The most commonly used methods for the detection and identification of small microplastics generally require a complex sample preparation procedure and only allow for static measurements. Quality control of food and drinking water therefore requires a lot of effort. Especially in view of the increasing amount of plastic waste in the environment, the rising public awareness of the issue and the indications for adverse effects of microplastics on human health, more sophisticated measuring methods are required. In this paper, we present a measuring setup for the detection and identification of microplastics using flow Raman spectroscopy. We demonstrate the ability to acquire Raman spectra of individual particles as small as about 4 µm, enabling the identification of their plastic type. We show measurements of differently generated and shaped particles and particles made of different plastic types, highlighting the observed challenges and differences. Finally, we show possible applications of the measuring method. We demonstrate that the measuring principle is suitable for detecting and identifying microplastic particles among other particles and that aged plastics can still be distinguished by their Raman spectra. Overall, our results show that flow Raman spectroscopy is a promising method that could significantly reduce the effort required to detect microplastics.

The increasing use of colored materials in 3D printing enables the creation of complex structures with realistic color appearances. Ensuring that the printed colors match the design requires advanced rendering methods to accurately predict the final product. We use a fully optical property-based pipeline for rendering the physically correct color of 3D-printed objects, achieving results nearly indistinguishable compared to the final printed samples. Our approach is based entirely on the intrinsic optical properties of the printing materials: the reduced scattering coefficient, absorption coefficient, refractive index, and scattering anisotropy factor. First, we employed an integrating sphere setup to measure the spectrally resolved absorption and reduced scattering coefficients of the base materials. This method was extended to handle both highly scattering and nearly non-scattering materials with precision. Next, a spectral Monte Carlo path tracing simulation was used to compute light transfer through the printed object, fully modeling the physics of light propagation in heterogeneous turbid media. Our results achieved CIE Δ E ₂₀₀₀ values below 2.0, with renderings accurately reproducing the colors of the specially designed validation samples printed using a Stratasys PolyJet system. This pipeline provides a robust tool for predicting and optimizing the color appearance of 3D-printed objects. It offers broad applicability across various printing systems and material compositions while eliminating the need for time- and cost-intensive trial-and-error test prints.

Accurate knowledge of the intrinsic optical properties of 3D printing materials, i.e., spectral absorption and scattering coefficients, phase function, and refractive index, is essential for simulating the appearance of translucent prints on displays (softproofing) or optimizing material arrangements to achieve desired optical effects in multi-material 3D prints. This information is also critical for designing printing materials that mimic the optical characteristics of other materials, a key requirement in applications like dental restorations. Current methods for measuring these properties rely on specialized laboratory equipment and expert knowledge. In this paper, we propose an approach that uses a commercial reflectance/transmittance spectrophotometer to determine the spectral absorption and scattering coefficients and refractive index of 3D printing materials. We model the light path of this device to simulate reflectance and transmittance measurements via a Monte Carlo path tracer. We then predict measurements for a large set of random but plausible intrinsic optical material properties for three different sample thicknesses. With these data, we train machine learning models to infer the intrinsic properties from phenomenological reflectance/transmittance measurements, considering a priori knowledge of smoothness as a regularization constraint. We validate our method by comparing results for real printing materials with accurate laboratory measurements and provide the trained machine learning models to the community.

In the original article ( Opt. Express 33 , 15187 ( 2025 ) 10.1364/OE.553642 ) [1], the discussion of certain related works was incomplete. Including these references provides additional context and places the topics examined in a broader perspective.