Experimental and computational intelligent techniques for predicting the thickness-dependent optical behavior of PbI2 thin films

IF 4.2 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Optical Materials Pub Date : 2025-09-19 DOI:10.1016/j.optmat.2025.117530

Nasser Almutlaq , A.H. Ammar , H.S. Metwally , Ali Ibrahim , A.A.M. Farag , E.H. Aamer

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Abstract

This study presents a comprehensive investigation of thermally evaporated lead iodide (PbI₂) thin films, focusing on the influence of film thickness (50–500 nm) on morphological, optical, and nonlinear properties. Scanning electron microscopy (SEM) and 3D surface topography reveal a distinct evolution in microstructure, transitioning from fine-grained uniformity in 50 nm films to coarse, aggregated crystallites in 300 nm films due to enhanced grain growth and stress accumulation. Optical analyses demonstrate tunable transmittance, reflectance, and absorption characteristics, with the direct bandgap decreasing from 2.89 eV (50 nm) to 2.50 eV (300 nm), attributed to reduced quantum confinement effects in thicker films. A robust artificial neural network (ANN) model is developed to predict key optical parameters—including refractive index (n), extinction coefficient (k), dielectric functions (ε₁, ε₂), and nonlinear susceptibility (χ⁽³⁾), with exceptional accuracy (MSE <10⁻⁷), validating its capability to simulate unmeasured thicknesses. Furthermore, nonlinear optical studies highlight PbI₂'s potential for photonic applications, with thickness-dependent variations in two-photon absorption (β_c) and skin depth. This work establishes a synergistic approach that combines experimental characterization and machine learning to optimize PbI₂ thin films for use in optoelectronic and nonlinear optical devices, providing a scalable framework for material design.

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预测PbI2薄膜厚度相关光学行为的实验和计算智能技术

本研究对热蒸发碘化铅（PbI2）薄膜进行了全面的研究，重点研究了薄膜厚度（50-500 nm）对形态、光学和非线性性质的影响。扫描电子显微镜（SEM）和三维表面形貌显示了微观结构的明显演变，由于晶粒生长和应力积累的增强，从50 nm薄膜中的细晶粒均匀过渡到300 nm薄膜中的粗糙聚集晶体。光学分析表明，透射率、反射率和吸收特性可调，直接带隙从2.89 eV （50 nm）减小到2.50 eV (300 nm)，这归因于较厚薄膜中量子约束效应的减少。开发了一个鲁棒人工神经网络（ANN）模型来预测关键光学参数，包括折射率(n)，消光系数(k)，介电函数（ε1, ε2）和非线性磁化率（χ(3)），具有优异的精度（MSE <10−7），验证了其模拟未测量厚度的能力。此外，非线性光学研究强调了PbI2在光子应用方面的潜力，其双光子吸收（βc）和蒙皮深度随厚度的变化而变化。这项工作建立了一种结合实验表征和机器学习的协同方法，以优化用于光电和非线性光学器件的PbI2薄膜，为材料设计提供了可扩展的框架。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Optical Materials 工程技术-材料科学：综合

CiteScore

6.60

自引率

12.80%

发文量

1265

审稿时长

38 days

期刊介绍： Optical Materials has an open access mirror journal Optical Materials: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. The purpose of Optical Materials is to provide a means of communication and technology transfer between researchers who are interested in materials for potential device applications. The journal publishes original papers and review articles on the design, synthesis, characterisation and applications of optical materials. OPTICAL MATERIALS focuses on: • Optical Properties of Material Systems; • The Materials Aspects of Optical Phenomena; • The Materials Aspects of Devices and Applications. Authors can submit separate research elements describing their data to Data in Brief and methods to Methods X.