Presented by: Parinaz Moazzezi

Date: Wednesday, August 12, 2026
Time: 4:00 pm
Place: Elliot Building, Room 230

Abstract:

Inverse temperature crystallization (ITC) is a widely used technique for growing high-quality perovskite single crystals; however, the chemical evolution of precursor solutions during prolonged crystal growth remains poorly understood. This seminar presents our recent discovery of a previously overlooked solvent–precursor interaction that governs the stability of formamidinium lead iodide (FAPbI3) single crystals grown in ambient air. We demonstrate that FAPbI3 promotes an esterification reaction between γ-butyrolactone (GBL) and 2-methoxyethanol (2ME), resulting in crystal redissolution and reduced phase stability during extended heating. By introducing a slight PbI2 deficiency in the precursor solution, this degradation pathway is suppressed, enabling stable growth of phase-pure α-FAPbI3 single crystals with reduced defect density and improved optoelectronic properties. The resulting single-crystal solar cells achieve a power conversion efficiency of 22.99%, representing a record for ambient-air-fabricated FAPbI3 single-crystal solar cells. This work provides new mechanistic insights into inverse temperature crystallization and establishes precursor stoichiometry control as an effective strategy for producing high-performance perovskite single-crystal photovoltaic devices.

Presented by: Saeed Farajollahi

Date: Monday, August 17, 2026
Time: 10:00 am - 11:00 am
Place: Zoom - see below.

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Abstract: Despite rapid advances in computational capabilities and commercial full-wave electromagnetic simulators, three-dimensional numerical analysis of whispering gallery mode micro-resonators remains infeasible for WGM cavities with large diameters. This is especially important for the case of local scatterers near the ideal azimuthally symmetric micro-resonators, where the common 2D axisymmetric analysis fails to explain important scatterer-resonator interactions. Second-order methods such as perturbation and coupled-mode theory are often applied to model these interactions, where the 2D axisymmetric solutions of the bare cavity are used in a post-processing step. However, the dipole model of spherical scatterers, which is essential for these analyses, shows discrepancies with experimental results for larger scatterers. We present a full-wave bi-directional mode matching method that significantly reduces the computational cost of first-order 3D simulation of the perturbed WGM resonator.

Presented by: Maryam Ahang

Date: Friday, August 28, 2026
Time: 7:00 am
Place: Zoom - see below.

Zoom Details:

Meeting link: https://uvic.zoom.us/j/86727261624?pwd=KrTN0wNZ2vrxrjdbbG6fRJYV7iBSep.1

Meeting ID: 86727261624

Password: 669808

Abstract: Reliable condition monitoring of industrial systems remains challenging because fault data are often scarce, operating conditions vary, previously unseen failure modes may emerge, and machine-learning models can produce overconfident diagnostic decisions. This seminar introduces solutions to these challenges to improve the accuracy, adaptability, and trustworthiness of intelligent fault detection and diagnosis methods through three approaches.

This seminar covers N2FGAN, a signal-to-signal generative framework that learns to synthesize fault signals under operating conditions where only normal data is available. Moreover, to address previously unseen severe faults, a variational autoencoder–based health index is introduced to model degradation and detect fault conditions absent from the training data.

Finally, a hybrid, uncertainty-aware condition-monitoring framework is proposed that integrates sensor measurements, temporal information, and physics-informed residuals derived from nominal process models. Both feature-level and decision-level fusion strategies are investigated, demonstrating improved fault-diagnosis performance on CSTR and Tennessee Eastman Process benchmarks. Conformal prediction is further incorporated to quantify diagnostic uncertainty through calibrated prediction sets, showing that high classification accuracy does not necessarily imply well-calibrated confidence.

Together, this seminar covers generating missing fault data, detecting unseen failure modes, and ultimately combining data-driven learning, physical knowledge, temporal information, and uncertainty quantification to support more reliable intelligent condition monitoring in industrial applications.