This work summarizes the development of a dynamic measurement platform in a cryostat to measure sample temperature response to space-like conditions and the creation a MATLAB theoretical model to predict sample temperature responses in the platform itself. An interesting variable-emittance sample called a Fabry-Perot emitter was studied for its thermal homeostasis behavior using the two developments. Using the measurement platform, it was shown that there was no thermal homeostatic behavior demonstrated by the sample at steady state temperatures. Theoretical calculations show other ways to demonstrate the cooling homeostasis behavior through time-varying heat inputs. Factors within the system such as heat loss and thermal mass contributed to an inhibited sample performance in the platform. Future work will have to be conducted, not only to verify the findings of the initial experiments but also to improve the measurement platform and the theoretical model.

A thermochromic mid-infrared filter is designed, where a spectrally-selective transmittance peak exists while vanadium dioxide layers are below their transition temperature but broad opaqueness is observed below the transition temperature. This filter takes advantage of interference effects between a silicon spacer and insulating vanadium dioxide to create the transmittance peak and the drastic optical property change between insulating and metallic vanadium dioxide. The theoretical performance of the filter in energy dissipation and thermal camouflaging applications is analyzed and can be optimized by tuning the thicknesses of the thin-film layers.

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.