A method of testing the tensile strength of an injectable hydrogel wound dressing

Abstract

The present application relates to the technical field of material physical performance testing, and discloses a kind of injectable hydrogel wound dressing tensile strength test method, comprising: establishing the correlation model of the mass of standard sample at different temperatures and maximum breaking load, and initial stiffness and temperature;For the sample to be measured, measure its mass and actual maximum breaking load, and determine its actual initial stiffness;The equivalent temperature of the test is converted using the actual initial stiffness, and the standard theoretical maximum breaking load is calculated by combining the sample mass and the corresponding model, and the performance is determined by comparing the two, the present application avoids the measurement of the geometric size of irregular samples by constructing the correlation of mass and mechanical properties, solves the quality control problem of in-situ forming materials, and the integrated temperature self-adaptive calibration mechanism further ensures the reliability of the determination result in the industrial environment using the mechanical response data of the same test.

Description

Technical Field

[0001] This invention relates to a method for testing the tensile strength of injectable hydrogel wound dressings, belonging to the field of material physical property testing technology. Background Technology

[0002] Currently, determining the intrinsic physical properties of materials by applying mechanical loads to samples and measuring their response is a fundamental and mature technical method. Its entire theoretical and practical system has long been based on a core premise: the sample being tested has a precisely known geometrically standardized cross-section. This allows the measured load to be converted into the intrinsic strength of the material, thereby enabling performance comparisons between different samples or materials.

[0003] However, with the development of materials technology, a series of in-situ molding materials, represented by injectable hydrogel dressings, have emerged. The core application value of these materials lies precisely in their ability to solidify in situ at complex application interfaces, forming conformal and functional non-standard entities. This characteristic fundamentally challenges the effectiveness of classic testing methods based on standardized samples when faced with the real application forms of these materials. This is because, in the quality control process, engineers are not really concerned with the theoretical strength that the material may achieve under ideal conditions, but rather whether the mechanical properties of each production batch, in its real non-ideal solidified form, are highly consistent with the standard batch that was initially rigorously verified and approved.

[0004] To address this issue, the current common practice is to use regular samples prepared in molds as substitutes for testing. However, this leads to a deeper contradiction: the ideal curing environment provided by the mold shields the material from minute defects or internal stresses that may be introduced in real-world applications due to factors such as interface temperature gradients or mixing uniformity. These factors are key to batch-to-batch performance fluctuations. Therefore, data obtained from testing ideal samples has a weakened correlation with the material's mechanical performance in actual use. This results in the quality control system being less sensitive to significant changes in the final product performance caused by slight drifts in raw materials or process parameters. Specifically, existing technologies have the following shortcomings: 1. There is a disconnect between the test sample and the actual application entity, and the test results cannot faithfully reflect the material's performance consistency under real-world conditions; 2. The detection sensitivity is insufficient for subtle anomalies that may lead to final performance degradation during the production process, potentially resulting in delayed detection of quality risks. Therefore, establishing a new testing method that does not rely on standardized sample geometry and can directly assess the mechanical performance consistency of in-situ molded materials in near-real-world application conditions with high sensitivity and repeatability is the technical problem this invention aims to solve. Summary of the Invention

[0005] This invention provides a method for testing the tensile strength of injectable hydrogel wound dressings. Its main purpose is to solve the problem that existing technologies cannot perform highly repeatable quality control of the mechanical properties of in-situ molded hydrogels without relying on sample morphology standardization.

[0006] To achieve the above objectives, the present invention provides a method for testing the tensile strength of injectable hydrogel wound dressings, comprising the following steps:

[0007] Step a: Prepare a set of standardized samples of standard batch hydrogels with known geometric dimensions at at least two different known constant calibration temperatures, and perform the operation of measuring the mass and maximum fracture load and determining the tensile initial stiffness of each of the standardized samples in the set. Based on all the data obtained, establish a fracture load correlation model characterizing the relationship between mass and calibration temperature and maximum fracture load, and establish a temperature correlation model characterizing the relationship between tensile initial stiffness and calibration temperature.

[0008] Step b: Provide a batch of hydrogel to be tested to prepare the test sample, measure the mass of the test sample, and obtain its uniaxial tensile force displacement response until fracture to record its actual maximum fracture load.

[0009] Step c: Determine the actual initial tensile stiffness of the sample under test based on the uniaxial tensile force displacement response. Calculate the equivalent temperature of the test using the actual initial tensile stiffness and a temperature correlation model. Calculate the standard theoretical maximum fracture load based on the equivalent temperature, the fracture load correlation model, and the mass of the sample under test. Finally, determine the tensile strength properties of the hydrogel batch under test based on the comparison between the actual maximum fracture load and the standard theoretical maximum fracture load.

[0010] Preferably, the comparison result is obtained by calculating the ratio of the actual maximum fracture load to the standard theoretical maximum fracture load to obtain the mechanical property consistency index, and then comparing the mechanical property consistency index with the judgment threshold determined based on the statistical data of standard batches of hydrogels.

[0011] Preferably, the mechanical property consistency index (MPI) is calculated as follows: ,in, This represents the actual maximum fracture load. This is the maximum fracture load according to standard theory.

[0012] Preferably, both the fracture load correlation model and the temperature correlation model are established through nonlinear regression analysis; the preparation process of standardized samples includes preparing a set of standardized samples with gradient changes in geometric dimensions in a series of molds with different and known standard geometries.

[0013] Preferably, the preparation process of the test sample includes injecting the hydrogel of the batch to be tested into a test mold with a biomimetic rough topological inner surface, and performing in-situ curing molding.

[0014] Preferably, before performing step b, an injection performance characterization step is included, which includes: providing a sample of the hydrogel to be tested in an uncured state and placing it in a syringe; pressing the plunger of the syringe at a constant rate using a device capable of applying controlled linear displacement and measuring reaction force; and measuring and recording the force on the plunger during the pressing process as an indicator characterizing the injection performance of the hydrogel to be tested.

[0015] Preferably, when acquiring the uniaxial tensile force displacement response in step b, the force data is acquired at a frequency higher than 1 kHz; and the method further includes a failure mode diagnosis step, which includes: separating a macroscopic response trend and a high-frequency residual signal from the acquired high-frequency force data; calculating at least one higher-order statistical moment of the high-frequency residual signal as a fingerprint feature characterizing the microscopic failure mode of the sample; and comparing the fingerprint feature with a benchmark fingerprint range established based on standard batch hydrogels to diagnose the failure mode of the sample.

[0016] Preferably, the higher-order statistical moments include the skewness and kurtosis of the high-frequency residual signal distribution; skewness reflects the symmetry of the signal distribution, and kurtosis reflects the peakness of the signal distribution.

[0017] Preferably, the method further includes a self-verification step for the integrity of the correlation model, which includes: providing a verification sample prepared from a standard batch of hydrogel with a known standard geometry and a known true tensile strength benchmark value; and simultaneously performing model path analysis and classical physical path analysis on the verification sample; performing steps b and c in the model path analysis to obtain model prediction results based on the correlation model; measuring the current geometry of the verification sample in the classical physical path analysis, and obtaining classical physical prediction results based on the geometry and its true tensile strength benchmark value; and finally, comparing the mechanical properties actually measured on the verification sample with the model prediction results and the classical physical prediction results to determine the integrity status of the correlation model.

[0018] Preferably, the logic for determining the integrity status of the correlation model is as follows: when the prediction result of the model path analysis does not match the actual measurement but the prediction result of the classical physical path analysis matches the actual measurement, it is determined that the test equipment has experienced systematic drift; when the prediction result of the model path analysis matches the actual measurement but the prediction result of the classical physical path analysis does not match the actual measurement, it is determined that the standard batch of hydrogel has deteriorated.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. This invention first establishes an intrinsic and stable functional relationship between sample mass and maximum fracture load in a standard batch of material. Then, when judging the sample to be tested, the measured mass of the sample is directly substituted into this relationship to obtain a standard theoretical fracture load. Finally, the consistency of its mechanical properties is evaluated by comparing the actual fracture load of the sample with this standard theoretical fracture load. This process no longer relies on the direct measurement of the irregular geometric dimensions of the sample, so that the mechanical property evaluation of in-situ molded materials can avoid the long-standing testing obstacle of not being able to accurately obtain the cross-sectional area due to its irregular shape. Thus, a highly repeatable industrial quality control process is established for such materials.

[0021] 2. During the execution process, in addition to recording the maximum fracture load, the force-displacement response at the beginning of the tensile process was used to determine the initial stiffness of the sample. This initial stiffness was then converted into the equivalent temperature of the test environment through a pre-established temperature correlation model. This equivalent temperature was then used to select or adjust the corresponding mass-fracture load correlation model as a benchmark for subsequent performance consistency judgment. Since the acquisition of the initial stiffness and the determination of the maximum fracture load both originated from the same tensile process, the perception and compensation of temperature changes in the test environment were integrated into a complete mechanical test, avoiding the introduction of external temperature sensors and the system complexity and potential measurement errors they might bring.

[0022] 3. Before conducting mechanical property tests on the sample, this invention includes an injection performance characterization step. This step utilizes the same device capable of applying controlled linear displacement and measuring reaction force. By pressing a syringe containing the uncured sample, the force on the plunger is recorded. After this step, subsequent curing and tensile tests are performed using samples from the same batch. In this way, two key physical performance data—the material's flow and transport characteristics before application and its structural load-bearing characteristics after curing—are obtained through the same core equipment in a continuous testing process, forming a comprehensive evaluation of the key performance throughout the product's application lifecycle. This provides more comprehensive data support for adjusting production processes and troubleshooting. Attached Figure Description

[0023] Figure 1 This is an overall flowchart of the hydrogel mechanical property consistency test of the present invention;

[0024] Figure 2 This is a comparison curve of the injection performance of different batches of hydrogel of the present invention;

[0025] Figure 3 This is a flowchart illustrating the interaction between the researchers and quality control personnel involved in this testing method. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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.

[0027] The present invention discloses a method for testing the tensile strength of injectable hydrogel wound dressings. Its technical architecture includes a modeling and calibration stage and a test sample stage. In the test stage, an adaptive calibration mechanism for ambient temperature, a synchronous injection performance table step, a microscopic failure mode diagnosis step, and a self-verification protocol for the integrity of the associated model are integrated. These constitute a set of test methods for determining the consistency of mechanical properties of in-situ molded materials.

[0028] In the field of materials physical property testing, how to quantitatively characterize the mechanical properties of samples that cannot be prepared into geometrically standardized forms due to in-situ molding at the application site has been a long-standing technical problem. Therefore, the core of this invention is to establish and utilize a mathematical model characterizing the physical relationship between material mass and mechanical properties, thereby transforming the problem of measuring irregular geometric cross-sections of samples into the problem of accurately measuring sample mass. This method is achieved through a modeling and calibration stage. First, a standard batch of hydrogel is provided, representing a baseline product state with verified performance. Simultaneously, a universal testing machine is prepared, with a force sensor accuracy of not less than 0.5%FS, and a [missing information - likely a specific type of testing machine]. The analytical balance; its operating procedure is as follows: using standard batch hydrogels, in a series of molds with different and known standard geometries, for example, inner diameters of... , , , , , In a standard cylindrical mold, a set of standardized samples, for example, n=30, with gradient variations in geometric dimensions, is prepared. After the samples have solidified, the mass of each of the standardized samples in the set is first measured using an analytical balance. Then it was clamped on a universal testing machine, to... A constant tensile rate was applied to perform uniaxial tensile tests until the sample fractured, and the maximum fracture load corresponding to the peak value of the force-displacement curve was recorded. After completing the testing of all standardized samples, the resulting multiple sets of data points will be... The data is plotted in a two-dimensional coordinate system, and nonlinear regression analysis methods, such as polynomial fitting, are used to establish a functional relationship that can characterize the distribution trend of these data points, i.e., a correlation model. The correlation model is digitally stored and used as the basis for subsequent quality control judgments.

[0029] Establish fracture load correlation model Temperature-related models The process involves applying a model selection algorithm to all data points collected during the calibration phase. This algorithm fits each model in a pre-defined candidate model library containing polynomial, power-law, and exponential functions, and calculates the Akaike Information Criterion (AIC) value for each model. The AIC value is an evaluation metric used to select among multiple statistical models. It balances goodness of fit and complexity by applying a penalty term proportional to the number of free parameters to the model's log-likelihood function value. Ultimately, the model with the lowest AIC value in the candidate model library is selected as the unique associated model for all subsequent calculations. Furthermore, in the entire process of performing all quality measurements and mechanical tests, from removing the sample from its hydration and curing environment and lightly touching the surface of a standardized absorbent material for 2 seconds to remove surface free water, to initiating the tensile test, the time consumption is strictly controlled within a specified timeframe. The process is completed within a few seconds. In quality control applications, fluctuations in ambient temperature are one of the factors affecting the mechanical properties of polymer materials such as hydrogels. To avoid judgment biases that may be introduced due to inconsistencies between the test environment temperature and the modeling and calibration stage, this invention introduces an adaptive calibration mechanism. The establishment of this mechanism is a synergistic enhancement of the aforementioned modeling and calibration stage. Its procedures require that the preparation and testing processes of the aforementioned standardized sample library be carried out separately at at least two known, constant calibration temperatures, for example, at... , , At three temperature points, the above-described sample preparation, mass measurement, and tensile testing procedures were fully executed; and the maximum breaking load of each sample was recorded. At the same time, it is also necessary to determine the initial stiffness by calculating the slope of the initial linear segment of its force-displacement curve. Based on all the obtained data, two correlation models were established. One is the fracture load correlation model, which is a family of models indexed by temperature that characterize the relationship between mass and calibration temperature and maximum fracture load, denoted as . The second is the temperature correlation model, which is a function characterizing the relationship between the initial stiffness and the calibration temperature, denoted as... Thus, when entering the sample testing phase, for a sample tested at the current ambient temperature, its uniaxial tensile force-displacement response is obtained and its actual maximum fracture load is recorded. Then, the actual initial tensile stiffness of the sample under test is first calculated from the response curve. Then use that The value is obtained through the inverse function of the temperature correlation model. The equivalent temperature of this test was calculated. Furthermore, based on the calculated The appropriate model for this equivalent temperature is selected from the family of fracture load correlation models or obtained through interpolation. This process transforms a static calibration model into a judgment benchmark that can respond to environmental changes, thereby improving the reliability of test results.

[0030] After the model is established, the testing phase for the test samples can begin. This phase aims to determine whether the mechanical properties of any given batch of hydrogel after curing are consistent with those of the standard batch. To ensure that the testing conditions closely approximate the application conditions of the material, the test samples can be prepared by injecting the hydrogel of the test batch into a test mold with a biomimetic rough topological inner surface and then curing it in situ. The surface morphology parameters of the test mold with the biomimetic rough topological inner surface used in the preparation of the test samples are determined through a standardized screening procedure. This procedure begins by preparing a set of candidate molds with different and quantified surface parameters, such as a series of arithmetic mean roughness Ra values ​​formed by electrical discharge machining. Then, using a pre-determined standard batch of hydrogel and a critical batch of hydrogel with a reduced crosslinking agent concentration, samples are prepared in each of the candidate molds, and their mechanical property consistency index (MPI) is calculated. Finally, for each mold surface, a surface differentiation index is calculated, defined as the ratio of the absolute value of the difference between the mean MPI values ​​of the two batches to the square root of the sum of the variances of the MPI values ​​of the two batches. ,Right now ,in, Assigning numbers to the candidate molds, ultimately, will generate the largest... The surface parameters of the candidate mold, for example This standard operating procedure is formalized for all subsequent tests. The sample to be tested is first measured for its mass. And a uniaxial tensile test was conducted to obtain its actual maximum fracture load. and actual initial stiffness Subsequently, the aforementioned temperature adaptive calibration procedure is performed to determine the equivalent temperature. And call the corresponding fracture load association model. The mass of the sample to be tested will be measured. Substituting into the model, a standard theoretical maximum fracture load is calculated. This value represents the fracture load that a standard batch of samples with the same mass and temperature as the sample under test should possess. Finally, a quantified mechanical property consistency index is obtained by calculating the ratio of the actual maximum fracture load to the standard theoretical maximum fracture load. The calculation formula is as follows: ,in, This represents the actual maximum fracture load. This is the standard theoretical maximum fracture load; for example, if the mass of a test sample... for The correlation model determined after temperature calibration is (in Units are , Units are Then its standard theoretical maximum fracture load Calculated as If its actual measured maximum fracture load for Then its Value ; By this The value is compared with a preset judgment threshold, such as 0.95 determined based on statistical data of standard batches of hydrogels, to determine whether the tensile strength performance of the tested batch of hydrogels is qualified.

[0031] For injectable hydrogels, injection smoothness before application is an important quality indicator. This invention provides an injection performance characterization step, implemented using a functionally reusable universal testing machine. Specifically, before performing post-curing performance testing, a sample of the test batch of hydrogel in its uncured liquid state is taken and placed into a standard-sized medical syringe. The syringe is vertically fixed to the base of the testing machine via an adapter, with the syringe plunger facing the machine's drive head. The testing machine is then set to simulate a constant rate of clinical injection, for example... The pressure head is moved downwards to press the push rod, and a force sensor is used to record the reaction force on the push rod during constant-speed pressing. The average force value of the stable segment of the force-displacement curve is taken as an indicator to characterize the injection performance of this batch of hydrogel. By comparing this indicator with the baseline value of the standard batch, the injection performance can be determined.

[0032] To extract information about the integrity of the material's microstructure from the test data, the method of this invention also provides a failure mode diagnosis step; this step requires that, when acquiring the uniaxial tensile force-displacement response, the force sensor data be obtained at a frequency higher than that of conventional tests, for example, higher than Data acquisition is performed. After testing, the acquired high-frequency force data sequence is processed. First, a response trend and a high-frequency residual signal are separated using low-pass filtering or moving average methods. This residual signal reflects the characteristics of internal micro-failure events during the tensile process. Subsequently, at least one higher-order statistical moment of the high-frequency residual signal is calculated as a fingerprint feature characterizing the micro-failure mode of the sample. The higher-order statistical moment may include the skewness and kurtosis of the signal distribution. Skewness reflects the symmetry of the signal distribution, while kurtosis reflects the peakness of the signal distribution. The fingerprint features calculated for the test sample are compared with a baseline fingerprint range established based on a standard batch of hydrogels. For example, if the skewness is within a certain range... Peak at By comparing the samples, it is possible to diagnose whether the failure modes of the samples have drifted, providing a reference for troubleshooting production process failures.

[0033] To ensure the long-term validity of the correlation model used as the basis for the testing system and to mitigate potential risks caused by the aging of standard materials or drift of testing equipment, the method of this invention also includes a self-verification step for the integrity of the correlation model. This step is achieved by introducing a verification based on classical physical paths. Specifically, in the initial modeling and calibration stage, an additional reference model with a known standard geometry, such as a cylinder, and a known true tensile strength, prepared from a standard batch of hydrogel, is provided. The verification sample is used; during the execution of the self-verification protocol, both model path analysis and classical physical path analysis are performed on the verification sample simultaneously; model path analysis involves performing the aforementioned conventional testing procedures to obtain a model prediction result based on the correlation model. Classical physical path analysis first uses high-precision calipers to measure the current geometric dimensions of the calibration sample, such as its diameter. And calculate its current actual cross-sectional area. Then, based on its known true tensile strength benchmark value, a classical physical prediction result is obtained. Finally, the actual mechanical properties of the calibration sample were measured. The results are compared with both model predictions and classical physics predictions; for example, when the model path analysis predictions do not match the actual measurements ( When the predictions from classical physical path analysis match the actual measurements () If the model path analysis prediction results are consistent with the classical physical path analysis prediction results, it can be determined that the standard batch of hydrogel has deteriorated, thus providing a diagnostic basis for system maintenance and recalibration.

[0034] Example 1: In a production facility for injectable hydrogel wound dressings, the quality control department faces a technical problem: verifying whether the mechanical properties of a newly produced batch of BatchN after in-situ curing are consistent with those of the clinically validated and approved reference batch, GoldenBatch. Traditional testing methods, using geometrically regular samples prepared in standard molds, show that BatchN's tensile strength is acceptable. However, process engineers have doubts about the validity of these test results based on records of batch fluctuations in raw materials. This is because idealized regular samples cannot reproduce the microscopic defects and stress concentrations caused by interface irregularities during in-situ curing of materials on real, complex wound surfaces. Consequently, batch-to-batch performance differences may go undetected, leading to potential quality risks.

[0035] To address this working condition, the quality control department applied the testing method of this invention. It should be noted that the department had previously used GoldenBatch material and, following the procedures of the modeling and calibration phase, established a fracture load correlation model characterizing the GoldenBatch material, which included a temperature adaptive calibration mechanism. Temperature-related models During the testing of BatchN, the operator injected the hydrogel of BatchN into a uniform test mold with a biomimetic rough topological inner surface and cured it in situ under controlled conditions. The irregularity of the inner surface of the mold was designed to simulate the conformal state of the material at the interface with biological tissues in application. Subsequently, the cured irregular sample was removed from the mold and its mass was measured using an analytical balance. The sample was then clamped onto a universal testing machine and stretched at a constant rate until fracture, during which its force-displacement response data were collected. In this testing process, a test mold with a biomimetic rough topological inner surface was used to reproduce potential performance weaknesses that might occur under real curing conditions, providing a basis for detecting performance differences. However, it also created an irregular sample that could not be directly measured for its effective load-bearing cross-section. A correlation model based on mass as a surrogate parameter was used. Its function is to provide a standard for judging mechanical properties without knowing the geometric dimensions of the sample. The former provides the latter with a test object that can reflect the performance of the sample, while the latter provides a quantitative analytical path for the non-standardized test objects generated by the former. The combination of the two transforms a test that was originally insufficient in sensitivity due to the disconnect between the sample shape and the application state into a test that combines working condition simulation and quantitative judgment. This test method does not directly solve the problem of measuring the cross-section of irregular samples, but rather changes the judgment criterion from absolute strength to the equivalence of mass-load, so that measuring the cross-section is no longer a necessary step under the new judgment framework.

[0036] Finally, based on the collected force-displacement response, the testing system first calculates the actual initial stiffness of the sample. And via temperature correlation model Calculate the equivalent temperature of the test. Next, according to Call the corresponding fracture load association model and the measured sample mass Substitute the values ​​and calculate the standard theoretical maximum fracture load. Finally, the actual maximum fracture load was measured. and By comparison, the mechanical performance consistency index is calculated. The calculation results show that BatchN's The value was 0.89, which is lower than the set pass threshold of 0.95, indicating that its mechanical properties have declined compared to GoldenBatch. As a result, this batch of products was deemed unqualified and isolated, thus avoiding a potential release of unqualified products due to insufficient sensitivity of the test method. The production and quality control processes also gained a repeatable test procedure that reflects the performance of the product.

[0037] Example 2: To objectively verify the effectiveness of the technical solution of this invention in detecting performance differences between batches of hydrogels, this example designed and executed a comparative experiment. The aim was to compare the method of this invention with traditional tensile strength testing methods that rely on standardized geometric samples, to evaluate the resolving power of the two methods when faced with a batch of samples whose performance has deteriorated due to formulation adjustments. The experimental platform consisted of a universal testing machine (force sensor accuracy 0.5%FS) and an analytical balance (accuracy...). And a constant temperature environment chamber (temperature control accuracy) The test materials consisted of two batches of hydrogels: one was the GoldenBatch, whose performance indicators met the standards according to the specific implementation method; the other was a DefectiveBatch, which was based on the GoldenBatch formulation but with the crosslinking agent concentration reduced by 5%. This fine-tuning was intended to simulate slight process deviations that might occur during production. The test sample group was divided into a control group and an invention sample group. The control group used the traditional testing method, i.e., standard dumbbell-shaped tensile samples with uniform geometric dimensions were prepared for both batches of materials. The invention sample group used the method of this invention, i.e., irregular samples were prepared for both batches of materials and cured in a unified test mold with a biomimetic rough topological inner surface. All tensile tests were conducted in [location missing]. The process was carried out at a constant temperature, and the stretching rate was uniformly set to... The rate setting is an industry-standard value chosen to obtain stable and repeatable data after balancing the viscoelastic response of the material and the test efficiency.

[0038] The experiment first used GoldenBatch material, following the modeling and calibration procedures outlined in the specific implementation method. The following establishes a fracture load correlation model for this batch of materials. Subsequently, mechanical property tests were performed on all samples from the control group and the sample group of the present invention; tests were conducted on the control group samples prepared using conventional methods, and the results showed that the tensile strength measurements of the GoldenBatch samples were as follows: and The measured value for the DefectiveBatch sample was... and The mean differences between the two sets of data were minimal, failing to reflect performance differences between batches; correspondingly, the mechanical property consistency index of the GoldenBatch samples prepared using the method of this invention was tested. The calculated values ​​are respectively , and All are within the acceptable threshold. The above, and the DefectiveBatch samples The calculated value is , and All values ​​were below the acceptable threshold, thus clearly indicating that the batch's performance was substandard.

[0039] Experimental data show that traditional methods are not sensitive enough to such subtle performance degradation because the ideal geometry of the standard dumbbell-shaped sample masks the decrease in toughness caused by insufficient crosslinking density. In contrast, in the sample group of this invention, the irregular sample, due to its biomimetic rough surface, introduces stress concentration points, amplifying the microstructural defects of the DefectiveBatch material, thus increasing the actual maximum fracture load required for it to fracture under tension. The reduction is ultimately reflected in The value is far below the qualified threshold and is consistent with GoldenBatch. The values ​​clearly distinguish the differences; the test results confirm that the test method of the present invention, by introducing stress conditions related to the application conditions and combining them with quality-based equivalence determination, can effectively identify the performance differences between material batches that may be missed by traditional standardized test methods.

[0040] Example 3: This example combines Figures 1 to 3 This document describes a method for testing the tensile strength of an injectable hydrogel wound dressing, as follows: Figure 1As shown, it begins with two core inputs: a standard batch of hydrogel and a test batch of hydrogel. The standard batch of hydrogel undergoes a modeling and calibration process, processing its physical data, including mass, dimensions, and fracture load, to generate correlation model parameters and store them in the correlation model library. The test batch of hydrogel generates both cured and uncured test samples. The former enters the physical performance measurement stage, while the latter enters the injection performance characterization stage. During the physical performance measurement process, the system not only outputs measured mechanical parameters, including mass, actual maximum fracture load, and actual initial stiffness, but also simultaneously collects high-frequency force data and inputs it to the microscopic failure mode diagnosis module. Finally, the data analysis and judgment module outputs the mechanical performance consistency index based on the measured mechanical parameters and the retrieved correlation model. The microscopic failure mode diagnosis module outputs failure mode fingerprint characteristics, while the injection performance characterization module outputs injection performance indicators. These three key indicators are collectively summarized in the judgment report generation stage, forming a final judgment report delivered to quality control personnel / systems as a basis for decision-making.

[0041] like Figure 2 As shown, the horizontal axis represents the displacement of the syringe plunger. The vertical axis represents the injection force measured by the testing machine. The figure shows three representative curves: a standard batch, a qualified batch, and a non-qualified batch. The curve of the standard batch quickly enters a stable plateau region after the initial stage, representing ideal injection performance. The curve shape and force value of the qualified batch closely follow the standard batch. However, the curve of the non-qualified batch shows a significant difference. It has a force peak value much higher than that of the standard batch in the initial stage and maintains a high force value level in the subsequent plateau region, thus distinguishing the advantages and disadvantages of different batches in terms of flow delivery characteristics.

[0042] like Figure 3 As shown, the R&D / validation engineer, as the creator and maintainer of the system, is responsible for performing the initial modeling and calibration, as well as periodically performing self-validation of the integrity of the associated model to ensure the accuracy and long-term effectiveness of the judgment criteria. The quality control technician, as a regular user, has the core task of performing tests on the samples to be tested. During the execution of this core task, the technician can selectively call two advanced diagnostic modules simultaneously: characterizing the injection performance of the sample and diagnosing the microscopic failure modes of the sample. After the test is completed, the system generates and displays a test report, which is simultaneously submitted to the quality control technician for immediate judgment and fed back to the R&D / validation engineer for long-term data traceability and process improvement.

[0043] Example 4: In a scenario where the method of this invention is applied for the first time in a materials testing laboratory for quality control of an injectable hydrogel with a specific formulation, the primary technical task is to establish a dedicated, traceable mathematical model and performance judgment thresholds for this specific material. The goal of this task is to transform a general methodology into an executable testing protocol for a specific object. It needs to ensure that the established model reflects the physicochemical properties of the material, and that the set judgment thresholds are statistically valid. To complete this calibration, the laboratory prepared the same standard batch of hydrogel (GoldenBatch), a universal testing machine equipped with an environmental temperature chamber, and an analytical balance. The first stage of the calibration procedure aims to establish the relationship between initial stiffness and temperature, i.e., a temperature correlation model. To this end, the operators prepared fifteen standardized samples with identical geometric dimensions, all with a diameter of [missing information]. The cylindrical shape was used, and the ambient temperature chamber was set to five gradient points, with 20.0°C as the starting point. 22.5 25.0 27.5 30.0 At each temperature point, three samples were taken for uniaxial tensile testing, and the initial stiffness was calculated from their respective force-displacement response curves. Subsequently, the average initial stiffness of the three samples at each temperature point was calculated, resulting in five sets of data points. Linear regression analysis was performed on these five sets of data points, resulting in a graph of the form shown below. The functional relationship, where for Theoretical stiffness at time, This is the coefficient for stiffness as a function of temperature; this function is the calibrated temperature correlation model for this material.

[0044] The second phase of the calibration procedure aims to establish the relationship between fracture load and mass temperature, i.e., the fracture load correlation model. And determine the mechanical property consistency index. The judgment threshold; the operator in , , Multiple sets of standardized samples with gradient variations in geometric dimensions were prepared at three core temperature points, with diameters covering... to Multiple specifications within the range were included, with multiple replicate samples prepared for each specification; all samples were tested at the corresponding constant temperature, and their mass was recorded. Compared with the actual maximum fracture load For each temperature point, based on multiple sets of data obtained at that temperature... The data is fitted using a nonlinear regression in the form of a power function. The selection of this value is based on the physical principle that the tensile fracture load is proportional to the effective bearing cross-sectional area of ​​the sample, and that mass is proportional to volume. This establishes an independent mass-fracture load relationship for each temperature point. Together, they constitute a family of fracture load correlation models; after all model parameters are determined, the fracture load correlation model is calculated for each of the GoldenBatch samples tested during the entire calibration process. The value is calculated by taking the measured mass of each sample. and test temperature Substitute into the newly established model In the process, its standard theoretical maximum fracture load is calculated. Then use its original measured value. Dividing by the theoretical value, we obtain the sample's... Value; thus, obtain a value containing multiple Given a set of data points, calculate the average of that set. with standard deviation And set the judgment threshold to In this calibration, The average of the total data is The standard deviation is Then the final determination The threshold for judgment is .

[0045] By executing the complete calibration procedure described above, the testing system now possesses a temperature correlation model and a fracture load correlation model for this specific hydrogel, including specific functional expressions and coefficients, as well as a model with a clear statistical source. Determination threshold All subsequent routine quality control tests for unknown batches of this product will be conducted based on this set of calibrated parameters, ensuring that each pass / fail determination has traceable and reproducible engineering basis.

[0046] Example 5: This example aims to describe a self-verification procedure for the integrity of the correlation model used to ensure the long-term stability of the test system in a specific implementation. To this end, the test system performed a self-verification step for the integrity of the correlation model. It should be noted that, during the initial system calibration phase, an additional reference model with known standard geometry and known true tensile strength, prepared from a standard batch of hydrogel (GoldenBatch), was prepared and stored. The self-validation process for the validation sample is as follows: First, perform model path analysis on the sample to be validated, i.e., measure its current mass. Compared with the actual maximum fracture load And based on the equivalent temperature of the current test. ,Will Substitute the fracture load correlation model stored in the system Calculate the fracture load predicted by the model. Simultaneously, a classical physical path analysis was performed on the calibration sample, which involved using high-precision measuring instruments to measure its current geometric dimensions to calculate the true cross-sectional area. And based on its known true tensile strength benchmark value. The fracture load predicted by classical physics was calculated. Ultimately, by measuring the actual maximum fracture load of the sample... Comparing with model prediction results and classical physics predictions By comparing the results, the integrity status of the correlation model is determined. When the prediction results of the model path analysis do not match the actual measurement, but the prediction results of the classical physical path analysis match the actual measurement, it is determined that the test equipment has experienced systematic drift. Otherwise, it is determined that the standard batch of hydrogel has deteriorated. This provides a diagnostic basis for the maintenance and recalibration of the system and completes the periodic confirmation of the effectiveness of the core model.

[0047] In a testing scenario where the method of this invention has been used for periodic quality control, the quality assurance system needs to perform a periodic verification to confirm that the validity of the previously calibrated and digitally stored correlation model, which serves as the judgment criterion, has not deviated due to potential long-term performance drift of the testing equipment or degradation of the standard material. This verification aims to avoid erroneous quality judgments that may result from the failure of the judgment criterion and is a crucial step in maintaining the long-term reliability of the entire testing method. To this end, the testing system performs a self-verification step on the integrity of the correlation model. It should be noted that, during the initial system calibration phase, an additional reference model with known standard geometry and known true tensile strength values, prepared from a standard batch of hydrogel GoldenBatch, has been prepared and sealed. The self-validation process for the validation sample is as follows: First, perform model path analysis on the sample to be validated, i.e., measure its current mass. Compared with the actual maximum fracture load And based on the equivalent temperature of the current test. ,Will Substitute the fracture load correlation model stored in the system Calculate the fracture load predicted by the model. Simultaneously, a classical physical path analysis was performed on the calibration sample, which involved using high-precision measuring instruments to measure its current geometric dimensions to calculate the true cross-sectional area. And based on its known true tensile strength benchmark value. The fracture load predicted by classical physics was calculated. Ultimately, by measuring the actual maximum fracture load of the sample... Comparing with model prediction results and classical physics predictions By comparing the results, the integrity status of the correlation model is determined. When the prediction results of the model path analysis do not match the actual measurement, but the prediction results of the classical physical path analysis match the actual measurement, it is determined that the test equipment has experienced systematic drift. Otherwise, it is determined that the standard batch of hydrogel has deteriorated. This provides a diagnostic basis for the maintenance and recalibration of the system and completes the periodic confirmation of the effectiveness of the core model.

[0048] Example 6: This example aims to describe the standardized engineering procedures used to establish the relevant judgment criteria for the injection performance and micro-failure mode diagnosis modules in a specific implementation. In a scenario where a research and development laboratory needs to apply the injection performance characterization and failure mode diagnosis functions of the present invention to the quality control system of a specific hydrogel product, the technical task is to establish quantifiable and statistically based qualification judgment criteria for these two diagnostic functions. The goal of this task is to ensure that the judgment of injection force or micro-failure fingerprint is not based on qualitative description, but on an objective numerical range defined by standard batch samples, so that all modules of the entire testing method have reproducible judgment logic.

[0049] To establish a criterion for judging injection performance, the laboratory used the same material as the standard batch of GoldenBatch hydrogel, took 30 uncured samples, and loaded them into standard-sized syringes; following the injection performance characterization procedure, in... Under the environment, using a universal testing machine to The constant rate of push-button pressure was used to record the stable injection force for each sample. After obtaining all 30 data points, calculate the average value of the entire dataset. with standard deviation The acceptable range of injection force for this material was determined as follows: If the measured average value is The standard deviation is Then the acceptable range of injection force for this product is determined to be... To establish a baseline fingerprint range for micro-failure modes, the laboratory utilized a higher [specification / method] during the calibration process in Example 3. The high-frequency force data from tensile tests of all GoldenBatch samples were collected at a frequency specified in the sampling frequency. This sampling frequency was chosen because the signal frequency generated by the energy release from microscopic cracking events within the hydrogel is typically no higher than [the specified frequency]. To capture the signal without distortion according to the Nyquist sampling theorem, the sampling frequency must be at least twice its highest frequency; for the original high-frequency force data sequence of each sample, a cutoff frequency of is first applied. A Butterworth low-pass filter was used to extract the macroscopic response trend, which was then subtracted from the original data to obtain the high-frequency residual signal. Next, the skewness and kurtosis of this high-frequency residual signal were calculated. After processing all GoldenBatch samples, two data populations were obtained, each consisting of the skewness and kurtosis values ​​of all samples. The average value of these two data populations was calculated. ) and standard deviation ( ), and the baseline fingerprint range was determined as follows: and If the final calculated acceptable range of skewness is The acceptable range for kurtosis is This range constitutes the baseline fingerprint characterizing the health failure mode of this material. By executing the above procedures, the two advanced diagnostic modules of injection performance and microscopic failure mode of this specific hydrogel product have obtained clear quantitative judgment benchmarks determined by experimental data and statistical methods. This enables the testing system to not only determine whether the macroscopic mechanical properties of the sample are consistent in subsequent routine quality control applications, but also to objectively and repeatably evaluate its rheological properties and microstructural integrity.

[0050] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An injectable hydrogel wound dressing tensile strength test method characterized by, The method comprises the following steps: Step a, preparing a set of standard samples with gradient variation in geometric size from a standard batch of hydrogel at at least two different known constant calibration temperatures, and performing the operation of measuring the mass and maximum breaking load and determining the tensile initial stiffness of each of the set of standard samples, based on the obtained overall data, establishing a breaking load correlation model characterizing the relationship between mass and calibration temperature and maximum breaking load, and establishing a temperature correlation model characterizing the relationship between tensile initial stiffness and calibration temperature; Step b, providing a to-be-tested batch of hydrogel to prepare a to-be-tested sample, measuring the mass of the to-be-tested sample, and obtaining its uniaxial tensile force displacement response until breaking to record its actual maximum breaking load; Step c, determining the actual tensile initial stiffness of the to-be-tested sample according to the uniaxial tensile force displacement response, using the actual tensile initial stiffness and converting the test equivalent temperature via the temperature correlation model, calling the model corresponding to the equivalent temperature from the breaking load correlation model according to the equivalent temperature, calculating the standard theoretical maximum breaking load in combination with the mass of the to-be-tested sample, and finally determining the tensile strength performance of the to-be-tested batch of hydrogel based on the comparison result of the actual maximum breaking load and the standard theoretical maximum breaking load.

2. A method of testing the tensile strength of an injectable hydrogel wound dressing according to claim 1, wherein, The comparison result is obtained by calculating the ratio of the actual maximum breaking load and the standard theoretical maximum breaking load to obtain a mechanical property consistency index, and comparing the mechanical property consistency index with a determination threshold value determined according to the statistical data of the standard batch of hydrogel.

3. A method of testing the tensile strength of an injectable hydrogel wound dressing according to claim 2, wherein, The calculation of the mechanical property consistency index MPI is: wherein is the actual maximum breaking load, is the standard theoretical maximum breaking load.

4. The injectable hydrogel wound dressing tensile strength test method of claim 1, wherein, Both the breaking load correlation model and the temperature correlation model are established by nonlinear regression analysis.

5. The injectable hydrogel wound dressing tensile strength test method of claim 1, wherein, The preparation process of the to-be-tested sample comprises injecting the to-be-tested batch of hydrogel into a test mold with a biomimetic rough topological inner surface and in-situ curing and forming.

6. The injectable hydrogel wound dressing tensile strength test method of claim 1, wherein, Before step b is performed, an injection performance characterization step is further included, which comprises: providing a to-be-tested batch of hydrogel in an uncured state and placing it in a syringe; using a device capable of applying controlled linear displacement and measuring reaction force to press the plunger of the syringe at a constant rate; and measuring and recording the force on the plunger during pressing as an indicator of the injection performance of the to-be-tested batch of hydrogel.

7. The injectable hydrogel wound dressing tensile strength test method of claim 1, wherein, When the uniaxial tensile force displacement response is obtained in step b, the force data is collected at a frequency higher than 1 kHz; and the method further comprises a failure mode diagnosis step, which comprises: separating a macroscopic response trend and a high-frequency residual signal from the collected high-frequency force data; calculating at least one high-order statistical moment of the high-frequency residual signal as a fingerprint feature characterizing the microscopic failure mode of the sample; and comparing the fingerprint feature with a reference fingerprint range established according to the standard batch of hydrogel to diagnose the failure mode of the sample.

8. A method of testing the tensile strength of an injectable hydrogel wound dressing according to claim 7, wherein, The high-order statistical moment includes the skewness and kurtosis of the distribution of the high-frequency residual signal; the skewness reflects the symmetry of the signal distribution, and the kurtosis reflects the degree of sharpness of the signal distribution. The high-order statistical moment includes the skewness and kurtosis of the distribution of the high-frequency residual signal; the skewness reflects the symmetry of the signal distribution, and the kurtosis reflects the degree of sharpness of the signal distribution.

9. The injectable hydrogel wound dressing tensile strength test method of claim 1, wherein, The method further comprises a self-verification step of the correlation model integrity, which comprises: providing a calibration sample prepared from the standard batch of hydrogel, having a known standard geometry and a known true tensile strength benchmark value; and simultaneously performing a model path analysis and a classical physical path analysis on the calibration sample; the model path analysis performs steps b and c to obtain a model prediction result based on the correlation model; the classical physical path analysis measures the current geometric size of the calibration sample, and based on the geometric size and the true tensile strength benchmark value thereof, obtains a classical physical prediction result; finally, the actual mechanical property of the calibration sample is compared with the model prediction result and the classical physical prediction result respectively to determine the integrity state of the correlation model.

10. A method of testing the tensile strength of an injectable hydrogel wound dressing according to claim 9, wherein, The logic for determining the integrity state of the correlation model is: when the prediction result of the model path analysis does not match the actual measurement and the prediction result of the classical physical path analysis matches the actual measurement, it is determined that the test equipment has systematic drift; when the prediction result of the model path analysis matches the actual measurement and the prediction result of the classical physical path analysis does not match the actual measurement, it is determined that the standard batch of hydrogel has deteriorated.

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