Raman spectroscopy is widely used in chemistry, material science and in biomedical applications such as cancer detection. A Raman spectrum of tissue shows DNA, amino acids, lipids, and proteins simultaneously, which makes the evaluation of the spectral content both complete but also challenging. The impact of the technique can increase substantially by access to big databases, but variations in the setup, e.g. quantum efficiency of Raman detectors, transmission profiles and disturbances of optical filters and components, may hinder direct data comparison. We here introduce a step prior to preprocessing that calculates spectral quotients to address system-dependent multiplicative differences and system-inherent background noise, thereby enabling analysis of Raman spectral data from different setups. Pre-processing was performed using an artificial neural network (ANN) trained on synthetic data to deal with fluorescent background and noise. Validation by multivariate analysis of the spectral quotients combined with ANN was performed on randomized synthetic data and, as a proof of principle, on experimental data from brain tumor biopsies. The results demonstrated clustering and feature extraction that was not possible without the introduction of the quotients. Data exploration revealed that the method enabled spectral feature identification even for weak Raman signals that are not in resonance with the excitation wavelength. Note, some distortions persist due to data dependency and additive errors but a system independent clustering was achieved. ANN-based preprocessing combined with spectral quotients for combined evaluation of Raman spectroscopic data from multiple setups opens the possibility for more robust multivariate studies in biomedical and other applications.

Carbon-based materials are highly sought after due to their superior properties, making them valuable for high-performance applications. However, most carbon-based materials are derived from fossil sources, and their synthesis often involves hazardous chemicals. Therefore, it is essential to develop sustainable methods for synthesising these materials from renewable resources, using fewer solvents, catalytic reagents, and generating minimal waste. In this study, few-layer graphene oxide (GO) was directly synthesised from waste biomass, without the formation of an amorphous intermediate, and its use as a fire retardant in two bioplastics was evaluated. Waste birch wood biomass was converted directly into graphitic carbon using manganese nitrate as a catalyst, with varying concentrations (0.003 to 0.1 mol-metal/g-wood) and treatment durations (1 and 2 h). The catalyst was doped through vacuum soaking and mild heating (90 °C), which facilitated the formation of graphitic carbon at relatively lower temperatures (< 1000 °C), eliminating the need for producing amorphous biochar prior to graphitisation. After pyrolysis at 900 °C and 950 °C for 2 h, the sample containing 0.005 mol-metal/g-wood, treated at 950 °C, exhibited the highest degree of graphitisation. This sample was further processed in a planetary ball mill with melamine as a dispersant for 30 min. Characterisation showed a broad absorption peak at 230 nm and the presence of semi-transparent sheets (3–8 layers), indicating the presence of GO. To evaluate its performance as a fire retardant, 2 wt% of the synthesised GO was added to polyamide 11 and wheat gluten bioplastics, which were then subjected to cone calorimeter tests. The results showed a 42% and 33% reduction in the peak heat release rate for polyamide 11 and wheat gluten, respectively, compared to their neat counterparts. The flame retardancy index further indicated that GO had a more significant impact on improving the fire safety of wheat gluten compared to polyamide 11. This study highlights a sustainable method for the preparation of few-layer GO at lower temperatures than contemporary methods, making the process more energy-efficient, environmentally friendly, and cost-effective. Additionally, the effectiveness of few-layer GO as a fire-retardant additive for enhancing the fire safety of bioplastics has been demonstrated.

Particle Image Velocimetry (PIV) for flow visualization has advanced with the integration of deep learning algorithms. These methods enable end-to-end processing, extracting dense flow fields directly from raw particle images. However, conventional deep learning-based PIV models, which predominantly rely on convolutional architectures, are limited in their ability to utilize contextual information and capture dependencies between pixels across sequential images, impacting the prediction accuracy. We introduce Twins-PIVNet, a deep learning framework for PIV optical flow estimation that leverages a spatial attention-based vision transformer architecture. Its self-attention mechanism captures multi-scale features of particle motion, significantly improving the dense flow field estimation. Trained on synthetic PIV datasets covering a wide range of flow conditions, Twins-PIVNet has been evaluated on both synthetic and experimental datasets, demonstrating superior accuracy and performance. In comparative studies, Twins-PIVNet outperforms existing optical flow and conventional methods, achieving accuracy improvements of 51% for backstep flow, 42% for DNS-turbulence, and 33% for surface quasi-geostrophic flow. Additionally, it also exhibits strong generalization on experimental PIV data, demonstrating robustness in handling real-world PIV uncertainties. Despite its attention mechanism, Twins-PIVNet maintains faster inference and training times compared to other PIV models, offering an optimal balance between complexity, efficiency, and performance.

A bi-directional digital holographic imaging system is presented that is designed to take images automatically out in the field. The main objective is to acquire sufficient information to be able to estimate the scattering phase function for different type of snowfall. The imaging system consists of a 3D-printed frame and two orthogonal telecentric imaging arms, one in the forward direction and one in the side scattering direction for which the reference arms are directed along different paths. All images are acquired using pulsed visible light.

Current optical flow-based neural networks for particle image velocimetry (PIV) are largely trained on synthetic datasets emulating real-world scenarios. While synthetic datasets provide greater control and variation than what can be achieved using experimental datasets for supervised learning, it requires a deeper understanding of how or what factors dictate the learning behaviors of deep neural networks for PIV. In this study, we investigate the performance of the recurrent all-pairs field transforms-PIV (RAFTs-PIV) network, the current state-of-the-art deep learning architecture for PIV, by testing it on unseen experimentally generated datasets. The results from RAFT-PIV are compared with a conventional cross-correlation-based method, Adaptive PIV. The experimental PIV datasets were generated for a typical scenario of flow past a circular cylinder in a rectangular channel. These test datasets encompassed variations in particle diameters, particle seeding densities, and flow speeds, all falling within the parameter range used for training RAFT-PIV. We also explore how different image pre-processing techniques can impact and potentially enhance the performance of RAFT-PIV on real-world datasets. Thorough testing with real-world experimental PIV datasets reveals the resilience of the optical flow-based method's variations to PIV hyperparameters, in contrast to the conventional PIV technique. The ensemble-averaged root mean squared errors between the RAFT-PIV and Adaptive PIV estimations generally range between 0.5–2 (px) and show a slight reduction as particle densities increase or Reynolds numbers decrease. Furthermore, findings indicate that employing image pre-processing techniques to enhance input particle image quality does not improve RAFT-PIV predictions; instead, it incurs higher computational costs and impacts estimations of small-scale structures.

The depth-gating capacity of a spatially quasi-incoherent imaging interferometer is investigated in relation to the 3D correlation properties of diffraction field laser speckles. The system exploits a phase-stepped imaging Michelson-type interferometer in which spatially quasi-incoherent illumination is generated by passing an unexpanded laser beam through a rotating diffuser. Numerical simulations and optical experiments both verify that the depth-gating capacity of the imaging interferometer scales as 𝜆/2NA2𝑝λ/2NAp2, where 𝜆λ is the wavelength of the laser and NA𝑝NAp is the numerical aperture of the illumination. For a set depth gate of 150 µm, the depth-gating capacity of the interferometer is demonstrated by scanning a standard USAF target through the measurement volume. The results obtained show that an imaging tool of this kind is expected to provide useful capabilities for imaging through disturbing media and where a single wavelength is required.