733 results about "Raman spectroscopy" patented technology

The invention discloses an automatic alignment and integrated control system and method for an optical crystal array. The automatic alignment and integrated control system comprises a quantum entanglement enhanced ultra-precise sensing array, a multi-factor coupling error prediction module, a full-link adaptive compensation execution module, a central control module and a federal learning collaborative optimization module. The sensing array is used for acquiring multi-dimensional error data such as sub-nano displacement, Raman spectrum deformation, infrared temperature rise and friction stress in real time; the prediction module is used for mining coupling characteristics between error sources based on an attention mechanism and predicting an instant alignment error and a hysteresis deformation error; the compensation execution module realizes full-link compensation through quantum traceability calibration, mechanical arm dynamic composite compensation and process parameter cooperative adjustment according to the prediction error; the central control module performs synchronous scheduling and conflict coordination; and the federated learning module realizes collaborative optimization and privacy protection of models among multiple devices. According to the invention, ultrahigh-precision and adaptive intelligent integration of the optical crystal array is realized.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention belongs to the technical field of biology, and particularly relates to a group of paired monoclonal antibodies for resisting H9N2 subtype avian influenza viruses and a surface enhanced Raman spectroscopy rapid detection test strip established based on the antibodies. The paired monoclonal antibody comprises a labeled antibody 1E9 and a capture antibody 3C10 which are prepared by taking hemagglutinin protein of the H9N2 subtype avian influenza virus as antigens and can be specifically combined with the H9N2 subtype avian influenza virus. According to the invention, an SERS (Surface Enhanced Raman Scattering) probe Au-coated Pt-4ATP is used as a marker and is coupled with a labeled antibody 1E9 to prepare a labeled probe, and the labeled probe is paired with a capture antibody 3C10 to establish an SERS immunochromatography test strip. The test strip has a colorimetric and Raman dual interpretation mode, can be used for qualitative and quantitative detection, has the advantages of strong specificity, high sensitivity, good repeatability and stability and the like, is simple and rapid in clinical detection operation, and provides reliable technical support for on-site rapid screening of subtype viruses.

The invention belongs to the technical field of power equipment state monitoring, and discloses a transformer oil dissolved gas surface enhanced Raman spectrum in-situ monitoring method and a transformer oil dissolved gas surface enhanced Raman spectrum in-situ monitoring system. The system comprises a membrane degassing unit, a Raman sensing unit, a light path unit, a data processing unit and a calibration early warning unit, the method comprises the following steps: firstly preparing a gold nanostar array sensing substrate with bifunctional groups to realize selective adsorption and signal enhancement of characteristic gas, then completing in-situ oil-gas separation through the membrane degassing unit, collecting a Raman spectrum through the light path unit, and calculating the Raman spectrum of the gold nanostar array sensing substrate according to the Raman spectrum. Spectral analysis and quantification are completed based on multiple algorithms, and fault early warning is achieved in combination with temperature and pressure compensation and an industrial standard threshold value. According to the scheme, the problems of lagging, weak interference resistance and the like of traditional monitoring are solved, high sensitivity and high specificity are achieved, full-process automatic closed-loop monitoring can be achieved, and accurate data support is provided for operation and maintenance of the transformer.

The invention relates to the technical field of analysis and detection, in particular to a surface enhanced Raman spectroscopy (SERS)-based nano material detection method, which is characterized in that a heterojunction composite structure consisting of noble metal nano stars and semiconductor oxide nano particles is used as an SERS active substrate; and a specific recognition molecule carrying a Raman reporter molecule is used as a detection probe. During detection, the recognition molecules are firstly combined with the to-be-detected nano material, and then the compound is adsorbed on the surface of the substrate. The tip structure of the noble metal nanostar and a heterojunction interface jointly generate a synergistic effect of electromagnetic enhancement and chemical enhancement, and a reporter molecule signal is doubly amplified, so that high-sensitivity and high-selectivity detection on a to-be-detected nano material is realized. The method solves the problems of weak signal and poor selectivity of the traditional method, and is suitable for rapid and quantitative analysis of trace nano materials in the environment.

The invention mainly relates to the technical field of spectral analysis, and provides a white spirit brewing spectral analysis method and system based on a multi-modal large language model in order to efficiently and accurately obtain white spirit brewing information according to multi-modal spectral data of all links of white spirit brewing. The method is characterized by comprising the following steps of: collecting multi-mode spectrum data including gas chromatography data, infrared spectrum data and Raman spectrum data in each link of white spirit brewing; converting the multi-modal spectral data and the natural language instruction into a feature embedding vector which can be recognized by a large language model to train the large language model which is finely adjusted based on professional knowledge in the field of white spirit, and receiving a question of a user in a natural language instruction form by the trained large language model; wine making analysis tasks such as flavor compound identification, key microorganism detection, fermentation state evaluation and base wine quality grading are carried out, and finally analysis results and wine making process optimization suggestions are output in a natural language form, so that the accuracy and efficiency of spectrum data interpretation in the white wine making process are improved.

The invention discloses a Raman spectrum joint classification and quantification method based on a prior fusion double-flow network, and belongs to the technical field of spectral analysis and chemometrics. The method comprises the following steps: acquiring Raman spectrum original data, and preprocessing to obtain standardized spectrum data; raman spectrum prior information is extracted, and a prior information double-flow network containing classification and quantitative flow sub-networks is constructed; standardized data and prior information are input, classification preheating-combined fine tuning training is adopted, conflict is relieved by combining dynamic weighting and PCGrad, and samples are expanded through a physical consistency strategy synchronously; processing unknown samples by using the trained network, and outputting categories and component contents; according to the method, weak peak information is mined through double-flow architecture and cross-flow attention, derivative noise is suppressed, classification-quantification performance is balanced through double-task optimization, and small sample overfitting is solved through physical expansion; on a polycyclic aromatic hydrocarbon data set, classification and regression indexes are superior to those of a traditional method, the low-concentration / weak-peak scene precision and robustness are better, and the engineering application prospect is good.

A system and method for determining charge variants in monoclonal antibodies using raman spectroscopy is disclosed.

An edible fungi quality detection method based on spectral characteristics. The method comprises: acquiring original Raman spectral data of an edible fungi sample and reference light source light intensity sequences at different ambient temperatures, and on the basis of the reference light source light intensity sequences, determining light source fluctuation intensities; in view of the light source fluctuation intensities and the original Raman spectral data at the different ambient temperatures, determining predicted weights of various original Raman light intensities at each Raman shift, and on the basis of the original Raman light intensities and the predicted weights thereof, determining a standard spectral Raman light intensity of each Raman shift, so as to determine standard Raman spectral data; and on the basis of the standard Raman spectral data, performing quality detection of the edible fungi sample. By means of reducing the interference of a baseline drift effect to the Raman spectral analysis of an edible fungi sample, the quality detection accuracy of edible fungi is improved.

The invention belongs to the technical field of coal quality detection, and particularly relates to a method for rapidly predicting coal quality parameters based on Raman spectrum in combination with machine learning. According to the method, a Raman spectrum technology and machine learning are combined, a Raman spectrum systematic preprocessing process is adopted, an adaptive iteration reweighted least square method Air-PLS is applied, spectral signals and baseline drift are accurately separated, Savitzky-Golay filtering is applied, peak shape features are reserved, random noise is effectively suppressed, and the method is suitable for large-scale popularization and application. Maximum-minimum normalization or Z-score standardization processing is provided, and the intensity difference between samples is eliminated; moreover, a data-driven Raman spectrum intelligent feature screening mechanism is introduced, and an optimal feature wavelength subset which is highly related to coal quality parameters and low in redundancy is screened out through automatic iteration; according to the method, the data quality in Raman spectrum coal quality detection is improved, the feature correlation is enhanced while the feature dimension is reduced, the model precision and generalization ability are improved, and rapid, lossless, high-precision and high-robustness coal quality parameter prediction is realized.

Methods and computing apparatus for implementing a radiation dosimetry process. An example process may include exposing a first radiation sensitive film to a series of known doses of radiation, measuring a first set of radiation data with a measurement device, and determining a calibration curve. The calibration curve may be determined by selecting Raman spectral ranges based on the first set of radiation data, determining a plurality of band area ratios, and generating the calibration curve based on plotting the band areas and ratios compared to known dose. The radiation dosimetry process then exposes a second radiation sensitive film to an unknown dose of radiation, measures a second set of radiation data for the second radiation sensitive film with the measurement device, and determines a dose exposure level for the second radiation sensitive film by comparing the second set of radiation data with the calibration curve.

The invention relates to an all-solid-state battery thin-layer electrode device for multi-mode in-situ interface research. The all-solid-state battery thin-layer electrode device comprises the following components: a solid electrolyte substrate; the functional thin layer is arranged on the surface of the solid electrolyte substrate; the functional thin layer has conductivity; the functionalized thin layer allows a detection signal of an in-situ characterization technology to effectively penetrate or be reflected from the surface of the functionalized thin layer; the in-situ characterization techniques include atomic force microscopes, optical microscopes, and Raman spectroscopy, X-ray photoelectron spectroscopy, infrared spectroscopy, or X-ray diffraction. The invention provides a universal thin-layer electrode design and system and a thin-layer electrode in-situ imaging and spectroscopic characterization method for all-solid-state battery interface reaction. The objective of the invention is to expose a solid electrolyte-electrode interface reaction process which is difficult to observe in an effective detection range of various characterization technologies so as to realize real-time and in-situ visualization and accurate analysis of a dynamic evolution process of an internal interface of an all-solid-state battery.

The invention discloses a Raman spectrum-based endometrial tissue spectral data processing method and equipment, and relates to the technical field of data processing. The method comprises the following steps: firstly, calculating a gradient change sequence of to-be-processed spectral data, carrying out continuity verification in combination with wavelength and width, and accurately dividing a Raman characteristic peak interval and a background baseline interval; then, iterative baseline fitting processing based on a Raman peak protection mechanism is adopted, blood fluorescence interference is eliminated, and pure Raman spectrum data is obtained. And further, inputting the pure spectral data into a pre-constructed segmented fusion feature extraction network, extracting local wave band features, fusing the local wave band features with global features, and outputting a data classification result. According to the embodiment of the invention, the problem of difficult feature extraction caused by low signal-to-noise ratio of a bleeding sample and non-uniform background in related technologies can be effectively solved, the integrity of spectrum fingerprint information is ensured while strong fluorescence interference is thoroughly eliminated, and the accuracy of data feature extraction and the robustness of classification and recognition are improved.

A positive electrode active material in a form of a single particle includes a lithium transition metal oxide in a form of a single particle, a coating portion containing cobalt which is formed on the lithium transition metal oxide in the form of the single particle, and LiCoO2 in a form of an island which is discontinuously formed on a surface of the positive electrode active material. A ratio of an intensity of a peak ranging from 550 cm−1 to 620 cm−1 corresponding to an A1g vibration mode of LiCoO2 to an intensity of a peak ranging from 500 cm−1 to 600 cm−1 corresponding to an A1g vibration mode of LiNiO2 in a Raman spectrum of the surface is greater than 1. Also provided is a method of preparing the same.

The invention relates to a microplastic visualization method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: obtaining a Raman spectrum of a micro-plastic sample; based on the Raman spectrum, determining the peak top and the peak bottom of the characteristic peak of the micro-plastic in the micro-plastic sample; and based on difference information between the peak top and the peak bottom of the characteristic peak of the micro-plastic, performing chemical imaging extraction on the Raman spectrum to generate a chemical imaging image of the micro-plastic sample.

An integrated detection method based on a liquid-filled hollow-core optical fiber enhanced Raman signal comprises the following steps: positioning an optical coupling system, a liquid adapter and a forward Raman signal collection system along the same dovetail groove guide rail, and operating a sliding block fine adjustment mechanism to adjust the relative positions of the optical coupling system, the liquid adapter and the forward Raman signal collection system so as to enable an optical path to be axially aligned; the micro-flow injection system is operated, target liquid is conveyed to the liquid adapter through the micro-flow guide pipe, the liquid adapter keeps the collimation alignment relation between the hollow-core optical fiber and the laser, and then the target liquid is introduced into a fiber core of the hollow-core optical fiber; the laser light source is started to emit laser, and after the laser is collimated and focused through the optical coupling system, a Raman signal is excited and generated; operating a forward Raman signal collection system to capture a Raman signal output by the hollow-core optical fiber, filtering out an exciting light Rayleigh scattering signal in the Raman signal, and transmitting the purified Raman signal to a Raman spectrometer; and analyzing and processing the purified Raman signal, and outputting a detection result of the target liquid.

The invention provides a process method for producing ferrotitanium powder by reducing ilmenite, which comprises the following steps of: uniformly mixing ilmenite concentrate with a biomass-coal mixed reducing agent, a nanoscale additive and the like, reducing in a rotary kiln under a temperature-controlled and oxygen-controlled atmosphere, quenching and cooling a product by water, and performing multi-stage ore grinding magnetic separation to obtain metal iron particles and a titanium-rich material; and preparing the two into a ferrotitanium powder product. The process integrates innovative units of raw material homogenization pretreatment, biomass-coal mixed reducing agent dynamic proportioning, LIBS + Raman spectrum on-line monitoring closed-loop control, nano CaO-Na2O composite catalyst buffer addition, finished product multistage magnetic separation reforming and the like. The product composition fluctuation is obviously reduced, the TiO2 content fluctuation is controlled within + / -0.4%, the FeO content fluctuation is controlled within + / -0.5%, the metallization ratio is improved to 90% or above, the heat efficiency is improved by about 20%, and the energy consumption per unit yield is reduced by about 15%. The ilmenite is directly, efficiently and cleanly utilized, a high-quality titanium-rich raw material can be provided for production of titanium dioxide, titanium sponge and the like, meanwhile, the byproduct high-grade iron powder is used for steelmaking, and the comprehensive utilization benefit of resources is remarkable.

The invention discloses a high-purity and low-damage single-walled carbon nanotube purification method which comprises the following steps: carrying out mechanical shearing treatment on a single-walled carbon nanotube to disperse the single-walled carbon nanotube and open a tube bundle structure to obtain a sheared single-walled carbon nanotube; carrying out pre-oxidation on the sheared single-walled carbon nanotube in a weak oxidizing atmosphere to obtain a pre-oxidized single-walled carbon nanotube; carrying out microwave treatment on the pre-oxidized single-walled carbon nanotube or the acidified single-walled carbon nanotube in an inert atmosphere to obtain a single-walled carbon nanotube subjected to microwave treatment; and acidizing the single-walled carbon nanotube subjected to microwave treatment to obtain the acidized single-walled carbon nanotube. The method is simple in process, mild in condition, easy to amplify and suitable for industrial production. The purity of the obtained single-walled carbon nanotube is higher than 99%, the raman spectrum IG / ID ratio is larger than 150 and higher than that before purification, and the single-walled carbon nanotube is high in structural integrity and suitable for the fields of high-performance composite materials, electronic devices, energy storage and the like.

Battery cell characterized in that it comprises a stacked electrical core, wherein the stacked electrical core comprises a cathode foil and an anode foil, wherein the cathode foil comprises a cathode collector and a cathode film layer provided on at least one side of the cathode collector, wherein the cathode film layer comprises lithium-containing transition metal phosphate particles provided with a carbon material on at least part of a surface; wherein a median C50 of the degree of graphitization in the cumulative distribution curve for the graphitization C value of the cathode film layer, obtained in the area-scanning mode of the laser microconfocal Raman spectrometer, is 0.95–1.20; wherein the graphitization C value is IG / ID, where IG is the intensity of the G-peak of the Raman spectrum at 1580 ± 100 cm⁻¹ and IG is the intensity of the D-peak. of the Raman spectrum at 1350±100cm-1;where the density of the cathode foil when the battery cell is in a fully discharged state is 2.3 g / cm3-2.6 g / cm3;

The invention discloses a carbon nano tube Raman spectrum batch simulation method based on Gaussian light spot modeling and a related device, and relates to the technical field of high-flux carbon nano tube characterization, the method comprises the following steps: constructing a laser light spot area with light spot intensity conforming to Gaussian distribution based on a Gaussian model; randomly generating a plurality of simulation space coordinates in the region, and enabling the simulation space coordinates to correspond to Raman spectrum data of the carbon nanotubes with different chiral indexes; obtaining the effective chord length of the carbon nanotube, and calculating the signal intensity of the single tube in combination with the light spot intensity distribution; calculating the spatial weight of each carbon nanotube by taking the signal intensity of the central position of the light spot as a reference; and obtaining corresponding Raman spectrum data from a pre-established single-spectrum database in combination with the chiral index, and carrying out weight superposition to obtain total Raman spectrum data. According to the scheme, an actual detection scene can be accurately reproduced, and time consumed by single-time carbon nanotube Raman spectrum superposition is lt; and multi-chirality combination is supported, the simulation precision and efficiency are remarkably improved, and the characterization requirement of the high-flux carbon nano tube is met.

A method of surface-enhanced Raman spectroscopy includes positioning an analyte in a field-enhancing region of a nanophotonic element, irradiating the analyte with a continuous wave excitation laser operating at a user-defined frequency in a range from near infrared (NIR) to short-wave infrared (SWIR) to generate a signal to cause photon scattering from the analyte and detecting the photons with one or more photodetectors in the SWIR. A surface-enhanced Raman spectroscopy system includes a laser operating at a frequency having a range from near-infrared (NIR) to short-wave infrared (SWIR) that emits light in a spectrum between 700 and 2500 nanometers, a nanophotonic element to receive the light from the laser, and one or more SWIR detector to detect photons scattered by an analyte on the nanophotonic element.

The invention discloses a method for detecting the content of aflatoxin in breeding feed, and relates to the technical field of feed safety detection.The method comprises the steps that near-infrared, fluorescence and Raman spectrums of a feed sample to be detected are synchronously collected, and an original spectrum data stream is formed; carrying out parallel preprocessing on the data stream, suppressing noise and correcting a baseline to obtain an optimized spectrum data set; extracting multi-modal spectral features from the data set, fusing the multi-modal spectral features, and constructing a multi-dimensional feature matrix representing the toxin content; inputting the matrix into a pre-trained grading detection model to obtain a preliminary prediction value of the toxin content and corresponding uncertainty evaluation; and in combination with calibration sample data in a historical detection database, performing dynamic calibration compensation on the preliminary predicted value, and outputting a final aflatoxin content report. Through multi-source spectral information fusion and dynamic self-calibration, the accuracy, stability and long-term reliability of aflatoxin detection in a complex feed matrix are improved.

The invention provides a micro-nano plastic detection method and system, and relates to the technical field of micro-nano plastic detection.The method comprises the steps that a to-be-detected sample is obtained, and the positions of all suspicious micro-nano plastics in the sample are determined based on hyperspectral imaging; eliminating a biological matrix on the surface of the suspicious micro-nano plastic by utilizing laser with stepped power based on a photo-thermal ablation pretreatment technology; the magnitude of the stepped power is determined according to a biological matrix; and collecting the Raman spectrum of the suspicious micro-nano plastic after photo-thermal ablation pretreatment, and outputting a detection result according to the Raman spectrum. The positions of all suspicious micro-nano plastics in a sample are determined based on hyperspectral imaging, and the sample is detected by using Raman spectrum, so that the efficient positioning capability of nanoscale hyperspectral imaging and the accurate identification capability of laser confocal Raman spectrum are organically combined, and a photo-thermal ablation pretreatment technology is introduced, so that the biological matrix interference is effectively overcome, and the detection accuracy is improved. The high-flux, high-resolution and high-accuracy detection on the micro-nano plastic is realized.

The present application belongs to the technical field of data analysis, and particularly relates to a high-throughput biosensing system for early warning of algal blooms and pathogens, which comprises multiple sampling units, a microfluidic chip with a multi-channel structure, and a high-throughput surface-enhanced Raman detection unit with an integrated metal nanostructure surface, and obtains the original surface-enhanced Raman spectrum of the water sample through a Raman spectrum acquisition mechanism. The system uses compressed spectrum sampling and sparse reconstruction methods to reduce the data volume and maintain the integrity of the spectrum information, constructs a pathogen Raman fingerprint dictionary through a dictionary learning method, and realizes the identification of different algal metabolites, algal toxins and pathogen-related molecular components and the concentration interval judgment thereof in combination with a sparse representation classification mechanism. Based on the type and concentration interval of the pathogens, the system further generates algal bloom risk warning and pathogen risk warning, and realizes early discovery of abnormal changes in the water body. The present application has significant advantages in stability, sensitivity, throughput and real-time performance.

The invention relates to a water-rock reaction mechanism in-situ measurement device and method, and belongs to the field of carbon dioxide geological sequestration experiment determination. The device comprises a formation water tank, a Raman probe, a reaction kettle, a vacuum pump, a gas tank, a formation water pipeline, a liquid circulating pump, a vacuum pipeline, a gas pipeline, a gas circulating pump, a connecting piece, a gas-liquid circulating pipeline and a sampling pipeline, wherein a sampler is arranged on the sampling pipeline. According to the invention, through combined arrangement of the formation water pipeline, the gas pipeline, the Raman probe and the pipeline where the connecting piece is located, real-time measurement of Raman spectrum data in the whole water-rock reaction process is realized; liquid and gas are mixed and circulated in the device in the water-rock reaction process by utilizing the combined arrangement of a gas-liquid circulating pipeline, a gas circulating pump and a liquid circulating pump, so that the accuracy of detection data is improved.

The invention belongs to the technical field of functional crystal materials, and discloses a neodymium-doped forsterite crystal, the chemical formula is Nd < 3 + >: Mg2SiO4, and the doping concentration range of Nd < 3 + > is 0.05 at%-0.2 at%. The preparation method of the neodymium-doped forsterite crystal comprises the following steps: S1, loading a synthesized Nd < 3 + >: Mg2SiO4 raw material into an iraurita crucible; s2, installing a zirconium oxide temperature field and an iraurita crucible in the single crystal furnace, and filling oxygen to inhibit decomposition and volatilization of the raw materials; and S3, melting the raw materials, carrying out constant-temperature treatment, selecting seed crystals for seeding, enabling the crystals to grow to a set length size in an equal-diameter manner until the crystals are separated from the melt after the growth is finished, and annealing in the crucible to obtain the large-size single crystals. The neodymium-doped forsterite grown by the method disclosed by the invention can still keep a stable structure in a lunar vacuum and extreme temperature environment, is not easy to oxidize or change phase, is suitable for being used as a standard sample for a long time, and can be used for calibrating a Raman spectrometer through specific wavelength and intensity signals.

The invention relates to the technical field of Raman spectrum detection, and discloses an automatic spectrum detection system for food grease, which comprises a detection box, a laser, an optical fiber module, a driving motor, a sample turntable, a heating piece and a lifting structure, the detection box comprises a detection cavity, the driving motor is rotatably connected with the sample turntable, the sample turntable is provided with a plurality of circumferentially spaced placement grooves, and the placement grooves are provided with light passing openings towards the center side; the heating element is cylindrical, the heating wire is buried in the side wall, the inner wall and the bottom surface of the placing groove form a sample groove, the heating element slides up and down along the placing groove through the lifting structure, and the bottom wall descends below the light passing opening when the heating element rotates to the position between the laser and the optical fiber module. The system can heat a plurality of grease samples at the same time, time is saved, heat loss is little, temperature rise is balanced, and spectrum detection is not hindered.