Asmida Herawati, Suhendra, Riza Ariyani Nur Khasanah, Eri Widianto, Iman Santoso, Edi Suharyadi, Takeshi Kato, Satoshi Iwata, Hao-Ting Lin
Rapid and label-free detection of microalgae is increasingly required for environmental surveillance and bio-industrial process control, where decisions must be made from subtle interfacial changes rather than from bulk concentration alone. In this work, chromium (Cr) thin films (20–75 nm) were deposited by RF magnetron sputtering and evaluated by spectroscopic ellipsometry (SE) to develop a thickness-optimized optical transducer for microalgae biosensing. Thickness-dependent optical constants (n, k) were extracted using a Drude–Lorentz dispersion model incorporating an effective-medium roughness layer. The 75 nm film exhibited the most bulk-like and spectrally stable response with comparatively low effective loss across the visible range, while 20–30 nm films showed larger deviations attributable to microstructure- and interface-mediated scattering contributions in the ultra-thin regime. Biosensing was implemented by forming Cr/PVA and Cr/PVA + microalgae stacks and quantifying the differential phase response. The 75 nm Cr/PVA platform delivered the strongest microalgae-induced modulation, exhibiting a Δ phase shift of 40.6° within 2.65–3.20 eV, thereby identifying a high-contrast spectral window for detection. Machine learning was required because Ψ–Δ spectra are high-dimensional, nonlinear, and strongly correlated, and multilayer spectral blending (Cr/PVA/biological loading) limits reliable thresholding and linear separation. A multitask deep neural network was trained to learn the coupled Ψ–Δ response for rapid prediction, and support vector machines were used for supervised discrimination of film stacks. By converting dense SE signatures into decision-ready labels on a thickness-optimized substrate, the proposed SE–ML framework advances an intelligent, non-destructive route for rapid microalgae screening and environmental diagnostics. Copyright © 2026. Published by Elsevier B.V.
Research Center for Photonics, National Research and Innovation Agency (BRIN), Bd. 442 Kawasan Puspiptek Serpong, Banten, South Tangerang, 15314, Indonesia; Department of International Doctoral Program in Agriculture, National Chung Hsing University, Taichung, 402202, Taiwan; Department of Agro-Industrial Technology, Universitas Bengkulu, Bengkulu, 38112, Indonesia; Department of Physics Education, Universitas Negeri Yogyakarta, Yogyakarta, 55281, Indonesia; Department of Physics, Faculty of Engineering, Universitas Singaperbangsa Karawang, Telukjambe Timur, Karawang, 41361, Indonesia; Department of Physics, Faculty of Mathematics and Natural Science, Gadjah Mada University, Sekip Utara, BLS 21, Yogyakarta, 55281, Indonesia; Department of Electronics, Nagoya University, Nagoya, Japan; Institute of Materials and System for Sustainability, Nagoya University, Nagoya, Japan; Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung, 402202, Taiwan
