使用激光进行聚合物表征的计算机视觉

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Seda Uyanik, Sam Parkinson, George Killick, Biplab Dutta, Rob Clowes, Charlotte E. Boott and Andrew I. Cooper
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引用次数: 0

摘要

对于寻求使用自动化和机器学习(ML)加速发现过程的科学家来说,计算机视觉是一种有用的反应监测和表征工具。在这里,我们报告了一种基于非侵入性激光的方法,该方法结合了计算机视觉和深度学习模型,可以对不同聚合物化合物在一系列溶剂中的溶解度进行分类。采用2 - 4个溶解度等级(可溶性、可溶性-胶体、部分可溶性和不溶性)进行分类,测试准确率从94.1%(2个等级)到89.5%(4个等级)不等。利用我们的溶解度筛选方法的结果,我们还使用优化算法确定了聚合物的汉森溶解度参数(HSP)。从我们的数据集获得的HSP值与文献中聚合物的HSP值之间的计算百分比欧几里得距离在11-32%之间。最后,我们开发了基于特征的线性调制(FiLM)条件卷积神经网络(CNN)回归模型来估计20-440 nm之间的聚合物纳米颗粒的尺寸,并获得了9.53 nm的平均绝对误差(MAE)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Computer vision for polymer characterisation using lasers

Computer vision for polymer characterisation using lasers

Computer vision is a useful reaction monitoring and characterisation tool for scientists seeking to accelerate discovery processes using automation and machine learning (ML). Here we report a non-invasive laser-based method that combines computer vision and deep learning models to classify the solubility of different polymeric compounds across a range of solvents. Classifications were conducted using two to four solubility classes (soluble, soluble-colloidal, partially soluble, and insoluble), achieving high test accuracy rates ranging from 94.1% (2 classes), to 89.5% (4 classes). Using results from our solubility screening method, we also determined the Hansen Solubility Parameters (HSP) of the polymers using an optimisation algorithm. The calculated percentage Euclidean distance between the HSP values obtained from our dataset and the literature HSP values for the polymers, ranged from 11–32%. Finally, we developed the feature-wise linear modulation (FiLM) conditioned Convolutional Neural Network (CNN) regression model to estimate the size of polymeric nanoparticles between 20–440 nm and achieved a Mean Absolute Error (MAE) of 9.53 nm.

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CiteScore
2.80
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