CN114657513B - Preparation method of antibacterial regenerated polyester film - Google Patents

Preparation method of antibacterial regenerated polyester film Download PDF

Info

Publication number
CN114657513B
CN114657513B CN202210560087.2A CN202210560087A CN114657513B CN 114657513 B CN114657513 B CN 114657513B CN 202210560087 A CN202210560087 A CN 202210560087A CN 114657513 B CN114657513 B CN 114657513B
Authority
CN
China
Prior art keywords
image
polyester film
convolution
film
stretching
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210560087.2A
Other languages
Chinese (zh)
Other versions
CN114657513A (en
Inventor
樊鹏鹏
赵越超
李海良
李沅鸿
张启纲
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Henan Yinjinda New Materials Co ltd
Original Assignee
Henan Yinjinda New Materials Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Henan Yinjinda New Materials Co ltd filed Critical Henan Yinjinda New Materials Co ltd
Priority to CN202210560087.2A priority Critical patent/CN114657513B/en
Publication of CN114657513A publication Critical patent/CN114657513A/en
Application granted granted Critical
Publication of CN114657513B publication Critical patent/CN114657513B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • C—CHEMISTRY; METALLURGY
    • C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/06—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the coating material
    • C23C14/12—Organic material
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01N—PRESERVATION OF BODIES OF HUMANS OR ANIMALS OR PLANTS OR PARTS THEREOF; BIOCIDES, e.g. AS DISINFECTANTS, AS PESTICIDES OR AS HERBICIDES; PEST REPELLANTS OR ATTRACTANTS; PLANT GROWTH REGULATORS
    • A01N43/00—Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds
    • A01N43/48—Biocides, pest repellants or attractants, or plant growth regulators containing heterocyclic compounds having rings with two nitrogen atoms as the only ring hetero atoms
    • A01N43/60—1,4-Diazines; Hydrogenated 1,4-diazines
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01N—PRESERVATION OF BODIES OF HUMANS OR ANIMALS OR PLANTS OR PARTS THEREOF; BIOCIDES, e.g. AS DISINFECTANTS, AS PESTICIDES OR AS HERBICIDES; PEST REPELLANTS OR ATTRACTANTS; PLANT GROWTH REGULATORS
    • A01N59/00—Biocides, pest repellants or attractants, or plant growth regulators containing elements or inorganic compounds
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C55/00—Shaping by stretching, e.g. drawing through a die; Apparatus therefor
    • B29C55/28—Shaping by stretching, e.g. drawing through a die; Apparatus therefor of blown tubular films, e.g. by inflation
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/18—Testing for antimicrobial activity of a material
    • C—CHEMISTRY; METALLURGY
    • C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C14/00—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material
    • C23C14/22—Coating by vacuum evaporation, by sputtering or by ion implantation of the coating forming material characterised by the process of coating
    • C23C14/24—Vacuum evaporation
    • C23C14/28—Vacuum evaporation by wave energy or particle radiation
    • C23C14/30—Vacuum evaporation by wave energy or particle radiation by electron bombardment
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N3/08—Investigating strength properties of solid materials by application of mechanical stress by applying steady tensile or compressive forces
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/0001—Type of application of the stress
    • G01N2203/0003—Steady
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/0014—Type of force applied
    • G01N2203/0016—Tensile or compressive
    • G01N2203/0017—Tensile
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/0058—Kind of property studied
    • G01N2203/006—Crack, flaws, fracture or rupture
    • G01N2203/0062—Crack or flaws
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/02—Details not specific for a particular testing method
    • G01N2203/026—Specifications of the specimen
    • G01N2203/0262—Shape of the specimen
    • G01N2203/0278—Thin specimens
    • G01N2203/0282—Two dimensional, e.g. tapes, webs, sheets, strips, disks or membranes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/02—Details not specific for a particular testing method
    • G01N2203/06—Indicating or recording means; Sensing means
    • G01N2203/0641—Indicating or recording means; Sensing means using optical, X-ray, ultraviolet, infrared or similar detectors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
    • Y02W30/00—Technologies for solid waste management
    • Y02W30/50—Reuse, recycling or recovery technologies
    • Y02W30/62—Plastics recycling; Rubber recycling

Landscapes

  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Organic Chemistry (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • General Health & Medical Sciences (AREA)
  • Mechanical Engineering (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Pest Control & Pesticides (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Materials Engineering (AREA)
  • Plant Pathology (AREA)
  • Metallurgy (AREA)
  • Agronomy & Crop Science (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Dentistry (AREA)
  • Toxicology (AREA)
  • Environmental Sciences (AREA)
  • Pathology (AREA)
  • General Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Inorganic Chemistry (AREA)
  • Biotechnology (AREA)
  • Microbiology (AREA)
  • Molecular Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • Genetics & Genomics (AREA)
  • Manufacture Of Macromolecular Shaped Articles (AREA)
  • Shaping By String And By Release Of Stress In Plastics And The Like (AREA)

Abstract

A process for preparing antibacterial regenerated polyester film includes such steps as (1) mixing nano TiO with the polyester film 2 The mass ratio of the particles to the regenerated polyester particles is 0.8: 99.2-1.5:98.5, preparing a polyester-based film, preparing a target material consisting of ZnO, ciprofloxacin and polyester according to the mass ratio of 0.5:1:2 to 0.8:1:2 or 0.5:1:1, and coating the film in a deposition mode; and carrying out bacteriostasis experiment and mechanical property detection on the formed film. Therefore, the polyester film which can keep better mechanical property and stable antibacterial performance under the illumination condition can be obtained.

Description

Preparation method of antibacterial regenerated polyester film
Technical Field
The present invention relates to the field of recycled polyester films having antimicrobial functionality.
Background
The regenerated polyester film is prepared by using waste polyester materials (such as PET, PP, PVC and the like) through processes of cleaning, sorting, decoloring, regenerating and the like, so that the utilization rate of resources can be improved. When producing the regenerated polyester film, the functionality of the regenerated polyester film can be increased by adding other substances according to the requirements of application occasions. Such as photochromic films, resistive films, etc. Of these, antibacterial recycled polyester films are an important class.
The conventional antibacterial regenerated polyester film is prepared by adding Ag into regenerated polyester and realizing an antibacterial function by utilizing the bactericidal action of silver. However, Ag is a noble metal and is relatively expensive. In addition, the amount of Ag added to the polyester film should not be too large, which may degrade the mechanical properties of the polyester film, while too small may impair the bactericidal effect. There are also chemicals (e.g. sarin compounds) deposited in polyester films that can kill bacteria, but most of these compounds slowly decompose and denature under light conditions, thereby losing their bactericidal function. The polyester film is frequently exposed to sunlight for use, and thus the antimicrobial life of the antimicrobial polyester film is greatly shortened. Some people also propose to use metals such as Ag and the like together with various pharmaceutical compounds (mixed coating or layered coating), and to improve the bactericidal effect by utilizing the co-sterilization effect of Ag and chemical drugs. However, the peak value of the sterilization effect of the new product is only improved, and the problem that the chemical medicine is slowly denatured by illumination cannot be solved. Therefore, how to realize long-term antibacterial effect on the basis of ensuring the mechanical property of the regeneration membrane is an urgent problem to be solved.
In addition, how to evaluate the bacteriostasis effect and the mechanical property of the prepared polyester film is also the key point for developing the antibacterial regenerated polyester film. The existing bacteriostatic experiments on the polyester film usually consider the bacteriostatic activity of the polyester film, but do not consider the bacteriostatic performance of the polyester film under the actual use conditions (illumination). Therefore, a complete bacteriostasis experiment is also needed to be solved. In addition, in the prior art, a stress field analysis method is mainly used for detecting the mechanical property of the film, but the method has the disadvantages of complex equipment and algorithm, difficulty in operation, long detection time, capability of being used only in scientific research experiments and unsuitability for application in a production line. Most of detection equipment is a tensile testing machine, is too complex and professional and is not suitable for automatic operation. In the prior art, an image processing mode is also used for detecting the mechanical property, but most algorithms do not find out proper detection characteristics, such as elongation and the like, which are used as indexes, and the mechanical property of the membrane cannot be accurately reflected. And the current image processing algorithm is not mature and cannot accurately detect. Therefore, the method can be only used for auxiliary detection and cannot be applied to a production line. Although the neural network model is already applied in other fields, no special model structure is still applicable in the field of membrane mechanical property detection, and other transfer models cannot adapt to the characteristics of high light transmission and diffuse light of polyester film images and cannot accurately detect and judge. In addition, uniform stretching does not accurately and comprehensively reflect the mechanical properties of polyester films, particularly with the addition of TiO 2 A recycled polyester film of ZnO or ciprofloxacin needs a more optimized drawing method.
Disclosure of Invention
To solve one or more of the above problems, the present invention proposes the following solutions.
1. A method for preparing antibacterial regenerated polyester film comprises
(1) Mixing nanometer TiO 2 The mass ratio of the particles to the regenerated polyester particles is 0.8: 99.2-1.5:98.5, fully stirring for 10-20min, and then performing extrusion granulation on the mixture to obtain polymer master batches; performing film blowing and stretching on the obtained master batch to obtain a polyester base film, wherein the surface of the base film is provided with TiO 2 Protrusions and depressions formed by the particles;
(2) preparing a target material consisting of ZnO, ciprofloxacin and polyester according to the mass ratio of 0.5:1:2 to 0.8:1:2 or 0.5:1:1, and putting the target material into a vacuum deposition chamber;
(3) placing the base film in a vacuum deposition chamber, and vacuumizing the vacuum deposition chamber to 2 × 10 -2 -8×10 -2 Pa, adjusting the voltage of an electron beam to be 1.5-2.5kV and the current to be 80-200mA, starting deposition, wherein the deposition time is 7-12min, so that target particles are filled into the depressions on the surface of the base film; after the deposition is finished, slowly returning the current and the voltage to zero, and after the temperature of the vacuum chamber is reduced, turning off the power supply to finish the film coating;
(4) carrying out bacteriostasis experiment on the coated antibacterial regenerated polyester film: comprises observing the diameter of the inhibition zone under the condition of strong light;
(5) and (3) placing the coated antibacterial regenerated polyester film into a stretching device to be stretched in the following way:
Figure 100002_DEST_PATH_IMAGE002
wherein Y is the outward stretching distance of the stretching mechanism, t is the stretching time, and a is the tensile strength coefficient;
the method comprises the steps of collecting an image of the antibacterial regenerated polyester film while stretching, preprocessing the image, identifying cracks of the polyester film image by using a neural network model, and judging the mechanical property of the antibacterial regenerated polyester film according to the time for collecting the image.
Nano TiO 2 2 Pellets and recycled polyester pelletsThe mass ratio is 1: 99.
the stirring time in the step (1) is 15 min.
The vacuum deposition chamber is vacuumized to 5 x 10 -2 Pa。
The electron beam voltage was 2 kV.
The deposition time was 8 min.
The recycled polyester is PVC, PE or PET.
The preprocessing includes brightness correction using cross-correlation coefficients of pixel value distributions of the acquired image and the sample image.
In the bacteriostasis experiment step, the method also comprises the steps of dripping the mixed liquid of the bacteria and the culture medium on the polyester film, culturing for 24 hours, observing the diameter D of the bacteriostasis zone and recording.
In the bacteriostasis experiment step, the method also comprises the steps of dripping the mixed liquid of the bacteria and the culture medium on the polyester film, culturing under the condition of strong light, and recording the time when the diameter of the bacteriostasis zone reaches 0.6D.
The invention has the advantages that:
1. by adding nano TiO into regenerated polyester particles 2 The particles thus form a depression for containing ciprofloxacin at the time of film formation, so that TiO 2 The particles can reduce the photolytic effect of ciprofloxacin; meanwhile, ZnO and ciprofloxacin are preferably deposited on the surface of the polyester film in an electron sputtering deposition mode, and the photolysis effect of the ciprofloxacin is further reduced under the action of ZnO. Particularly, the proportion, the film forming mode and the parameters of the three are optimized, so that the polyester film can stably exert the bacteriostatic effect for a long time under the illumination condition on the premise of ensuring the mechanical property of the polyester film.
2. A bacteriostasis experiment using the illumination condition is provided, and the bacteriostasis effect of the polyester film in the actual use process can be more accurately and comprehensively reflected.
3. The real use condition is simulated through the optimized stretching motion equation, and the mechanical property of the polyester film can be detected more accurately. Particularly, image preprocessing is carried out through a correlation method, and a special neural network is adopted for identification and detection, so that the cracks caused by stretching can be quickly and accurately detected, and the detection can be applied to a production line.
Detailed Description
Preparation of polyester film
1. Mixing nanometer TiO 2 The mass ratio of the particles to the regenerated polyester particles is 0.8: 99.2-1.5:98.5, fully stirring for 10-20min, and then extruding and granulating the mixture to obtain the polymer master batch. And carrying out film blowing and stretching on the obtained master batch to obtain the polyester base film. The thus-obtained base film was observed to have TiO on the surface thereof under an electron tunneling microscope 2 The bulges and the depressions formed by the particles are ready for the next step of coating the antibacterial film. That is, the antibacterial film, particularly ciprofloxacin component, can uniformly fill the depressions on the base film. This can prevent degradation of the ciprofloxacin component by light during use of the polyester film. The principle is TiO 2 The particles can reflect and absorb light rays, reduce the light receiving amount of the ciprofloxacin component, and can generate a photocatalysis antibacterial effect after absorbing the light rays, so that the sterilization effect is further improved. However, excessive addition of TiO 2 Particles are not preferable, and too high a content thereof causes repeated reflection of light rays between them, and on the contrary, causes an increase in the amount of ciprofloxacin component received, and an excessive amount of TiO 2 The particles cause a severe deterioration in the mechanical properties of the film. Furthermore, TiO 2 The particles reduce the aging effect of the polyester film and thereby avoid affecting the stability of ciprofloxacin during aging of the polyester film.
2. Preparing ZnO, ciprofloxacin and polyester targets with the mass ratio of 0.5:1:2, 0.8:1:2 or 0.5:1:1, and putting the targets into a vacuum deposition chamber. ZnO not only has an antibacterial effect, but also can inhibit the photolysis of ciprofloxacin according to a large number of experiments. Therefore, ZnO is added in a small amount and is deposited together with ciprofloxacin, so that the contact area of the ZnO and the ciprofloxacin is increased, and photolysis of the ciprofloxacin in a light environment is reduced. However, the proportion of ZnO should not be too large, and the too large ZnO may agglomerate to reduce the mechanical performance of the film.
3. The vacuum deposition chamber is vacuumized to 2 x 10 -2 -8×10 -2 Pa, adjusting the voltage of the electron beam to be 1.5-2.5kV and the current to be 80-200mA, and starting deposition for 7-12 min. When the deposition time is over, or the film thickness reaches the desired film thickness, the deposition can be stopped, and the current is appliedAnd slowly returning the voltage to zero, turning off the power supply after the temperature of the vacuum chamber is reduced, and finishing the film coating. By the electron sputtering deposition, the deposition particles of ZnO and ciprofloxacin were very small and could be filled in the recess formed in step 1, and this effect could not be achieved by other formation means. In addition, not only TiO is formed during the preparation 2 The protrusions and depressions provide space for ciprofloxacin to deposit, thereby reducing the influence of light, and controlling TiO 2 The proportions of ZnO and ciprofloxacin can influence the photolysis rate of ciprofloxacin and the mechanical strength of formed films. The control of the ratio of the three components is realized by the mass part ratio of the components, and the deposition condition and time in the deposition process are strictly controlled, so that the regenerated polyester film with stable antibacterial effect can be obtained.
The recycled polyester can be PVC, PE, PET, etc.
Preparation of sample 1
1. Mixing nanometer TiO 2 The mass ratio of the particles to the regenerated polyester particles is 0.8: 99.2 mixing and fully stirring for 15min, and then extruding and granulating the mixture to obtain the polymer master batch. And carrying out film blowing and stretching on the obtained master batch to obtain the polyester base film.
2. Preparing ZnO, ciprofloxacin and polyester target materials with the mass ratio of 0.5:1:2, and putting the target materials into a vacuum deposition chamber.
3. The vacuum deposition chamber is evacuated to 5X 10 -2 Pa, adjusting the voltage of the electron beam to be 1.5kV and the current to be 80mA, and starting deposition for 7 min. When the deposition is finished, the current and the voltage are slowly returned to zero, and after the temperature of the vacuum chamber is reduced, the power supply is turned off, and the film coating is finished.
Preparation of sample 2
1. Mixing nanometer TiO 2 The mass ratio of the particles to the regenerated polyester particles is 1.5:98.5 mixing and fully stirring for 30min, and then extruding and granulating the mixture to obtain the polymer master batch. And carrying out film blowing and stretching on the obtained master batch to obtain the polyester base film.
2. Preparing ZnO, ciprofloxacin and polyester target materials with the mass ratio of 0.5:1:1, and putting the target materials into a vacuum deposition chamber.
3. The vacuum deposition chamber is vacuumized to 2 x 10 -2 Pa, adjusting the voltage of the electron beam to be 2kV and the current to be 100mA, and starting deposition for 10 min. When the deposition is finished, the current and the voltage are slowly returned to zero, and after the temperature of the vacuum chamber is reduced, the power supply is turned off, and the film coating is finished.
Preparation of sample 3
1. Mixing nanometer TiO 2 The mass ratio of the particles to the regenerated polyester particles is 1: 99 and fully stirring for 20min, and then extruding and granulating the mixture to obtain the polymer master batch. And carrying out film blowing and stretching on the obtained master batch to obtain the polyester base film.
2. Preparing ZnO, ciprofloxacin and polyester target materials with the mass ratio of 0.5:1:1, and putting the target materials into a vacuum deposition chamber.
3. The vacuum deposition chamber is pumped down to 8 x 10 -2 Pa, adjusting the voltage of the electron beam to be 2.5kV and the current to be 120mA, and starting deposition for 8 min. When the deposition is finished, the current and the voltage are slowly returned to zero, and after the temperature of the vacuum chamber is reduced, the power supply is turned off, and the film coating is finished.
(II) antibacterial test
(1) Adding 0.5g of yeast extract powder, 1.0g of peptone and 0.5g of NaCl into 50ml of deionized water, stirring for dissolving, adjusting the pH value of the solution to 7, and then adding 2.0g of agar to obtain a culture medium;
(2) 1.5mL of cultured Escherichia coli bacterial liquid is rapidly taken by a pipette and mixed with a culture medium to obtain a mixed solution.
(3) A square with 2cm side length was cut from the sample and placed in a petri dish with the antimicrobial layer facing upwards. And dripping the mixed liquid on the sample, culturing for 24h, observing the diameter D of the inhibition zone, and recording.
(4) Repeating the steps, and meanwhile, adding strong light irradiation (the light intensity is 3500-.
The samples obtained above were subjected to the same antibacterial test as the antibacterial film of the prior art, and the following test results were obtained.
Figure 100002_DEST_PATH_IMAGE004
(III) detection of mechanical Properties
The antibacterial regenerated polyester film has various components added, so the mechanical property of the antibacterial regenerated polyester film is influenced. In particular, mechanical properties deteriorate drastically after aging during use. The material usually cracks in the early stage of aging, and through a large number of experiments, the cracks are found to be a key clue reflecting the aging degree and the mechanical property of the material. With the continuous increase of the stretching degree, the tested material starts to generate cracks, the cracks are continuously expanded and become round, and finally the whole body is torn; accordingly, the stress distribution of the material is gradually concentrated at the crack and continuously develops. Based on the above analysis, the present invention proposes a detection device for determining the mechanical properties of a polyester film by detecting the occurrence of cracks. Therefore, the problems that the stretching ratio is difficult to measure in an image mode and the image representation of the polyester film is not obvious after the polyester film is stressed are solved.
Due to the addition of TiO 2 And ZnO, may suffer from a decrease in mechanical properties of the polyester film, for which special experiments are required for testing. For more accurate detection, the present invention preferably proposes the use of non-uniform motion for stretching, simulating the various conditions that may occur in actual use. The movement process of the specific stretching mechanism is as follows:
Figure DEST_PATH_IMAGE002A
wherein Y is the outward stretching distance of the stretching mechanism, t is the stretching time, and a is the tensile strength coefficient.
Therefore, the stretching action can be richer, multiple conditions can be simulated in one-time stretching test, the mechanical property detected in this way is more accurate, and the method is more practical.
Step 1: capturing polyester film images
A visible light camera is adopted to collect the front images of the polyester film material at a certain frame rate in a polyester film mechanical tensile experiment, and each collected image is transmitted to an image recognition module after being preprocessed.
The material to be measured is laid on a tensile test bed and fixed, a visible light camera is vertically aligned with the test bed, and the material is kept in the central part of the visual field of the camera; the distance of the camera from the test rig was controlled so that the material covered approximately 2/3 areas in the center of the camera's field of view. The distribution of pixel values of the material is known in advance and can be obtained by collecting sample image statistics of the tested material, and the distribution is recorded as
Figure 100002_DEST_PATH_IMAGE006
，
Figure 100002_DEST_PATH_IMAGE008
The pixel values representing the image are typically discrete values for a digital visible light image, with a range of values from 0 to 255.
The sample image refers to an image with known characteristics prepared in advance according to needs, and the specific known characteristics are determined according to needs. A known feature of a sample image here is the pixel distribution of the image.
Taking any one of the collected images
Figure 100002_DEST_PATH_IMAGE010
Called the captured image, having a histogram distribution of
Figure 100002_DEST_PATH_IMAGE012
，
Figure 134960DEST_PATH_IMAGE008
Representing pixel values of an image. Calculating cross-correlation coefficient of pixel value distribution of collected image and sample image
Figure 100002_DEST_PATH_IMAGE014
. Preferably, the distribution of pixel values of the sample image can be obtained by averaging the sample values, that is, obtaining an average distribution of pixel values of a plurality of sample images. Define according to the cross-correlation:
Figure 100002_DEST_PATH_IMAGE016
wherein
Figure 100002_DEST_PATH_IMAGE018
，
Figure 100002_DEST_PATH_IMAGE020
The offset of the sample image from the distribution of the acquired image pixels. And calculating the offset which enables the sample image and the collected image to be closest to the pixel distribution according to the following formula:
Figure 100002_DEST_PATH_IMAGE022
wherein max means in
Figure 848838DEST_PATH_IMAGE014
At maximum time
Figure 894155DEST_PATH_IMAGE020
The value of (c).
Because the brightness distribution of different images is easily influenced by the environment, the average value of the measured material in the sample image and the acquired image may be different; calculating the distributed offset of the measured material in the collected image relative to the sample image according to the formula (2), and calculating the distribution of the measured material in the collected image according to the offset
Figure 100002_DEST_PATH_IMAGE024
Carry out luminance correction to the collection image, make the relevant pixel distribution of material more even in the collection image to keep the uniformity of material pixel distribution in a plurality of collection images, be favorable to subsequent identification process:
Figure 100002_DEST_PATH_IMAGE026
Figure 100002_DEST_PATH_IMAGE028
for acquiring a modified pixel distribution of an image, corresponding to the image
Figure 100002_DEST_PATH_IMAGE030
To be composed of
Figure 568325DEST_PATH_IMAGE010
The corrected image. And finishing the pretreatment.
Step 2: detection of cracks in images of mylar material
And performing spatial filtering on the preprocessed polyester film material image to obtain filter responses of different-scale subgraphs in the image, and obtaining filter parameters with high correlation with cracks through sample learning so as to detect the cracks in the image.
Inputting a preprocessed image:
Figure 100002_DEST_PATH_IMAGE032
Figure 100002_DEST_PATH_IMAGE034
representing the spatial coordinates of the image.
Figure 100002_DEST_PATH_IMAGE036
Express coordinates in
Figure 117118DEST_PATH_IMAGE034
The value of the image pixel of (2). Spatial filtering means the convolution response between an image and a particular filter. The filter, also called filter window, refers to a two-dimensional matrix, the size of which is usually much smaller than the image size. Assume that the filter is
Figure 100002_DEST_PATH_IMAGE038
And u and v are filter matrix coordinates, the convolution response is:
Figure 100002_DEST_PATH_IMAGE040
the original will obtain different responses for different filters by equation (4), each response being a two-dimensional matrix of equal size to the original, also called a convolution response map. The filter is also called a convolution kernel.
According to the formula (4), 16 independent convolution kernels are selected
Figure 100002_DEST_PATH_IMAGE042
、
Figure 100002_DEST_PATH_IMAGE044
、…、
Figure 100002_DEST_PATH_IMAGE046
. A corresponding 16 independent sets of convolution responses can be generated:
Figure 100002_DEST_PATH_IMAGE048
in the formula (5), each convolution kernel
Figure 100002_DEST_PATH_IMAGE050
Has a size of 13 x 13.
Figure DEST_PATH_IMAGE052
Which represents the response of the convolution,
Figure DEST_PATH_IMAGE054
is a linear offset used for rectifying the brightness of the whole image.
Convolution response map
Figure 838781DEST_PATH_IMAGE052
Including at a certain scale a certain convolution kernel
Figure 371394DEST_PATH_IMAGE050
Information about this. If the pixel of the crack in the image is divided intoThe distribution of the cloth just accords with the distribution of a certain convolution kernel, and then the convolution kernel generates strong response, so that whether cracks exist in the image can be judged according to the strong response. But since the scale (i.e., relative size) of the crack in the image is unknown, detection can be re-performed using convolution kernels of different sizes according to the foregoing teachings. Therefore, the following definitions are provided:
Figure DEST_PATH_IMAGE056
according to the formula (6),
Figure DEST_PATH_IMAGE058
corresponding to the to-be-responded figure
Figure 163769DEST_PATH_IMAGE052
The scale is reduced 1/4 and each pixel value is equal to the maximum of the 16 pixels it corresponds to before the scale is reduced. The reduced image is processed by convolution kernel again to obtain a convolution response image under a new scale:
Figure DEST_PATH_IMAGE060
according to the formula (7),
Figure DEST_PATH_IMAGE062
,
Figure DEST_PATH_IMAGE064
for the 16 convolution kernels at this scale,
Figure DEST_PATH_IMAGE066
in order to be a linear offset amount,
Figure 458747DEST_PATH_IMAGE058
is the output of the previous step.
Figure DEST_PATH_IMAGE068
Is a non-linear excitation function for making the linear model formed from convolution kernel possess non-linear approximation capability, defined asThe following:
Figure DEST_PATH_IMAGE070
in the formula (8), the reaction mixture is,
Figure DEST_PATH_IMAGE072
to control the parameters of convergence, having a certain influence on the filter model performance, the preferred values are selected according to a large number of data tests
Figure DEST_PATH_IMAGE074
.
Further reducing the image to obtain a new scale, defining:
Figure DEST_PATH_IMAGE076
according to the formula (9),
Figure DEST_PATH_IMAGE078
corresponding to the to-be-responded figure
Figure DEST_PATH_IMAGE080
The scale is reduced 1/4 and each pixel value is equal to the maximum of the 16 pixels it corresponds to before the scale is reduced. The reduced image is processed by convolution kernel again to obtain a convolution response image under a new scale:
Figure DEST_PATH_IMAGE082
according to the formula (10),
Figure DEST_PATH_IMAGE084
,
Figure 996651DEST_PATH_IMAGE064
for the 16 convolution kernels at this scale,
Figure DEST_PATH_IMAGE086
in order to be a linear offset amount,
Figure 157505DEST_PATH_IMAGE078
is the output of the previous step.
Figure 425675DEST_PATH_IMAGE068
Is defined as (8) for the nonlinear function.
Defining a one-dimensional vector for mapping the matrix convolution kernel to a one-dimensional space further simplifies the feature space of the response:
Figure DEST_PATH_IMAGE088
Figure DEST_PATH_IMAGE090
for one pixel in the response map calculated according to equation (10),
Figure DEST_PATH_IMAGE092
is a linear weight corresponding thereto, the value of which is based on
Figure DEST_PATH_IMAGE094
The values of (a) are different.
Figure DEST_PATH_IMAGE096
Is a linear offset.
Figure 230951DEST_PATH_IMAGE068
Is defined as (8) for a non-linear function
Mapping the feature space of equation (11) to the crack detection result y:
Figure DEST_PATH_IMAGE098
wherein,
Figure DEST_PATH_IMAGE100
is prepared by reacting with
Figure DEST_PATH_IMAGE102
The linear weight of interest is then calculated,
Figure DEST_PATH_IMAGE104
in order to calculate the result in the last step,
Figure DEST_PATH_IMAGE106
is a linear offset.
Figure 51009DEST_PATH_IMAGE068
Is defined as (8) for the nonlinear function. y has a value in the range of [0, 1 ]]When y =0, it indicates that the image is input
Figure 699159DEST_PATH_IMAGE030
No crack was detected, and when y =1, it indicates that a crack was detected.
According to the above definitions (5) - (12), the filter parameters, linear offset parameters and weight parameters in the formula are also determined. Several sample images can be prepared for learning, including two types of images, one containing intact film material and one containing cracked film material, each having a known characteristic
Figure DEST_PATH_IMAGE108
And corresponds to the two types of sample images,
Figure DEST_PATH_IMAGE110
it is shown that the image has no cracks,
Figure DEST_PATH_IMAGE112
indicating a crack in the image.
For the sample image for learning in the previous step, all filter parameters are preset to be 1, the linear offset parameter is 0, the weight parameter is 1 as an initial value, and the final output value can be calculated according to the formulas (5) to (12)
Figure DEST_PATH_IMAGE114
With known features
Figure 462365DEST_PATH_IMAGE108
The error in the values is defined as follows:
Figure DEST_PATH_IMAGE116
Figure DEST_PATH_IMAGE118
the method is beneficial to improving the robustness of the detection method to noise by controlling the coefficient. Preferably, take
Figure DEST_PATH_IMAGE120
。
According to the formula (13), and by adopting BP algorithm, iterative calculation can be carried out and
Figure DEST_PATH_IMAGE122
tends to be minimized when
Figure 230470DEST_PATH_IMAGE122
When the iteration condition is reached, the obtained parameters are used as parameters finally adopted in the formulas (5) to (12).
And (5) detecting whether any input image contains cracks according to the steps (5) to (12). When outputting
Figure DEST_PATH_IMAGE124
And if not, the image is considered to contain no cracks.
The method in the step 1 is adopted to collect and preprocess the image, and the method in the step 2 is adopted to detect the preprocessed image, so that whether a crack exists in the polyester film material image can be detected. The method can find the appearance abnormality of the polyester film material before the polyester film material is subjected to tensile fracture, and the method can indirectly detect the mechanical property of the polyester film material because the crack of the material is highly related to the change of the mechanical property of the material, and can react in advance when the abnormal condition occurs. Through a large number of experiments, the detection accuracy of the method is more than 99%, and the delay time is 3 seconds, so that the method is more excellent than the prior art.
The mechanical property of the sample is detected by the method, and the following results are obtained. From the results, it can be seen that the samples of the present invention can achieve substantially the same mechanical properties as the conventional methods. But the bacteriostatic activity is far ahead. It will therefore be appreciated that the mechanical properties of the polyester film of the invention are far superior to those of the prior art if the same bacteriostatic properties are obtained.
Figure DEST_PATH_IMAGE126
It is understood that the bacteriostasis test and the mechanical property test mentioned in the invention are not limited to the polyester film prepared by the invention, and other bacteriostasis polyester films can be used.
It will be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been shown and described in detail herein, many other variations or modifications can be made, which are consistent with the principles of this invention, and which are directly determined or derived from the disclosure herein, without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.

Claims (1)

1. A method for detecting the performance of an antibacterial regenerated polyester film is characterized by comprising the following steps: comprises that
(1) Carrying out bacteriostasis experiment on the antibacterial regenerated polyester film: comprises observing the diameter of the inhibition zone under the condition of strong light;
(2) the antibacterial regenerated polyester film is placed into a stretching device to be stretched in the following way:
Figure DEST_PATH_IMAGE002
wherein Y is the outward stretching distance of the stretching mechanism, t is the stretching time, and a is the tensile strength coefficient;
collecting the image of the antibacterial regenerated polyester film while stretching, preprocessing the image, identifying the crack of the polyester film image by using a neural network model, and judging the mechanical property of the antibacterial regenerated polyester film by combining the time for collecting the image;
wherein the nonlinear excitation function of the neural network model is:
Figure DEST_PATH_IMAGE004
；
wherein the neural network model is composed of: for the pre-processed image
Figure DEST_PATH_IMAGE006
And performing convolution operation as follows to obtain a first convolution response diagram:
Figure DEST_PATH_IMAGE008
(ii) a First convolution response map
Figure DEST_PATH_IMAGE010
Down-sampling to obtain a first sampling image
Figure DEST_PATH_IMAGE012
(ii) a For the first sampling chart
Figure 104778DEST_PATH_IMAGE012
And performing convolution operation as follows to obtain a second convolution response diagram:
Figure DEST_PATH_IMAGE014
wherein
Figure DEST_PATH_IMAGE016
Is a nonlinear excitation function; mapping the second convolution response map
Figure DEST_PATH_IMAGE018
Down-sampling to obtain a second sampling map
Figure DEST_PATH_IMAGE020
(ii) a For the second sampling chart
Figure 772651DEST_PATH_IMAGE020
And performing convolution operation as follows to obtain a third convolution response graph:
Figure DEST_PATH_IMAGE022
；
and performing the following mapping operation to obtain a feature space vector:
Figure DEST_PATH_IMAGE024
wherein,
Figure DEST_PATH_IMAGE026
、
Figure DEST_PATH_IMAGE028
、
Figure DEST_PATH_IMAGE030
convolution kernels in the steps of obtaining a first convolution response diagram, obtaining a second convolution response diagram and obtaining a third convolution response diagram respectively;
mapping the feature space vector to a crack discrimination result:
Figure DEST_PATH_IMAGE032
wherein
Figure DEST_PATH_IMAGE034
、
Figure DEST_PATH_IMAGE036
、
Figure DEST_PATH_IMAGE038
、
Figure DEST_PATH_IMAGE040
、
Figure DEST_PATH_IMAGE042
In order to be a linear offset,
Figure DEST_PATH_IMAGE044
、
Figure DEST_PATH_IMAGE046
in order to be a linear weight, the weight,
Figure DEST_PATH_IMAGE048
Figure DEST_PATH_IMAGE050
。
CN202210560087.2A 2022-05-23 2022-05-23 Preparation method of antibacterial regenerated polyester film Active CN114657513B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210560087.2A CN114657513B (en) 2022-05-23 2022-05-23 Preparation method of antibacterial regenerated polyester film

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210560087.2A CN114657513B (en) 2022-05-23 2022-05-23 Preparation method of antibacterial regenerated polyester film

Publications (2)

Publication Number Publication Date
CN114657513A CN114657513A (en) 2022-06-24
CN114657513B true CN114657513B (en) 2022-09-20

Family

ID=82038021

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210560087.2A Active CN114657513B (en) 2022-05-23 2022-05-23 Preparation method of antibacterial regenerated polyester film

Country Status (1)

Country Link
CN (1) CN114657513B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118407015A (en) * 2024-06-29 2024-07-30 江苏派莱特光电科技有限公司 Coating thickness monitoring system of evaporation coating machine

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107352616A (en) * 2017-06-30 2017-11-17 中南大学 A kind of method using cadmium wastewater photocatalytic degradation Ciprofloxacin
CN108654586A (en) * 2018-05-07 2018-10-16 山东大学 A kind of graphitization mesoporous carbon-TiO2Composite photocatalyst material and the preparation method and application thereof
CN110665529A (en) * 2019-10-12 2020-01-10 南京农业大学 Method for catalytically degrading antibiotics by nitrogen-containing doped modified nano titanium dioxide and evaluation method

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016087841A (en) * 2014-10-31 2016-05-23 正尚 曹 Manufacturing method of composite mixture
CN104573731B (en) * 2015-02-06 2018-03-23 厦门大学 Fast target detection method based on convolutional neural networks
CN106563175A (en) * 2016-11-21 2017-04-19 南京理工大学 Medical antimicrobial coating and preparation method thereof
CN107066995A (en) * 2017-05-25 2017-08-18 中国矿业大学 A kind of remote sensing images Bridges Detection based on convolutional neural networks
CN111317865A (en) * 2018-12-17 2020-06-23 南京理工大学 Preparation method of double-layer antibacterial composite film
CN112342496B (en) * 2019-08-06 2022-11-25 南京理工大学 Preparation method of double-layer composite film
CN110982158A (en) * 2019-10-23 2020-04-10 江阴市德惠热收缩包装材料有限公司 Film and method for producing same
CN111223087B (en) * 2020-01-15 2022-11-15 东南大学 Automatic bridge crack detection method based on generation countermeasure network
CN111333888A (en) * 2020-03-26 2020-06-26 江阴市德惠热收缩包装材料有限公司 Degradable polylactic acid antibacterial film and preparation method thereof

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107352616A (en) * 2017-06-30 2017-11-17 中南大学 A kind of method using cadmium wastewater photocatalytic degradation Ciprofloxacin
CN108654586A (en) * 2018-05-07 2018-10-16 山东大学 A kind of graphitization mesoporous carbon-TiO2Composite photocatalyst material and the preparation method and application thereof
CN110665529A (en) * 2019-10-12 2020-01-10 南京农业大学 Method for catalytically degrading antibiotics by nitrogen-containing doped modified nano titanium dioxide and evaluation method

Also Published As

Publication number Publication date
CN114657513A (en) 2022-06-24

Similar Documents

Publication Publication Date Title
CN114657513B (en) Preparation method of antibacterial regenerated polyester film
EP3904859B1 (en) System and method for determining a platelet volume for a blood sample, computer program and computer readable medium
CN111814867A (en) Defect detection model training method, defect detection method and related device
US6577754B2 (en) Robust stain detection and quantification for histological specimens based on a physical model for stain absorption
CN113393461B (en) Method and system for screening metaphase chromosome image quality based on deep learning
CN110363218B (en) Noninvasive embryo assessment method and device
CN117517325B (en) Machine vision-based aluminum veneer spraying quality detection and analysis system
CN111080561A (en) Time domain high-pass filtering method
CN112098485B (en) Photoelectrochemical aptamer sensor based on sensing separation strategy and preparation method and application thereof
Huang et al. A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning
CN116563205A (en) Wheat spike counting detection method based on small target detection and improved YOLOv5
CN112151192A (en) Genetic metabolic disease screening method based on implicit space reprojection
Cannet et al. Wing interferential patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification
CN101048491A (en) Membrane filter for microbe detection
CN113505649A (en) Tap water chlorination control method and device
CN111368625A (en) Pedestrian target detection method based on cascade optimization
JP3607258B2 (en) Method for diagnosing duct contamination
Sophia et al. A Novel method to detect Disease in leaf using Deep Learning Approach
Orevi et al. Droplet size and surface hydrophobicity enhance bacterial plasmid transfer rates in microscopic surface wetness
Enriquez et al. Algortihm for Daphnia motion detection in toxicological studies
CN116994064B (en) Seed lesion particle identification method and seed intelligent screening system
CN118430714A (en) Perovskite degradation behavior quantitative characterization method based on gray tracking
CN117926367B (en) Preparation method of titanium alloy micro-arc oxidation coating
CA3194045A1 (en) Continuous detection and species classification of biological particles in a sample
CN117934350B (en) Image processing method for intelligent inspection robot of chemical plant

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant