A turbidity current deposit micro-plastic extraction, identification and physical and mechanical property testing method

Abstract

The present application relates to the technical field of sediment environment monitoring, and discloses a turbidity current sediment microplastic extraction, identification and physical and mechanical property testing method, which comprises the following steps: turbidity current observation data are screened through a quality control threshold value, and the concentration of suspended bodies and hydrodynamic environment characteristic data are obtained by using an acoustic inversion model and a vertical integral algorithm; the position of sediment sampling is determined by using computer tomography technology, and a C-M diagram is constructed in combination with particle size analysis to accurately identify the turbidity current sediment horizon; the horizon sample is subjected to multi-stage density flotation by using saturated potassium iodide solution, and microplastic samples to be tested are obtained by cooperating with chemical digestion; the polymer material is identified by using spectrum technology, and the hardness, elastic modulus and yield strength of the microplastic are calculated by using nanoindentation technology to record the load and displacement curves. The present application solves the problems of low extraction efficiency of deep-sea microplastics and lack of mechanical constitutive parameters, and realizes the coupling analysis of macroscopic dynamics and microscopic physical properties.

Description

Technical Field

[0001] This invention relates to the field of sediment environmental monitoring technology, specifically a method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments. Background Technology

[0002] Marine microplastic pollution has become a global environmental problem, and turbidity currents are a key dynamic mechanism for the transport of terrestrial microplastics to the deep sea. Existing monitoring technologies mainly focus on the abundance statistics and composition identification of microplastics, but they still have shortcomings in the detailed coupled analysis of turbidity current dynamic processes.

[0003] In terms of hydrodynamic environment acquisition, existing acoustic observation data processing typically fails to correct for acoustic attenuation and near-field effects in the deep-sea environment, leading to distorted suspended matter concentration inversion results and a lack of depth-averaged dynamic parameters that can characterize the overall transport scale of turbidity currents. Regarding sediment processing, conventional equidistant physical cutting lacks prior identification of stratigraphic structures, easily resulting in the mixing of sedimentary layers from different periods, making it difficult to accurately pinpoint turbidity current events. Simultaneously, traditional single-density flotation processes struggle to overcome the adsorption and trapping effects of deep-sea viscous sediments, resulting in low extraction and recovery rates for high-density or microplastic-encapsulated sediments.

[0004] Furthermore, existing technologies lack experimental characterization of the micromechanical properties of actual microplastic samples. Numerical simulations of microplastic transport often directly use the mechanical parameters of standard industrial materials, neglecting the changes in particle hardness and elastic modulus caused by long-distance transport and environmental weathering. This results in models lacking support from real physical constitutive parameters, thereby reducing the reliability of predicting the trajectory and sedimentation behavior of microplastics under turbidity shear conditions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for the extraction, identification, and physical and mechanical property testing of microplastics in turbidity current sediments. This method solves the problems of low accuracy, poor extraction efficiency, and lack of microscopic mechanical property characterization in existing technologies for monitoring microplastics in complex deep-sea dynamic environments.

[0006] This invention provides a method for the extraction, identification, and physical and mechanical property testing of microplastics in turbidity current sediments. This method integrates hydrodynamic inversion, accurate geological stratum identification, multi-level physicochemical extraction, and micromechanical characterization techniques to achieve a complete chain analysis of microplastics in deep-sea turbidity current environments, from macroscopic flux to microscopic constitutive structure.

[0007] The method first acquires turbidity current observation data through a turbidity current observation data processing module. After filtering out valid data according to a preset quality control threshold, the backscattering intensity of the valid data is converted into suspended body concentration using an acoustic inversion model. Based on this, turbidity current dynamic parameters containing information on flow velocity, concentration, and thickness are calculated, thereby constructing hydrodynamic environmental characteristic data of the turbidity current. Subsequently, a sediment processing and identification module acquires original sediment samples corresponding to the hydrodynamic environmental characteristic data in time and space. Computed tomography is used to visualize the internal structure of the samples to determine the sampling location. The acquired particle size test samples undergo chemical pretreatment and particle size testing. Based on the particle size test results, a graph showing the relationship between the first percentile particle size and the median particle size is constructed to identify the specific turbidity current sedimentary layer. Next, a microplastic extraction and pretreatment module acquires samples from the turbidity current sedimentary layer. After drying and dispersion, a multi-stage density flotation process is performed using a saturated potassium iodide solution to separate and collect the supernatant containing microplastics. The supernatant is then chemically digested and filtered to obtain clean microplastic test samples. Finally, the microplastic basic property testing module uses spectral analysis to determine the polymer material type of the microplastic and uses nanoindentation technology to record the load and displacement curves of the microplastic sample under load. Based on the characteristics of these curves, the mechanical property parameters of the microplastic are calculated.

[0008] In the specific implementation of data quality control and inversion, to ensure the authenticity of hydrodynamic environmental data, this invention sets correlation coefficient thresholds and backscattering intensity thresholds as screening criteria. By traversing the observation data, invalid points with correlation coefficient values ​​or backscattering intensity values ​​below the set thresholds are eliminated, retaining only valid data that simultaneously meets both indicators. Based on the screened valid data, an acoustic inversion model is used to calculate the suspended body concentration. In its calculation logic, the acoustic inversion model comprehensively considers the influence of seawater temperature, salinity, and pH of the observed sea area on signal attenuation, and introduces the near-field correction factor and sensor sensitivity parameters of the equipment. At the same time, it sets scattering characteristic parameters based on the particle size characteristics of the suspended body, and uses the above parameters to correct the backscattering intensity in the valid data, thereby converting the acoustic electrical signal into a physical concentration value.

[0009] To quantify the macroscopic dynamic characteristics of turbidity currents, this invention employs a vertical integration algorithm. First, the vertical distance from the seabed to the point where the velocity decays to zero is determined as the upper limit of the turbidity current height. Then, within the range from the seabed to this upper limit, velocity profile data and suspended particle concentration data are integrated. Through integration, depth-average velocity, turbidity thickness, and depth-average turbidity concentration, which characterize the overall transport scale of the turbidity current, are obtained, providing hydrodynamic boundary conditions for subsequent analysis of microplastic flux.

[0010] Regarding the processing and stratigraphic identification of sediment samples, this invention employs non-destructive computed tomography (CT) technology to guide physical sample separation. By analyzing the changes in grayscale values ​​with depth in the scanned images, stratigraphic interfaces are identified based on abrupt changes in grayscale data, and areas with relatively high grayscale values ​​are marked as coarse-grained enrichment zones as sample separation locations. Before particle size analysis, organic matter is removed using hydrogen peroxide solution, carbonates and iron-manganese oxide cementation are removed using hydrochloric acid solution, and sodium hexametaphosphate solution is added for dispersion using ultrasonic vibration to obtain the true single-particle size. In terms of stratigraphic determination logic, a double logarithmic coordinate system is established with the median particle size as the x-axis and the first percentile particle size as the y-axis. When sample points exhibit a distribution parallel to the baseline in this coordinate system, or exhibit a characteristic of deviating from the baseline and an increasing first percentile particle size, the stratigraphic level is confirmed as a turbidity current sedimentary layer.

[0011] For the efficient extraction of microplastics, this invention designs a multi-stage density flotation and deep purification process. A saturated potassium iodide solution is used as the flotation medium to process the dried and dispersed sediment samples. To address the mechanical retention of microplastics by deep-sea sediments, a multi-stage circulation strategy is employed: first, the sample and solution are mixed, stirred, and allowed to settle and separate into layers, yielding the first batch of supernatant; then, fresh saturated potassium iodide solution is added to the remaining bottom sediment layer, and the stirring, settling, and separation steps are repeated to obtain subsequent batches of supernatant; finally, the supernatants from each batch are combined. The combined supernatant is further treated with hydrogen peroxide solution as an oxidant, and a digestion reaction is carried out under static conditions to remove organic impurities from the surface of the microplastics. After vacuum filtration and washing with ultrapure water, a microplastic sample for subsequent testing is obtained.

[0012] For characterizing the properties of microplastics, this invention combines spectroscopic and micro / nanomechanical testing techniques. On one hand, functional group information is obtained using Fourier transform micro-infrared spectroscopy, and fingerprint spectra are acquired using laser confocal micro-Raman spectroscopy. The combined spectral data is compared with a standard library to determine the polymer material. On the other hand, a nanoindenter is used to apply a set load to the microplastic sample and record the entire loading and unloading process, generating a load-displacement curve. Based on the characteristics of the unloading phase of this curve, the hardness and elastic modulus are calculated, and the yield strength is calculated based on the characteristics of the elastoplastic transition point in the curve, thereby quantifying the mechanical constitutive properties of microplastics in a deep-sea environment.

[0013] This invention provides a method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments. It offers the following advantages:

[0014] 1. This invention establishes an acoustic inversion model incorporating near-field correction and signal attenuation compensation, combined with a vertical integration algorithm, to accurately quantify the hydrodynamic characteristics of turbidity currents. The method of this invention can accurately convert the backscattered intensity electrical signal acquired by an acoustic Doppler current profiler into the physical quantity of suspended body concentration, and calculate key parameters such as depth-average velocity, turbidity thickness, and depth-average concentration. This solves the problem of high randomness in traditional single-point measurement data, making it difficult to characterize the overall transport scale of turbidity currents, and provides a reliable hydrodynamic environmental data foundation for calculating the transport flux of microplastics.

[0015] 2. This invention combines computed tomography (CT) with grain size mapping (CM) to achieve precise stratigraphic localization and genetic determination without damaging the original sediment structure. Physical sampling locations are determined by analyzing abrupt changes in the grayscale sequence of CT images, and the depositional type is verified using the mapping relationship between the first percentile grain size and the median grain size, ensuring that the extracted microplastic samples strictly correspond to specific turbidity current depositional events. This targeted sampling strategy effectively avoids stratigraphic mixing and geological information distortion caused by traditional blind sampling, guaranteeing the spatiotemporal representativeness of the analysis results.

[0016] 3. This invention employs a multi-stage cyclic density flotation process using saturated potassium iodide solution and innovatively introduces nanoindentation technology for micromechanical characterization, enhancing the dimensionality and application value of microplastic monitoring data. Multi-stage flotation combined with chemical digestion overcomes the mechanical retention effect of fine-particle sediments on microplastics in deep-sea environments, improving the recovery rate of high-density microplastics. Furthermore, constitutive parameters such as hardness, elastic modulus, and yield strength obtained through nanoindentation testing fill the technical gap in traditional methods that focus solely on abundance statistics while neglecting physical and mechanical properties, providing crucial physical boundary conditions for constructing a high-fidelity numerical model of microplastic particle transport. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the test method flow of the present invention;

[0018] Figure 2 This is a schematic diagram of the multi-stage density flotation process of the present invention;

[0019] Figure 3 This is a CM diagram for identifying turbidity deposit layers according to the present invention;

[0020] Figure 4 This is a flowchart illustrating the processing and testing analysis of sediment and microplastic samples according to the present invention.

[0021] The module includes: 10. Turbidity flow observation data processing module; 20. Sediment processing and identification module; 30. Microplastic extraction and pretreatment module; and 40. Microplastic basic property testing module. Detailed Implementation

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] See attached document Figure 1 A method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments includes: a turbidity current observation data processing module 10, a sediment processing and identification module 20, a microplastic extraction and pretreatment module 30, and a microplastic basic property testing module 40.

[0024] The turbidity flow observation data processing module 10 receives data from the acoustic Doppler velocity profiler, removes invalid data based on the correlation coefficient and backscatter intensity threshold, converts the backscatter intensity into suspended body concentration using an acoustic inversion model that includes near-field correction factor, sensor sensitivity, scattering characteristics and attenuation coefficient, and calculates the depth-average velocity, thickness and concentration of the turbidity flow using a vertical integration algorithm.

[0025] The sediment processing and identification module 20 performs computed tomography scans on sediment samples to extract grayscale sequences and determine sample locations. After chemical removal of organic matter and cement and ultrasonic dispersion, the turbidity current sedimentary layers are identified by combining particle size analysis results with the CM diagram method, and radiocarbon dating is performed.

[0026] The microplastic extraction and pretreatment module 30 dries and disperses the sample, and uses a saturated potassium iodide solution to perform multi-stage density flotation to separate the supernatant containing microplastics; then the supernatant is oxidized and digested to remove organic impurities, and the microplastic sample is obtained after filtration and washing.

[0027] The microplastic basic property testing module 40 uses spectroscopic technology to identify polymer components and uses scanning electron microscopy and energy dispersive spectroscopy to obtain three-dimensional morphological information; it uses nanoindentation technology to record load and displacement curves and calculates the hardness, elastic modulus and yield strength of microplastics accordingly.

[0028] The turbidity current observation data processing module 10 specifically performs the data quality control process, which mainly includes steps S101 to S103.

[0029] In step S101, the turbidity current observation data processing module 10 reads the raw observation data file acquired by the acoustic Doppler current profiler. The raw observation data file contains velocity component data, flow direction data, correlation coefficient data, and backscattering intensity data at different depth layers and different time series. The specific hardware working principle of the acoustic Doppler current profiler for data acquisition is well-known to those skilled in the art and will not be described in detail here.

[0030] In step S102, the turbidity current observation data processing module 10 sets key indicator thresholds for filtering valid data. These key indicator thresholds include at least a correlation coefficient threshold and a backscattering intensity threshold. In this embodiment, the correlation coefficient threshold is set to 70%, and the backscattering intensity threshold is set to 120 counts. The correlation coefficient characterizes the quality of the echo signal received by the acoustic Doppler current profiler and the consistency between pulses. A lower correlation coefficient typically indicates interference in the measurement environment or a low signal-to-noise ratio. The backscattering intensity characterizes the ability of suspended particles in the water to scatter sound waves. The backscattering intensity is expressed in counts, representing the original echo intensity count recorded by the device. A lower backscattering intensity typically indicates that the water is clear or at the instrument noise level, failing to effectively capture turbidity current suspended particle signals.

[0031] In step S103, the turbidity current observation data processing module 10 performs point-by-point traversal and discrimination of each data point in the original observation data file. The turbidity current observation data processing module 10 reads the correlation coefficient value and backscattering intensity value of the current data point. The turbidity current observation data processing module 10 compares the correlation coefficient value of the current data point with a correlation coefficient threshold and compares the backscattering intensity value of the current data point with a backscattering intensity threshold. When the correlation coefficient value of the current data point is less than 70%, or the backscattering intensity value of the current data point is less than 120 counts, the turbidity current observation data processing module 10 marks the current data point as invalid data and removes it. Only when the correlation coefficient value of the current data point is greater than or equal to 70%, and the backscattering intensity value is greater than or equal to 120 counts, the turbidity current observation data processing module 10 retains the current data point and stores it in the valid turbidity current velocity dataset. Through the above screening steps, non-turbidity current signals and low-quality noise signals can be effectively removed, ensuring that the data basis for subsequent inversion calculations is true and reliable.

[0032] After completing the data quality control and removing invalid data, the turbidity current observation data processing module 10 performs the suspended body concentration inversion process, which converts the electrical signal into physical concentration parameters based on the acoustic backscattering principle, mainly including steps S201 to S205.

[0033] In step S201, the turbidity current observation data processing module 10 acquires the filtered effective backscattering intensity data and converts the effective backscattering intensity data into backscattering equivalent voltage. Backscattering equivalent voltage is a direct physical quantity reflecting the strength of the received acoustic wave energy.

[0034] In step S202, the turbidity current observation data processing module 10 determines the near-field correction factor. The near-field correction factor is jointly determined by the physical measurement position of the acoustic Doppler current profiler, the distance to the transducer, the transducer radius, and the wavelength of the transmitted signal. For the hardware model of the acoustic Doppler current profiler, the transducer radius and signal wavelength are fixed parameters, and the near-field correction factor varies with the measurement distance to correct for the nonlinear effects of the acoustic beam diffusion characteristics in the near-field range.

[0035] In step S203, the turbidity current observation data processing module 10 determines the sensor sensitivity parameters and suspended body scattering characteristic parameters. The sensor sensitivity parameters are fixed constants of the measurement system, depending on the instrument's hardware calibration characteristics. The suspended body scattering characteristic parameters depend on the particle size of the suspended particles and their backscattering form function. The turbidity current observation data processing module 10 sets the suspended body scattering characteristic parameters based on the sediment particle size characteristics of the monitored area. In an embodiment involving deep-sea coarse-grained sedimentary environments, the turbidity current observation data processing module 10 adjusts the suspended body scattering characteristic parameters to values ​​suitable for the scattering cross-section of coarse particles (e.g., set to 1.2 × 10⁻⁶). -5 In a typical fine-particle environment or under default settings, the scattering characteristic parameters of the suspended body are set to a baseline value (e.g., 1.0 × 10⁻⁶). -5 ).

[0036] In step S204, the turbidity current observation data processing module 10 calculates the signal attenuation coefficient caused by seawater absorption. The turbidity current observation data processing module 10 acquires the on-site environmental parameters of the observation area, including seawater temperature, salinity, and pH value. Based on the seawater temperature, salinity, and pH value, the turbidity current observation data processing module 10 calculates the signal attenuation coefficient of the sound wave during propagation to compensate for energy loss along the sound wave's propagation path in the water.

[0037] In step S205, the turbidity observation data processing module 10 uses the constructed acoustic inversion model of suspended body concentration, combined with the parameters obtained in the above steps, to calculate the suspended body concentration at each measurement location. The calculation formula for the acoustic inversion model of suspended body concentration is as follows:

[0038] ;

[0039] in, For distance transducers The concentration of suspended matter at that location, The backscattering equivalent voltage, The near-field correction factor is determined by the distance between the measurement position and the transducer, the transducer radius, and the signal wavelength. To measure the distance from the location to the transducer, For sensor sensitivity, and for fixed parameters of the measurement system, The scattering characteristic parameters of the suspended body depend on the particle size of the suspended particles and their backscattering form function. The backscattering equivalent voltage, The signal attenuation coefficient due to seawater absorption is determined by seawater temperature, salinity, and pH.

[0040] Through the above steps, the turbidity flow observation data processing module 10 converts the acoustic echo intensity into spatiotemporal distribution data of suspended body concentration with clear physical meaning. The correlation between the spatiotemporal distribution data of suspended body concentration and the measured value by the gravimetric method can meet the requirements of high-precision observation, providing basic scalar field data for subsequent calculation of the flux and dynamic properties of turbidity flow.

[0041] After acquiring turbidity flow velocity profile data and inverted suspended matter concentration profile data, the turbidity observation data processing module 10 performs a vertical integration parameter calculation process to convert the two-dimensional or three-dimensional profile data into a quantified single characteristic parameter. This process mainly includes steps S301 to S303.

[0042] In step S301, the turbidity current observation data processing module 10 determines the physical boundary of the vertical integration. The turbidity current observation data processing module 10 reads velocity data at different heights above the seabed and identifies the location where the turbidity current velocity decays to zero. The turbidity current observation data processing module 10 defines the vertical distance from the location where the turbidity current velocity decays to zero to the seabed as the upper limit of the turbidity current height, denoted as […]. As the upper limit of subsequent integration operations, the lower limit of integration is set to the seabed surface (i.e., the height from the bottom is 0).

[0043] In step S302, the turbidity flow observation data processing module 10 acquires the discrete variable data required for integration. The discrete variable data includes the flow velocity at different locations. and the concentration of suspended matter at different locations .

[0044] In step S303, the turbidity flow observation data processing module 10 calculates the characteristic parameters of the turbidity flow vertical profile based on the integral definition. The characteristic parameters include the depth-average turbidity velocity, turbidity thickness, and depth-average turbidity concentration. The turbidity flow observation data processing module 10 performs vertical integration processing on the discrete profile data using numerical integration methods (such as the trapezoidal rule or Simpson's rule).

[0045] The turbidity flow observation data processing module 10 calculates the depth-average velocity of the turbidity flow based on the following set of formulas. and turbidity thickness :

[0046] ;

[0047] ;

[0048] The turbidity observation data processing module 10 calculates the depth-average concentration of turbidity using the following formula. :

[0049] ;

[0050] in, It is the depth-average velocity of the turbidity flow. It is the thickness of the turbidity flow. The average concentration at turbidity depth. The unit width volume flux of the turbidity stream. For turbidity flow, the momentum flux per unit width is... This represents the turbidity flow unit width suspended transport flux. It is the distance from the seabed where the turbidity current velocity is zero. and The values ​​represent the flow rate and suspended matter concentration at different locations. It is the height above the bottom where the flow velocity and suspended concentration of each layer are measured.

[0051] Through the above integral calculations, the turbidity flow observation data processing module 10 transforms the complex velocity and concentration profiles that vary with depth into key dynamic parameters that can characterize the overall transport capacity of the turbidity flow, providing a quantitative basis for analyzing the scale and evolution of turbidity flow events.

[0052] See attached document Figure 4 The sediment processing and identification module 20 performs a non-destructive testing process, using computed tomography (CT) technology to obtain information about the internal structure of the sediment without opening the sample tube. This process mainly includes steps S401 to S404.

[0053] In step S401, the sediment processing and identification module 20 receives the sediment samples to be analyzed. The sediment samples include loose samples recovered by a sediment trap and columnar sediment samples obtained by a gravity sampler or a box sampler. The sediment processing and identification module 20 scans the sediment samples using a whole-core CT scanning device. In this embodiment, a CT scanning device from GEOTEK is used for data acquisition.

[0054] In step S402, the sediment processing and identification module 20 sets the CT scan parameters to obtain high-resolution images. To identify fine layering and small particle aggregates, the sediment processing and identification module 20 sets the scan interval to 0.05 mm. For the deep-sea coarse-grained sediment environment, the sediment processing and identification module 20 sets the scan range to 2 cm × 2 cm to ensure coverage of statistically representative particle distribution areas. The CT scanning equipment penetrates the sediment sample with X-rays and generates corresponding CT slice images based on the density differences of the materials within the sample.

[0055] In step S403, the sediment processing and identification module 20 digitizes the acquired CT slice images using image processing software. In this embodiment, ImageJ software is used for image analysis. The sediment processing and identification module 20 extracts grayscale data from the CT images and generates a grayscale value sequence that varies with depth. The magnitude of the grayscale value characterizes the density characteristics of the sediment; high grayscale value areas correspond to high-density materials, typically indicating the presence of coarse grains, gravel, or heavy minerals; low grayscale value areas correspond to low-density materials, typically indicating clayey silt, high-water-content layers, or organic-rich layers.

[0056] In step S404, the sediment processing and identification module 20 determines the physical sampling scheme based on the grayscale value distribution characteristics. The module analyzes abrupt changes in the grayscale value sequence, identifying hidden bedding interfaces or grain size abrupt changes that are difficult to observe with the naked eye. Based on the identified interface locations, the module determines the sampling interval for subsequent physical cutting. The default sampling interval is set to 1 cm to 2 cm; when the grayscale values ​​indicate the presence of fine bedding with a thickness smaller than the normal interval, the sampling interval is adjusted to 1 cm. For areas with high grayscale values, the module marks these areas as coarse-grained enrichment zones, serving as key sampling targets for subsequent studies on the adhesion relationship between microplastics and coarse particles.

[0057] After determining the sample distribution location and completing the physical cutting, the sediment processing and identification module 20 performs a particle size analysis pretreatment process, which aims to remove organic matter, carbonates and iron and manganese oxide cements in the sediment, so as to thoroughly disperse the primary mineral particles. This mainly includes steps S501 to S504.

[0058] In step S501, the sediment processing and identification module 20 weighs and performs preliminary sieving of the sample to be tested. The sediment processing and identification module 20 weighs 0.5g of the freeze-dried sediment sample and places it in a centrifuge tube. For deep-sea coarse-grained sedimentary environments, the sediment processing and identification module 20 checks whether the sample contains gravel with a particle size greater than 5mm before weighing. If gravel with a particle size greater than 5mm is present, the sediment processing and identification module 20 removes the gravel to prevent clogging or damage to the laser particle size analyzer used subsequently.

[0059] In step S502, the sediment treatment and identification module 20 removes organic matter from the sample using an oxidant. The module adds excess hydrogen peroxide solution to the centrifuge tube. Depending on the organic matter content in the sediment, the volume of the hydrogen peroxide solution is set to 40 mL to 60 mL, and the concentration is set to 5% to 30%. The module then heats the sample in a water bath at 85°C for 1.5 to 2 hours. During heating, the module continuously observes the reaction bubbles. For estuarine samples with high organic matter content, after one heating cycle, the module centrifuges for 10 minutes and removes the supernatant. Hydrogen peroxide solution is added again, and the heating process is repeated, typically twice, until the reaction bubbles completely disappear, ensuring that the organic matter is thoroughly oxidized and removed.

[0060] In step S503, the sediment treatment and identification module 20 uses an acidic reagent to remove chemical binders from the sample. These chemical binders mainly include carbonates, iron and manganese oxides, and hydroxides. The sediment treatment and identification module 20 adds a sufficient amount of hydrochloric acid solution to the sample after organic matter removal. In this embodiment, the concentration of the hydrochloric acid solution is 0.5 mol / L, and the added volume is 40 mL. The sediment treatment and identification module 20 heats the sample in a water bath at 85°C for 1.5 hours. After heating, the sediment treatment and identification module 20 centrifuges the sample for 10 minutes and removes the supernatant to eliminate the chemical adhesion between particles.

[0061] In step S504, the sediment processing and identification module 20 disperses the chemically treated sample. The module adds a 51 g / L sodium hexametaphosphate solution as a dispersant. Before laser particle size analysis, the module disperses the sample using an ultrasonic vibrator for 10 minutes. The ultrasonic vibration time is set to 10 to 15 minutes depending on the particle size. For deep-sea samples containing more coarse particles, the ultrasonic time is extended to 15 minutes to ensure that particles aggregated due to physical adsorption are completely broken up, thereby obtaining accurate single-particle size distribution data.

[0062] After completing the preprocessing for particle size analysis and acquiring particle size distribution data using a fully automated laser particle size analyzer, the sediment processing and identification module 20 performs the sediment genesis determination and dating process, which mainly includes steps S601 to S603.

[0063] In step S601, the sediment processing and identification module 20 constructs a CM map based on the grain size distribution data. The sediment processing and identification module 20 extracts characteristic grain size parameters, namely the C value and M value, from the grain size data of each sub-sampling layer. The C value is defined as the grain size value corresponding to the 1% grain content on the physical accumulation curve of the sample, representing the coarsest percentile grain size in the sediment, used to characterize the maximum hydrodynamic agitation capacity during deposition. The M value is defined as the grain size value corresponding to the 50% grain content on the physical accumulation curve of the sample, representing the median grain size of the sediment, used to characterize the average hydrodynamic transport capacity during deposition. The sediment processing and identification module 20 establishes a double logarithmic coordinate system, with the M value on the x-axis and the C value on the y-axis, projecting the grain size data of different depth layers into the double logarithmic coordinate system to form the CM map.

[0064] In step S602, the sediment processing and identification module 20 identifies turbidite deposits based on the distribution morphology of sample points and vertical grain size variation characteristics on the CM map. The sediment processing and identification module 20 analyzes the spatial distribution trend of sample points relative to the C=M baseline. When sample points exhibit a distribution parallel to the C=M line on the CM map, the sediment processing and identification module 20 determines the corresponding layer as a graded suspension turbidite deposit, indicating that particles in the fluid mainly form deposits through a graded suspension mechanism via gravity settling due to reduced flow velocity. When the sample point shows an increase in the C value relative to the M value on the CM map, the sediment processing and identification module 20 determines the corresponding layer as a high-energy, coarse-grained turbidite deposit. Furthermore, the sediment processing and identification module 20 uses grain size abrupt changes and erosion surface characteristics on the vertical profile for auxiliary discrimination. When a certain layer exhibits a sharp change in grain size parameters, poor sorting, or obvious bottom erosion structures, the sediment processing and identification module 20 confirms the layer as a turbidite depositional event layer.

[0065] In step S603, the sediment processing and identification module 20 performs geological dating sampling in the identified turbidite sedimentary layers. Under a microscope, the module selects foraminiferal shells from the sedimentary residue of the selected layers. To eliminate interference from redeposition on the dating results, the module specifically selects planktonic foraminifera with clean shell surfaces, intact structures, and no signs of recirculation abrasion. The module then sends the selected planktonic foraminiferal samples to an accelerator mass spectrometer for carbon-14 isotope analysis to obtain the absolute geological age of the turbidite depositional event. The specific operation of the accelerator mass spectrometer is well-known to those skilled in the art and will not be described further here.

[0066] The microplastic extraction and pretreatment module 30 performs sample drying and dispersion operations, aiming to effectively dissociate microplastic particles from the sediment matrix without damaging the physical morphology and chemical structure of microplastics, laying the foundation for subsequent density flotation. It mainly includes steps S701 to S703.

[0067] In step S701, the microplastic extraction and pretreatment module 30 performs low-temperature drying on the wet sediment sample selected through sedimentation site identification. The microplastic extraction and pretreatment module 30 places the wet sediment sample in an aluminum foil box and then puts it into an electrically heated constant-temperature drying oven. To prevent the polymer materials (such as polyethylene, polypropylene, etc.) from thermal melting, deformation, or thermal degradation, the microplastic extraction and pretreatment module 30 strictly controls the drying temperature. In this embodiment, the drying temperature is set to 100°C, and the drying duration is set to 12 hours until the sample reaches a constant weight. After drying, the microplastic extraction and pretreatment module 30 uses a high-precision electronic balance to weigh the dried sample and uniquely marks this physical quantity as the dry weight of the sediment sample, denoted as W. d Dry weight W of sediment sample d This serves as the baseline denominator for subsequent calculations of microplastic abundance (units: cells / kg or items / kg). For surface samples rich in moisture, the microplastic extraction and pretreatment module 30 can optionally employ a freeze dryer for vacuum freeze drying to preserve the original surface morphology of the microplastics to the greatest extent possible.

[0068] In step S702, the microplastic extraction and pretreatment module 30 prepares the dispersion and chemically disperses the dried sample. The microplastic extraction and pretreatment module 30 adds a sodium hexametaphosphate solution with a mass concentration of 51 g / L to 306 g / L (corresponding to a molar concentration of approximately 0.08 mol / L to 0.5 mol / L) as a dispersant to a beaker, with the added volume being at least 2 to 3 times the sample volume. In a preferred embodiment, a sodium hexametaphosphate solution with a concentration of 51 g / L is used; for sediment samples with high clay content or severe organic matter adhesion, the dispersant concentration can be further adjusted to 0.2 mol / L to 0.5 mol / L. After adding the dispersant, the system performs stirring for 2-5 minutes to break down the flocs formed by fine-particle sediments through chemical complexation, ensuring the sample is completely submerged and fully wetted.

[0069] In this embodiment, the microplastic extraction and pretreatment module 30 adds a sodium hexametaphosphate solution with a concentration of 0.2 mol / L to 0.5 mol / L, and the added volume is at least 2 to 3 times the sample volume to ensure that the sample is completely immersed in the dispersant solution.

[0070] In step S703, the microplastic extraction and pretreatment module 30 mechanically disperses the mixture through oscillation. The module seals the container holding the sample and dispersant solution and places it on a horizontal reciprocating or rotary shaker. The module sets the oscillation frequency and time. To avoid severe mechanical breakage that could cause large microplastics to fracture into smaller fragments (i.e., artificially generated secondary microplastics), the module sets the oscillation frequency to a low-speed mode, specifically 100 to 120 rpm. The oscillation time is set to 12 to 24 hours. Through continuous and gentle mechanical agitation combined with the action of the chemical dispersant, the module releases microplastic particles encapsulated or adhered to by the sediment into the liquid phase system, forming a homogeneous solid-liquid suspension for subsequent density flotation separation. The specific mechanical structure and driving principle of horizontal reciprocating oscillators and gyroscopic oscillators are well-known technologies to those skilled in the art, and will not be elaborated here.

[0071] See attached document Figure 2 After the sample drying and dispersion pretreatment is completed, the microplastic extraction and pretreatment module 30 performs a multi-stage density flotation operation. The aim is to use a high-density salt solution as a medium and allow the less dense microplastic particles to float to the surface of the liquid through long-term static sedimentation, thereby completely separating the less dense microplastic particles from the complex sediment matrix. It strictly follows the physical sorting principle and mainly includes steps S801 to S804.

[0072] In step S801, the microplastic extraction and pretreatment module 30 prepares a flotation solution with a specific density. The microplastic extraction and pretreatment module 30 uses potassium iodide (KI) as the solute to prepare a saturated salt solution. To ensure effective flotation of various microplastic components, including high-density polymers, and to utilize the high solubility of the saturated salt solution, the microplastic extraction and pretreatment module 30 precisely prepares the potassium iodide solution to a density of 1.66 g / cm³. 3 Density value (1.66 g / cm³) 3 After optimization, the density is significantly higher than that of common microplastics (such as polyethylene, polypropylene, polyvinyl chloride, etc.) and lower than that of the main silicate minerals in sediments, thus forming an ideal density separation medium.

[0073] In step S802, the microplastic extraction and pretreatment module 30 mixes the dispersed sediment sample with a high-density flotation solution and stirs it for a long time. The microplastic extraction and pretreatment module 30 adds the prepared saturated potassium iodide solution to the sample after drying and sodium hexametaphosphate dispersion treatment. To break the physical adsorption and agglomeration effect between sediment particles and microplastics, the microplastic extraction and pretreatment module 30 thoroughly stirs the mixture. In this embodiment, the microplastic extraction and pretreatment module 30 strictly sets the stirring time to 30 minutes. This duration is designed to ensure sufficient contact between the solid phase and the liquid phase medium, allowing the encapsulated lightweight components to completely dissociate and disperse in the high-density solution.

[0074] In step S803, the microplastic extraction and pretreatment module 30 performs a long-term static sedimentation operation. After stirring is stopped, the microplastic extraction and pretreatment module 30 places the mixture in a stable environment for gravity sedimentation. The microplastic extraction and pretreatment module 30 sets the settling time to 12 hours. During this period, the density value is greater than 1.66 g / cm³. 3 Sand, silt, and clay mineral particles slowly settle to the bottom of the container under gravity, with a density value less than or equal to 1.66 g / cm³. 3 The microplastic particles overcome fluid resistance and gradually float to the surface, eventually gathering on the solution surface to form a clear solid-liquid stratification structure.

[0075] In step S804, the microplastic extraction and pretreatment module 30 performs supernatant filtration and multi-stage circulating flotation. The module carefully collects the microplastic-rich supernatant and filters it through a membrane to retain the microplastics. To maximize extraction efficiency and prevent mechanical retention of microplastics by the bottom precipitate, the module implements a multi-stage circulating extraction strategy. Specifically, the module adds fresh saturated potassium iodide solution to the bottom precipitate generated in step S803 and repeats the entire process of stirring thoroughly for 30 minutes, allowing it to stand for 12 hours, and then filtering the supernatant. The module repeats the flotation process three times. Through three cycles of extraction, the extraction efficiency for microplastics with a particle size range of 0.05 mm to 5 mm can reach 85% to 90%, and the extraction efficiency for microplastics with a particle size greater than 0.1 mm can reach over 92%, ensuring the statistical representativeness of the analytical results. The specific operation of the filtration device is well-known to those skilled in the art and will not be described in detail here.

[0076] After obtaining a microplastic-rich recovered supernatant through a multi-stage density flotation process, the microplastic extraction and pretreatment module 30 performs organic digestion and washing operations, mainly including steps S901 to S904.

[0077] In step S901, the microplastic extraction and pretreatment module 30 performs oxidative digestion on the supernatant recovered from flotation. The microplastic extraction and pretreatment module 30 adds a chemical digestion reagent to the supernatant containing suspended microplastic particles recovered in step S804. To effectively remove biofilms, algae, and other organic impurities from the sediment adhering to the microplastic surface, while avoiding damage to the microplastic polymer matrix, the microplastic extraction and pretreatment module 30 selects a 30% hydrogen peroxide solution as the oxidant. Based on the specific volume and organic matter content of the supernatant, the microplastic extraction and pretreatment module 30 adds 35 mL to 40 mL of hydrogen peroxide solution to ensure an excess of oxidant for sufficient reaction.

[0078] In step S902, the microplastic extraction and pretreatment module 30 performs a static digestion reaction. The module places the solution system mixed with hydrogen peroxide at room temperature for static digestion. To ensure complete oxidative decomposition of the organic matter, the module sets the static digestion time to 24 to 36 hours. During this period, the highly oxidizing hydrogen peroxide can destroy the molecular structure of the organic matter, causing impurities adhering to the microplastic surface to detach and dissolve, thereby exposing the original surface features of the microplastics, facilitating subsequent spectral identification and morphological observation.

[0079] In step S903, the microplastic extraction and pretreatment module 30 performs a filtration operation. After the digestion reaction is completed, the microplastic extraction and pretreatment module 30 repeats the filtration step. The microplastic extraction and pretreatment module 30 filters the digested mixed solution through a vacuum filtration device, so that the purified microplastic particles are retained on the surface of the filter membrane, while the waste liquid containing degraded organic matter and residual chemical reagents is filtered out.

[0080] In step S904, the microplastic extraction and pretreatment module 30 performs multi-stage cleaning and preservation of the filter membrane. To thoroughly remove residual flotation media (potassium iodide) and digestion reagent (hydrogen peroxide) from the filter membrane, the microplastic extraction and pretreatment module 30 rinses the filter membrane and the inner wall of the filter cup with ultrapure water, with the rinsing frequency set to 3 to 5 times. Subsequently, to remove residual lipid-soluble impurities and accelerate the drying of the filter membrane, the microplastic extraction and pretreatment module 30 may use ethanol solution to perform auxiliary rinsing of the filter membrane, with the rinsing frequency set to 1 to 2 times. After cleaning, the microplastic extraction and pretreatment module 30 performs a quality inspection of the filter membrane. After confirming that there are no obvious impurities obstructing the flow, the filter membrane is dried and sealed for preservation for subsequent component identification and mechanical property testing.

[0081] After the extraction and cleaning of microplastics are completed, the microplastic basic property testing module 40 performs non-destructive component analysis and high-precision morphology reconstruction on the sample, aiming to accurately identify the polymer type of microplastics and quantify the physical deformation characteristics of polymer types under complex deposition dynamic environment, mainly including steps S1001 to S1003.

[0082] In step S1001, the microplastic basic property testing module 40 performs dual-spectral joint component identification. The microplastic basic property testing module 40 places the filter membrane carrying the microplastic sample on a microscopic spectral analysis platform. To overcome the limitations of single-spectral techniques in dealing with microplastics of different particle sizes and surface aging degrees, the microplastic basic property testing module 40 uses a combination of Fourier transform micro-infrared spectroscopy and laser confocal micro-Raman spectroscopy for joint testing. The microplastic basic property testing module 40 obtains functional group information through infrared spectroscopy for rapid screening of major polymer matrices (such as polyethylene, polypropylene, polyvinyl chloride, polystyrene, etc.); simultaneously, it utilizes the high spatial resolution of Raman spectroscopy to collect fingerprint spectra of microplastic particles with small particle sizes or those exhibiting fluorescence interference on their surfaces. The microplastic basic property testing module 40 compares the collected spectral data with a standard polymer spectral library; when the matching degree reaches a preset threshold, the chemical composition of the microplastic is determined. Through the joint identification strategy of Fourier transform micro-infrared spectroscopy and laser confocal micro-Raman spectroscopy, the accuracy rate of microplastic component identification can reach over 93%.

[0083] In step S1002, the microplastic basic property testing module 40 acquires microscopic morphology and elemental distribution information. The module uses a scanning electron microscope equipped with an X-ray energy dispersive spectroscopy (EDS) instrument to perform high-resolution imaging of the microplastic sample. Utilizing the secondary electron and backscattered electron signals from the EDS instrument, the module observes the surface microstructure of the microplastic particles, including scratches, dents, cracks, and edge wear features caused by sediment transport and abrasion. Particularly for deep-sea coarse-grained sediment samples, the module focuses on observing the adhesion relationship between microplastics and sediment particles and surface wear marks. Simultaneously, the module uses an EDS instrument to perform surface scanning or point analysis of the elemental composition of the particle surface, detecting characteristic elements (such as chlorine, titanium, and other additive elements or clay mineral elements) to help distinguish microplastics from inorganic mineral particles and assess the composition of surface deposits.

[0084] In step S1003, the microplastic basic property testing module 40 performs three-dimensional structure reconstruction and geometric parameter extraction. Based on the high-resolution electron micrograph and multi-angle projection data obtained in step S1002, the microplastic basic property testing module 40 performs digital reconstruction using image processing and analysis software. The microplastic basic property testing module 40 first performs image segmentation processing on the original image to separate the microplastic target area from the background; then, it uses a three-dimensional surface modeling algorithm to recover the three-dimensional spatial structure of the microplastic particles based on grayscale changes and texture features. On this basis, the microplastic basic property testing module 40 automatically extracts three-dimensional spatial geometric parameters that reflect the dynamic characteristics of the microplastics. These three-dimensional spatial geometric parameters include, but are not limited to, particle volume, surface area, three-dimensional sphericity, roughness, and the ratio of major to minor axes. These quantified geometric parameters are directly used in the subsequent CFD-DEM numerical simulation to construct the particle model, thereby providing realistic physical model boundary conditions for inverting the transport laws of microplastics in turbidity currents. The specific algorithm implementation of the above image processing and analysis software is a well-known technology in the field of computer vision and will not be elaborated here.

[0085] After completing the three-dimensional morphological characterization, the microplastic basic property testing module 40 further conducts quantitative testing of the micromechanical properties of the microplastic particles, mainly including steps S1101 to S1104.

[0086] In step S1101, the microplastic basic property testing module 40 configures the nanoindentation testing environment and indenter parameters. To accurately measure the mechanical response of microplastics at the micrometer scale, the microplastic basic property testing module 40 selects a Berkovich diamond indenter as the contact probe. The indenter has a triangular pyramidal geometry, with a tip radius controlled between 10 nm and 20 nm. The microplastic basic property testing module 40 fixes the microplastic sample to be tested on the testing stage and calibrates the zero-point of contact between the indenter and the sample surface to ensure the reference accuracy of the displacement measurement.

[0087] In step S1102, the microplastic basic property testing module 40 performs a loading test and collects load and displacement data. The microplastic basic property testing module 40 controls the indenter to apply continuously varying loads to the surface of the microplastic sample. During the complete loading and unloading cycle, the microplastic basic property testing module 40 records the load value (P) applied by the indenter and the depth displacement (h) of the indentation into the sample in real time, generating a load-displacement curve (Ph curve). The Ph curve reflects the elastic deformation, plastic deformation, and viscoelastic creep characteristics of the microplastic material during the stress process.

[0088] In step S1103, the microplastic basic property testing module 40 calculates the micromechanical parameters. Based on the Ph curve obtained in step S1102, the microplastic basic property testing module 40 analyzes the constitutive relationship of the microplastic. By analyzing the initial slope of the unloading curve and the peak displacement corresponding to the maximum load, the microplastic basic property testing module 40 calculates the hardness, elastic modulus, and yield strength of the microplastic. These parameters quantify the microplastic's ability to resist external mechanical deformation and its deformation trend under fluid shear force.

[0089] In step S1104, the microplastic basic property testing module 40 outputs mechanical model parameters. The microplastic basic property testing module 40 stores the calculated hardness, elastic modulus, and yield strength data in a structured manner and transmits them as physical property boundary conditions to the CFD-DEM numerical simulation system. These measured mechanical parameters are used to construct a realistic microplastic particle model, thereby ensuring the authenticity and accuracy of subsequent simulation results regarding the migration, collision, and sedimentation of microplastics in turbid flows. For the specific operating procedures of the nanoindentation experiment and the basic calculation principles based on the Ph curve, those skilled in the art can refer to relevant materials mechanics testing standards, which are well-known technologies in the field and will not be elaborated upon here.

[0090] After obtaining the composition, three-dimensional morphology and micromechanical properties of microplastics, the microplastic basic property testing module 40 performs cross-scale fusion of the above data with the turbidity dynamic parameters obtained in the previous steps to construct a high-fidelity physical model for numerical simulation, mainly including steps S1201 to S1203.

[0091] In step S1201, the microplastic basic property testing module 40 performs multi-source data standardization and integration. The microplastic basic property testing module 40 retrieves the turbidity current vertical profile characteristic parameters calculated by the turbidity current observation data processing module 10. These parameters include depth-average velocity, turbidity current thickness, and depth-average concentration obtained through vertical integration. Simultaneously, the microplastic basic property testing module 40 summarizes the physicochemical test data of the microplastic samples, establishing a comprehensive dataset containing polymer type labels, three-dimensional geometric parameters (volume, surface area, three-dimensional sphericity, roughness), and micromechanical parameters (hardness, elastic modulus, yield strength). The microplastic basic property testing module 40 uses a unified timestamp and spatial coordinate index to correlate the physical properties of microplastics with the hydrodynamic conditions of their depositional environment, generating a fundamental database for studying microplastic transport mechanisms.

[0092] In step S1202, the microplastic basic property testing module 40 constructs a discrete element method (DEM) particle physics model. To realistically reproduce the motion behavior of microplastic particles in numerical simulation, the module maps the three-dimensional geometric parameters extracted in step S1003 to shape descriptors of DEM particle elements. Using the ratio of particle volume to its major and minor axes, it approximates the irregular contour of the microplastic using a multi-sphere combination or hyperquadratic surface method. The module then defines the particle contact constitutive model using the nanoindentation mechanical parameters measured in step S1103. Specifically, the module substitutes the elastic modulus and Poisson's ratio into the contact theory model (such as the Hertz-Mindlin contact model), sets the normal and tangential stiffness between particles and between particles and the wall, and estimates the coefficient of restitution and coefficient of friction of the particles during collision using yield strength and hardness data. In this way, the microplastic basic property testing module 40 transforms the experimentally measured micromechanical properties into key physical parameters for controlling particle collision, rebound, and energy dissipation in numerical simulations.

[0093] In step S1203, the microplastic basic property testing module 40 generates the boundary condition file for calculating the CFD-DEM coupled simulation. The microplastic basic property testing module 40 sets the depth-average velocity of the turbidity flow and the suspended mass concentration field as the inlet velocity boundary and initial volume fraction distribution of the fluid phase. The microplastic basic property testing module 40 uses the constructed microplastic DEM particle model as the discrete phase injection flow field. Based on the CFD-DEM coupled simulation input file, the subsequent CFD-DEM coupled solver can calculate the momentum exchange and interaction forces between the fluid mesh and the discrete particles, thereby inverting the sedimentation trajectory, suspension distribution characteristics, and collision behavior with sediment particles of different types of microplastics in turbidity shear flow. The specific numerical solution process for the CFD-DEM coupled calculation can be implemented by those skilled in the art using open-source OpenFOAM and LIGGGHTS code or commercial software; these are well-known techniques in the field of computational fluid dynamics and will not be elaborated upon here.

[0094] The following is a specific implementation example of this solution:

[0095] Example 1: Extraction and identification of microplastics in turbidity currents under high organic matter background in estuaries

[0096] Application scenario: River delta region, water salinity 15-25‰, sediment rich in plant debris (organic matter content about 28%), requiring identification of flood-induced turbidity flow events and extraction of microplastics.

[0097] 1. Turbidity Current Depositional Identification: Technicians first performed CT scans and grain size tests on the collected sediment column samples. (See attached document.) Figure 3 The system uses step S601 to construct the CM diagram.

[0098] Calculation process: The system extracts the first percentile particle size (C value) and median particle size (M value) from the particle size accumulation curve. For example... Figure 3 As shown, data points are projected for each layer.

[0099] Identification results: Sample points (blue dots) in the 65-90cm depth layer exhibit a distribution pattern clearly parallel to the C=M baseline in the logarithmic coordinate system, which is consistent with the typical characteristics of graded suspension turbidity current deposition; while the background deposition (gray triangles) does not show this pattern. Based on this, the system accurately identified the turbidity current deposition layers, avoiding invalid analysis of non-turbidity current layers.

[0100] 2. Dynamic parameter inversion: The system processes the ADCP data corresponding to this layer.

[0101] Calculation process: using the acoustic inversion model (formula) The backscattering intensity is converted into a suspension concentration profile.

[0102] Using the vertical integration algorithm (step S303), the upper limit of the height from the seabed to the turbidity current is calculated. Flow velocity within the range of ) ) and concentration ( Integrate the results.

[0103] Calculate depth-average flow velocity .

[0104] Calculate the transport flux of a single-width suspended body .

[0105] Results: The study revealed structural characteristics of high concentrations at the bottom of the turbidity stream and a logarithmic velocity distribution. The calculated peak concentration of suspended matter reached 1200 mg / L.

[0106] 3. Microplastic Extraction Optimization: To address interference from high organic matter, the digestion time with 30% hydrogen peroxide in step S901 was extended to 36 hours. The final extracted microplastic concentration was 85 particles / g dry weight, mainly polyethylene fragments.

[0107] Example 2: Mechanical Damage Analysis of Microplastics in Deep-Sea Coarse-Particle Environment

[0108] Application scenario: Deep-sea fans at a water depth of 2000m, with sediments containing 35% coarse particles (sand and gravel), focusing on the mechanical damage of microplastics under strong dynamic transport.

[0109] 1. Physical and Mechanical Property Testing: Obvious scratches were observed on the extracted microplastic surface. To quantify this damage, technicians used nanoindentation technology for testing. Typical load-displacement curves (Ph curves) were recorded systematically.

[0110] Calculation process:

[0111] Loading phase: The indenter presses in the microplastic, maximum load. Maximum depth .

[0112] Unloading phase: Initial slope of the unloading curve This reflects the contact stiffness.

[0113] Hardness calculation based on the Oliver-Pharr model ,in The contact projection area.

[0114] Results: Compared with the undamaged original standard sample (dashed line), the microplastics extracted from the deep-sea turbidity current (solid line) showed higher hardness (H≈2.4GPa vs 1.2GPa) and lower elastic recovery rate, confirming the work hardening effect caused by coarse particle collision.

[0115] Example 3: Experimental Verification and Effect Comparison

[0116] To verify the effectiveness of this invention, a comparative experiment was conducted: the control group used a traditional single-density (NaCl, 1.2 g / cm³) solution. 3 Flotation method; the experimental group used the multi-stage density (KI, 1.66 g / cm³) method of this invention. 3 ) + Deep digestion method.

[0117] The experimental results show that:

[0118] Improved recovery rates: The recovery rate of polyvinyl chloride (PVC, high density) in the experimental group increased from 15% in the control group to 92%; the recovery rate of polyethylene (PE) increased from 85% to 96%.

[0119] Digestion effect: Due to the severe organic matter residue in the control group, the spectral recognition accuracy was only 72.4%; while the recognition accuracy of the experimental group of this invention reached 97.8%.

[0120] Flux estimation: The microplastic flux estimated based on the control group data was only 52% of the actual amount applied (severely underestimated), while the estimation value of the method of this invention reached 91%, with the relative error controlled within 10%.

Claims

1. A method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments, characterized in that, Includes the following steps: The turbidity flow observation data processing module filters turbidity flow observation data according to a preset quality control threshold to obtain effective data. It then uses an acoustic inversion model to convert the backscattering intensity in the effective data into suspended body concentration and calculates turbidity flow dynamic parameters to obtain hydrodynamic environment characteristic data. The sediment processing and identification module acquires the original sediment samples that correspond to the hydrodynamic environment characteristic data in time and space, performs computed tomography to determine the sampling location, acquires sediment grain size test samples, performs chemical pretreatment and grain size testing on the sediment grain size test samples, and constructs a graph of the relationship between the first percentile grain size value and the median grain size value based on the grain size test results to identify turbidity current sedimentary layers. The microplastic extraction and pretreatment module dries and disperses the sediment samples extracted from the turbidity sedimentary layer, collects the supernatant containing microplastics by multi-stage density flotation using saturated potassium iodide solution, and obtains the microplastic test sample by chemical digestion and filtration. The microplastic basic property testing module uses spectral analysis technology to determine the polymer material type of the microplastic sample as the material identification result, uses nanoindentation technology to record the load and displacement curves, and calculates the mechanical property parameters of the microplastic based on the load and displacement curves.

2. The method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments according to claim 1, characterized in that, The turbidity flow observation data is filtered according to the preset quality control threshold to obtain the effective data, specifically including: The preset quality control threshold is specifically set as the correlation coefficient threshold and the backscattering intensity threshold; Traverse the turbidity observation data to obtain the correlation coefficient and backscattering intensity values ​​for the current data point; When the correlation coefficient value is less than the correlation coefficient threshold, or the backscattering intensity value is less than the backscattering intensity threshold, the data point is marked as invalid data and discarded. Data points that simultaneously satisfy the condition that the correlation coefficient value is greater than or equal to the correlation coefficient threshold and the backscattering intensity value is greater than or equal to the backscattering intensity threshold are retained as valid data.

3. The method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments according to claim 1, characterized in that, The acoustic inversion model is used to convert the backscattering intensity in the effective data into the suspended body concentration, specifically including: The seawater temperature, salinity, and pH of the observed sea area are obtained to calculate the signal attenuation coefficient; The scattering characteristic parameters of the suspended body are set according to the particle size characteristics of the suspended body corresponding to the concentration of the suspended body; Obtain the near-field correction factor and sensor sensitivity of the equipment used to collect the turbidity observation data; The backscattering intensity in the effective data is corrected and calculated using the signal attenuation coefficient, the scattering characteristic parameters of the suspended body, the near-field correction factor, and the sensor sensitivity, thereby obtaining the concentration of the suspended body.

4. The method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments according to claim 1, characterized in that, The calculation of the turbidity dynamics parameters based on the suspended body concentration specifically includes: The vertical distance from the seabed to the location where the velocity value in the valid data decays to zero is determined, and the vertical distance is defined as the upper limit of the turbidity current height. Extract flow velocity profile data from the valid data; Using a vertical integration algorithm, the velocity profile data and the suspended body concentration are integrated within the range from the seabed to the upper limit of the turbidity current height to obtain the turbidity current dynamic parameters. The turbidity dynamic parameters include depth-average turbidity velocity, turbidity thickness, and depth-average turbidity concentration.

5. The method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments according to claim 1, characterized in that, The computed tomography scan of the original sediment sample is performed to determine the sampling location, specifically including: The scanned image generated by the computed tomography scan is acquired, the gray value data in the scanned image is extracted, and the gray value data is used to generate a gray value sequence that varies with depth. Analyze the grayscale value sequence, identify the stratification interface or particle size abrupt change interface based on the abrupt change position of the grayscale value data in the grayscale value sequence, determine the physical sampling interval, and determine the sampling position based on the physical sampling interval; The regions with relatively high grayscale values ​​are marked as coarse-grained enrichment areas, and these coarse-grained enrichment areas are determined as the sampling locations.

6. The method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments according to claim 1, characterized in that, The chemical pretreatment of the sediment grain size test sample was performed by sequentially executing the following steps: Organic matter in the sediment particle size test sample was removed using hydrogen peroxide solution under heating conditions; The sediment particle size test sample after the organic matter has been removed is treated with hydrochloric acid solution to remove carbonates and iron-manganese oxide chemical cements from the sediment particle size test sample; Sodium hexametaphosphate solution was added as a dispersant to the sediment particle size test sample purified by the above steps, and ultrasonic dispersion was performed using an ultrasonic oscillator.

7. The method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments according to claim 1, characterized in that, Based on the results of the particle size test, a graph showing the relationship between the first percentile particle size value and the median particle size value is constructed, specifically including: A particle size accumulation curve is generated using the results of the particle size test; Extract the particle size value corresponding to the first percentile particle size value at the point where the cumulative particle content on the particle size accumulation curve is 1%. Extract the particle size value corresponding to the point where the cumulative particle content is 50% on the particle size accumulation curve, and define it as the median particle size value. Establish a double logarithmic coordinate system with the median particle size value as the abscissa and the first percentile particle size value as the ordinate, and set a baseline representing the characteristics of uniform suspension deposition in the double logarithmic coordinate system. The data pair consisting of the median particle size value and the first percentile particle size value is projected as sample points into the double logarithmic coordinate system; The turbidity deposit layer is identified when the sample points exhibit a distribution characteristic parallel to the baseline in the double logarithmic coordinate system, or when the sample points exhibit a distribution characteristic deviating from the baseline and the first percentile particle size value increases.

8. The method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments according to claim 1, characterized in that, The multi-stage density flotation of the dried and dispersed sediment extract sample using the saturated potassium iodide solution specifically includes: The sediment extract sample after the drying and dispersion treatment is mixed with the saturated potassium iodide solution. After stirring and standing to separate the layers, a supernatant layer containing the microplastics and a bottom sediment layer are formed. The supernatant layer is separated and used as the first batch of supernatant. Fresh saturated potassium iodide solution is added again to the bottom precipitate layer after the supernatant layer is separated, and the steps of stirring, standing and separating the supernatant layer formed after separation are repeated to obtain subsequent batches of supernatant. The first batch of supernatant is combined with the subsequent batches of supernatant to form the supernatant containing microplastics.

9. The method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments according to claim 1, characterized in that, The supernatant containing microplastics is subjected to chemical digestion and filtration to obtain the microplastic test sample, specifically including: Hydrogen peroxide solution was added as an oxidant to the supernatant containing microplastics; The supernatant containing microplastics was subjected to a digestion reaction under static conditions to remove organic impurities from the surface of the microplastics. The supernatant after the digestion reaction is filtered using a vacuum filtration device, and the filter membrane containing the microplastics is washed with ultrapure water to obtain the microplastic test sample.

10. The method for extracting, identifying, and testing the physical and mechanical properties of microplastics in turbidity current sediments according to claim 1, characterized in that, The process of using spectral analysis to detect the microplastic sample to determine the polymer material type of the microplastic as the material identification result, and calculating the mechanical property parameters of the microplastic based on the load-displacement curve, specifically includes: The functional group information of the microplastic sample was obtained by using a Fourier transform micro-infrared spectrometer, and the fingerprint spectrum of the microplastic sample was acquired by using a laser confocal micro-Raman spectrometer. The functional group information and the fingerprint spectrum were then combined to form spectral data. The spectral data is compared with a standard polymer spectral library to determine the polymer material type; A set load is applied to the microplastic sample to be tested using a nanoindenter, and the load and displacement data of the entire loading-unloading process are recorded to generate the load and displacement curve. Based on the unloading phase portion of the load-displacement curve, the mechanical property parameters, including hardness and elastic modulus, are calculated. Based on the elastoplastic transition point of the load-displacement curve, the mechanical property parameters, including yield strength, are calculated.

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