoded splicing feature map to obtain the decoded feature map. The application can simultaneously consider the spatial dimension and the spectral dimension to perform Raman spectrum or Raman image denoising, and the denoising effect is strong.

The invention discloses a micro-plastic organism in-vivo in-situ integrated analysis method based on phthalocyanine blue labeling and Raman imaging, and belongs to environmental toxicology and analytical chemistry technologies. The method comprises the following steps: firstly, exposing organisms to be researched and analyzed in phthalein blue dyed micro-plastic particles; then obtaining exposed biological tissues, preparing paraffin sections and staining the tissues; positioning a biological tissue area under an optical microscope, acquiring a spectrum of the area by adopting a Raman spectrum point scanning mode, and confirming a target area by identifying characteristic Raman peaks of phthalocyanine blue and micro-plastic; raman surface scanning is carried out on a target area, intensity information of characteristic Raman peaks of phthalocyanine blue or micro-plastic is obtained through a peak clamping method, a spatial distribution image of corresponding components in biological tissue is generated, and quantification of the micro-plastic can be achieved. According to the method, the problem that the marking stability of a traditional plastic dyeing method is difficult to be compatible with a standard slide making process is solved, in-situ association of micro-plastic distribution and tissue lesion can be realized, and the method is suitable for accurate detection of submicron micro-plastics.

The application discloses a preparation and characterization method for tracking heavy metal chelating microspheres based on a Raman imaging method, and belongs to the technical field of protein purification materials, and comprises the following steps: establishing a Raman molecule spectrum library, wherein the Raman molecule spectrum library comprises products involved in the preparation process of the heavy metal chelating microspheres; preparing the heavy metal chelating microspheres, and acquiring surface Raman spectrum images of each stage in the preparation process of the heavy metal chelating microspheres by using the Raman imaging method, and tracking the preparation of the heavy metal chelating microspheres according to functional group changes by comparison with the Raman molecule spectrum library; and characterizing the prepared heavy metal chelating microspheres by using the Raman imaging method, including characterizing the heavy metal chelating microspheres after the influence of external factors, and characterizing ion leakage of the microspheres after the microspheres are combined with metal ions. The application tracks the preparation and characterization of the heavy metal chelating microspheres through the Raman spectrum, and the prepared microspheres have good protein purification capacity.

This invention discloses a low-power laser line-scanning Raman imaging system and method for detecting blood lipids and blood glucose in the nailfold. The system includes: a low-power laser rapid line-scanning light source module for generating a linear light spot and performing rapid line-scanning Raman imaging on the nailfold region; a positioning imaging module for observing the nailfold region under visible light and guiding the line-scanning light spot to be precisely projected onto the non-keratinized area; a Raman signal collection module for collecting Raman scattered light emitted by stimulated emission from the nailfold tissue; a spectral dispersion and detection module for dispersing the Raman scattered light and converting it into an electrical signal; and a data processing and control module for simultaneously extracting blood lipid-related features and / or blood glucose-related features from the Raman imaging data or the spatially averaged aggregated spectrum, and calculating the blood lipid index and blood glucose concentration or relative index respectively. This invention achieves completely non-invasive, safe, and rapid combined detection of blood lipids and blood glucose, suitable for simultaneous screening and home monitoring of people with diabetes and dyslipidemia.

This invention relates to a method, apparatus, device, and medium for single-cell feature analysis based on hyperspectral coherent Raman scattering imaging, used for single-cell feature analysis. The invention provides an apparatus for performing hyperspectral coherent Raman scattering imaging of samples at different Raman shifts; the method performs hyperspectral coherent Raman imaging on samples enriched with cells using medium A, acquiring images and Raman spectra of different substances at multiple Raman shifts; a multivariate curvature resolution method is used to obtain concentration distribution maps of different substances, and an image is obtained after removing medium A; deep learning technology is used to segment single-cell regions, extracting morphological and metabolic features of cells; simultaneously, a spectral phasor method combined with Lorentz fitting is used to extract morphological and metabolic features of single cells and their subcellular structures. This invention solves the problem of medium structure occlusion, resulting in richer extracted morphological and metabolic features of cells, facilitating more accurate differentiation of cell types.

1. The name of the design product: full quantitative Raman imaging analyzer. 2. The use of the design product: the design product is used for microscopic imaging and long-time observation of biological samples, and sample analysis according to the pictures of microscopic imaging. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

This invention discloses a high-speed Raman imaging method based on physical dimensionality reduction and latent space restoration, comprising: S1, constructing a spectral physical encoder (SPE) module and training the SPE module; S2, constructing a spatial structure restoration (SSI) module and training the SSI module; S3, acquiring the original Raman hyperspectral image I based on sparse sampling. sparse S4. Use the trained SPE module to test I. sparse Compression is performed to output the latent vector map IZ. sparsei S5. Extract Z using the trained SSI module. sparsei Spatial context information in Z, and Z sparsei After repairing the zero-value region, the latent vector map II Z is output. dense_recon S6. Using the SPE decoder corresponding to the SPE module, the low-dimensional feature vector is nonlinearly mapped back to the high-dimensional space to obtain the initial reconstructed image Irecon_raw; S7. Linear calibration is performed on each pixel value in Irecon_raw to obtain the reconstructed hyperspectral image Irecon, completing high-speed Raman imaging. This invention can achieve high-speed, high-fidelity Raman imaging under highly sparse sampling conditions (e.g., 5%-10%).

The invention discloses a preparation and characterization method for tracking heavy metal chelate microspheres based on a Raman imaging method, and belongs to the technical field of protein purification materials, and the preparation and characterization method comprises the following steps: establishing a Raman molecular spectrum library which comprises products involved in the preparation process of the heavy metal chelate microspheres; preparing the heavy metal chelating microspheres, in the preparation process of the heavy metal chelating microspheres, acquiring surface Raman spectrum images of all stages by adopting a Raman imaging method, comparing the surface Raman spectrum images with the Raman molecular spectrum library, and tracking the preparation of the heavy metal chelating microspheres according to the change of functional groups; and characterizing the prepared heavy metal chelating microspheres by adopting a Raman imaging method, including characterizing the heavy metal chelating microspheres affected by external factors and characterizing ion leakage after the microspheres are combined with metal ions. The preparation and characterization of the heavy metal chelating microspheres are tracked in the whole process through Raman spectrum, and the prepared microspheres have good protein purification capacity.

The invention provides a microscopic Raman imaging super-resolution detection method, device and equipment and a medium, and the method comprises the steps: collecting low-resolution Raman images and high-resolution images of a plurality of samples, and constructing a data set; inputting a low-resolution Raman image in a sample into the constructed diffusion adversarial network, and outputting a super-resolution Raman image; carrying out similarity calculation on the super-resolution Raman image and the high-resolution Raman image; when the similarity is smaller than a preset similarity threshold value, the training process is continued, and training is ended until the similarity is larger than the preset similarity threshold value; and collecting a low-resolution Raman image of a to-be-detected sample, inputting the low-resolution Raman image of the to-be-detected sample into the trained diffusion adversarial network, outputting a super-resolution Raman image of the to-be-detected sample, and detecting the quality of the to-be-detected sample based on the super-resolution Raman image of the to-be-detected sample. The problem of low efficiency in the traditional grain grade detection process can be solved.

The invention relates to the technical field of spectral analysis and microscopic imaging, and discloses a dynamic surface scanning Raman imaging system and a spectral data acquisition method, and the system comprises a light beam output module which is used for outputting a collimated laser beam; the device comprises a spatial light modulator, an excitation light path module, a sample stage, a collection light path module, an imaging spectrometer and a control module, the control module generates a corresponding driving signal according to a preset image sequence, and the preset image sequence comprises a plurality of preset excitation images arranged according to a set sequence; controlling a spatial light modulator to generate a corresponding dynamic structure light field through a driving signal, obtaining Raman spectrum data of a plurality of excitation light field distribution diagrams through an imaging spectrometer, and reconstructing and decoupling the Raman spectrum data to obtain Raman imaging cube data. Complete Raman imaging cube data can be reconstructed through a small amount of pattern switching and exposure, and the spectrum acquisition efficiency is greatly improved.

The present application relates to a kind of PLS-based raman imaging method and the quantitative analysis method of polymer material, the imaging method includes (1) start raman testing instrument and calibration;(2) to the sample with single state change is carried out large spot raman test;(3) after using raman spectrum analysis technique to the raman spectrum data obtained in step (2) is preprocessed, using PLS is analyzed, and thus establish state-spectrum model;(4) using the wavelength range where characteristic peak is located carries out raman mapping scanning test;(5) using raman spectrum analysis technique to the mapping result obtained in step (4) is preprocessed, to make mapping scanning data and the model obtained in step (3) compatible;(6) according to the peak intensity of characteristic peak or the model to the mapping scanning data obtained in step (5) is plotted, to obtain distribution diagram.The present application patent broadens raman imaging application field, and it is important to obtain the sample topography and component distribution under microscale.