The effect of mass transfer on electrochemical impedance of a solid oxide fuel cell anode

This paper presents electrochemical impedance simulation of a solid oxide fuel cell (SOFC) anode in order to investigate the effect of mass transport processes on the impedance spectra. The current model takes in to account the gas-phase transport processes both in the gas channel and within the porous electrode and couples the gas transport processes with the electrochemical kinetics. The impedance simulation is carried out in time domain, and the correlation between the anode harmonic responses to the sinusoidal excitation and the impedance spectra is analyzed. In order to solve the system of non-linear equations, an in-house code based on the finite difference method is developed and utilized. Results show a depressed semicircle in the Nyquist plot, which originates from gas transport processes in the gas channel, in addition to a Warburg diffusion impedance originates from gas transport in the thick porous anode. The influence of parameters such as electrode thickness, inlet gas composition, and temperature is also investigated and the results are discussed. The simulation results are in good agreement with published data.     

Data-driven discovery of molecular photoswitches with multioutput Gaussian processes

Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications. Engineering these properties into a system through synthetic design however, remains a challenge. Here, we present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning with Gaussian processes. In the prediction of electronic transition wavelengths, we demonstrate that a multioutput Gaussian process (MOGP) trained using labels from four photoswitch transition wavelengths yields the strongest predictive performance relative to single-task models as well as operationally outperforming time-dependent density functional theory (TD-DFT) in terms of the wall-clock time for prediction. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules. Through this screen, we identified several motifs that displayed separated electronic absorption bands of their isomers, exhibited red-shifted absorptions, and are suited for information transfer and photopharmacological applications. Our curated dataset, code, as well as all models are made available at https://github.com/Ryan-Rhys/The-Photoswitch-Dataset.

We present a data-driven discovery pipeline for molecular photoswitches through multitask learning with Gaussian processes. Through subsequent screening, we identify several motifs with separated and red-shifted electronic absorption bands.