Riccardo Marin

Intracellular luminescence thermometry has long promised to reveal how heat is generated, dissipated, and regulated inside living cells. Yet, despite substantial progress, the field remains shaped by disagreement over the magnitude and physical plausibility of reported intracellular temperature gradients. In this manuscript, we discuss luminescence thermometry as a powerful approach for probing temperature at subcellular length scales, while emphasizing the experimental care required to make such measurements meaningful. After outlining the field’s evolution, we summarize the relevant heat transfer concepts, before introducing luminescence thermometry and defining the performance metrics used to describe readout uncertainty and accuracy within a metrological framework. We then examine how thermometer design, intracellular localization, calibration, microscopy configuration, and data treatment influence the final thermal readout. Particular attention is given to sources of loss in readout accuracy. Among them, we highlight cross-sensitivity, which arises when the probe responds to parameters other than temperature, such as pH, viscosity, ionic strength, or biomolecular interactions. Finally, we propose practical directions for improving reproducibility, including multifeature readouts, machine-learning-assisted analysis, and FAIR data practices, while suggesting future research directions.

Near‐infrared (NIR)‐emitting Ag ₂ S nanocrystals (NCs) are promising contrast agents in nanomedicine, thanks to extensive efforts to improve their photochemical stability, biocompatibility, and brightness by tuning their size, composition, surface chemistry, and core/shell architecture. However, when dispersed in biological media, these NCs undergo non‐specific interactions with surrounding species, including biomolecules. Such interactions may compromise their colloidal and physicochemical stability, alter their photoluminescence properties, and impair both in vitro and in vivo imaging and sensing performance. Here, we show that incorporating NIR‐emitting Ag ₂ S‐based NCs into colloidal dendritic silica effectively minimizes environmental interference in complex biological media, including blood plasma and whole blood. Steady‐state and time‐resolved photoluminescence measurements reveal that both emission intensity and lifetime are substantially better preserved following incorporation into silica colloids. This preservation has a dual benefit: maintaining photoluminescence intensity enables precise NIR imaging, while retention of spectral and temporal responses preserves the NCs’ thermal sensing capability. Proof‐of‐concept experiments in tissue phantoms and small‐animal models confirm these findings. The approach supports reliable optical readouts under biologically relevant conditions without modifying the NC composition. Overall, incorporation into dendritic silica colloids offers a viable strategy for protecting luminescent nanoparticles from biological interference and retaining their optical properties for biomedical imaging and sensing applications.

A design strategy for NIR-emitting lanthanide nanoparticles resolves the fundamental tension between core-shell passivation and dye sensitization. By engineering a Yb³⁺-doped interlayer as an active energy relay between surface-bound ICG and an heavily Er³⁺-doped core, a ~ 2000-fold brightness enhancement is achieved, enabling high-contrast in vivo vascular imaging.

Copper-based semiconductor nanocrystals are versatile nanomaterials with diverse compositions, structures, morphologies, and optical properties, enabling applications in biomedicine, photocatalysis, photovoltaics, and lighting.

Luminescence sensing is a powerful technique for remote monitoring of chemical and physical parameters. Yet, measurement accuracy is often compromised by cross‐sensitivity—confounding modulation of the optical readout feature by nontarget parameters. Conventionally treated as a “bug”, cross‐sensitivity is generally mitigated through strategies aimed at minimizing or compensating for its effects. In this work, we reconceptualize cross‐sensitivity as a “feature” and unveil its latent potential to enable multiparametric luminescence sensing using a single sensor. We achieve this by employing ruby (Al ₂ O ₃ :Cr ^{3 +} ) microspheres as a model system—a popular luminescent pressure standard with marked cross‐sensitivity to temperature. First, we explore the cross‐sensitivity of various conventional optical readout features in ruby and propose a framework for quantifying accuracy loss as a function of the interfering parameter, enabling a fair evaluation of cross‐sensitivity across features. We then harness cross‐sensitivity using Linear Discriminant Analysis (LDA)—a supervised machine learning algorithm trained to isolate the effects of pressure and temperature, enabling simultaneous measurement of both parameters without the need for correction. This work lays the foundation for multiparametric luminescence sensing using a single sensor, expanding the capabilities of this technique and broadening its application potential.

Accurate, rapid, and remote temperature sensing at the nanoscale is essential for applications ranging from monitoring cellular thermodynamics to thermal management of microelectronic devices. Luminescent nanothermometers are promising candidates; however, their deployment is hindered by limited thermal sensitivity and cross-sensitivity to environmental factors that mimic temperature-induced luminescence changes. We introduce fluorescent chromatic nanoswitchers (CNSs), comprising silica nanocapsules incorporating a fluorescent dye within a thermoresponsive matrix. The matrix undergoes a solid-to-liquid phase transition, yielding an exceptional fluorescence lifetime sensitivity of 19% °C^-1 at 37 °C. Crucially, the lifetime-based thermal readout provided by CNSs is resistant to environmental interference, ensuring reliable, reproducible temperature measurements. To enhance CNS performance, we integrate artificial neural networks (ANNs) for advanced lifetime signal processing, enabling faster and robust thermal readouts. Proof-of-concept experiments show that the synergy between high-sensitivity lifetime-based nanothermometers and ANN-driven analysis paves the way for next-generation thermal sensing technologies, offering improved responsiveness, real-time capabilities, and enhanced accuracy.

Luminescence thermometry affords remote thermal readouts with high spatial resolution in a minimally invasive way. This technology has advanced our understanding of biological mechanisms and physical processes from the macro- to the submicrometric scale. Yet, current approaches only allow obtaining 2D thermal images. This aspect limits the potential of this technology, given the inherent three-dimensional nature of heat diffusion processes. Despite initial attempts, a credible method that allows extracting 3D thermal images via luminescence is missing. Here, we design such a method combining Ag₂S nanothermometers and machine learning algorithms. The approach leverages the distortions in the emission spectra of luminescent nanothermometers caused by changes in temperature and tissue-induced photon extinction. The optimized neural network-based algorithm can extract this information and provide 3D thermal images of complex nanothermometer patterns. Although tested for luminescence thermometry at the in vivo level, this method has far-reaching implications for luminescence-supported 3D sensing in biological systems in general.

Two‐dimensional (2D) semiconductor materials are being explored for applications including photocatalysis and optoelectronic devices. This class of materials features high surface‐to‐volume ratio that imparts unique physical properties. Yet, this feature makes 2D semiconductor materials prone to chemical changes that translate to variations in their photophysical behavior. Large‐scale application of these materials requires an understanding of degradation processes toward developing strategies to increase their stability. Here, the effect of air exposure on underexplored 2D semiconductor material γ‐In₂S₃ is studied. This In₂S₃ polymorph is stabilized as nanoplatelets, which are then characterizes before and after exposure to air. A marked surface oxidation , accompanied by drastic variations in photoluminescence is observed. Temperature‐dependent spectroscopic measurements show that the emission spectrum of fresh γ‐In₂S₃ nanoplatelets is dominated by two defect‐related bands, while oxidation enhances the contribution of a third band. An energy level scheme is proposed based on the analysis of the spectroscopic data. This study finally portrays how a postsynthesis treatment with an oxygen‐containing molecule (oleic acid) changes the photoluminescence of γ‐In₂S₃ nanoplatelets similarly to a prolonged air exposure. The work shines light on the optical properties of γ‐In₂S₃, paving the way for their control and the use of this material in luminescence sensing.

In line with the inaugural Light-nanoMatter Interaction Summer School , Liyan Ming, Riccardo Marin and Erving Ximendes introduce this themed collection focused on light–(nano)matter interactions.