The thickness of thin layers of the conductive polymer PEDOT:PSS in the range between about 60 and 300 nm was determined by a near-infrared spectroscopic method using a hyperspectral camera. The reflection spectra of the layers do not contain bands, but consist of a moderate slope of the overall reflectance in the range between 1320 and 1850 nm. Despite the low thickness, the spectra show an extremely strong dependence on the thickness of the layers, which allows their use for quantitative measurements. The prediction of quantitative thickness data from the reflection spectra was based on a chemometric approach using the partial least squares (PLS) algorithm. Calibration was carried out by means of spin-coated layers of PEDOT:PSS, whose thickness was determined by white-light interferometry and stylus profilometry. Finally, this resulted in a calibration model with a root mean square error of prediction (RMSEP) of about 9 nm. After external validation of this model, it was used for quantitative imaging of the thickness distribution in PEDOT:PSS layers. The precision of the predicted values was confirmed by comparison with data from the reference methods. Moreover, it was shown that this approach can be also used for hyperspectral imaging of the thickness of thin printed layers and structures of this conductive polymer on polymer film or paper with excellent thickness resolution. This analytical approach opens new possibilities for in-line process control by large-scale monitoring of thickness and homogeneity of thin layers of conductive polymers.

导电聚合物PEDOT：PSS的薄层的厚度在约60至300nm之间的范围内，通过使用高光谱照相机的近红外光谱法来确定。这些层的反射光谱不包含谱带，而是由总反射率的适度斜率组成，介于1320和1850 nm之间。尽管厚度很薄，但光谱显示出对层厚度的极强依赖性，这使得它们可用于定量测量。来自反射光谱的定量厚度数据的预测是基于使用偏最小二乘（PLS）算法的化学计量学方法。通过旋涂的PEDOT：PSS层进行校准，其厚度由白光干涉法和测针轮廓仪确定。最后，这导致了校准模型，其预测均方根误差（RMSEP）约为9 nm。在对该模型进行外部验证后，将其用于PEDOT：PSS层中厚度分布的定量成像。通过与参考方法的数据进行比较，确认了预测值的精度。而且，已经表明，该方法也可以用于薄印刷层的厚度的高光谱成像以及该导电聚合物在聚合物膜或纸上具有优异的厚度分辨率的结构。通过大规模监测导电聚合物薄层的厚度和均匀性，这种分析方法为在线过程控制开辟了新的可能性。它用于定量成像PEDOT：PSS层中的厚度分布。通过与参考方法的数据进行比较，确认了预测值的精度。而且，已经表明，该方法也可以用于薄印刷层的厚度的高光谱成像以及该导电聚合物在聚合物膜或纸上具有优异的厚度分辨率的结构。这种分析方法通过大规模监控导电聚合物薄层的厚度和均匀性，为在线过程控制开辟了新的可能性。它用于定量成像PEDOT：PSS层中的厚度分布。通过与参考方法的数据进行比较，确认了预测值的精度。而且，已经表明，该方法也可以用于薄印刷层的厚度的高光谱成像以及该导电聚合物在聚合物膜或纸上具有优异的厚度分辨率的结构。这种分析方法通过大规模监控导电聚合物薄层的厚度和均匀性，为在线过程控制开辟了新的可能性。结果表明，该方法还可用于薄印刷层的厚度的高光谱成像，以及该导电聚合物在聚合物膜或纸上具有优异的厚度分辨率的结构。通过大规模监测导电聚合物薄层的厚度和均匀性，这种分析方法为在线过程控制开辟了新的可能性。结果表明，该方法还可用于薄印刷层的厚度的高光谱成像，以及该导电聚合物在聚合物膜或纸上具有优异的厚度分辨率的结构。通过大规模监测导电聚合物薄层的厚度和均匀性，这种分析方法为在线过程控制开辟了新的可能性。