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Method for optimizing gradient titanium dioxide nanotube micro-patterns under assistance of machine learning

A machine learning, titanium dioxide technology used in the field of gradient TiO2 nanotube micropatterning

Pending Publication Date: 2021-04-09
XIAMEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Construction of gradient TiO by bipolar electrochemical method 2 Nanotubes, high-throughput screening of optimal nanotube diameters used in different fields, but how to quickly obtain TiO with the widest range of diameters 2 Micropatterning of nanotubes is currently a challenge

Method used

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  • Method for optimizing gradient titanium dioxide nanotube micro-patterns under assistance of machine learning
  • Method for optimizing gradient titanium dioxide nanotube micro-patterns under assistance of machine learning
  • Method for optimizing gradient titanium dioxide nanotube micro-patterns under assistance of machine learning

Examples

Experimental program
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Effect test

Embodiment 1

[0035] Apply an active learning-based algorithm to search for boundary conditions and maximize TiO 2 Diameter range of nanotube micropatterns. After collecting a small dataset from bipolar electrochemical experiments, a machine learning algorithm builds a learned model and predicts the best outcome. The predictions are then tested experimentally, and the new results update the training dataset for the next active learning cycle. Such as figure 1 As shown, each active learning cycle consists of four steps: (1) acquire raw data from experiments; (2) define effective data boundaries with classification models; (3) data analysis / regression and prediction; (4) use grid Search for optimal experimental parameters for search and prediction.

[0036] First of all, we first pass a certain amount of experiments to accumulate raw data. Among the original data, the eigenvalues ​​mainly include the specific parameters in the preparation process: voltage, reaction time, water bath temper...

Embodiment 2

[0040] In the active learning framework of Example 1, different classification and regression models are trained and generated respectively, and the optional models include linear models, polynomial models, decision tree models, support vector machine models, GBDT models or neural network models. Finally, the model with the highest accuracy is selected, the decision tree is used for classification, and GBRT (GBDT is used for regression algorithm) is used for regression processing. The selected model is established based on computer programming languages ​​such as Python, Scikit-learning, xgboost, TensorFlow, and stored in one or more computer storage media. Decision tree is a decision analysis method for evaluating project risk and judging its feasibility by forming a decision tree to obtain the probability that the expected value of net present value is greater than or equal to zero on the basis of knowing the probability of occurrence of various situations. It is an intuitive...

Embodiment 3

[0042] A titanium foil with a thickness of 0.1 mm and a purity of 99.6% was cut into a shape of 38 mm × 10 mm, and then washed with an ultrasonic cleaner in the order of acetone, deionized water and ethanol for 20 min each. The titanium foil was then placed in air to dry. Preparation of TiO by Bipolar Electrochemical Anodization Based on the Best Protocol Recommended by Machine Active Learning 2 Nanotube micropatterns. Prepare 120mL electrolyte solution, which contains 0.75wt% ammonium fluoride, 10v% deionized water and 90v% glycerol, and stir evenly to obtain electrolyte solution. Fix the titanium sheet in the electrolytic cell described in step 1.3 (between the two platinum electrodes) with a high-temperature-resistant polyimide tape, and the distance between the metal titanium edge and the two platinum electrodes is 1 mm. Connect the positive and negative poles of the constant voltage power supply to the two platinum electrodes of the device to output a voltage of 160V. T...

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Abstract

The invention discloses a method for optimizing a gradient titanium dioxide nanotube micro-pattern under the assistance of machine learning, and relates to preparation of the gradient titanium dioxide nanotube micro-pattern. The method comprises the following steps: 1) setting related experimental conditions to prepare a TiO2 nanotube micro-pattern and characterize the TiO2 nanotube micro-pattern to obtain experimental data; 2) preprocessing the obtained experimental data and performing machine learning modeling; 3) performing, by the machine learning model, prediction and recommending an optimization experiment scheme; and (4) verifying a prediction result through an experiment, supplementing data, and iterating the steps (1)-(4). By means of the technical scheme, sample data expansion, self-learning and automatic training of a model meeting preset precision can be automatically realized, and then an active learning framework for predicting the parameter structure property of the material is automatically constructed, and intelligent generation and reverse design of the material are realized. The TiO2 nanotube micro-pattern sample with the maximum gradient range, which is prepared in one step by utilizing a bipolar oxidation method under an ammonium fluoride / water / glycerol system, and the experimental conditions of the TiO2 nanotube micro-pattern sample can be found under fewer experimental conditions. Operation is simple and convenient, and operation time is short.

Description

technical field [0001] The present invention relates to gradient TiO 2 Preparation of nanotube micropatterns, especially a method for machine learning-assisted optimization of gradient titania nanotube micropatterns. Background technique [0002] Micropatterning technology can miniaturize and integrate various materials with different properties into one sample platform, which can be used for high-throughput screening of biological materials with fewer samples and higher efficiency. Popular micropatterning techniques include soft lithography, photolithography, jet patterning, scanning probe lithography, laser patterning, bipolar electrochemistry, etc. Among all micropatterning techniques, bipolar electrochemistry is the easiest and has been widely used to construct chemical / structural gradient micropatterns. [0003] Construction of gradient TiO by bipolar electrochemical method 2 Nanotubes, high-throughput screening of optimal nanotube diameters used in different fields,...

Claims

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Application Information

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IPC IPC(8): G16C60/00C25D11/26G06N3/08G06N20/00G06N20/10
CPCG16C60/00G06N3/08G06N20/00G06N20/10C25D11/26
Inventor 王斯黄巧玲沈子傲
Owner XIAMEN UNIV
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