Feeling blue: the luminescence of a triarylboron compound has a high quantum yield (at least 0.64) over a wide temperature range (-50 to +100 °C) and changes from green to blue as the temperature is increased. The luminescence color was determined by the population of the two distinct excited-state conformations-a local excited state (high temperature) and a twisted intramolecular charge-transfer state (low temperature).

The color purple: A siloxy‐functionalized benzamide (see picture) is a highly efficient fluoride ion sensor in water. The sensor, which is activated when the OSi bond is cleaved by fluoride ions, provides two independent modes for signal recognition. In colorimetric mode, the fluoride ion concentration is transformed into a fluorescence signal that can be observed directly with the naked eye.

[This paper is part of the Focused Collection on Upper Division Physics Courses.] We review and extend existing frameworks on modeling to develop a new framework that describes model-based reasoning in introductory and upper-division physics laboratories. Constructing and using models are core scientific practices that have gained significant attention within K-12 and higher education. Although modeling is a broadly applicable process, within physics education, it has been preferentially applied to the iterative development of broadly applicable principles (e.g., Newton's laws of motion in introductory mechanics). A significant feature of the new framework is that measurement tools (in addition to the physical system being studied) are subjected to the process of modeling. Think-aloud interviews were used to refine the framework and demonstrate its utility by documenting examples of model-based reasoning in the laboratory. When applied to the think-aloud interviews, the framework captures and differentiates students' model-based reasoning and helps identify areas of future research. The interviews showed how students productively applied similar facets of modeling to the physical system and measurement tools: construction, prediction, interpretation of data, identification of model limitations, and revision. Finally, we document students' challenges in explicitly articulating assumptions when constructing models of experimental systems and further challenges in model construction due to students' insufficient prior conceptual understanding. A modeling perspective reframes many of the seemingly arbitrary technical details of measurement tools and apparatus as an opportunity for authentic and engaging scientific sense making.