Secondary organic aerosol (SOA) is ubiquitous in the atmosphere and plays important roles in environmental chemical processes, influencing air quality and the Earth’s radiative budget. In the present work, 1-octen-3-ol (OTL) was identified as one of several prominent green leaf volatiles (GLVs) emitted as a result of sugarcane wounding. GLVs, a subset of volatile organic compounds (VOCs), act as SOA precursors and are a potentially underrepresented source of the overall SOA budget. Here, ozonolysis experiments of OTL standards were carried out in Teflon chambers in conjunction with a scanning mobility particle sizer (SMPS), an electrical low pressure impactor (ELPI+), and a near-infrared laser desorption ionization aerosol mass spectrometer (NIR-LDI-AMS). Under our experimental conditions, the OTL ozonolysis rate constant and aerosol yield were estimated to be 5.00 ± 0.58 × 10–24 cm3 s–1 molecule–1 and 1.03 ± 0.07%, respectively. Bounce factor (BF) calculations based on the ELPI+ data at relative humidity (RH) levels of 5, 30, 60, and 90% suggest that the OTL-derived SOA exhibits largely non-liquid characteristics regardless of RH levels at particle genesis. Furthermore, high RH at particle genesis also appears to decrease the hygroscopicity of the SOA, impacting its ability to activate as cloud droplets. Online chemical analysis of the SOA using a NIR-LDI-AMS supports the production of oxygenated products ranging from 45 to 161 m/z, in addition to prominent oligomers well beyond this m/z range.

Over the past decade, fluorophores that exhibit "mega" Stokes shifts, defined to be Stokes shifts of greater than 100 nm, have gained considerable attention due to their potential technological applications. A subset of these fluorophores have Stokes shifts of at least 10,000 cm −1 , for whom we suggest the moniker "giga" Stokes shift. The majority of "giga" Stokes shifts reported in the literature arise from the twisted intramolecular charge transfer mechanism, but this mechanism does not fit empirical characterization of triazolopyridinium (TOP). This observation inspired a density functional theory (DFT) and time-dependent DFT study of TOP, and several related fluorophores, to elucidate the novel photophysical origin for the "giga" Stokes shift of TOP. The resulting computational models revealed that photoexcitation of TOP yields a zwitterionic excited state that undergoes significant structural relaxation prior to emission. Most notably, TOP has two orthogonal moieties in the ground state that adopt a coplanar geometry in the excited state. According to Huckel's rule, both the heterocycle and phenyl moieties of TOP should be aromatic in an orthogonal ground state. However, according to Baird's rule, these individual moieties should be anti-aromatic in the excited state. By relaxing to a coplanar conformation in the excited state, TOP likely forms a single aromatic system consisting of both the heterocycle and phenyl moieties.

Recent advances in computer hardware and software, particularly the availability of machine learning (ML) libraries, allow the introduction of data-based topics such as ML into the biophysical curriculum for undergraduate and graduate levels. However, there are many practical challenges of teaching ML to advanced level students in biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogic tools, and assessments of student learning, to develop the new methodology to incorporate the basis of ML in an existing biophysical elective course and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply ML algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using ML algorithms. This work establishes a framework for future teaching approaches that unite ML and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.