Upconverting nanoparticles (UCNPs) have been proposed for a variety of applications ranging from biomedical probes to luminescent sensors and security tags. Yet, bringing UCNPs into real-life, ...technologically relevant products requires implementation into industry-friendly processes. The need for stable dispersions, clean films or dry powders challenges users who look for a way to use UCNPs. In this work, an ink formulation was developed that offers a straightforward way to print UCNPs on glass and metallic substrates. The use of Gum Arabic as biocompatible emulsifier allowed to implement the NaGdF4:Er,Yb/NaGdF4 core/shell UCNPs into water-based ink formulations without the need of complex surface chemistry. The formulation, based on water, glycerin, and propanediol, exhibited good stability and applicability for printing with a commercial aerosol jet printer. Bright upconversion emission was retained upon printing, and the obtained UCNP films were used in proof-of-concept luminescent thermal sensing.
•An aqueous ink formulation for printing applications of upconverting nanoparticles (UCNPs) was developed.•Biopolymer Gum Arabic was used for modification of UCNPs, showing its suitability as emulsifying agent in UCNP-based inks.•Using a commercial aerosol jet printer, various patterns were successfully printed on glass and metallic substrates.•Proof-of-concept nanothermometry with decent thermal sensitivity and excellent repeatability was done on the printed UCNPs.
Raman spectroscopy imaging is a technique that can be adapted for intraoperative tissue characterization to be used for surgical guidance. Here we present a macroscopic line scanning Raman imaging ...system that has been modified to ensure suitability for intraoperative use. The imaging system has a field of view of 1 × 1 cm
and acquires Raman fingerprint images of 40 × 42 pixels, typically in less than 5 minutes. The system is mounted on a mobile cart, it is equiped with a passive support arm and possesses a removable and sterilizable probe muzzle. The results of a proof of concept study are presented in porcine adipose and muscle tissue. Supervised machine learning models (support vector machines and random forests) were trained and they were tested on a holdout dataset consisting of 7 Raman images (10 080 spectra) acquired in different animal tissues. This led to a detection accuracy >96% and prediction confidence maps providing a quantitative detection assessment for tissue border visualization. Further testing was accomplished on a dataset acquired with the imaging probe's contact muzzle and tailored classification models showed robust classifications capabilities with specificity, sensitivity and accuracy all surpassing 95% with a support vector machine classifier. Finally, laser safety, biosafety and sterilization of the system was assest. The safety assessment showed that the system's laser can be operated safetly according to the American National Standards Institute's standard for maximum permissible exposures for eyes and skin. It was further shown that during tissue interrogation, the temperature-history in cumulative equivalent minutes at 43 °C (CEM43 °C) never exceeded a safe threshold of 5 min.
