The temperature and solvent dependent emission spectra of fac-[(bpy)Re(CO)3(4-Etpy)]PF6 were recorded in 4:1 (v:v) EtOH−MeOH and CH2Cl2−MeOH mixtures (bpy = 2,2‘-bipyridine; 4-Etpy = 4-ethylpyridine). The temperature-dependent emission of [Ru(bpy)3](PF6)2 and the emission spectra of [(bpy)2Ru(bpyc-NHMe)](PF6)2 (bpyc-NHMe = 4-methyl-4‘-(N-methyl carboxamido)-2,2‘-bipyridine) in 10 solvents were also analyzed for comparison purposes. The data reveal a significant dependence of the effective vibrational quantum spacing (ℏωM ) with the energy difference between the ground and excited states (E 0) as the medium properties are systematically varied. Initial attempts to explain this dependence in terms of a medium induced modulation of the metal-to-ligand charge transfer energy were not fully successful. That model is based on the relationship between the energy gap, the extent of charge transfer, and excited state distortions. A more satisfactory explanation of the experimental data was achieved by a model that includes hydrogen bonding interactions between the CO groups of the Re complex and a protic solvent. The inclusion of extended hydrogen-bonding interactions between the solute and the solvent are required to fully explain ℏωM and solvent reorganizational energy dependencies on medium properties and temperature.

Meeting regulatory and customer demands requires detailed powertrain calibration which can be expensive and time-consuming. There is often a reliance on mathematical optimization tools to convert experimental learnings into a final calibration. This work focuses on developing multiple neural network machine learning (ML) models which were trained on different test-train data splits of test-cell recorded steady-state medium-duty (MD) diesel engine data. The output data was used to develop engine actuator maps by utilizing a genetic algorithm (GA). The genetic algorithm contains a fitness function which was varied to target different combinations of low NOx and CO2 emissions. The input variables used for the ML model were engine speed, engine torque, fuel rail pressure, exhaust gas recirculation (EGR) valve command, main injection timing, and wastegate valve command. The output variables predicted were NOx mass flow rate, exhaust temperature, fuel flow rate, and dry intake mass flow rate. The ML models were used to predict cycle-averaged engine-out emissions and time-series predictions of all output variables for different transient drive cycles. The drive cycles used for this case were the Heavy-Duty Federal Test Procedure (HDFTP) transient cycle, the Non-Road Transient Cycle (NRTC), the Ramped Mode Cycle (RMC) and the newly proposed on-road Low-Load Cycle (LLC).

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Despite increasing risks from sea-level rise (SLR) and storms, coastal communities continue to attract wealthier residents, and coastal property values continue to rise. To understand this seeming paradox and explore policy responses, we develop the Coastal Home Ownership Model (C-HOM) and analyze the long-term evolution of coastal real estate markets. C-HOM incorporates changing physical attributes of the coast, economic values of these attributes, and dynamic risks associated with storms and flooding. Resident owners, renters, and non-resident investors jointly determine coastal property values and the policy choices that influence the physical evolution of the coast. In the coupled system, we find that subsidies for coastal management, such as beach nourishment, tax advantages for high-income property owners, and stable or increasing property values outside the coastal zone all dampen the effects of SLR on coastal property values. The effects, however, are temporary and only delay precipitous declines as total inundation approaches. By removing subsidies, prices would more accurately reflect risks from SLR but also trigger more coastal gentrification, as wealthier owners enter the market and self-finance nourishment. Our results suggest a policy tradeoff between slowing demographic transitions in coastal communities and allowing property markets to adjust smoothly to risks from climate change.