CN112345417A - A kind of calculation method of nanobubble particle size distribution - Google Patents
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Abstract
本发明属于微纳粒径测量技术领域，涉及一种纳米气泡粒径分布计算方法。该方法用于测量含纳米气泡水中的纳米气泡平均粒径大小及其分布规律。其应用NTA捕捉的气泡的布朗运动与扩散系数关系初步获得含所有颗粒粒径的数据源，再对准确的数据进行筛选，结合对数正态分布对数据进行拟合，得到准确的粒径分布规律。该方法更新了颗粒粒径数据处理方法，避免了粒径测量过程中因光源信号值差异过小问题所带来的误差，弥补了单独依靠NTA捕捉布朗运动与扩散系数关系计算粒径大小的准确性的不足，提供了新的准确的粒径测量方法。
The invention belongs to the technical field of micro-nano particle size measurement, and relates to a method for calculating nano-bubble particle size distribution. The method is used to measure the average particle size and distribution of nanobubbles in water containing nanobubbles. It uses the relationship between the Brownian motion and diffusion coefficient of bubbles captured by NTA to initially obtain the data source containing all particle sizes, then screen the accurate data, and fit the data in combination with the log-normal distribution to obtain accurate particle size distribution. law. This method updates the particle size data processing method, avoids the error caused by the small difference of the light source signal value in the particle size measurement process, and makes up for the accuracy of calculating the particle size by relying solely on NTA to capture the relationship between Brownian motion and diffusion coefficient. It provides a new and accurate particle size measurement method.
Description
Technical Field
The invention belongs to the technical field of micro-nano particle size measurement, and relates to a method for calculating nano bubble particle size distribution by combining nano particle tracking analysis (NTA) and lognormal distribution.
Background
In recent years, the nano bubbles are widely applied, and the body shadow of the nano bubbles can be seen in the fields of aquaculture, environmental protection, agriculture, industry, sanitation, medical treatment, electrolysis, ships and the like. Because the particle size of the nano bubbles is small, the nano bubbles can survive in water for a long time, so that the dissolved oxygen in the water is kept sufficient; the nano bubbles with static electricity on the surface can also inhibit and eliminate algae in water, so that the water area keeps better water quality; the water containing nano bubbles irrigates crops, can increase the population and activity of rhizosphere soil microorganisms, improve the physical and chemical properties of soil, enhance the capability of root systems for absorbing water and nutrients, improve the utilization efficiency of water and fertilizer, obviously improve the quality and yield of crops, and further achieve the purposes of increasing yield and saving water. The size of the particle size of the nanobubbles is a main factor affecting the application effect, so that it is very necessary to measure the accurate particle size distribution of the nanobubbles.
Generally, nanobubble particle size measurement techniques are classified into direct methods and indirect methods. The direct method can directly observe and measure the nano-bubble particle size through an electron microscope, but the vacuum measurement environment required by the electron microscope generally has certain influence on the bubble particle size, the particle size measurement result is limited by the resolution of the electron microscope to generate errors, and meanwhile, the method has the advantages of high capital operation cost, expensive sample preparation and poor sample integrity after sample preparation. The indirect method can calculate the particle size of the nano-bubbles by measuring the motion track and the speed of the nano-bubbles through dynamic light scattering, but the measurement method is easily influenced by impurities, small difference of optical signal values of the bubbles with different particle sizes and the like, so that the measurement result contains more error data, and the particle size distribution result of the nano-bubbles is non-unimodal. In nanobubble production, the bubbles produced are generally centered on a certain particle size, i.e., the particle size distribution map appears as a single peak.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and develop a nano bubble particle size distribution calculation method for measuring the average particle size and the distribution rule of nano bubbles in water containing nano bubbles. The method comprises the steps of preliminarily obtaining a data source containing all particle sizes by using the relationship between the Brownian motion and the diffusion coefficient of bubbles captured by NTA, screening accurate data, and fitting the data by combining with the lognormal distribution to obtain an accurate particle size distribution rule. The method updates the particle size data processing method, avoids errors caused by the problem that the difference of light source signal values is too small in the particle size measuring process, makes up for the defect of accuracy of calculating the particle size by independently depending on the relationship between the NTA capture Brownian motion and the diffusion coefficient, and provides a new accurate particle size measuring method.
The technical scheme of the invention is as follows:
a method for calculating the particle size distribution of nano bubbles uses dynamic light scattering and Einstein equation to obtain the motion rule and approximate hydrodynamic diameter of nano bubbles in solution; the data are screened and grouped, and data fitting is carried out by using the lognormal distribution, so that the accurate and visual bubble distribution rule and the average particle size are obtained.
The method comprises the following specific steps:
a method for calculating the particle size distribution of nano bubbles comprises the following steps:
the first step is as follows: primarily obtaining all particle size data of the nano bubbles by using NTA;
generating a water solution containing nano bubbles by using a nano bubble generating device, loading the solution by using a glass syringe, and measuring each group of solution for more than two times; capturing the Brown motion characteristic of the nano bubbles by using NTA; calculating the hydrodynamic diameter of the bubbles by using an Einstein equation to obtain all particle size data;
the second step is that: screening and classifying data;
screening the data obtained by the NTA in the first step, reserving all True data, and deleting all False data; then, carrying out interval classification counting on the retained data, setting a specified step length, and accumulating the number of bubbles in a corresponding interval, wherein the accumulation times are the same as the measurement times;
the third step: fitting a particle size distribution rule by lognormal distribution;
drawing a histogram by using the bubble number data of different intervals obtained in the second step and taking the particle size as a horizontal axis and the bubble number of the corresponding interval as a vertical axis, performing log-normal distribution fitting on the drawn histogram, and fitting to obtain the bubble distribution rule and the average particle size of the measurement;
meanwhile, for the calculation of multiple groups of data, a stacked histogram is drawn, and then the bubble distribution rule and the average particle size of the multiple groups of data are obtained by performing lognormal distribution fitting.
The invention has the beneficial effects that:
according to the technical scheme, the particle size distribution and the average particle size of the nano bubbles in the solution are calculated by utilizing NTA and lognormal distribution, so that the distribution rule of the nano bubbles in the solution and the average particle size of the bubbles are accurately obtained.
Obtaining rough nano-bubble particle size distribution rule and average particle size by using the self-dynamic light scattering of NTA and Einstein equation algorithm, carrying out histogram or stacked histogram drawing and lognormal distribution function fitting bubble particle size distribution rule on the sorted data by screening and classifying the derived data, and searching the parameter X obtained by fittingCAnd determining the average particle size of the bubbles in the solution. The distribution rule change and the larger average particle size caused by the data processing and selection of software are avoided, and the result shows that the particle size distribution is changed from multimodal to unimodal and the average particle size is changed from 159.1nm to 114.53nm when the bubble generator with the particle size of 100nm is used for continuous ventilation for 10 min; the particle size distribution changed from multimodal to unimodal and the average particle size changed from 149nm to 115.89nm by continuous aeration for 20min using a bubble generator producing a particle size of 100 nm. The accuracy of the average particle size approaching the conditional particle size was improved by 44.58% and 33.11%, respectively.
Drawings
Fig. 1 is a flow chart of a calculation method of nano-bubble particle size distribution.
FIG. 2 is a graph showing the distribution of the bubble particle size at 10min of aeration time output by the software itself.
FIG. 3(a) is a graph showing the distribution of the particle diameter of the air bubbles obtained by fitting the air flow time to 10 min.
FIG. 3(b) is a graph showing the distribution of the particle diameter of the air bubbles obtained by fitting the air flow time to 20 min.
FIG. 4 is a graph of the bubble size distribution for different aeration times as fitted.
Detailed Description
The following detailed description of the invention refers to the accompanying drawings. The examples are intended to further illustrate the invention, but not to limit it.
Comparative example
In order to verify the accuracy of the method for calculating the particle size distribution and the average particle size of the nanobubbles in the solution by combining NTA and lognormal distribution, the result of the particle size distribution of the nanobubbles in the solution and the average particle size derived by software can be compared. The comparative example compares the results under the experimental condition of using a nanobubble generator generating 100nm and the aeration time of 10 min. The software itself derived profile is shown in fig. 2, and the result calculated by this method is shown in fig. 3 (a).
From the aspect of particle size distribution rule, the result calculated by the method is more consistent with the target result of single peak; the result of this calculation is 114.53nm, compared to 159.1nm, which is directly derived from the software, and is closer to the condition value of 100nm in terms of average particle size.
Example 1
The method is a method for calculating the particle size distribution and the average particle size of nano bubbles in a solution by NTA and lognormal distribution under the condition of continuous ventilation for 10min of an air bubble generator with the generated particle size of 100 nm.
The method comprises the following specific steps:
the first step is as follows: primarily obtaining the total particle size data of the nano bubbles by using NTA
Generating a water solution containing nano bubbles by using a nano bubble generating device, loading the solution by using a glass syringe, measuring each group of solution for 20 times, wherein each measuring time is 10 seconds; capturing the Brown motion characteristic of the nano bubbles by using NTA; then using software to calculate the hydrodynamic diameter of the bubble by using the Einstein equation; using software to dump all particle size data;
the second step is that: screening and classification of data
Screening data poured out by NTA software, reserving all True data, and deleting all False data; then, performing interval classification counting of 0-500nm on the selected data, wherein the step length is 25nm, the number of bubbles in the corresponding interval is accumulated, and the accumulated number of times is the same as the number of times of measurement;
the third step: log-normal distribution fitting particle size distribution rule
Introducing the bubble number data of different intervals into Origin drawing software, drawing a histogram by taking the particle diameter as a horizontal axis and the bubble number of the corresponding interval as a vertical axis, and drawing the histogram for the drawn vertical axisFitting the graph with lognormal distribution to obtain parameter XCThe average bubble particle size required by the target is obtained, and the bubble distribution rule and the average particle size of the measurement are obtained.
The method comprises the following specific steps: and (3) drawing a histogram by taking the particle size as a horizontal axis and the number of bubbles in the corresponding interval as a vertical axis: selecting Plot in an Origin page, pulling down the Plot to select a Historgram option, and successfully drawing a data histogram at the moment; in the plotted histogram, a log normal distribution fit is performed on the data: selecting Analysis in the Origin page, then selecting Fitting, finding Nonlinear customer Fit in the Fitting, selecting Function Selection, and checking the LogNormal option, so that the data is successfully subjected to log-normal distribution Fitting. Parameter X obtained by fittingCThe average bubble particle size required by the target is obtained, and the bubble distribution rule and the average particle size of the measurement are obtained.
For multiple sets of data analysis, the data can be analyzed by plotting a stacked histogram: selecting Plot in the Origin page, and pulling down the Plot to select a Stacked Column option, wherein the Stacked Column diagram is successfully drawn; in the plotted stacked histogram, a log normal distribution fit was performed simultaneously on all data: selecting Analysis in the Origin page, then selecting Fitting, finding Nonlinear customer Fit in the Fitting, selecting Function Selection, and checking the LogNormal option, so that log-normal distribution Fitting is successfully carried out on all data. Parameter X obtained by fitting each group of dataCThe average bubble particle size required by the target is obtained, and a plurality of groups of measured bubble distribution rules and average particle sizes are obtained.
The obtained bubble distribution is shown in FIG. 3(a), and the average bubble diameter is 114.53 nm.
Example 2
The method is a method for calculating the particle size distribution and the average particle size of nano bubbles in a solution by NTA and lognormal distribution under the experimental working condition of continuously ventilating a bubble generator with the particle size of 100nm for 20 min.
The specific steps are shown in example 1, and only the calculation results are given here: the obtained bubble distribution is shown in FIG. 3(b), and the average bubble diameter is 115.89 nm.
Claims (2)
1. A method for calculating the particle size distribution of nano bubbles is characterized by comprising the following steps:
the first step is as follows: primarily obtaining all particle size data of the nano bubbles by using NTA;
generating a water solution containing nano bubbles by using a nano bubble generating device, loading the solution by using a glass syringe, and measuring each group of solution for more than two times; capturing the Brown motion characteristic of the nano bubbles by using NTA; calculating the hydrodynamic diameter of the bubbles by using an Einstein equation to obtain all particle size data;
the second step is that: screening and classifying data;
screening the data obtained by the NTA in the first step, reserving all True data, and deleting all False data; then, carrying out interval classification counting on the retained data, setting a specified step length, and accumulating the number of bubbles in a corresponding interval, wherein the accumulation times are the same as the measurement times;
the third step: fitting a particle size distribution rule by lognormal distribution;
drawing a histogram by using the bubble number data of different intervals obtained in the second step and taking the particle size as a horizontal axis and the bubble number of the corresponding interval as a vertical axis, performing log-normal distribution fitting on the drawn histogram, and fitting to obtain the bubble distribution rule and the average particle size of the measurement;
meanwhile, for the calculation of multiple groups of data, a stacked histogram is drawn, and then the bubble distribution rule and the average particle size of the multiple groups of data are obtained by performing lognormal distribution fitting.
2. The method for calculating the particle size distribution of nanobubbles according to claim 1, wherein the step of screening and classifying the data in the second step comprises: and screening the data obtained by each group of measurement, selecting all True data, putting each group of data in a new column, screening and counting each column of data, and superposing the data in the same size range and different columns through a summation function to obtain the rough particle size distribution of the sample solution.
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| CN115824973A (en) * | 2022-12-12 | 2023-03-21 | 西南石油大学 | Trace photo-thermal conversion efficiency testing and calculating method |
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