Green and facile approaches aiming at the manufacture of biocompatible paper-based optical sensors reporting the presence of photoluminescence (PL) modulating compounds is an emerging field of research. This study investigates the preparation of bacterial cellulose nanopaper containing covalently immobilized carbon dots for potential biosensing applications. Preliminary work of this feasibility study included TEMPO-mediated ((2,2,6,6-tetramethylpiperidin-1-yl)oxyl-mediated) oxidation and nanofibrillation of bacterial cellulose (TOBC) on the one hand as well as synthesis and comparative analysis of different types of carbon dots (CDs) on the other hand. The two source materials of the targeted functional nanopaper were finally linked to each other by two different N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride/ N-hydroxysuccinimide (EDC/NHS) coupling approaches to clarify whether grafting of CDs prior to or after TOBC paper formation would be the method of choice. Synthesis of the carbon nanodots was accomplished by microwave-assisted co-hydrothermolysis of appropriate precursor compounds. After isolation and purification by dialysis particles in the single-digit nanometer-range were obtained and characterized with regard to their photoluminescence properties in terms of emission wavelength, pH stability, and quantum yield. All types of synthesized CDs reached their PL maxima (450–480 nm; light blue) in a narrow excitation wavelength range of 340–360 nm. Variation of molar (C/N) ratio of the CD precursors and substitution of the nitrogen donor EDEA by urea increased PL and quantum yield (QY), respectively. The highest relative QY of nearly 32% was obtained for CDs synthesized from citric acid and urea. PL of all CDs was virtually insensitive to pH changes in the range of 4–10. Tensile testing of hybrid nanopaper prepared after EDC/NHS-mediated grafting of GEA-type CDs onto TOBC (0.52 mmol·g⁻¹ COOH) in dispersion state revealed that both stiffness and strength are not compromised by incorporation of carbon dots, while plastic deformation and elongation at break increased slightly compared to nanopaper formed prior to decoration with CDs. Water contact angle of the nanopaper is unaffected by introduction of carbon dots which is supposedly due to the presence of surface amino- and amide groups compensating for the loss of carboxyl groups by grafting.

Rapid and accurate reconnaissance in the event of radiological and nuclear (RN) incidents or attacks is vital to launch an appropriate response. This need is made stronger by the increasing threat of RN attacks on soft targets and critical infrastructure in densely populated areas. In such an event, even small radioactive sources can cause major disruption to the general population. In this work, we present a real-time radiological source localization method based on an optimization problem considering a background and radiation model. Supported by extensive real-world experiments, we show that an airborne system using this method is capable for reliably locating category 3–4 radioactive sources according to IAEA safety standards in real time from altitudes up to 150 m. A sensor bundle including a LiDAR sensor, a Gamma probe as well as a communication module was mounted on a UAV that served as a carrier platform. The method was evaluated on a comprehensive set of test flights, including 28 flight scenarios over 316 min using three different radiation sources. All additional gamma sources were correctly detected, multiple sources were detected if they were sufficiently separated from each other, with the distance between the true source position and the estimated source averaging 17.1 m. We also discuss the limitations of the system in terms of detection limit and source separation.

Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera—installed on the ground or possibly a drone—and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train a classifier to detect people and cars. Our results show that, based only on spectral signature classification, we can achieve an Matthews Correlation Coefficient of 0.83. We evaluate our classification method in areas with varying vegetation and discuss the limitations and constraints that the current hyperspectral imaging technology has. We conclude that spectral signature classification is possible with high accuracy in uncontrolled outdoor environments. Nevertheless, even with state-of-the-art compact passive hyperspectral imaging technology, high dynamic range of illumination and relatively low image resolution continue to pose major challenges when developing object detection algorithms for areas of dense vegetation.