Prof Yen-Hung Lin

Abstract:

Achieving efficient and stable formamidinium lead iodide (FAPbI3) perovskite solar cells (PSCs) requires integrated control of crystallization kinetics and defect suppression. While ionic liquids (IL) have shown promise as multifunctional additives, their rational design remains challenging. Here, we develop an attention-focus graph neural network (GNN) framework that combines the molecular features of IL with device-level characteristics of FAPbI3 PSCs. Our model identifies N-methyl-N-butylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([MBPY][TFSI]) as an ideal dual-functional passivator. The [MBPY]⁺, acting as a Lewis base, passivates undercoordinated Pb²⁺ via Pb-N coordination bonds, whereas the [TFSI]⁻ anion mitigates interfacial defects via hydrogen bonding with FA⁺. It is found that the [MBPY]⁺ cation not only suppresses non-radiative recombination but also enhances the moisture resistance of the perovskite layer due to its hydrophobic alkyl chains. With the synergetic effect of [MBPY]⁺ and [TFSI]⁻ additives, the PSCs achieve a power conversion efficiency (PCE) of 25.03% with an open circuit voltage of 1.182 V, and retain 90.5% of their initial PCE after 1200 h storage at room temperature in air atmosphere (35% relative humidity). This work contributes to ongoing computational and experimental efforts in accelerating the exploration and prediction of potential ionic liquid passivation materials for perovskite solar cells. (Figure presented.).

Abstract:

The rational design of perovskite heterojunctions is crucial for advancing the efficiency and operational stability of perovskite solar cells (PSCs). However, conventional methods face challenges in precisely controlling interfacial phase purity at the nanoscale and achieving conformal heterojunction coverage. Herein, we report a 'soft-soft' interaction-guided strategy to tailor perovskite heterojunction formation by introducing dimethyl sulfide (DMS) as a soft Lewis base additive in the organic cation solution. The resulting DMS-modulated PSCs achieve a remarkable power conversion efficiency (PCE) of up to 26.70%, with a certified PCE of 26.48%. The devices exhibit exceptional operational stability, retaining over 94% of their initial PCE after 2000 h of maximum power point tracking under continuous 1-sun illumination (ISOS-L-1 protocol). Furthermore, the universality of this 'soft-soft' interaction strategy is validated across a range of diverse perovskite compositions and ligand systems, demonstrating its potential for scalable and reproducible PSC fabrication.

Abstract:

As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components. Here, we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic "control-on-demand" architecture. Integrating signal sensing, featuring, and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module, and minimizes circuit integration complexity. The system achieves 97.15% fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption. Furthermore, we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication. By centralizing core functionalities on the same in-sensor platform, we propose a resilient and adaptable framework for energy-efficient and economical edge computing.